The E-Business Handbook

The E-Business Handbook SL3054_frame_FM Page 2 Wednesday, November 21, 2001 10:31 AM The E-Business Handbook Edit...

Author: Paul B. Lowry | J. Owen Cherrington | Ronald R. Watson

376 downloads 5686 Views 7MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

The

E-Business Handbook

SL3054_frame_FM Page 2 Wednesday, November 21, 2001 10:31 AM

The

E-Business Handbook Edited by

Paul Benjamin Lowry J. Owen Cherrington Ronald R. Watson

ST. LUCIE PRES S A CRC Press Company Boca Raton London New York Washington, D.C.


Library of Congress Cataloging-in-Publication Data The e-business handbook / [edited by] Paul Benjamin Lowry, J. Owen Cherrington, Ronald R. Watson p. cm. Includes bibliographical references and index. ISBN 1-57444-305-4 (alk. paper) 1. Electronic commerce. 2. Business enterprises--Computer network resources. I. Title: Ebusiness handbook. II. Lowry, Paul Benjamin. III. Cherrington, J. Owen. IV. Watson, Ronald R. V. Title. HF5548,32 ,E1742001 658.8′4—dc21 2001048644 CIP

This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. All rights reserved. Authorization to photocopy items for internal or personal use, or the personal or internal use of specific clients, may be granted by CRC Press LLC, provided that $.50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA. The fee code for users of the Transactional Reporting Service is ISBN 1-57444-3054/02/$0.00+$.50. The fee is subject to change without notice. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe.

Visit the CRC Press Web site at www.crcpress.com © 2002 by CRC Press LLC St. Lucie Press is an imprint of CRC Press LLC No claim to original U.S. Government works International Standard Book Number 1-57444-305-4 Library of Congress Card Number 2001048644 Printed in the United States of America 1 2 3 4 5 6 7 8 9 0 Printed on acid-free paper


Introduction Before embarking on this journey commonly called “e-business,” it is important to not get lost in the mind-boggling array of technologies, business strategies, and jargon. Unfortunately, since the IT industry has started appending “e-” to almost every word in Webster’s dictionary, a false illusion has been created that “new economic rules” exist. So, while e-business represents a significant shift in academic research and business strategy (especially for trendsetters and followers), it is important to remember that this so-called revolution has been in the making for at least 50 years. Arguably, the e-business revolution may have started with the advent of mainframe computing (i.e., UNIVAC I in 1951), which was soon followed by computers’ being built with transistors in 1958, and the introduction of procedural languages (e.g., COBOL in 1960 by Dr. Grace Hopper). The revolution picked up steam with the introduction of ARPANET in 1969 (providing the foundation for the Internet), followed by the production of large-scale integrated circuits (LSIs) in 1970, the creation of the microprocessor in 1971 by Dr. Ted Hoff of Intel, the release of the Altair home computer in 1975, and the introduction of the IBM PC in 1981. The revolution further gained momentum with the introduction of the Mosaic Browser by Marc Andreesen in 1993, the subsequent launch of Netscape and Linux in 1994, and the introduction of Java in 1995. In the 1990s, we witnessed the recombinant synergistic application of many existing technologies (i.e., cryptology, packetswitching networks, protocols, standards, scalable servers, and relational databases) that finally caught the notice of mass markets and made e-commerce and e-business household terms. But, given this history, can we really call e-business a radical revolution? — perhaps if you were sleeping through the last five decades. E-business is not so much a revolution as an acceleration of some of the underlying fundamentals of economics and technology. No economic rules have changed; instead, certain economic principles have been simplified or more strongly accentuated because of technological advances. Thus, the rules of the game remain the same: A firm must deliver quality products and services faster and cheaper than its competitors; likewise, it must post a reasonable profit, create tangible value, differentiate itself from competitors, provide excellent customer service, find ways to produce “lock in” and to take advantage of network externalities. Similarly, intellectual capital has always been important in strategic competition, but with ebusiness, it becomes increasingly crucial to strategic survival, as many firms no longer trade in physical goods, but in ideas and services in a global arena. In the same way, while standards have been pivotal to business success for many decades, they now have an increased importance. Early on, the potential utility of standards was brought to light when gun manufacturers learned to utilize replaceable parts in the manufacturing process. The importance of standards was further high-


lighted during the Industrial Revolution when standard widths were created for railroad tracks and replaceable parts were made for gasoline engines. Now standards can be used to interconnect suppliers and customers to shore up vertical supply chains and to create value-added intranets and extranets. Thus, a business focused on e-business products or services cannot survive without embracing or creating standards that facilitate access to a critical mass of target users. For example, a given e-mail system is virtually useless if it does not interconnect with hundreds of disparate e-mail systems. Suppliers and customers need to be able to rapidly exchange product and inventory data using preset exchange protocols. Ultimately, business is still about accurately delivering on transactions. Ebusiness now allows a much higher volume and more accurate quality to transfer between companies much faster than was previously possible. This nearly frenetic pace has increased the need for integrated supply chains, partners, trust, security, and verification. We call this electronic acceleration of internal and external business networks “e-business.” This book presents leading research on a wide range of e-business topics such as strategy, web development, net auctions, XML, emerging Internet-based technologies, virtual teams, international issues, intelligent agents, e-transactions, customer relationship management and security. Because of the multidimensional nature of its content, this book is an appropriate manual for a wide range of academic and advanced practitioner audiences. For example, this book can be used for upperdivision undergraduate courses in CS and MIS, for graduate courses in business and e-commerce, and as a professional primer. Paul Benjamin Lowry Editor


Acknowledgments I would like to personally thank the international body of authors and researchers who made this work possible. Their persistence and hard work, conducted in a short period of time, have enabled us all to benefit from the timely production and distribution of this knowledge on e-business. I would also like to thank my coeditors, Drs. Owen Cherrington and Ronald Watson, for their outstanding work, as well as Jessica Stant and Bethany Stevens for their administrative assistance. Moreover, I appreciate the support I have received from the Center for the Management of Information (CMI) at the University of Arizona, including from Dr. Queen Booker, Dr. James Lee, Dr. Judee Burgoon, and Betty Albert. Finally, I would like to thank Dr. Jay F. Nunamaker, Jr. for the vision that made CMI possible — for all of us, Jay is truly an international scholar, leader, friend, and visionary. Paul Benjamin Lowry Editor



Editors Paul Benjamin Lowry is a research associate at the distinguished international Center for the Management of Information (CMI) at the University of Arizona, under the direction of Dr. Jay Nunamaker, Jr., a Regents Professor of MIS and founder of Group Systems.com. CMI conducts leading research in collaboration, knowledge management, and e-business. Cumulative research over the years at CMI has produced hundreds of research articles and scores of books, and many international honors. Paul’s professional MIS and e-business experience comes from several Fortune 100 companies including Ernst & Young Management Consulting, Ameritech, Novell, Price Waterhouse Management Consulting, and IBM. His key clients have included organizations such as 3M, Imation, Dial Corporation, the United Nations, the Wyoming Transportation Department, InfoPak, Pacific Telesis, Vanstar, Computerland, and PG&E. Paul attended Brigham Young University, receiving a B.S. in Information Systems and later an MBA from the Marriott School of Management. He is scheduled to complete his Ph.D. in MIS from the University of Arizona in April 2002. At CMI, Paul conducts research emphasizing Internet-based collaboration (ecollaboration), e-business, GroupWare/GSS, technology-assisted virtual teams, autofacilitation, and distributed group work. His current research involves creating technologies and improved collaborative processes to enable distributed groups to effectively work together over the Internet. J. Owen Cherrington is the Mary & Ellis Distinguished Professor of Accounting and Information Systems at Brigham Young University. He is currently the director of the information systems faculty and programs in the Marriott School of Management, and the director of the Rollins Center for eBusiness. Dr. Cherrington earned MBA and Ph.D. degrees at the University of Minnesota with an emphasis in accounting and information systems. He is a CPA, a member of the AICPA and the UACPA, and is licensed to practice public accounting in Utah. He was a principal in the management consulting division of Arthur Young & Co. Dr. Cherrington has an extensive list of publications, including four major college textbooks in introductory accounting, cost and managerial accounting, information systems, and CPA review. He has published more than 50 articles and monographs in professional books and journals. In addition, he has written training materials or conducted training programs for IBM, AICPA, Utah Association of CPAs, Arthur Young, Ernst & Young, Alexander Grant & Co., Price Waterhouse, and BYU Conferences and Workshops. Dr. Cherrington’s awards and recognitions have been numerous. In 1997, he received the Marriott School Outstanding Professor Award. He has been recognized


by the Marriott School for his outstanding teaching and awarded the NAC Outstanding Faculty Award, Exxon Outstanding Teaching Award, William C. Brown Teaching Excellence Award, and the Outstanding Educator Award given by the Utah Association of CPAs. Ronald Ross Watson is a professor at the Arizona Prevention Center in the Division of Health Promotion Sciences, College of Public Health, as well as School of Medicine and College of Agriculture at the University of Arizona. His distinguished career has led to the production of 450 journal articles and reviews and has included many distinctions, awards, and grants. He has also edited 55 books. He received his Ph.D. in Biochemistry from Michigan State University and completed a postdoctoral fellowship at Harvard School of Public Health. He currently directs research funded by four National Institutes of Health grants. He is also president of the company developing natural products from plant extracts for sale via the Internet.


Contributors Conan C. Albrecht Marriott School of Management Brigham Young University Provo, Utah [email protected] W. Steve Albrecht Marriott School of Management Brigham Young University Provo, Utah [email protected] Lynda M. Applegate Harvard Business School Cambridge, Massachusetts [email protected] Ravi Bapna Operations & Information Management School of Business University of Connecticut Storrs, Connecticut [email protected] Rida A. Bazzi Department of Computer Science and Engineering Arizona State University Tempe, Arizona [email protected] Bonka Boneva Carnegie Mellon University Pittsburgh, Pennsylvania

Brian Carini Carnegie Mellon University Pittsburgh, Pennsylvania J. Owen Cherrington Marriott School of Management Brigham Young University Provo, Utah marriottschool.byu.edu/emp/joc/ [email protected] Karen Clay The Heinz School Carnegie Mellon University Pittsburgh, Pennsylvania [email protected] Anne Crawford Carnegie Mellon University Pittsburgh, Pennsylvania Jonathon Cummings Carnegie Mellon University Pittsburgh, Pennsylvania Dorothy E. Denning Georgetown Institute for Information Assurance Georgetown University Washington, D.C. [email protected] Suzanne W. Dietrich Department of Computer Science and Engineering Arizona State University Tempe, AZ dietrich}@asu.edu


Michael Doran United Nations Center for Trade Facilitation and Electronic Business, Business Process Analysis Working Group (UN/CEFACT/BPAWG) ETT Division, CERN, Geneva, Switzerland [email protected] Amitava Dutta School of Management George Mason University Fairfax, Virginia Yuan Gan Intelligent Systems Laboratory Industrial Engineering University of Iowa Iowa City, Iowa Nahum Goldmann CEO ADDSecure.Net Inc. Ottawa, Ontario [email protected] ADDSecure.Net Alok Gupta Information and Decision Sciences Department Carlson School of Management University of Minnesota Minneapolis, Minnesota [email protected] Vicki Helgeson Carnegie Mellon University Pittsburgh, Pennsylvania Gaby Herrmann Department of Information Systems University of Essen Essen, Germany [email protected]

Blake Ives Decision and Information Sciences C.T. Bauer College of Business University of Houston Houston, Texas [email protected] Sirkka L. Jarvenpaa Center for Business, Technology & Law McCombs School of Business University of Texas at Austin Austin, Texas [email protected] Angie Jensen Brigham Young University Provo, Utah Ying Jin Department of Computer Science and Engineering Arizona State University Tempe, Arizona [email protected] Sunitha Kambhampati Department of Computer Science and Engineering Arizona State University Tempe, Arizona [email protected] Feras Karablieh Computer Science and Engineering Department Arizona State University Tempe, Arizona [email protected] Robert J. Kauffman Carlson School of Management University of Minnesota Minneapolis, Minnesota [email protected]


Sara Kiesler Human Computer Interaction Institute Carnegie Mellon University Pittsburgh, Pennsylvania [email protected] Stefan Klein University of Muenster Muenster, Germany [email protected] Robert Kraut Carnegie Mellon University Pittsburgh, Pennsylvania http://www.hcii.cmu.edu/People/Facult y/RobertKraut.html [email protected] Ramayya Krishnan The Heinz School Carnegie Mellon University Pittsburgh, Pennsylvania [email protected] Andrew Kusiak Intelligent Systems Laboratory Industrial Engineering University of Iowa Iowa City, Iowa [email protected] http://www.icaen.uiowa.edu/~ankusiak Louise Lane Department of Mathematics and Computer Science Laurentian University Sudbury, Ontario [email protected] Jae Kyu Lee Graduate School of Management Korea Advanced Institute of Science and Technology Seoul, Korea

Ronald M. Lee Institute for Decision and Information Systems (EURIDIS) Erasmus University Rotterdam, The Netherlands [email protected] Paul Benjamin Lowry Center for the Management of Information (CMI) University of Arizona Tucson, Arizona [email protected] [email protected] Sanjay Kumar Madria Department of Computer Science University of Missouri-Rolla Rolla, MO [email protected] Mukesh Mohania Department of Computer Science Western Michigan University Kalamazoo, Michigan [email protected] Yinghui Na Department of Computer Science and Engineering Arizona State University Tempe, Arizona [email protected] Martin Nemzow Network Performance Institute Miami, Florida http://www.networkperf.com/ [email protected]


William T. Neumann Department of Management Information Systems Eller College of Business and Public Administration University of Arizona Tucson, Arizona http://www.bpa.arizona.edu/~mis/facult y/bneumann.shtml [email protected] Edward Orton ADDSecure.Net Inc. Ottawa, Ontario [email protected] http://www.ADDSecure.Net

Kai Riemer Institute of Information Systems University of Muenster Muenster, Germany [email protected] Steven E. Roberts Georgetown University Washington, D.C. [email protected] Alexander W. Röhm Department of Information Systems University of Essen Essen, Germany [email protected]

Kalpdrum Passi Department of Mathematics and Computer Science Laurentian University Sudbury, Ontario, Canada [email protected]

Ramesh Sambasivan Itradefair.Com, Inc. Stillwater, Oklahoma [email protected]

Günther Pernul Department of Information Systems University of Essen Essen, Germany [email protected]

Tarun K. Sen Pamplin College of Business Virginia Tech Falls Church, Virginia http://www.cob.vt.edu/accounting /faculty/tksen/tksen.htm [email protected]

G. Prem Premkumar Union Pacific Chair of Information Systems College of Business Iowa State University Ames, Iowa [email protected] Balasubramaniam Ramesh Robinson College of Business Georgia State University Atlanta, Georgia [email protected]

Ramesh Sharda College of Business Administration Oklahoma State University Stillwater, Oklahoma [email protected] Simeon J. Simoff Faculty of Information Technology University of Technology Sydney, Australia [email protected]


Michael Smith The Heinz School Carnegie Mellon University Pittsburgh, Pennsylvania [email protected] Fay Sudweeks School of Information Technology Murdoch University Perth, Australia [email protected] Amy Sundermier Department of Computer Science and Engineering Arizona State University Tempe, Arizona Emerson H. Tiller Department of Management Science and Information Systems University of Texas Austin, Texas [email protected] Amrit Tiwana Goizueta Business School Emory University Atlanta, Georgia [email protected] [email protected] Carsten Totz Institute of Information Systems University of Muenster Muenster, Germany [email protected] Efraim Turban California State University Long Beach, California

Susan D. Urban Department of Computer Science and Engineering Arizona State University Tempe, Arizona [email protected] Kenneth R. Walsh Information Systems and Decision Sciences (ISDS) Department Louisiana State University Baton Rouge, Louisiana http://www.kenwalsh.com [email protected] Bin Wang Carlson School of Management University of Minnesota, Minneapolis, Minnesota [email protected] Ronald R. Watson Arizona Prevention Center Tucson, Arizona Bradley C. Wheeler Kelley School of Business Indiana University Bloomington, Indiana [email protected] Yurong Yao Ourso College of Business Administration Louisiana State University Baton Rouge, Louisiana [email protected] Sai K. Yayavaram Department of Management McCombs School of Business University of Texas at Austin Austin, Texas [email protected]


Vladimir Zwass Computer Science and Management Information Systems Fairleigh Dickinson University Saddle River, New Jersey [email protected]


Contents Chapter 1

Building Information Age Businesses for the 21st Century ...............1

Lynda M. Applegate Appendix A E-Business Models .............................................................................19 Appendix B E-Business Revenue and Cost Models...............................................29 Chapter 2

The Embedding Stage of Electronic Commerce ...............................33

Vladimir Zwass Chapter 3

Web Evaluation ..................................................................................45

Carsten Totz, Kai Riemer and Stefan Klein Chapter 4

Supply Chain Management................................................................67

G. Prem Premkumar Chapter 5

Online Auctions: A Closer Look .......................................................85

Alok Gupta and Ravi Bapna Chapter 6

Bid Together, Buy Together: On the Efficacy of Group-Buying Business Models in Internet-Based Selling..........99

Robert J. Kauffman and Bin Wang Chapter 7

The Great Experiment: Pricing on the Internet...............................139

Karen Clay, Ramayya Krishnan, and Michael Smith Chapter 8

Virtual Trade Fairs: An Emerging Internet Application ..................153

Ramesh Sharda and Ramesh Sambasivan Chapter 9

Planning Business-to-Business E-Procurement Marketplaces ........167

Jae Kyu Lee and Efraim Turban


Chapter 10 Internet Evolution and Social Impact ..............................................189 Sara Kiesler, Robert Kraut, Jonathon Cummings, Bonka Boneva, Vicki Helgeson, and Anne Crawford Chapter 11 Designing a Curriculum for the Death of E-Business: Five Principles ..................................................................................203 Bradley C. Wheeler Chapter 12 Electronic Commerce Partnerships Between Business and Academia...................................................................................213 J. Owen Cherrington and Brian Carini Chapter 13 The Covered Bazaar on the Internet: Culturally Specific Alternatives to “Web-Marts”..............................................227 Fay Sudweeks and Simeon J. Simoff Chapter 14 Electronic Government ....................................................................243 Yurong Yao and Blake Ives Chapter 15 E-Business Goes Global: Institutional Environments and Governance of Global Internet Firms.......................................261 Sirkka L. Jarvenpaa and Sai K. Yayavaram Chapter 16 Paradigm for Financial Modernization in E-Commerce .................279 Martin Nemzow Chapter 17 Knowledge Management in E-Services: From Mass Customization to Service Individualization.................297 Amrit Tiwana and Balasubramaniam Ramesh Chapter 18 Preventing and Detecting Fraud in Electronic Commerce Systems ....................................................315 W. Steve Albrecht and Conan C. Albrecht Chapter 19 Protecting a Borderless World: Recognizing and Understanding Security Threats to E-Commerce.....................339 Steven E. Roberts and Dorothy E. Denning


Chapter 20 The Critical Role of Independent Security Audits ..........................353 Nahum Goldmann and Edward Orton Chapter 21 Trusted Electronic Market Transactions: A Macro- and Micro-Level View.....................................................365 Günther Pernul, Alexander W. Röhm and Gaby Herrmann Chapter 22 Development of Reliable E-Commerce Applications in Large Open Distributed Systems.................................................379 Rida A. Bazzi and Feras Karablieh Chapter 23 Distributed Software Component Integration: A Framework for a Rule-Based Approach......................................395 Susan D. Urban, Suzanne W. Dietrich, Amy Sundermier, Ying Jin, Sunitha Kambhampati, and Yinghui Na Chapter 24 Collaborative Architectures that Support Electronic Business ..........................................................................423 Conan C. Albrecht Chapter 25 A Business Component-Based Approach to E-Business Process Design .................................................................................443 Amitava Dutta and Tarun K. Sen Chapter 26 Reducing Distance in Electronic Commerce Using Virtual Reality ..................................................................................457 Kenneth R. Walsh Chapter 27 XML, A Collaborative Enabler of E-Business through the Mediation of Heterogeneous Data between Trading Partners ...............................................................................467 Paul Benjamin Lowry and William T. Neumann


SL3054_frame_C01 Page 1 Tuesday, November 20, 2001 8:47 AM

1

Building Information Age Businesses for the 21st Century* Lynda M. Applegate

CONTENTS 1.1 1.2 1.3 1.4

Introduction ......................................................................................................1 Business Models: Something Old and Something New .................................2 Emerging E-Business Models..........................................................................4 Crafting Value Webs.........................................................................................9 1.4.1 Intuit Crafts a Value Web Inside its Organization.............................10 1.4.2 Intuit’s Value Web Extends to its Business Community...................13 1.5 Putting the Ideas To Work .............................................................................16 1.5.1 A Step-by-Step Approach to Analyzing Emerging E-Business Models.............................................................................16 Appendix A: E-Business Models ............................................................................19 Appendix B: E-Business Revenue and Cost Models..............................................29 References................................................................................................................32

1.1 INTRODUCTION A fundamental shift in the economics of information is under way — a shift that is less about any specific new technology than about the fact that a new behavior is reaching critical mass. Millions of people at home and at work are communicating electronically using universal open standards. This explosion in connectivity is the latest — and, for business strategists, the most important — wave in the information revolution. A new economics of information will precipitate changes in the structure of entire industries and the ways that companies compete.1

Few would dispute that rapid technological advancements over the latter half of the 20th century spawned dramatic worldwide socioeconomic changes. By the mid-1980s, a new economic paradigm was emerging that many called the Information Age. Its promise caused large established firms to embark upon business transformation initiatives designed to shed static, rigid structures,

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

1


2

The E-Business Handbook

processes, and business mindsets that remained as a legacy of the Industrial Age. Today, as we stand at the gateway to a new millennium, the Internet and associated technologies of the network era form the foundation upon which Information Age businesses are being built. Initially, entrepreneurs and executives in established firms approached the Internet in much the same way that fortune seekers of the 1800s prospected for gold. Although there are still frontiers to explore, the “gold rush” mentality has given way to a search for frameworks and analytics to guide us in building successful — and sustainable — Information Age businesses. Drawing on more than 6 years of work with hundreds of Internet pioneers, this chapter analyzes how emerging Information Age business models are revolutionizing the way business is conducted around the world. Portals, aggregators, exchanges, and marketplaces are but a few of the models examined.*

1.2 BUSINESS MODELS: SOMETHING OLD AND SOMETHING NEW If there is one lesson we can learn from the continuing evolution of work and competition in the new economy, it’s this: Change the question and you change the game. The old question was, “What business am I in?” The new question is: “What is my business model?”2

Why is a focus on business models so important today? If you think about it, we spent nearly a century building and perfecting the Industrial Age models that defined how companies conducted business throughout most of the 1900s. As a result, we knew what it meant if someone said, “I sell insurance” or “I sell cars.” We had developed a shorthand way of describing how a business was structured, what type of people were needed, and what roles they filled. That shorthand told us how our company interacted with others in the industry and, most importantly, how it made money and delivered value to customers, suppliers, partners, employees, and owners. It also told everyone who did business with us what they could expect. The Industrial Age business models became so familiar that they no longer required explanation. In contrast, the Internet enables us to create new business models and redefine existing ones. It provides a flexible channel for procuring and distributing products and services and the tools needed to create and package content in all of its many forms, including data, voice, and video. This highly interactive and engaging channel offers new opportunities and enables development of new capabilities that were difficult to achieve before the commercialization of the Internet. Figure 1.1 shows the building blocks of a business model. The categories of analysis and representative outcomes for each category. As you review the business model framework, it is important to recognize that the components and relationships depicted here are not new. Indeed, this * This chapter is adapted from papers and materials in Professor Applegate’s Building E-Businesses online course series (HB5 order #5238BN).


Building Information Age Businesses for the 21st Century

TABLE 1.1 Analyzing a Business Model What is it? An organization's business concept defines its: • Market opportunity • Product and services offered • Competitive dynamics • Strategy for capturing a dominant position • Strategic options for evolving the business

An organization's capabilities are built and delivered through its: • People and partners • Organization and culture • Operating model • Marketing/sales model • Management model • Business development model • Infrastructure model

Value is measured by: • Benefits returned to all stakeholders • Benefits returned to the firm • Market share and performance • Brand and reputation • Financial performance

How will we? • • • • • • • • • • • • • • • • • • • • • • •

Attract a large and loyal community? Deliver value to all community members? Price our product to achieve rapid adoption? Become #1 or #2? Erect barriers to entry? Evolve the business to "cash in on strategic options"? Generate multiple revenue streams? Manage risk and growth? Achieve best-in-class operating performance? Develop modular, scalable, and flexible infrastructure? Build and manage strong partnerships with employees and the community? Increase the lifetime value of all members of the community? Build, nurture, and exploit knowledge assets? Make informed decisions and take actions that increase value? Organize for action and agility? Deliver value to all stakeholders? Claim value from stakeholder relationships and transactions? Increase market share and drive new revenues off existing customers? Increase brand value and reputation? Generate confidence and trust? Ensure strong growth in earnings? Generate positive equity cash flow? Increase stock price and market value?

3


4


FIGURE 1.2 The value chain.

basic approach has been used for decades to analyze a wide variety of industrial business models. What is new is the business rules and assumptions that form the mental models that, in turn, guide how we make decisions and take actions. As we define new models, we don’t immediately throw out the old. In fact, the best inventions leverage old paradigms, relaxing assumptions to define new models that are both familiar and decidedly superior to the old.3 Scott Cook, founder of Intuit, explained: Some of the best innovations involve a paradigm shift, a real mental change of assumptions and certainties. In fact, the process of innovating and entrepreneuring is much less about invention or new ideas. It’s much more about rethinking and questioning the assumptions people already make.… The ability to rethink fundamental assumptions and take what people accept as certain and question it is the central talent of being a great entrepreneur.4

1.3 EMERGING E-BUSINESS MODELS Consumers are looking for the ability to bundle the products they want in a fashion unique to each individual, and the Web will provide this capability.... We believe that vertical portals will do the best job of providing the consumer empowerment that the Internet makes possible.... Not only will vertical portals have a profound effect on traditional distribution networks, but because many vertical portals will have production capabilities, they may also pose a threat to specialty producers that choose to downplay the significance of the Internet channel.5.

For decades, executives have used the value chain framework (see Figure 1.2) to define the set of activities through which products and services are created and delivered to customers.6 Once activities are defined, it is then possible to analyze the economics at each step in the chain by identifying both costs incurred and value created. These activities can be located inside a firm or across firm boundaries. In the latter case, activities may involve customers, suppliers, partners, or other stakeholders. Accompanying the physical value chain is a related



5

FIGURE 1.3 Value chain roles.

information value chain through which involved parties coordinate and control activities. Participants within a business market assume one or more of four primary roles to carry out these value-creating activities (see Figure 1.3): 1. Suppliers create component products or provide services, raw materials or talent. 2. Producers design and build products, services, and, most importantly, solutions that meet a specific customer or market need. They might sell and maintain the product or share that role with others in an industry or with those outside traditional industry boundaries. 3. Distributors enable buyers and sellers to connect, communicate, and transact business. These distributors may connect suppliers to business customers, forming what is often called a supply chain, or they may connect producers to consumers, forming what can be called a buy chain. 4. Customers might be either individual consumers or businesses willing to pay for a product, service, or solution. When selling to business customers, individual consumers — the actual end users — are often located inside the customer firm. This can create a two-stage adoption cycle — first the business must decide to purchase a product or service and then individuals must decide to use it. The point within a value chain where maximum economies of scale and scope are created determines market power. Economies of scale are achieved when a market


6


participant or network of participants is able to leverage capabilities and infrastructure to increase revenues and profitability within a single product line or market. Economies of scope are achieved when a market participant or network of participants is able to leverage capabilities and infrastructure to launch new product lines or businesses, or enter new markets. Industrial Age business innovations favored producers. The innovations included: • Physical or analog production and distribution technologies (machines, railroads, steam engines, telephones) • An operating model (the assembly line, marketing, sales, and after-sales service channels) • A management model (the hierarchy) • A social or regulatory system (specialized work, pay-for-performance incentives, worker education, unions, antitrust laws) As we enter the 21st century, Information Age pioneers such as AOL (now AOL Time Warner) are defining the business models that are reshaping the global business landscape and redefining power. Once again, emerging models exploit the power of technological, business, and social innovations within a regulatory and policy framework — the latter of which emerges over time. Information Age business innovations include: • Digital production and distribution technologies (broadband and wireless networks, sophisticated content creation, flexible knowledge management) • An operating model (integrated supply chains and buy chains) • A management model (teams, partnerships, consortia) • Social or regulatory systems (ownership incentives, freelancing, virtual work, distance learning, digital copyright laws) Although Industrial Age markets and power bases were built on proprietary infrastructure, participants within Information Age markets leverage a shared digital business infrastructure to enable new entrants and established firms to create and exploit network economies of scale and scope. Network economies of scale are achieved when a “community” of firms shares its infrastructure, capabilities, and customer base to produce and distribute products faster, better, and cheaper than competitors. Network economies of scope are achieved when the community uses its shared infrastructure to produce and distribute new products and services, enter new markets or launch new businesses more quickly, at less cost, and more successfully than competitors. The interorganizational IT systems of the 1980s and early 1990s (e.g., American Airlines’ Sabre reservation system and American Hospital Supply Corporation’s ASAP system) foreshadowed how network economics could create value. Because they were built using proprietary technologies, however, access, reach, and flexibility were limited. Table 1.2 compares Industrial Age and Information Age economics.



7

TABLE 1.2 Comparison of Industrial Age and Information Age Economics Characteristics

Industrial Age

Information Age

Criteria for Economic Success

Internal, proprietary and specialized economies of scale, and scope; Economies of scope are limited by the level of infrastructure specialization required

Core Technological Innovations

Production technologies

Core Operating Innovations

Standardization of work, job specialization, assembly line operations, value chain industry structure

Core Management Innovations

Hierarchical coordination structures and supervision, compliance-based control, pay-for-performance incentives, centralized planning and control Urban growth, mass transportation, social security and welfare, unions, federal regulations, domestic economy Decades

External, networked and shared economies of scale and scope; Economies of scale and scope are dramatically increased by the ability to build new businesses on the nonproprietary, flexible, shared and ubiquitous Internet infrastructure Distribution, communication and information technologies, and the ability to “assemble” component pieces Knowledge work, job expansion, work teams (face-to-face and virtual), extended enterprise, outsourcing and partnerships, value web industry and inter-industry structures Networked coordinating structures, ownership incentives, information-based (“learning”) models of control, distributed planning and control

Societal Innovations

Length of Time to Achieve Economies of Scale and Scope Dominant Industry Power

Producers

Work-at-home, self-employment, personal pension and savings programs, global economy

Uncertain

Solution assemblers and channel managers

The new e-business models emerging on the Internet can be classified within one or more of the generic market roles (see Figure 1.4). In addition, the models can be grouped into two categories. First, and most relevant for our discussion, are the digital businesses being built and launched on the Internet. The second major category of e-business model comprises businesses that provide the platform upon which digital businesses are built and operated. Appendix A describes the various emerging e-business models and Appendix B summarizes revenue, cost and asset models. It is recommended that the reader review these appendices before proceeding to the next section. The e-business model classification presented above suggests that there is a separation between companies that produce and sell technical infrastructure and


8


FIGURE 1.4 Emerging e-business models.

businesses that use the technology to support business strategy and design. As we will see in this chapter, however, the distinction is blurring as adoption of Internetbased business models penetrates to the very core of how firms do business. IBM, AOL, Time Warner, Microsoft, and Intuit no longer just sell technology products; these companies are now content aggregators, portals, and media companies. At the same time, non-high-tech businesses, such as Charles Schwab, are becoming technology infrastructure providers. David Pottruck, co-CEO of Charles Schwab, explained: “[Charles Schwab] is a technology company that just happens to be in the brokerage business.… If we are going to be successful, technology is going to have to be built into our DNA.” Another interesting feature of emerging e-business models concerns relationships within an online market. Traditionally, market participants performed their roles sequentially. Each participant in a value chain received inputs from those downstream, added value, and delivered outputs to the next participant in the chain. As shown in the next section, in emerging Internet markets, this orderly sequence of value-creating activities, transactions, and relationships may no longer apply. Participants in an e-business marketplace may assume more than one role and often relate through a complex series of interdependent transactions and relationships that are best modeled as a value web. An excellent example of a value web in action can be found by analyzing the multiple e-business models and relationships adopted by Citigroup (see Figure 1.5). Initially, each business unit within the Citigroup family of companies (for example, Citicard, Citibank, Travelers Insurance, and Salomon Smith Barney) strengthened offline channels and integrated them with new online channels to market. Thus, each business unit adopted a focused distributor business model. By 2000, the company had combined these independent focused distributors within two vertical portals that provided customers with an integrated solution — one portal, myCiti, could be accessed directly on the Citigroup Web site and the other, AOL Citi Center, was available to individuals with accounts on America Online (AOL).



9

FIGURE 1.5 Citigroup value web.

1.4 CRAFTING VALUE WEBS The key to reconfiguring business models for the knowledge economy lies in understanding the new currencies of value. A value network or value web generates economic value through complex dynamic exchanges between one or more enterprises and its customers, suppliers, strategic partners, and the community. These networks engage in more than just transactions involving exchange of goods and services, they also exchange knowledge and intangible value, for example, community, brand recognition, and reputation.7

E-Businesses are built by artfully combining a variety of business models. These businesses are then linked with others across multiple value chain networks to create what Frank Getman, CEO and president of HoustonStreet Exchange, refers to as a “web for the Web.”8 By incorporating multiple business models that generate separate revenue streams from the same infrastructure, a network of businesses can more efficiently use resources, more effectively meet customer needs for integrated solutions, and drive additional value from the same level of investment. By linking the web of businesses inside a firm with a business network composed of a much larger web of businesses, an organization can leverage the resources of the community to


10


further enhance value delivered to all members. The evolution of Quicken.com, Intuit’s vertical portal for consumers, is an excellent example of how value webs are crafted within organizations, industries, and markets.

1.4.1 INTUIT CRAFTS

A

VALUE WEB INSIDE

ITS

ORGANIZATION

In 1983, Intuit founders Scott Cook and Tom Proulx embarked on a quest to revolutionize the way individuals and small businesses managed their finances. A decade later, Intuit had emerged as the worldwide leader in the market for personal and small-business finance software. Its Quicken (personal finance), QuickBooks (small-business accounting), and TurboTax software accounted for 70% or more market share in their respective markets. Intuit executives were quick to recognize the potential opportunities and threats presented by the commercialization of the Internet, the World Wide Web (WWW), and user-friendly browser software. They believed these technologies could be used to deliver new products and services and to dramatically expand the company’s customer base and the range of products and services delivered. Although the market opportunity for Intuit’s traditional software business was estimated at $300 million in 2002, the market opportunity for its online businesses was estimated to exceed $202 billion.9 To exploit this opportunity, Intuit launched Quicken.com in 1996. Initially, Quicken.com operated as an information aggregator through which consumers could access financial services news and information from a number of different information providers. Quicken.com added value by synthesizing the content, categorizing it for easy search and retrieval, packaging it, and then distributing it over the Internet to a rapidly growing network of consumers. In an effort to expand its customer base, Quicken.com executives decided not to charge consumers a subscription fee for its service, but, instead, would generate revenues through advertising. The more consumers visited Quicken.com, the more the company learned about what those consumers wanted, and the more valuable the site became to advertisers. Between 1996 and 1998, six focused distributors were launched under the Quicken.com umbrella brand. By summer 2000, Quicken.com logged an impressive 6 million visitors per month with 20 million regular users during the year. The business models adopted by the six focused distributors within the Quicken.com vertical portal are described below. • QuickenInsurance, QuickenLoan, and Quicken Bill Manager were marketplaces where consumers could purchase insurance, apply for and receive loans, and pay bills online. Revenues were primarily from suppliers that were charged a commission on each transaction. Suppliers were also charged development, consulting, and maintenance fees for system integration. Advertising and referral fees provided additional revenues. • QuickenRetirement and QuickenInvestment were aggregators. Consumers could access information but could not invest online. Revenues were



11

generated primarily from sponsors that were charged advertising and referral fees. • QuickenShopping was a retailer. Consumers and small businesses could purchase Quicken’s packaged software products online and could immediately download the software and all documentation to their personal computer. Alternatively, a consumer could request that Intuit ship the software in its traditional packaging, which included printed documentation. • QuickenTurboTax enabled consumers to access information, tutorials and advice, prepare tax returns and file online. With the launch of QuickenTurboTax, Intuit evolved its traditional packaged software business model to become an application service provider (ASP).* But, Intuit did not eliminate its traditional packaged software when it adopted its online model. In early 2001, consumers could buy TurboTax packaged software for $39.95 and prepare their taxes on a personal computer. The tax forms could be printed out and submitted manually or they could be submitted online using QuickenTurboTax. Alternatively, consumers could bypass purchasing the software package and use QuickenTurboTax to prepare and submit their taxes online. Intuit saved money by shifting to the hosted online software model, and it passed the savings to the customer. There was no charge to prepare taxes online. A fee of $9.95 to $19.95 was charged to file online. Within 1 year of its launch in 1999, QuickenTurboTax for the Web had captured 80% market share for online tax preparation. Although not discussed in depth here, a second vertical portal, Quicken.com for Small Business was launched in the late 1990s. This vertical portal, or ASP, provided a wide range of online services (including payroll, bookkeeping, invoicing, and purchasing) to Intuit’s installed base of more than 2 million small-business users of its packaged software QuickBooks. By the summer of 2000, the ASP had more than 5 million users worldwide. Once again, Intuit found ways to link its packaged software to its online business and, as the number of Quicken for Small Business users grew, so too did the user base for QuickBooks packaged software, which increased to 3 million. During 2000, Intuit earned almost $300 million from its online businesses, and the majority of these businesses were profitable. Figure 1.6 provides an overview of Intuit’s consumer business models in summer 2000. Each Intuit online business leveraged a common infrastructure to generate multiple streams of revenues while also building knowledge assets and strengthening the brand — not just for Intuit but for all members of its value web (see Figure 1.7). Because the Internet and its associated technologies offered a common standardized * An application service provider, or ASP, provides online access to business software applications. Rather than buy a software package or build custom software that is then run on a personal computer or in a company’s data center, an organization pays a fee to access software that runs on computers that are managed by an independent service provider.

Focused Distributors

Aggregators

Producer

Software Firm

• E-commerce and software development know-how • New venture development and partnership know-how • Strong supplier/partner network • Over 25 million loyal customers

Retailers

12

Digital Business Infrastructure

FIGURE 1.6 Quicken.com business model

• Technical infrastructure and know-how • Obsession with customer service deeply embedded in business design • People with a passion for innovation and serving customers • Strong consumer and small business brand

Marketplaces





13

FIGURE 1.7 Leveraging e-business infrastructure.

interface for linking value webs inside and outside the organization, Intuit’s Internetbased digital infrastructure provided a modular platform upon which individual businesses could be integrated and built. The marginal cost of adding a new business to the infrastructure was low, and the revenue potential increased dramatically. Over time, new businesses could be built, launched, and grown to scale in months, dramatically increasing the company’s agility and innovation potential while dramatically decreasing risk.

1.4.2 INTUIT’S VALUE WEB EXTENDS TO ITS BUSINESS COMMUNITY The Quicken.com and Quicken for Small Business value webs do not stop at the door of the organization. Instead, these two vertical portals and the individual online businesses within them unite a network of suppliers, partners, and customers. For example, in the fall of 2000, QuickenInsurance linked 50 insurance carrier suppliers to more than 500,000 visitors per month. Approximately 30% of its visitors came through Quicken.com, 20% through AOL, and 17% through its 55 other distribution partners.10 Indeed, America Online was a key member of the Quicken.com value web and, as such, AOL’s success increased the success of Quicken.com (see Figure 1.8). Founded in 1985 as Quantum Computer Services, the AOL.com online information service was launched in 1989 as a proprietary news, information, communication, and entertainment service. From the beginning, AOL also served as a network services provider, giving away its content and community services while charging per-minute network-access fees. In summer 1995, AOL had approximately 500,000 members in the United States, revenues of $344.3 million, and was losing money. Losses continued as AOL shifted from a proprietary to an Internet infrastructure, and as it shifted its revenue model to a flat monthly fee. During 1997, the company lost almost $500 million, and many doubted that it would survive its painful evolution to an Internet business model. But survive it did, and, by the summer of 2000, AOL had more than 23 million


14


FIGURE 1.8 Linking value webs across boundaries

members worldwide (approximately 35% of total worldwide Internet users), revenues of almost $7 billion, profits of almost $2 billion, and over $1.5 billion in cash. The completion of the merger with Time Warner in January 2001 was expected to generate an additional $1 billion in equity cash flow to investors by the end of 2001.11 Even before the merger with Time Warner, AOL had evolved a complex business model. To most consumers, it was a horizontal portal that enabled free access to the Internet and its “World Wide Web” of businesses, information, and services anywhere and anytime. Revenues for this component of the business model were collected from advertisers and sponsors. For example, Intuit paid AOL $16.2 million in 1998 to become the exclusive provider of personal and small-business financial services within AOL’s Finance Web Center. It also paid a “click through” fee every time an AOL customer accessed its Quicken.com and Quicken for Small Business vertical portals through AOL. Finally, AOL received a percentage of every transaction conducted on Quicken.com by AOL customers. In early 2001, Quicken.com was one of 25 vertical portal web centers offered through AOL, and AOL commanded 20 to 25% of worldwide online advertising revenues.12 In addition to providing a gateway to content and services, AOL’s business model also reflected its roots as a proprietary network services provider. With the launch of its Internet service in the mid-1990s, it quickly became a leading Internet service provider (ISP). In this role, it developed and maintained the network infrastructure and services that enabled individuals to access the Internet across

Cable Networks

7,000 feature films 32,000 television titles 13,500 animated titles 1 million+ music copyrights

Entertainment

86 magazine titles 8 book publishers+e-book 39 worldwide news bureaus

22 additional Vertical Portals

Vertical Portals

Building Information Age Businesses for the 21st Century*

FIGURE 1.9 AOL Time Warner business model.

• Content, media and broadcasting know-how • New venture development and partnership know-how • #1 consumer online/offline brands touches consumers over 2.5 billion times/month

Aggregators/ Community

• Strong supplier and partner online/ offline network • AOL has over 28 million paid subscribers averaging over 70 min/day online • Together AOL Time Warner has over 130 million paid subscribers

Horizontal Portal ISP Broadband Network Services Provider

Software Firm

• Technical infrastructure and operating know-how

Producers

Digital Business Infrastructure

Distributors

Publishing and Entertainment Publishing


15


16


telephone (dial-up) or high-speed (broadband) networks. The completion of the merger of AOL and Time Warner in January, 2001 created the complex online/offline media conglomerate shown in Figure 1.9.13 Because it provides a common infrastructure for sharing information and coordinating business transactions, the Internet dramatically increases the ability to create value webs like those of Quicken.com and AOL Time Warner. And, like the spider webs upon which they were modeled, these networked value webs, although they may appear delicate on the surface, are surprisingly strong, “sticky,” flexible and resilient. But, as multiple new business webs are added, the complexity of managing these dense networks of relationships increases. It remains to be seen whether complex multimodel businesses such as AOL Time Warner will be able to achieve the synergies that appear so powerful on paper.

1.5 PUTTING THE IDEAS TO WORK It is just an incredible time to be in business and have the rules of business changing … . For many years, we operated under a pretty consistent set of rules. They evolved maybe … but now they’re morphing and that presents a situation that challenges entrepreneurs to figure out: Are these rules real, or are they temporary? Should we respond to them? Do we create new rules? How do we run a company in a world like this when we have 13,000 employees trying to figure out where we are going and what we should do?14

If you think about it, we spent most of the 20th century creating the business rules that were used to build and run a successful company in the Industrial Age. And, we spent the last two decades breaking those rules. Today, as we enter the 21st century, we’re searching for new business models that enable a company to achieve the efficiency, power, resources, and reach of being big, and the speed, agility, and responsiveness that comes from being small. As executives attempt to sort through the options available for building firms that can compete and succeed in the 21st century, they are finding that it is becoming less important to watch the actions of competitors and more important than ever to make decisions based on a deep understanding of the “business fundamentals” that define the structure and dynamics of markets, industries, and the organizations that compete within them. The business fundamentals that guide strategic decision making and action are discussed in this chapter. The following steps can be used to help guide business model analysis.

1.5.1 A STEP-BY-STEP APPROACH E-BUSINESS MODELS

TO

ANALYZING EMERGING

1. Evaluate the concept (opportunity): Assess market opportunity and dynamics, industry and competitive dynamics, business context and risk, product/service positioning, basis of differentiation, and evolutionary



2.

3.

4. 5.

potential (e.g., strategic options). The analysis of the concept provides the foundation for developing a pricing model and revenue forecast. Evaluate the capabilities and resources required to execute strategy: Assess the ability to attract, engage and retain key stakeholders; the appropriateness of operating and marketing plans; and the proposed infrastructure requirements. The analysis of the business design provides the foundation for developing cost forecasts. Evaluate the value proposition (returns to all stakeholders): Evaluate benefits to all stakeholders, revenue, cost, and asset models, profit model, cash flow projections, break-even timing, and financing needs. Check the consistency of assumptions used to build the financial model with the opportunity and resource analysis. Use the analysis as a benchmark to develop real-time performance monitoring systems. Revise the plan and performance measurement systems on an ongoing basis.

17



Appendix A E-Business Models A-1.1 FOCUSED DISTRIBUTORS Focused distributors provide products and services within a specific industry or market niche. For example, E-Loan is a marketplace that connects buyers and sellers in the financial services industry and landsend.com is an online retailer that sells clothing and accessories. The five types of focused distributor business models — retailers, marketplaces, aggregators, infomediaries, and exchanges — are differentiated from each other by the following characteristics. Differentiating Features • Does the business assume control of inventory? • Does the business sell online? • Is the price set outside the market or is online price negotiation and bidding permitted? • Is there a physical product or service that must be distributed? Focused Distributor E-Business Trends • Focused distributors that do not allow customers and the business community to transact business online are losing power. • Aggregators are evolving to marketplaces or vertical portals. • Multiple business models are required to ensure flexibility and sustainability. • Focused distributors must align closely with vertical and horizontal portals or evolve their model to become vertical portals.

A-1.2 PORTALS The American Heritage Dictionary of the English Language defines the term portal as “a doorway or gate—especially one that is large and imposing.”15 To many, this definition seems a fitting description of the portal business model that has emerged on the Web. Although the terminology is rather recent, the earliest online business portals (for example, American Hospital Supply’s ASAP and American Airlines’ Sabre) were launched in the late 1960s and 1970s.16 Online consumer portals (for example, America Online and CompuServe) emerged in the 1980s with the adoption of the personal computer. Built on proprietary technology, these pre-Internet portals 1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

19


20


provided limited access. In fact, in late 1993, AOL’s proprietary consumer portal had only 500,000 members. By early 2001, the number had grown to over 27 million.17 Differentiating Features • Does the business provide gateway access to a full range of Internet information and services, including search, calendar, e-mail, instant messaging, chat, and other community-building tools? • Does the business provide access to deep content, products, and services within a vertical industry (e.g., financial services) or related industries (e.g., travel)? • Does the business provide information and services for all types of users or are the information and services specific to a well-defined affiliation group (e.g., women, the elderly, lawyers, families)? Portal E-Business Trends • Horizontal and vertical portals are emerging as dominant sources of power within e-business markets. • Horizontal portals are joining forces with horizontal infrastructure portals to not just provide access to content and services, but also to network and hosting services. • Large media and entertainment portals that represent convergence of data, telephone, television, and radio networks are emerging in the consumer space. • B2B portals provide both horizontal access to business networks and vertical industry-wide solutions.

A-1.3 PRODUCERS Producers design and make, and may also directly market, sell, and distribute products and services. As mentioned earlier, producers often held the position of power within traditional business markets. During the late 1990s, many worried that new online entrants would dominate the Information Age. The demise of many once-powerful “dot-coms” shifted the balance of power in favor of established players. Differentiating Features • Does the business sell physical products or provide face-to-face services? • Does the business sell information-based products or services? • Does the business provide customized products/or services?



21

Producer E-Business Trends • Producers must be best-in-class – the #1 or #2 brand – to survive. • Some large full-service producers, such as American Express and Citigroup in the financial services industry, are acquiring a full range of products and services and then integrating them to provide vertical solutions required by customers. • Industry supplier coalitions are forming to enable business-to-business commerce within industry groups and with key business customers.

A-1.4 INFRASTRUCTURE DISTRIBUTORS Infrastructure distributors enable technology buyers and sellers to transact business. Four categories of focused distributor, key differentiating features and trends are shown below. Differentiating Features • Does the business assume control of inventory? • Does the business sell online? • Is the price set outside the market or is online price negotiation and bidding permitted? • Is there a physical product or service that must be distributed? Infrastructure Distributor E-Business Trends • The speed of obsolescence of the technology, coupled with the complexity of the solution and slim margins, has forced massive consolidation in network and computing technology channels. For many, service revenues are driving profitability. • Those distributors that take ownership of inventory are searching for inventory-less, just-in-time business models. • Distributors that have the capability for custom configuration of products and services are gaining power.

A-1.5 INFRASTRUCTURE PORTALS Infrastructure portals enable consumers and businesses to access online services and information. Five categories of infrastructure portal, key differentiating features and trends are shown below. Differentiating Features • Does the business enable users to connect to the Internet? • Does the business enable users to outsource the operation and maintenance of Web sites? • Does the business host applications and solutions?


22


Infrastructure Portal E-Business Trends • Horizontal infrastructure portals (ISPs, Network Service Providers, and Web hosting providers) are merging or partnering with horizontal content portals to increase value created through intangible assets such as information, community and brand. • Horizontal content portals such as AOL are vertically integrating with horizontal infrastructure providers, such as Time Warner’s cable networks. (Note: Prior to the merger, AOL was an ISP.) • Convergence of voice, data, and video channels and global acceptance of a common set of standards are leading to global industry convergence. • Aggressive pursuit of the growing market for hosted application services is leading to confusion as players with markedly different business models converge on the common space. • Two competing vertical infrastructure portal (ASP) models are emerging: producer-ASPs (for example, Oracle, Siebel, SAP) provide online access to their brand-name software; distributor-ASPs (for example, US Internetworking) offer application hosting of many software brands.

A-1.5 INFRASTRUCTURE PRODUCERS Infrastructure producers design, build, market and sell technology hardware, software, solutions, and services. Producers may sell and provide after-sales service directly or they may share this responsibility with online or offline channel partners including retailers, distributors, and portals. Differentiating Features • Does the business manufacture computer or network components or equipment? • Does the business develop packaged software? • Does the business provide infrastructure services or consulting? Infrastructure Producer E-Business Trends • Many hardware and software producers were early adopters of online commerce, selling directly to Internet-savvy customers and through online distributors. For example, in 1999, over 80% of Cisco’s sales — most of which were through online distribution partners — were through online channels.

Yes

Possibly

No

Possibly

Marketplace

Aggregator/ Infomediary

Exchange

Own Inventory

Retailer

Model & Examples

Possibly

No

Yes

Yes

Sell Online

Yes

No

No

No

Price Set Online

Model Differentiators

TABLE A-1 Focused Distributor E-Business Models

Possibly

Possibly

No

Yes

Physical Product or Service

Depends on model

Referral fees; Advertising & marketing fees

Transaction fees; Service fees; Commissions

Sales

Likely Revenues

Advertising & marketing; staff support for auctions (especially B-2-B); inventory & logistics if inventory control; R&D; Technical infrastructure

Advertising & marketing; R&D; IT infrastructure

Advertising & marketing; physical facilities, inventory & customer svc.; R&D; IT infrastructure Advertising & marketing; R&D; IT infrastructure

Likely Costs


Appendix A E-Business Models 23

Yes

Limited

Possibly

Vertical Portals

Affinity Portals

Gateway Access

Horizontal Portals

Model & Examples

TABLE A-2 Portal E-Business Models

Within affinity group

Through partnerships with vertical and affinity portals Yes

Deep Content & Solutions

Affinity Group Focus

Yes

Possibly; often through partnerships No


Referral fees; advertising, affiliation and slotting fees

Advertising, affiliation and slotting fees; possibly subscription or access fees Transaction fees; commissions; advertising, affiliation and slotting fees

Likely Revenues

Advertising, marketing and sales; content/info asset mgmt.; R&D; IT infrastructure Advertising, marketing and sales; content/info asset mgmt.; R&D; IT infrastructure; Legacy system integration to support transactions Advertising, marketing and sales; content/info asset mgmt.; R&D; IT infrastructure

Likely Costs



Yes Yes Yes Yes

Yes

Manufacturers

Service Providers

Educators

Advisors

Information & News Services

Model & Examples

Sell/Serve Online

TABLE A-3 Producer E-Business Models

Possibly

Usually

Possibly

Possibly

Yes

Sell/Serve Offline

Moderate to High

Moderate to High

Moderate to High

Moderate to High

Low to Moderate

Level of Customization


Commission, service or transaction fees; Registration or event fee; subscription fee; hosting fee subscription fee; registration or event fee; membership fee; commission, transaction or service fee Subscription fee; commission, transaction or service fee

Product sales; service fees

Likely Revenues

Content/info asset mgmt.; advertising, marketing & sales; IT infrastructure

Advertising, marketing & sales; content/info asset mgmt.; R&D; IT infrastructure Advertising, marketing & sales; Content/info asset mgmt.; R&D; IT infrastructure Content/info asset mgmt.; R&D; IT infrastructure Content/info asset mgmt.; IT infrastructure

Likely Costs



Yes Possibly

Usually Possibly

Infrastructure Marketplaces Infrastructure Exchanges

Yes

Sell Online

Yes

Control Inventory

Infrastructure Retailers

Models & Examples

Yes

Possibly

Usually

Price Set Online


TABLE A-4 Infrastructure Distributor E-Business Models

Yes

Yes

Yes

Physical Product or Service

Transaction fees; service fees; Commission Depends on model

Product sales; service fees

Likely Revenues

Advertising and marketing; physical facilities, inventory and customer svc.; R&D; IT infrastructure Advertising and marketing; R&D; IT infrastructure Advertising & marketing; staff support for auctions (especially B-2-B); inventory & logistics if inventory control; R&D; technical infrastructure

Likely Costs



Horizontal Infrastructure Portals Includes: Internet Service Providers (ISPs), Network Service Providers, and Web Hosting. Vertical Infrastructure Portals Includes: Producer and Distributor Application Service Providers (ASPs)

Models & Examples Through partnerships with non-infrastructure portals and ASPs Yes

Yes

Often through partnerships with horizontal infrastructure portals

Hosted Applications and Solutions

Model Differentiators Internet/Network Access and Hosting

TABLE A-5 Infrastructure Portal E-Business Models

Access fees; commission, service or transaction fees; subscription fees; hosting fees Licensing fees; service & transaction fees; maintenance & update fees; hosting fees

Likely Revenues

Advertising, marketing & sales; content/info asset management; R&D; IT infrastructure

R&D; IT infrastructure; advertising, marketing and sales

Likely Costs



Yes

Yes

No

Yes

Equipment/Component Manufacturers

Software Firms

Custom Software and Integration

Infrastructure Services Firms

Models and Examples

Sell/Serve Online

High

High

Moderate to High

Low to Moderate

Level of Customization

Commission, service or transaction fee; hosting fee

Commission, service or transaction fee

Product license or sales; installation and integration fees; maintenance, update and service fees

Product license or sales; installation and integration fees; maintenance, update and service fees

Likely Revenues

R&D; advertising, marketing and sales; production; physical facilities and infrastructure; specialized equipment, materials and supplies; IT infrastructure R&D; advertising, marketing and sales; production; physical facilities and infrastructure; specialized equipment, materials and supplies; IT infrastructure Access to specialized talent.; professional development and training; travel Content/info asset mgmt.; R&D; IT infrastructure

Likely Costs

28

Yes

Yes

Yes

Yes

Sell/Serve Offline


TABLE A-6 Infrastructure Producer E-Business Models




Appendix B E-Business Revenue and Cost Models TABLE B-1 Sample Revenue Options Revenue Category

Description

Product Sales Commission, Service or Transaction Fees

Commerce Revenues Sell or license physical or information-based products. Charge a fee for services provided; can be a set fee or a percentage of the cost of a product or service.

Subscription Fees Registration or Event Fees

Advertising, Slotting, Affiliate and Referral Fees Membership Fees

Software/Hardware Sales Installation and Integration Fees

Maintenance and Update Fees Hosting Fees Access Fees

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

Content Revenues Charge for receipt of updated information on a particular topic or a broad range of topics for a specified period of time (e.g., annual). Charge a fee for attendance at an online event, workshop or course.

Community Revenues Collect a fee for hosting a banner advertisement or special promotion. Collect a fee for an exclusive or nonexclusive partnership relationship. Collect a fee each time a visitor clicks through from your site to another company’s site. Charge a fee to belong to a private group or service. Infrastructure Revenues Sell or license a technology product. Charge either a set or variable fee for services provided; large-scale fixedprice projects are often broken into a series of discrete projects with well-defined timeframes and deliverables; variable fees are often based on time, materials and expenses incurred while working on a project. Charge a fee for software/hardware maintenance and updates. Charge a fee for hosting a software application, Web site, data center or network. Charge a fee for providing access to a network or to an Internet service.

29


30


TABLE B-2 Sample Cost Categories Cost Category People and Partners Advertising, Marketing, Sales Business Development Materials & Supplies

Specialized Equipment (does not include IT) Research & Development Physical Facilities and Infrastructure Information Technology (IT) Infrastructure

Description Cost to acquire, develop and retain skills and expertise needed to execute strategy; includes employees and partnerships. Cost of offline and online advertising, marketing and sales. Cost of designing and launching new businesses, developing alliances and acquiring partners. Cost of physical materials used in production of products and delivery of services; includes general purpose and specialized supplies and components. Cost of equipment—especially capital equipment—used in design, production, delivery, and distribution. Cost of designing and developing digital business products and services; may overlap with IT infrastructure costs. Cost of corporate and regional headquarters, sales offices, factories, warehouses, distribution centers, retail stores, service centers etc. Cost of computers and equipment (e.g., printers, data storage devices). Cost to operate and maintain data centers. Cost to design, develop, implement and maintain software. Cost of voice, data and video network equipment (e.g., physical cables, routers). Cost to operate and maintain networks.

TABLE B-3 Sample Asset Categories Asset Category

Financial Assets Marketable Securities

Property, Plant and Equipment Inventory

Description Current Assets Accounts receivable. Cash and convertible notes. Investments made as part of a cash management program. Tangible Assets Physical facilities. Fixed assets required to produce goods and services. Assets held for resale.



31

TABLE B-3 (CONTINUED) Sample Asset Categories Asset Category

Securities

Real Estate Relationships

Strength of Online & Offline Brand

Knowledge & Expertise

Agility & Responsiveness

Intellectual property Goodwill

Description Investments Stock held by one firm to enable joint control over shared business activities. Stock held by one firm in anticipation of a return at some time in the future. Investment in property in anticipation of a future return. Breadth and depth of relationships with customers and the business community. Loyalty and commitment of customers and business community members. Strong brand recognition among business and consumer communities (includes corporate brand, business unit brands, product brands and global brand). Ability to generate strong personal identification with brand. Ability to leverage “Internet” brand image. Reputation and image. Experience, skills and intellectual capabilities of employees and partners. Understanding of market and business dynamics. Scope and granularity of stored information. Flexibility and ease of accessing, customizing and distributing information. Information literacy. Understanding of technical and business evolution and ability to identify opportunities and threats. Ability to quickly recognize and act on new opportunities and threats. Ability to access and efficiently utilize resources required to execute strategy. Ability to capture the attention and mobilize the commitment of customers and members of the business community to implement new strategies. Patents, copyrights, etc. for which an objective measure of value can be assessed. Value of an acquired company over and above current and tangible assets. The value of an acquired company’s “franchise”—e.g., loyalty of its customers, the expertise of its employees—that can be objectively measured at the time of a sale or change of control.


32


REFERENCES 1. Evans, P. and Wurster, T., Strategy and the economics of information, Harvard Business Review, Boston, 1997. 2. Slywotzky, A. and Morrison, D., Profit Patterns. N.Y. Times Business, 1999. 3. Kuhn, T., The Structure of Scientific Revolution, University of Chicago Press, 1970. 4. Cook,Scott, address to MBA students at Harvard Business School, September 1998. 5. U.S. Internet and Financial Services Equity Research Team, The Internet and Financial Services, Morgan Stanley Dean Witter, August 1999. 6. Porter, M., Competitive Advantage: Creating and Sustaining Superior Performance, New York, The Free Press, 1985. 7. Allee, V., Reconfiguring the value network, Journal of Business Strategy 21 (4): 3639, July/August 2000. 8. HoustonStreet Exchange weaves new round of investments and strategic partnerships into its “web for the Web, HoustonStreet press release, March 23, 2000. 9. Intuit Annual Report, 2000. 10. Applegate, L.M., QuickenInsurance: The Race to Click and Close (HBS order #800295). 11. AOL Time Warner Annual Report, 2000. 12. Applegate, L.M., QuickenInsurance: The Race to Click and Close, HBS No. 800-295. 13. Data on the AOL Time Warner business model are from The AOL Time Warner 2001 Fact Book. 14. Pottruck, David, address to executives at Harvard Business School, October 1999. 15. American Heritage Dictionary of the English Language, Boston, Houghton Mifflin, 1971. 16. Applegate, L.M., McFarlan, F.W. and McKenney, J.L., Electronic Commerce: Trends and Opportunities, Corporate Information Systems Management, New York, McGraw-Hill Irwin, 1999. 17. AOL company Web site (www.corp.aol.com/who_timeline.html), March 2, 2000.


2

The Embedding Stage of Electronic Commerce Vladimir Zwass

CONTENTS 2.1 2.2 2.3 2.4

Introduction ....................................................................................................33 The Volume of E-Commerce Today and in the Future.................................34 The Scope and Structure of EC Enterprise ...................................................35 Embedding of Electronic Commerce ............................................................38 2.4.1 More Sophisticated Mix of Market vs. Hierarchy Approaches in Procurement and Supply-Chain Management...............................38 2.4.2 Multichannel Sales Encompassing the E-Channel............................40 2.4.3 Thick Connectivity Between the Physical and Virtual Worlds and the Ubiquitous Web .....................................................................41 2.5 Conclusions ....................................................................................................42 References................................................................................................................42

2.1 INTRODUCTION Electronic commerce (EC) is a broad frontier of business transformation deploying computer telecommunication networks and allied information technologies. Although EC as we understand it today was born in the early 1990s, with the Internet becoming its primary vehicle, we can see its precursor emerging decades earlier based on wide-area networks and electronic data interchange (EDI). Properly understood, EC encompasses inter- and intraorganizational segments, as well as businessto-consumer commerce. For a period of time, EC was seen by many as limited to the last segment, or, roughly, to selling consumer goods and services over the Internet. As will be seen in this chapter, this segment was always the most visible — if least weighty — part of EC, the tip of the iceberg. The main thrust of EC, which can best be seen in its totality as e-business, is in transforming intraorganizational processes and interorganizational collaboration and exchange by relying on the capabilities derived from the inexpensive universal connectivity of the Internet. Based on our new understanding of the field, the early notion of the autarkic development of EC into a “New Economy” has to be reconceptualized into that of the development of a more productive economy by embedding EC into it.

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

33


34


This chapter will first describe the scope of EC, proceed to analyze briefly the initial stage of its development, and go on to describe the ever more strongly emerging trend of embedding EC-based processes, methods, and technologies into the previously existing structures. We outline several promising areas of research engendered by this new stage of economic transformation.

2.2 THE VOLUME OF E-COMMERCE TODAY AND IN THE FUTURE EC has been defined as “the sharing of business information, maintaining business relationships, and conducting business transactions by means of telecommunications networks.”27 This definition places the long-term relationships and collaborative effects ahead of the more apparent and transient transactions.* The transaction phase is a consummation of more enduring relationships, enabled by more enduring infrastructures. All of these are within the domain of EC, which includes the business-to-business (B2B), business-to-consumer (B2C), and intraorganizational segments. Because, just as in the traditional enterprises and marketplaces, a B2C transaction is the result of multiple B2B ones, the B2B segment volume, both in actuality and in forecasting, has surpassed the B2C by many times. The volume of the intraorganizational segment is difficult to capture; however, the movement to intranets and other Internet-enabled information systems has been inexorable, displaying impressive return-on-investment results. The definition has proven robust to the evolution of EC. For those who saw the EC enterprise as selling goods over the Internet by green-field firms, the recent contraction in that subsegment heralded a fading of the EC promise. It is our purpose in this chapter to show that the contrary is true, and the EC enterprise is becoming embedded in the economies at large, at least in those regions and countries where it has developed sufficiently. The estimates of EC volume differ, owing to the relative immaturity of the field and vast differences in how it is encompassed. To cite those of highly respected Forrester Research, the total of B2B and B2C EC in 2000 globally was $657.0 billion, of which the lion’s share of $488.7 billion took place in the United States.10 This is forecast by the business-research firm to grow to $6,789.8 billion in the year 2004, to account for 8.6% of the total sales, of which $3,189.0 billion is to take place in the United States, accounting for 13.3% of the total sales in the country. 2 ** Two things are notable about the forecast. It predicts more than tenfold growth of the transactional component of EC over 4 years. It also forecasts a vastly more rapid growth in several regions other than the United States. While the U.S. transaction volume is to grow by a factor close to 6.5 between the years 2000 and 2004, in Asia Pacific, it is to grow by a factor close to 31, Western Europe by a factor of 17.5, and Latin America by a factor of 22.7. Such projections may strain credulity * In its scope, the definition resembles that of e-business, introduced some time later, notably by IBM. However, the EC definition goes back to an earlier usage of “commerce” to mean “dealings.” 22 ** We note that this estimate is in quite close agreement with the recently lowered estimate by Gartner Group of $6 trillion for the worldwide B2B commerce in 2004.8


The Embedding Stage of Electronic Commerce

35

and their object is subject to the vicissitudes of the business cycles. Yet, the rate of growth has been undeniable. According to the findings of the Center for Research in Electronic Commerce at the University of Texas at Austin, the revenues of the U.S. Internet economy grew by 58% from 1999 to 2000.11 Thus, although the U.S. EC share will still amount to almost half of the global EC sales volume, this share will fall as compared with the present. Indeed, countries such as Australia, South Korea, and Taiwan are expected by Forrester Research to have a greater penetration of EC into sales (all at 16.4%) than the United States (at 13.3%), according to the data set cited above. In other words, we should expect the globalization of EC or, to be more precise, the international spread of EC into national economies. Although, at first glance, EC does appear to be an inherently global phenomenon, in practice, it is not so at the present. While certain countries, notably in Scandinavia and in the Asia Pacific region, have moved “virtually” closer to the center of the world than their geographic endowment affords, others, notably large parts of Africa, are not as yet at the virtual table. Also, the degree to which EC penetrates international trade and, as could have been expected, fosters it, remains to be established. The composition of the trade with respect to its sales to businesses vs. to consumers can be gauged by comparing the above data with the NRF/Forrester Online Research Index.21 In 2000, the total estimate of B2C online sales in North America (USA and Canada) amounted to $48.3 billion, which, when compared with the Forrester Research North American sales total for the year10 amounts to about 10%. When compared with results for previous years, the 1-to-9 relationship between B2C and B2B electronic commerce appears quite stable. The larger effects of EC penetration are to be seen in its business-oriented segments.

2.3 THE SCOPE AND STRUCTURE OF EC ENTERPRISE EC has emerged from the confluence of several business practices grounded in the telecommunications and computer technologies. The earliest of these, EDI, has a decades-long history as the means of computer-to-computer business communications. As an early form of so-called digital commerce, unmediated by human agency, it still has not come to full fruition. The contemporary EC was born when the Internet was opened to business uses, was rendered broadly useful by the development of the World Wide Web, and was opened to access by the invention of the browser. The Internet’s precursor, the ARPANet, was originated in 1969, became the Internet with the TCP/IP protocol suite introduced in 1982–83, and was opened to commercial pursuits by the U.S. government in 1991. The exponential growth of the number of hosts began that year. Perhaps the most decisive feature in the success of the overall Internet design is that it easily accommodates the organic growth as a network of networks. By the spring of 1993, the World Wide Web and the browser as its universal front end began the inexorable expansion of the Internet-based EC. A three-level hierarchical structure of EC has emerged as a result of exceptionally rapid activity seeking to exploit the new opportunities in the competitive context.27, 28 At its foundation lies the infrastructure of the wide-area networks,


36


TABLE 2.1 The Hierarchical Framework of E-Commerce with Embedding Directions Metalevel Products and Structures

Services

Infrastructure

Level

Function

Examples

7

Electronic marketplaces and electronic hierarchies

Electronic exchanges Interorganizational supplychain management

6

Products and systems

5

Enabling services

4

Secure messaging

Remote consumer services (retailing, banking, stock brokerage) Infotainment-on-demand (content sites, educational offerings) On-line marketing Electronic benefit systems Intranet-based collaboration Extranet-based linkages E-portals and search/directory engines Electronic catalogs, smart agents E-money, smart-card systems Digital authentication services Digital libraries, copyrightprotection services Traffic auditing EDI, E-mail, EFT, instant messaging

3

Hypermedia/ multimedia object management Public and private communication utilities

2

1

Wide-area telecommunications infrastructure

World Wide Web with Java

Internet and value-added networks (VANs)

Guided- and wireless-media networks

Embedding Directions Exchange speciation Physical ownership to meet guarantees CPFR Mutlimodal marketplaces Brick-and-click services Multichannel sales Linked on- and offline media Multichannel marketing

Services for ubiquitous Web: device-oriented agents, authentication, payments

Messaging for devices/appliances /vehicles Ubiquitous Web, with flexible user/device interface Embedded servers, sensors, actuators E-tags for universal identification of physical objects Protocols for mobility and dynamic ad hoc connections



37

many — but not all — of which are managed by Internet protocols, and the hypermedia object management of the World Wide Web and allied technologies. On this infrastructure, the service layer of secure messaging and enabling services is provided. The enabling services, such as electronic catalogs, search engines, digital authentication, copyright protection, and many others, have been an area of extensive entrepreneurial activity. Many of them, such as consumer certification or copyright protection, have not reached the tipping point leading to mass adoption as yet; others, such as e-money, have not met with any appreciable degree of success in the marketplace. At the top metalevel, the framework includes the levels of products and structures. The products that lie in the pure digital domain, such as remote consumer services, or information-based systems such as intranet- and extranet-based collaboration, lend themselves especially well to EC. The structures at the apex of the hierarchical framework are electronic marketplaces and interorganizational supply chains. The consumer-oriented marketplaces such as eBay, and B2B marketplaces such as e-Steel, of which a number are successful and more yet attract only marginal activity, act in the spot-buying domain. The Internet-based supply chain management serves long-term linkages between the enterprises. Between the two poles of market and hierarchy lie a variety of hybrid arrangements that are meant to combine the price discovery of the marketplace with the efficiency of the long-term quasihierarchic linkage. The hierarchical framework reflects a wide swath of economic activity. This structure is supported by burgeoning entrepreneurial and intrapreneurial activities in technology development and business deployment. E-marketplaces that furnish only a nexus for transactions have proven to be largely unattractive, particularly if provided by intermediaries or by buyers seen as squeezing suppliers. In general, private marketplaces, often run by corporate consortia, succeed far more frequently than the public ones. Much work is needed to discover how to leverage the value and increase the efficiency of the existing supply chains, while taking advantage of the price-discovery and competitive capabilities of marketplaces. The performance of many sites in the B2C domain has been poor, leading to a collapse of the firms beyond them. Many of these displayed what was called by McKinsey consultants “fatal attraction” — the more customers they attracted, the more money they lost.1 Few attracted visitors were converted into customers, and few of those who did convert became repeat clients. In the McKinsey survey of hundreds of dot-coms, fewer than 1% of visits to pure transactional (i.e., noncommunity) sites were made by people who became repeat customers. A noticeable pattern of heightened online competition, with declining customer value, was discerned toward the end of 2000. The click-through rates of banner-based online advertising fell below 1%. Indeed, during the initial stage of Internet-based EC, it was often perceived as a world apart, giving birth to the New Economy as the opposite of the Old Economy, which, in the eyes of many, e-commerce has come to bury. The perceived image of EC was that of a dot-com phenomenon, where the idea of business model was highly flexible, with the external equity owners targeted as the principal customers. Neither idea, however, is fundamental to EC.


38


2.4 EMBEDDING OF ELECTRONIC COMMERCE It appears now that rather than become a replacement for the Old Economy, the emerging New Economy will be an amalgam of the old and the new, an economy transformed. The major directions of this transformation require a far more careful analysis than can be carried out here. What we can do, however, is look briefly at some of the developments that point to more evolved interconnections between the e-world — the world of pure EC and bits — and the p-world, the physical world of things that are not reducible to bit streams. We hope that further research will be stimulated by looking at these interconnections, at the embedding of EC in the existing structures. The hierarchical framework with some of the directions of EC embedding mapped onto it is shown in Table 2.1. As can be seen in the table, the embedding of EC in the physical world takes a number of directions. These can be generalized as three principal thrusts.

2.4.1 MORE SOPHISTICATED MIX OF MARKET VS. HIERARCHY APPROACHES IN PROCUREMENT AND SUPPLY-CHAIN MANAGEMENT The Web supports marketplaces of a great variety of ownership and design that can be used to seek out goods and services, as well as supply-chain management in longer-term buyer–supplier relationships. The discipline of exchange-based procurement can, in some cases, be applied to squeeze the inefficiencies out of the supply chain; it is, however, not a replacement for relationship-based regular supply streams, where the efficiencies are largely gained by cooperation, increasingly relying on collaborative planning, forecasting and replenishment (CPFR) systems to minimize inventories while ensuring reliable supplies. Physical products are manipulated by physical means, and operations and logistics consume a major part of production costs. However, a significant component of value in supply chains is informational in nature. The virtual value chain that mirrors the physical value chain can be created to move some of the activities onto the Internet.23 These activities can be further interfaced with the business processes of partner companies. The examples include procurement, electronic prototyping, extranet-based collaboration, marketing and sales, telemaintenance, and customer service, all of which are subject to the efficiencies of EC-based approaches. As the companies concentrate on their core competencies, business webs emerge, centering on the firms that provide and direct the overall structure, and generally concentrate on product development and marketing. Sun Microelectronics, a division of Sun Microsystems, has become a case in point. The division has 150 supply-web links around the world and maintains no inventory. The tiers of suppliers, partners, and customers are connected over the Web in ever tighter links as the firm implements CPFR.6 Traditional companies are realizing significant efficiencies, with EC becoming embedded in the intra- and interorganizational processes. Carrier, the world’s largest supplier of air conditioners, presently saves $100 million annually, or 1% of its revenues, by having moved part of its upstream supply chain (procurement) and of



39

its downstream chain (sales) to the Web.12 In 1995, furniture maker Herman Miller opened its SQA (Simple, Quick, and Affordable) division, with a new factory, linked backward with the suppliers and forward with the customers over the Internet. The new method of supply chain management enables the company to deliver products within 2 weeks of the order, instead of 8 weeks previously. SQA has since been used as a template for the entire company, with its largest customers ordering over the Web and production and fulfillment ordered digitally.20 Furniture dealers can establish Web-based links with Herman Miller that enable them to get current information on configuration, availability, and pricing, and specify to their customers the delivery date within 2 hours of the order receipt. Aside from the newly emergent cybermediaries, traditional intermediaries can successfully deliver value-added by relying on the capabilities of the Web.7 Differentiation and speciation of B2B market makers is taking place. Most of the exchanges are going beyond pure price discovery in an auction regime and simple transaction processing to provide the value-added necessary for the specific industry.26 Ultimately, this leads to multimodal marketplaces. Called allin-one markets by Kambil et al.,13 they offer a single platform for multiple interorganizational commercial mechanisms, ranging from pure spot buying (with auctions where desired) to long-term supply-chain relationships. Several business-travel sites and maintenance-repair-and-operations (MRO) sites working on this principle are available, with a gradual move into direct goods as well. Two recently established exchanges aiming to serve as procurement vehicles for the retail industry, GlobalNetXchange (GNX) and WorldWide Retail Exchange (WWRE) aim to move beyond reverse auctions to provide CPFR facilities to the participating firms.25 In many cases, ownership of physical goods has been recognized as necessary to fulfilling the commitments made online. Thus, Enron is able to guarantee the delivery of some of the products the firm auctions by taking the ownership of paper mills and bandwidth. Against the previously touted advantages of almost purely virtual stock, Amazon.com now owns warehouses, where it stocks books and other goods. Extensive multidisciplinary research is required to establish the categories of goods and services that benefit from exchange-type acquisition vs. long-term contracting. The organization of marketplaces and the distribution of power and benefits that lead to stable marketplaces need to be investigated. The principles of operation of business webs, relatively durable alliances of companies that combine their business processes to serve a marketplace, are an important study field. At this time, most of the firms mentioned here are unable to quantify the benefits derived from the new approaches (or lack thereof). The idea of the specificity of Internet economics was buried some time ago, in particular in Shapiro and Varian.24 A thorough framework and analysis of the economics of EC is provided by Kauffman and Walden.15 Economic theory has to be complemented by the studies of market strategies. For example, the presumed buyer dominance on the Web can be eroded by a variety of supplier strategies.9 Mass customization can lead to mass profit erosion.5 Economic studies of alternative supply-chain structures are beginning to appear.18


40

2.4.2 MULTICHANNEL SALES ENCOMPASSING

The E-Business Handbook THE

E-CHANNEL

The realization of the limitations of purely virtual selling leads to the click-andmortar organizational designs, which combine Web-site customer access with physical presence. This permits the combination of the capabilities of the e-channel with the time-tested advantages of the p-channel. Thus, the e-channel offers the opportunity of (quasi) one-to-one selling, such as customer selection, access convenience, interactivity, continuing customer service, value-added surrounds, and support for product retirement. The trust and the infrastructure for the handling of physical products (distribution network, handling of returns, etc.) are furnished by the physical channel. Partnerships with rapid-response delivery services, such as UrbanFetch, are being formed. Third-party return clearinghouses, such as Return Exchange and GENCO Distribution Systems, have been established. Mail Boxes Etc. is planning to pursue opportunities as a physical front end for a variety of virtual financial services and retail, for example, by accepting deposits from online bank customers or enabling buyers to inspect goods acquired in e-auctions. An emblematic example is the success of Gap, selling over the Web from gap.com since 1997. The firm garners, not simply incremental online sales, but meaningful synergies.2 The campaign conducted in the retail stores to collect the customers’ e-mail addresses has broadened the e-channel customer base. Gap customers who shop in both physical and electronic channels have been found to spend 50% more than single-channel customers; opportunities to cross-sell and up-sell are exploited. Many e-customers who use physical stores to return goods make additional purchases in the process.* Because of the needed speed of roll-out combined with capitalization, EC is, to a large degree, an art of alliances, also in the embedding pursuits. For example, Fodor’s, the company that puts out well-known paperback guidebooks in the field of travel, believes in the need of having strong presence on all online platforms,4 including the wireless. The firm sees wireless products as an adjunct to the guidebooks. Fodor’s has formed strategic alliances with the Finnish telecommunications provider Sonera Zed, with Avant Go, and with OmniSky. The platform providers need established content. Marketing research indicates that the Web facilitates, and often changes, the flows of information and of promotion, negotiation, and transactions, as well as the flows of digital products and services.14 New marketing opportunities emerge for the established firms from these changed flows. For example, Osram Sylvania researches its business customer behavior online and translates this customer knowledge into the marketing and product decisions. Firms such as Gateway, which have reengineered the flow of their physical products using the capabilities of the Web, continually modify the digital flows, substituting for or complementing the physical flows. Yet, Gateway finds it advantageous to maintain a network of physical outlets that serve as showrooms and help sell higher-margin services. * Anyone wishing to see how different this transformation is from the retailing revolution brought on by department stores at the turn of the 19th century can read Emile Zola’s Au Bonheur des Dames (translated into English as The Ladies Paradise).



41

A great variety of research issues includes the optimization of the physical vs. virtual mix and minimization of costs, while providing the optimal customer service. Beyond that, competitive advantage can be sought and risks can be reduced by a multichannel approach to marketing and sales, but this area still awaits scholarly study.

2.4.3 THICK CONNECTIVITY BETWEEN THE PHYSICAL AND VIRTUAL WORLDS AND THE UBIQUITOUS WEB The means are being introduced to more deeply embed the Web in the physical world. Internet-enabled kiosks, acting as retail computer terminals, complement floor personnel. Far beyond that, bar codes and e-tags can be used as entry points from physical objects onto the Web. Several magazines print the GoCode bar codes, to be read in by handheld scanners. Although this may not necessarily have wide reader appeal yet, it could be a harbinger of important developments ahead. A corporate alliance that includes Motorola and Symbol Technologies has been formed to develop and host a directory of barcode to URL mappings. A highly ambitious project to tag all the physical objects with e-tags whose contents can be remotely examined is MIT’s auto-id.19 If realized, this would enable the objects to act as read-only e-nodes. Exploited to provide complete visibility of physical products in supply chains, the approach would lead to extensive savings. Business benefits, such as the reduction of safety stock in the supply chains, are envisaged. Beyond passive bar codes or tags, Web servers can be embedded in the components of the physical environment. Sensors and actuators embedded in a variety of appliances and devices and relying on such Java-based technologies as Jini for location by clients that need to access them, and on such technologies as Bluetooth for mobile applications, can be deployed in a variety of systems.3 Health monitoring, home automation, and intelligent warehousing are only some of these. A variety of enabling services can be envisaged that would combine the universal bar codes with mobile technology to inform or transact anytime, anywhere. Ubiquitous computing can spread the deployment of digital commerce — direct computer-to-computer information transfer and transacting. Digital commerce can be expected to free humans from the need to monitor relatively simple transactions, and to free those transactions from the delays and errors of human input. We can expect that the Internet-based EDI, when lent a degree of semantic interoperability by the standardization of the extensible markup language (XML), business scenarios of open-EDI and other technologies, will be exploited to standardize business practices, then broadly assimilated. Aside from vital technological issues, including verification and standardization of the business protocols, many research questions present themselves regarding the delineation of the scope of digital commerce and its interface with the physical space, redesign of organizational practices, and the definition of responsibility. The work on electronic trade scenarios17 and formal languages for business communication16 contributes to this domain.


42


2.5 CONCLUSIONS At the core of EC are its B2B and intraorganizational segments. Economies are being changed as the EC approaches and technologies penetrate more deeply into the way companies operate and interact with their suppliers, customers and the evergreater numbers of business partners. What could be termed New Economy emerges from this transformation, with e-processes replacing a number of physical ones, supply chains being reconfigured into dynamic supply webs, and the ways of seeking competitive advantage changing as well. If we consider the new stage of EC to be the embedding of the virtual in the physical world, three major thrusts present themselves. Electronic marketplaces and electronic hierarchies are both supported by EC, and a continuum of approaches to procurement and supply chain management emerges. This is expected to lead to significant savings across industries. Fascination with pure virtual selling gives place to multichannel selling approaches, with greater capabilities to serve the multifaceted needs of the customer. As the Web becomes ubiquitous and, perhaps, the universal means of identification of physical objects are established, new opportunities for delivering services will emerge. A rich research agenda is generated by the embedding of EC.

REFERENCES 1. Agrawal, Vikas, Arjona, Luis D., and Lemmens, Ron, E-performance: the path to rational exuberance, McKinsey Q., 1, 2001, 31-49. http://mckinseyquarterly.com. 2. Anderson, Brett, Clicks and mortar: one channel is not enough, Knowledge Mgmnt, March 2001, e8-e10. 3. Borriello, Gaetano and Want, Roy, Embedded computation meets the World Wide Web, Comm. ACM, 43, 5, May 2000, 59-66. 4. Cohn, Michael, Places to go, people to see, Internet World, March 1, 2001, 48-51. 5. Dewan, Rajiv, Jing, Bing, and Seidmann, Abraham, Adoption of Internet-based product customization and pricing strategies, J. Mgmt. Info. Syst., 17, 2, Fall 2000, pp. 9-28. 6. Donahue, Sean, Supply traffic control, Business 2.0, February 2000, pp. 130-132. 7. El Sawy, Omar A., Malhotra, Arvind, Goasin, Sanjay, and Young, Kerry M., ITintensive value innovation in the electronic economy: insights from Marshall Industries, MIS Quarterly, 23, 3, September 1999, pp. 305-335. 8. Gartner Group, Press Release, March 13, 2001. 9. Grover, Varun and Ramanlal, Pradipkumar, Six myths of information and markets: information technology networks, electronic commerce, and the battle for consumer surplus, MIS Quarterly, 23, 4, December 1999, 465-495. 10. Internet Commerce, Forrester Research, http://www.forrester.com/ER/Press/FoorFind/0,1768,0.00.html, accessed on February 22, 2001. 11. The Internet Economy Indicators, http://www.internetindicators.com/keyfindings.html, accessed on February 22, 2001. 12. Judge, Paul C. How I saved $100 million on the Web, Fast Company, 174-181, February 2001, 174-181.



43

13. Kambil, Ajit, Nunes, Paul F., and Wilson, Diane, Transforming the marketspace with all-in-one markets, Int .J. Electronic Comm., 3, 4, Summer 1999, 11-28. 14. Kannan, P.K. Introduction to the special issue on marketing in the e-channel, Int .J. Electronic Comm., 5, 3, Spring 2001, 3-6. 15. Kauffman, Robert J. and Walden, Eric A., Economics and electronic commerce, Int .J. Electronic Comm, 5, 4, Summer 2001, to appear. 16. Kimbrough, Steve O. Formal language for business communication: sketch of a basic theory,” Int. J. Electronic Comm., 3, 2 Winter 1998-99, pp.23-44. 17. Lee, Ronald M., Distributed electronic trade scenarios: representation, design, prototyping, Int. J. Electronic Comm., 3, 2 Winter 1998-99, pp. 105-136. 18. Netessine, Serguei and Rudi, Nils, Supply chain structures on the internet: marketingoperations coordination, Oct. 2000, http://ebusiness.net.edu/papers/ERF/ERF88.pdf 19. The networked physical world, MIT Auto-ID Center, Dec. 2000, http://autoid.mit.edu/ 20. Newsome, Melba, IS for efficiency, Context, April-May 2001, pp. 50-53. 21. NRF/Forrester Online Retail Index, http://www.forrester.com/NRF/1,2873,0,00.html, accessed on February 22, 2001. 22. Oxford English Dictionary, 2nd edition, CD-ROM, 1992. 23. Rayport, Jeffrey F. and Sviokla, John J., Exploiting the Virtual Value Chain, Harvard Business Review, Nov.-Dec. 1995, pp. 75-85. 24. Shapiro, Carl and Varian, Hal R. Information Rules: A Strategic Guide to the Network Economy, Boston: HBS Press, 1999. 25. Sliwa, Carol, Dueling exchanges make big plans, Computerworld, March 19, 2001, pp. 1, 16. 26. Wise, Richard, and Morrison, David, Beyond the exchange: the future of B2B, Har. Bus. Rev., Nov.-Dec. 2000, 86-96. 27. Zwass, Vladimir, Electronic commerce: structures and issues, Int .J. Electronic Comm., 1, 1, Fall 1996, 3-23. 28. Zwass, V. Structure and macro-level impacts of electronic commerce, in Emerging Information Technologies: Improving Decisions, Cooperation, and Infrastructure, K.E. Kendall, Ed., Thousand Oaks, CA, Sage, 1999, 289-315, http://www.mhhe.com /business/mis/zwass



3

Web Evaluation Carsten Totz, Kai Riemer and Stefan Klein

CONTENTS 3.1 3.2

Introduction ....................................................................................................45 Web Evaluation: an Overview .......................................................................46 3.2.1 The Many Facets of Web Evaluation ................................................47 3.2.2 Web Evaluation in the Web Development Management Process .....50 3.2.3 Contingencies of Web-based Interaction Quality..............................51 3.2.3.1 Company-Oriented Determinants .......................................51 3.2.3.2 Customer-Oriented Determinants .......................................52 3.3 Web Evaluation Methods ...............................................................................53 3.3.1 Objective Methods .............................................................................54 3.3.2 Subjective Attribute-Specific Methods ..............................................56 3.3.3 Subjective Event-Specific Methods ...................................................58 3.4 A Web Evaluation Project..............................................................................62 3.4.1 Planning Web Benchmarking 3.4.2 Preparation, Execution and Analysis .................................................63 3.5 Conclusions ....................................................................................................64 References................................................................................................................64

3.1 INTRODUCTION Electronic commerce (e-commerce) is widely acknowledged as the cause of fundamental changes in organizations, industries, market structures and entire economies — changes that increasingly affect almost every aspect of every kind of business. Forced by market pressure and the necessity to build or protect competitive advantages, companies have started to invest heavily in Internet-related technologies. As requisite expertise could not be established as rapidly as businesses began to invest, and as the complexity of Internet applications is also growing rapidly, many EC ventures must be qualified as real world experiments with mission-critical implications. Although a decreasing number of companies favor the idea of deploying the Internet as their exclusive or major sales channel, and combinations of online and offline channels as best-of-breed approaches are emerging, the importance of a Web site as a crucial interface and customer touch point is beyond dispute. Similar to contact situations in offline environments, Web-based interactions can be referred to as “moments of truth,” as any Web site-related measure must prove its appropri1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

45


46


ateness to meet customer expectations and needs. Nevertheless, insights into Webspecific success factors, determinants of customer Web site acceptance and quality perception, as well as drivers of changing customer behavior and satisfaction, are still limited. Beyond simple usability tests, companies are confronted with the requirement of assessing the quality of their Web-based customer contact and the measurement of their e-commerce performance. This chapter develops a framework for the evaluation of Web-based customer touch points, while supporting the assessment of real-life Web experiments with appropriate methods and guidelines, the efficiency of organizational learning and the allocation of e-commerce investments to customer needs and value-creating applications. In addition to a detailed conceptualization of different Web evaluation methods and their modi operandi, a contingency model of Web-based business-to-customer (B2C) interactions is used to illustrate determinants of the customer’s quality perception. We distinguish between Web-specific approaches that have been developed from scratch, and approaches that are modifications or adaptations of proven methods deployed in other areas, e.g., research on marketing of services. The prime objective of the chapter is to establish a sound basis for the setup and realization of Web evaluation measures. It begins with a conceptual overview of Web evaluation and the outline of potentials for Web evaluation activities as a source for crucial information throughout typical e-commerce planning cycles. To illustrate the design, the tasks to be taken into consideration and the pursuit of Web evaluation projects, a model Web benchmarking project is presented in the last section of the chapter.

3.2 WEB EVALUATION: AN OVERVIEW Evaluating, in general, can be defined as, “to judge or determine the worth or quality of something,”1 and, furthermore, “evaluation is the process of examining a subject and rating it based on its important features,”2 or “evaluation is how we determine the quality of a product in the context of its intended use.”3 In this sense, Web evaluation might be defined as the methodical assessment and determination of the quality level of Web activities, specifically of a Web site, with regard to important features and contextual objectives. We can further refine this definition by asking: • • • • • • •

Why evaluate? What can be evaluated? Which criteria should be used to evaluate? Who are the assessors? How can evaluation data be collected? When should evaluations take place? What is an area of interest for an evaluation ?

Following are possible answers to these questions to show the broad variety of Web evaluation opportunities.


Web Evaluation

3.2.1 THE MANY FACETS

47 OF

WEB EVALUATION

The reasons and aims for carrying out a Web evaluation are many. First, Web evaluation projects typically are focused on performance assessment in relation to e-commerce goals. The overall goal of an e-commerce activity is economic success, which can be controlled by the business ratios turnover, profit, market shares or purchase rates. To achieve these economic goals, customer-oriented goals must be accomplished. According to Figure 3.1, quality aspects such as Web site usability, the company image, or overall customer satisfaction with e-commerce activities, can be assessed to identify potentials and needs for further quality improvements. This is the main application area of Web evaluation. Technical Web systems must guarantee a certain quality level regarding availability, security and responsiveness. Performance tests and ongoing monitoring activities help to ensure the achievement of technology-oriented goals. Because these measures have already found tremendous attention throughout a variety of e-commerce and information systems literature, we will barely touch on them and will not be subject to a detailed description (for a deeper insight into Web and IS testing compare Refs. 4 and 5). Several areas can be the object of an evaluation. These can be further subdivided into internal and external transactions. Internal refers to transactions that cannot be observed from outside the company, e.g., by customers or competitors. Therefore, they cannot be the objects of a comparative analysis. Internal operations are technical systems, internal processes, economic aspects and other areas such as advertising instruments. In contrast, external areas are referred to as market-oriented and visible from outside the company. They can be the Web site of competitors, the company’s own Web site, or the Web sites of a whole industry, e.g., to benchmark the industry’s Web practices. In addition to the distinction by areas and the questions mentioned above, methods can be distinguished by further dimensions: applied evaluation criteria, subjects of evaluation, the evaluation mode, the project horizon or the occasions when evaluation could be facilitated. Web evaluation criteria can be either objective or subjective. Objective is the verification of the existence of items and the measurement of time and cost (e.g., technical and economic measures), whereas quality assessment is highly subjective and dependent upon individual attitudes and previous experiences. The necessary evaluation data can be gathered in several ways. First, as discussed in most of the Web evaluation literature, an evaluation can be questionnaire based, surveying evaluation subjects such as customers, external experts, internal employees or other neutral people regarding the quality of Web site functionality. Alternatively, an evaluation can be carried out in a laboratory environment or by running automated technical systems. Figure 3.2 gives an outline of the discussed Web evaluation dimensions. Discussions on Web evaluation aspects with company representatives dealing with e-commerce initiatives have led to the impression that the initial development of an e-commerce strategy and the test of an Internet application in terms of usability during or after the technical implementation are primary triggers for gathering

avail -

respon -

quality

image

assessment

usability

t est, monitoring

siveness

IT performance

security satisfaction

customer

customer -oriented

profit

turnover/

controlling

share

market -

repur chase rate

management -oriented

48

FIGURE 3.1 Overview of Web evaluation approaches derived from e-commerce goals.

ability

technology -oriented

goals



• efficiency: cost,

• financial para-

evaluation)

FIGURE 3.2 Dimensions of Web evaluation.

sector

• sites of a whole

quality

• effectiveness:

• own Website

• competitors’ sites

(automated

• technical systems

• partners

• employees

• experts

subjective:

• features/attributes

collection

technical data

evaluation:

• automated

• ongoing monitoring

before Web site start

• quality check

engineering

interviews

observations,

entrance • requirements

• lab experiments:

offline interview

e-mail q.,

before market

• market evaluation

• continuous, longterm (monitoring)

• strategic position

• once (project)

based: Web q.,

• questionnaire

• neutral persons

• customers

...occasions

...horizon

...mode

...subjects

external:

• processes

•technical systems

time

objective:

internal visibility:

meters, cash flow

...criteria

...objects

Web Evaluation...


Web Evaluation 49


50


information on Web site features and quality. Although these occasions are evident, we regard Web evaluation activities as a source of vital information throughout the entire e-commerce planning and development process.

3.2.2 WEB EVALUATION IN THE WEB DEVELOPMENT MANAGEMENT PROCESS Supplementing the above-mentioned types of Web evaluation shown in Figure 3.1, Web evaluation can be performed before setting up e-commerce activities during Web strategy development, as shown in Figure 3.3. In this sense, an analysis of competitive or inspiring Web sites can be carried out to determine the state-of-theart quality level or to decide on opportunities for market differentiation and the (strategic) positioning of Web applications. Customer surveys and further benchmarks can complement this information by explicitly identifying required or implemented features on relevant sites. In the second phase, customer surveys can be carried out to identify required Web features. Furthermore, benchmarking approaches can be used to supplement features by learning from competitors. During the design period, prototypes should be subjected to detailed usability tests to ensure frictionless operation and ease of use by customers after the rollout. Beyond any kind of customer-oriented test, back-end processes might be analyzed with simulations to check their interfaces for the integration with Web applications and to identify (the causes of) potential shortcomings. Collateral testing throughout the implementation period minimizes the risk of missing scheduled milestones as well as the risk of having to compromise on quality. The measurement of customer satisfaction regarding the Web interface and Web-based interactions, the monitoring of technical parameters and controlling of financial figures during operation enables the management to intervene in time and assure a continuous improvement of Web applications. Web Strategy

Web Concept

Web Design

Implementation

Aligning Web strategy with business strategy, positioning

Configure online marketing mix, identify features and contents

Conceptual design of site navigation, interface, processes ( e.g. fulfillment)

Technical implementation of Web software and hardware systems

Competitive intelligence; market research

survey of customer needs -(importance of site features); benchmarking approaches

prototype usability testing (interfaces); process simulations; error probability and effect analysis

testing: --performance; --bug/error; --site usability “Web checker”

(Strategic) positioning

Concept selection & prioritization

User centered design

Quality checks

Operation Doing business; Maintenance of systems

financial and technical controlling customer satisfaction analysis

Continuous improvement

FIGURE 3.3 Integration of Web evaluation into the Web development process.


Web Evaluation

51

The following paragraphs concentrate on the quality assessment of Web-based customer touch points from the customers’ point of view. The primary focus of our analysis, disregarding technical aspects such as security of transmission, system stability, responsiveness, etc., will be the Web site as a combination of aesthetics, services, content, features and Web site-mediated interaction processes between a company and its customers.

3.2.3 CONTINGENCIES

OF

WEB-BASED INTERACTION QUALITY

3.2.3.1 Company-Oriented Determinants To generate vital and valid information, a set of sector-specific contingencies, as well as company-specific determinants, influence business practices, strategies, organizational settings and the deployment of information technology. These contingencies affect a company’s capabilities, objectives and intentions regarding the design and operation of its Web-based customer touch points in terms of: • Contact and communication (aesthetics, atmosphere, experience, information provided, wording, etc.) • Products and services offered (Web site as a service, Web site features, transactions, sales process, online shop and Web catalog) • Software implemented or required to access a site (site structure, navigation, interface, technical performance, plug-ins, etc.) The Web site integrates these attributes into a single package representing the company’s input coefficients for Web-based interaction processes. Although most definitions of e-commerce emphasize the execution of transactions via electronic channels,6,7 nowadays almost any Internet-related initiative is referred to as electronic commerce. In fact, companies may deploy Internet applications for a broad range of purposes. Therefore, the function of a Web site will likely depend on a set of objectives. Prevalent functions of a Web site are: • A channel for selling, sales promotion and commerce (transaction based) • A medium for the information of different stakeholders: customers, employees, applicants, partners, shareholders and investors • A medium for communication and interaction with stakeholders • A tool for the delivery of Web-specific services (e.g., search functionality) • An entertainment platform The different functions are not mutually exclusive. They might coexist or even be interdependent. These different cases can be classified under the term “genre.” Genres can be defined as “typified communicative actions characterized by similar substance and form and taken in response to recurrent situations”8 (for genres on the World Wide Web see also Ref. 9). We will interpret a genre as a specific type of Web site regarding the type of business in a specific domain, the nature of information and interaction provided and the objectives of a Web site.


52


However the objectives of a Web site are defined, the quality of a Web-based customer touch point has a bearing on customer satisfaction and the attitude of customers toward the touch point and its operator. Furthermore, it affects the image and identity of a product or service, customer relationships, brands or a company from the customer’s point of view. The genre of a Web site must be taken into account when trying to evaluate a company’s Web site. It must be considered in the selection of a Web evaluation method and the identification of appropriate evaluation criteria. Otherwise, the evaluation results lack relevance, and evaluated Web sites won’t be comparable among each other. Overall, we can distinguish between three major genres of Web sites: 1. Communication-related Web sites (advertising, image penetration and entertainment aspects) 2. Product- and service-related Web sites (sales, transaction aspects) 3. Web sites with software character (Web information systems, Web tools like search engines or online banking tools). 3.2.3.2 Customer-Oriented Determinants Customers are affected by their cultural background and social context. Relatives, friends or colleagues as model peer group members influence personal characteristics referred to as activating factors (emotions, needs, motivations, etc.), personality (skills, involvement, values, perceived risks) and cognitive traits (experiences, adoption processes, perception).10,11 Corresponding to different motivations or intentions to interact with a Web site operator, online behavior can be divided into browsing and searching activities.12 Whereas browsing bears primary emotional impact, searching entails stronger cognitive implications and constitutes a higher level of activation and involvement surfing the Web. These parameters affect the capabilities, motivation and involvement of customers dealing with Web interfaces in terms of medium, genre and company. A customer might be quite confident surfing the Internet (positive attitude toward the medium), but unmotivated or hesitant to carry out electronic transactions via the Web (negative attitude toward the Web as a transaction platform). In another case, a customer might favor shopping online (positive attitude toward one genre), but dislike any kind of Web advertising (negative attitude toward another genre) or might allocate online transactions to a small and stable set of vendors (positive or negative attitude toward different companies). The outlined determinants constitute customers’ prerequisites in Web-based interaction processes. The analysis and assessment of business–customer interactions and their quality has been subjected to research ever since marketing of services became a recognized research topic. Derived models have in common that they emphasize the interaction between service providers and customers as a crucial determinant regarding the perception of service quality from the customer’s perspective.13–17 Furthermore, service quality is not seen as the consistency of certain factors or parameters but as the customers’ perception of the service suitability to meet their needs. Therefore, the determination of interaction or service quality must be con-


Web Evaluation

53

ducted from the subjective customer’s perspective, comparing the expected with the perceived level of service. Customer satisfaction is likely in the case of a positive net quality, whereas a negative outcome might cause dissatisfaction. Because Web-based interaction processes can be interpreted as special instances of service processes (intangibility, constitutive cooperation of service provider and customer to render the service, etc.) we have developed the following contingency model of Web-based interaction quality from service-marketing literature. We have included the central evaluation process of interaction quality and customer- and company-oriented determinants.

3.3 WEB EVALUATION METHODS As mentioned before, Web evaluation methods can be divided into internal and external methods according to the evaluated objects (see Figure 3.418,19). Even though internal methods are concerned with the evaluation of technical systems and internal processes, etc., they can also have external relevance. The Web-usage mining concept for example, presented by Spiliopoulou,20 tries to improve Web site navigation by using data mining methods such as sequence analysis for analyzing the user’s navigation paths stored in Web log files.20 Although this method evaluates a certain part of the (externally visible) Web site, the relevant data has only an internal visibility. External evaluation methods can be divided into objective and subjective methods, according to the criteria used (see Figure 3.2). Beyond this, external subjective methods can be further divided into attribute-specific methods, which evaluate a set

Company focus: internal visibility

Market focus (customers, competitors): external visibility

objective

• Weblog analysis, Web usage mining • monitoring of technical systems (e.g. system responsiveness) • process cycle times (internal, back-end processes)

subjective

• FMEA: failure probability and effect analysis: e.g. integration of website with back -end processes • employee interviews: e.g., fit of Web site with corporate identity

objective

• check for the existence of features and attributes • measurement of efficiency and performances • error reports (broken links etc.) -> automated Web checker attribute specific

• ServQual • Web-specific methods: Web Assessment, WebQual, QEM, etc.

event specific

• lab experiments: observation, interviews, story telling • interaction blueprinting • critical incident method • problem-detecting method • frequency relevance analysis of problems

subjective (customer’s quality) perception)

FIGURE 3.4 Classification of Web evaluation methods.


54


sector, domain culture company company strategy strategy

organization organization

technology technology

company regarding :: company capabilities, capabilities, goals, goals, intentions intentions regarding contact/ service/ service/ contact/ software software communication product product communication

service & company website interaction web interaction quality consumer

perceived perceived quality quality matching expected quality

quality quality assess assess -ment ment

-

-

+

+

consumer capabilities, motivation and involvement towards medium medium

activation activation

genre genre

personality personality

company company

cognition cognition

consumer consumer culture social context

FIGURE 3.5 Contingency model of Web-based interaction quality.

of single Web quality attributes, and event-specific methods, which perform an evaluation of entire Web interaction processes as shown in Figure 3.5. Selected external methods will be presented in the next paragraphs.

3.3.1 OBJECTIVE METHODS While quality depends on the subjective perception of the Web interaction outcome and is assessed according to individual attitudes,21 objective evaluation methods focus on intersubjective verifiable and nonambiguous attributes. They do not consider individual attitudes or perceptions and they lack measures for relevance. For example, the fact that a Web site offers a community functionality (which is stated as being a relevant e-commerce feature by some authors22) may have no relevance to the specific target group of this particular Web site, whereas it may be important for the customers of another site. Therefore, objective evaluation methods’ contribution to Web quality assessment is limited. But, with objective criteria such as performance measures, error checks, or the verification of the existence of attributes, objective methods can perform certain quality evaluations of a company’s capabil-


Web Evaluation

55

ities and Web site elements, as shown in Figure 3.5. The advantage of objective methods lies in the easy applicability and the objective nature of the criteria in the comparability of evaluation results of different Web sites for benchmarking purposes. Selected Methods — Objective Web evaluation methods can be divided into methods focused on the software and system quality of Web sites, and methods to survey the existence of certain attributes such as e-commerce concepts on a Web site, which allow a more customer-centric perspective on e-commerce activities than the Web-software-oriented methods do. The first group is concerned with the external assessment of Web sites’ software quality. These methods check broken links, performance or HTML quality and have been implemented into Webbased software tools called Web checkers (compare the example of a Web site checker in Refs. 23 and 24). As examples for the second group, we will look at the methods by Bauer and Scharl25 and Elliot et al.26 1. Bauer and Scharl developed a quantitative evaluation method for the classification of Web sites based on the type of content and the Web site’s document structure. This classification can be used to cluster a group of Web sites (e.g., of an industrial sector) concerning different genres and to classify a Web site into one specific cluster. Such classifications can be valuable when analyzing the kinds of predominating Web activities in a sector and when comparing sectors. Different genres are, for example, characterized by a different content structure (number and size of documents), interaction elements (forms, etc.) and navigation elements (frames, links, etc.). It becomes clear, for instance, that entertainment Web sites and Web sites for customer information may differ widely concerning the use of images and interactive elements. The evaluation can be automated and tool-based in a three-step approach. First, the Web sites are downloaded by a tool; second, the mentioned classification data is extracted by a tool called WebAnalyzer; and the classification is done in a third and final step. Here, Bauer and Scharl propose the application of neural networks for the classification and clustering. 2. Elliot et al. propose a method for the evaluation of commercial Web sites based on an e-commerce criteria framework, with 30 criteria in six categories regarding e-commerce activities and requirements (e.g., product information, transaction processing, ease of use, etc.). The method is questionnaire-based and analyzes the existence of attributes. For each criterion, the rating can be 0 or 1, depending on the existence of one or more attributes for each criterion (if more than one attribute is required, the criterion is rated 1, if a specific number of the attributes exists). The existence of attributes can be easily checked, so that it is not necessary to perform a representative sample of assessments for a Web site. The results for different Web sites can easily be compared, but their interpretation is difficult, as they do not reflect the varying relevance of attributes for different Web sites.


56


3.3.2 SUBJECTIVE ATTRIBUTE-SPECIFIC METHODS Attribute-specific methods are based on the assumption that the overall quality of an object can be broken down into several single attributes whose quality can be evaluated separately.27 Attribute-specific evaluations typically consist of a multiattribute-criteria catalog, which can be evaluated using a questionnaire. Therefore, these methods are related to the evaluation of company capabilities as pointed out in Figure 3.5 — the quality of a Web site as a software tool, the quality of services and products and of information and communications. Evaluation Criteria — An increasing number of publications tend to identify relevant attributes that determine the quality and success of effective (e-commerce) Web sites and can therefore be used as a starting point for the identification of generic criteria for subjective attribute-specific evaluations. Although a valuable evaluation of a single Web site must be based on domain- and company-specific criteria, depending on the company’s strategy and implemented genre, comparative evaluations must be based on a generic criteria catalog. The following examples give evidence of the growing body of empirical and hypothesis-based research on Web site quality determinants: • A well-known examination is the Fortune 1000 Webmasters’ Evaluation by Liu et al., who carried out an empirical study among Webmasters to analyze the significance of consumer marketing as well as information system attributes on Web quality, including attributes such as information quality, playfulness, service and system quality, etc.28,29 The findings of the study offer a suggestion for the design of consumer-oriented Web sites and, therefore, a clue for relevant generic criteria. • Similar studies have been made by van der Heijden30 and Ho and Wu.31 Van der Heijden tries to identify attributes affecting the attraction of Web site traffic and influencing whether a customer will revisits the site, considering such attributes as usefulness, ease of navigation, attractiveness and interactivity.30 • Ho and Wu examine antecedents of customer satisfaction for online shopping stores.31 The examined hypotheses relate to logistical support, home page presentation, technological, information and product characteristics. Integration of Subjective Quality Perceptions into the Evaluation — Quality perceived by users depends on their capabilities and attitudes. To integrate subjective quality perceptions of evaluated attributes into the evaluation, two aspects must be considered: 1. Group of assessors can consist of various people with different attitudes, capabilities and experiences with the medium or genre. To evaluate ease of use of a Web interface, i.e., experienced as well as less-experienced persons may evaluate the interface. Thus, it can be checked whether the implemented interface is suitable, regardless of the user’s Web experience. 2. More important, not only the perceived quality of an attribute must be surveyed but also its perceived importance and relevance, which depend heavily on the assessor’s attitudes toward this specific attribute.


Web Evaluation

57

To survey the expected relevance of an attribute supplementary to the perceived quality, additional questions may be integrated into the questionnaire, as is done in ServQual, the well-known instrument for service quality assessment used in services marketing.32 For each quality attribute, two questions are formulated and presented as a double scale. The questions have the following nature: 1. A first-class Web site has an easy-to-use navigation interface. (Answers range on a scale from “totally disagree” to “totally agree.” 2. Web site X has an easy-to-use navigation interface. (Answers use same scale.) When applying such a design, the answers of the two questions can be compared by weighing the quality assessment of the second question with the surveyed relevance of the first to obtain a valuable and varied judgment. ServQual approaches this, for instance, by subtracting the second scale value from the first one. The higher the remaining number, the higher the perceived quality. Such a two-stage questionnaire design surveys not only the perceived quality, but also a certain degree of influence on the overall quality perception for each single assessor. Selected Web Evaluation Methods — Having discussed identification of multiattribute evaluation criteria and considered integration of subjective customer perceptions and expectations into Web evaluation, we will introduce selected methods specifically developed for the evaluation of e-commerce Web site quality: • Selz and Schubert have developed one of the first Web evaluation approaches, called Web assessment.33 They implemented a Web-based questionnaire with which Internet users assess commercial Web sites to get an interbusiness comparison of sites as a kind of benchmarking. The evaluated attributes have been derived from a two-dimensional framework that consists of a Web performance system with several service layers34 and the four phases of an extended market transaction. So, four phases (information, agreement, settlement and community) with five performance elements exist in which detailed attributes have been identified. The layer model considers not only core services and emotional customer experiences, but also external bundling of specific and generic services from partner companies. Although the criteria model is very extensive, it must be criticized for some lack of external criteria visibility, especially of bundled services that must be assessed by users (customers), but often cannot be identified as bundled from external partners. Nevertheless, the method has been used in several surveys22 and has significantly contributed to Web evaluation research. • Barnes and Vidgen have developed a method called WebQual, which was first developed using the quality function deployment (QFD) method35 and later enhanced by the integration of findings from the ServQual method for the measurement of services. Thus, not only the two-stage questions (importance and quality survey) but also ServQual criteria have been integrated into WebQual to emphasize the Internet’s interaction nature within the WebQual criteria.36 Therefore, Web-specific criteria corresponding to the five servicequality categories of ServQual (tangibles, reliability, responsiveness,


58


assurance and empathy) have been identified and further consolidated to get a set of 10 subcategories (two for each ServQual category) with, overall, 24 questions. The method also includes a procedure to calculate a “WebQualIndex” for each subcategory of the questioning results for comparison of Web sites. This has been shown by the authors for the Online-Bookstore genre.36 • Yet another method is the QEM (Quality Evaluation Method) by Olsina,37– 39 based on a detailed generic criteria catalog for evaluation of software quality offered by the International Standard Organization (ISO). Its four-steps are: 1. A domain- or genre-specific set of criteria (attributes requirements tree) is derived from the generic catalog. 2. An “elementary evaluation” is used to assess the perceived quality of single attributes (some criteria are also objective and prove the existence of attributes) regarding subjective perceptions of different user types. For this, scales and ranges for each attribute must be identified and criteria functions for their normalization must be defined. 3. To obtain a global quality indicator for each Web site, the evaluator defines the aggregation process and implements it (third step: global evaluation). For this, the logic scoring of preferences (LSP) model or a simpler additive scoring model can be used to weigh the attributes and identify their relevance to overall quality. 4. The last phase carries out the analysis and comparison of results and the identification of improvement needs and potentials for each Web site. Applications of the method have been done, for instance, for online bookstore,39 academic,38 and museum Web sites.37 The three models (see Table 3.1), represent the questionnaire-based evaluation of Web sites from a customer perspective, considering the subjective nature of quality. They differ in the used criteria catalogs and methods for the aggregation of resulting data to global quality indices.

3.3.3 SUBJECTIVE EVENT-SPECIFIC METHODS The event-specific quality assessment of Internet applications is based on the consideration that Web-based interactions consist of a number of sub-processes or events that drive or bias customers’ quality perception. Therefore, event-specific evaluation methods seem to be appropriate to supplement attribute-oriented methods facilitating an integrated view on the condition and quality of companies’ Web interfaces. The identification and analysis of these events provides management information on how to allocate resources to the development or improvement of crucial features and events. Different event-specific evaluation methods might be integrated in a three-step approach: (1) analysis of common navigation and interaction processes regarding the disclosure of process determinants, (2) identification of critical incidents that might occur throughout the interaction process and (3) the rating of identified shortcomings in relation to resource allocation for development and enhancement efforts (see Figure 3.6). Step 1: Disclosure of Interaction Process Determinants To examine the interaction process from the customers’ point of view and to identify relevant process determinants, a set of appropriate approaches can be applied:


Web Evaluation

59

TABLE 3.1 Overview of Objective and Subjective Attribute Specific Web Evaluation Methods Method name Elliot et al. (2000)26

Bauer and Scharl (2000)25

Selz and Schubert (1997, 1998)22,33

Barnes and Vidgen (2000): WebQual35,36

Olsina (1999): QEM37–39

Short Description Questionnaire-based evaluation for the comparison of commercial Web sites. Objective method surveying the existence of attributes.

Criteria

6 Categories, each with 5 attributes: • Company information and functions • Product/ service information • Transaction processing • Customer service • Ease of use • Degree of innovation Automated approach for the 3 categories for the classification of classification of Web sites into genres genres: using neural networks. Allows • Content: number, size of documents insights into e-commerce adoption of etc. sectors. • Interactivity: forms, usage of programming languages for interactive elements etc. • Navigation: links, frames etc. Web questionnaire-based subjective Criteria in two dimensions: 1) phases evaluation for the comparison of of transaction process; 2) layers of commercial Web sites. Web performance system. Sector-specific adjustment of criteria necessary. Web questionnaire-based subjective 24 attributes in 10 subcategories: evaluation for the comparison of • Aesthetics commercial Web sites with detailed • Navigation method for data aggregation to • Reliability calculate global quality indices. • Competence • Responsiveness • Access • Credibility • Security • Communication • Understanding the individual Stepwise method for the Criteria: domain specific quality questionnaire-based subjective requirements trees based on a generic evaluation and comparison of criteria catalog by the ISO. Main commercial Web sites with detailed categories: method for data aggregation to • Usability calculate global quality indices • Functionality (using LSP). • Site Reliability • Efficiency


60


First, blueprints of observed Web-based interactions must be developed. The preparation of such interactive blueprints can be supported by the analysis of clickstream data or the monitoring of browsing behavior in a laboratory environment, e.g., with Lotus ScreenCams or video cameras (documenting on-screen behavior and expressions of test subjects; for typical usability testing approaches using video cameras in a laboratory environment.40 Adding qualitative information on page features and content to click-stream data is essential as facts taken into account are otherwise very fragmentary. However, click-stream analysis always provides very limited insight into interaction processes, as information on contingencies of surfing activities are not captured in the user navigation record. (Click-stream analysis is an internal or company-oriented Web-evaluation method.) The developed interaction blueprints serve as a framework for the interview and guide the interviewer’s enquiries. With regard to the identified interaction process or subprocess (e.g., searching of product or service information, enter personal information, download software, online orders conducted, etc.), the test persons are encouraged to express their perception of the navigation flow, their impressions and particular evaluations. The interviews validate the appropriateness of the interaction blueprints and relevant process determinants. Compared with internal or expertdriven supervision, the outlined approach proves favorable, as interaction processes and determinants are evaluated from the subjective customer point of view. Step 2: Identification of Critical Incidents Due to the assumption that interaction processes imply a certain number of events determining the overall quality perception — positively or negatively — Step 2 of the integrated evaluation approach concentrates on the identification of such critical incidents (compare Critical Incident Method in Ref. 41). The identification of critical incidents can be achieved by the analysis of phrased customer perceptions and impressions of Step 1, the analysis of clickstream data indicating breakups, the breakdown of complaints or the direct inquiry via questionnaire in customer interviews. Appropriate questions in a questionnaire might be the following: • • • • •

Was any incident during your Web surfing troublesome? Please describe what happened. Did you expect this incident to occur? Do you have an explanation why this incident might have occurred? What did you do after the incident occurred? Were you able to bypass the interference or did you break up the interaction? • Did you expect any help from the Web site or its operator to solve the problem or to prevent it from cropping up?

The result of this evaluation step provides strong evidence on subjective relevant processes and events. Expected process design and quality levels are explicitly described, as well as the expression of expected responsiveness (interaction feedback, complaint handling, etc.). The propositions are unambiguous, as customers can focus on their significant experiences and are not called upon to


Web Evaluation

Disclosure of interaction process determinants

Development of interaction blueprints

61

Identification of critical Incidents

Mapping of critical incidents

Monitoring online behavior by

Analysis of

¥ personal observation,

¥ voiced customer im-

¥ clickstream analysis, ¥ ScreenCams etc.

Rating of critical Incidents

Problem assessment :: frequency and importance

Analysis of voiced deficits regarding frequency of

pressions and

occurrence and effects

¥ complaints/feedback,

of critical incidents on

¥ clickstreams

Importance

customer behavior

ii22 ii11

ii33

ii55

ii66 ii44

frequency

FIGURE 3.6 Three-step approach of event-specific Web evaluation.

rate a number of predetermined and abstractly phrased quality attributes.42 The understanding of attribute implications on personal interaction and quality requires a level of imagination, which only experienced users will show. As customers can use their own words to phrase their impressions, users of any experience level can provide vital information. The flip side of these methods for blueprinting the Web interaction process and identifying critical incidents is the high degree of effort they require. Surveys and assessment executed in a laboratory environment, as well as one-to-one or focus group interviews, require considerable resources for manual supervision and can hardly be automated. The same is true for the supplementary assessment of complaints, whereas a limited significance must be considered as they consist mostly of partial descriptions. As mentioned before, click-stream analyses are limited due to the lack of information on Web-surfing contingencies and should not be deployed as the primary tool to analyze critical incidents during Web interactions. Step 3: Rating of Shortcomings and Potential Objects of Refinement After identifying critical incidents as shortcomings of Web-based interaction processes, priorities must be set for the improvement of crucial features and further development. The problem-detecting method tries to assess the importance of shortcomings by ranking the voiced deficits. The approach is based on the assumption that problems that occur more frequently and are perceived as particularly significant are more urgent and therefore demand a higher degree of management attention. Shortcomings must be distinguished by frequency of occurrence and by their importance in the quality perception of the customer.27 Whereas the frequency can be


62


calculated easily, importance must be estimated by the use of substitutes, e.g., expected or observed customer reaction, level of satisfaction, etc. Furthermore, derived values can be transferred into a matrix to illustrate the nature of identified shortcomings. To summarize the outlined approach: Event-specific methods explicitly evaluate customers’ Web interaction process and provide vital management information. This advantage is partly offset by the fact that incident-oriented methods are extremely resource intensive. Tests in laboratory environments, for instance, require the presence of test subjects and the manual execution and analysis of interviews, as it proves crucial to break down the expressed quality perception into attributes for improvement. In contrast, multi-attribute methods can be carried out online (e.g., with Web questionnaires, etc.) and can be automated to a significantly higher extent. The choice of appropriate methods should be determined by the central objective of the Web evaluation project and resources disposable. Whereas attributeoriented approaches facilitate the deployment of (online) questionnaires and therefore enable representative samples, event-specific approaches provide deeper insight into the customer quality perception of a company’s Web interface and interactions taking place.

3.4 A WEB EVALUATION PROJECT Based on the discussion of Web evaluation methods, we will now discuss the process of a Web evaluation project. Given the complexity of Web evaluation methods and approaches, a Web evaluation project must be carefully planned and structured. It can be divided into classical project phases (Figure 3.7).

3.4.1 PLANNING WEB BENCHMARKING The planning phase is a core phase in which the evaluation goals must be identified and suitable evaluation methods selected. If using a company-specific set of criteria, they must be recalibrated. As an example of a Web evaluation project, we have selected a benchmarking case for a competitive Web site evaluation.43,44 The goals are to identify improvement areas and potentials for further Web activities regarding the customer’s needs. Therefore, a customer-oriented benchmarking with the use of subjective attributespecific methods can be applied and company-specific criteria identified. In a further step, reference Web sites must be identified. For this purpose, two main types of customer-oriented benchmarking approaches can be distinguished:45,46 Planning Evaluation goals Method selection

Preparation Criteria selection Scales and weights Selection of assessors

Evaluation Interview and questionnaire design Carrying out evaluation

FIGURE 3.7 Web evaluation process.

Analysis Consolidation and aggregation of results Interpretation, identification and prioritization of improvements

Implementation Re-implementation, improvement of website & services Implementation of new features


Web Evaluation

63

1.Competitive benchmarking compares one’s own Web activities with those of direct competitors to identify performance gaps for improvements. 2. Functional benchmarking compares single Web site functions or activities (attributes) with those of companies in other sectors to identify new ideas for the improvement and enlargement of the company’s own Web activities. The benchmarking company first must decide which type of benchmarking to apply and then must identify suitable benchmarking partners.

3.4.2 PREPARATION, EXECUTION

AND

ANALYSIS

In the next step, questionnaires must be prepared. Questions, scales, weights, etc., and a method for the aggregation of data and the calculation of quality indices must be selected or designed. Furthermore, addressees for the questioning must be selected and modalities must be identified. Several possibilities exist: • Web questioning of customers (critical if carried out on the company’s Web site, because of the evaluation of competitors’ sites) • Offline interviews with customers (expensive) • Interviews with neutral experts or internal employees who try to evaluate the sites from a customer’s point of view (problem of relevance, no real customer perspective regarding motivations and attitudes) After evaluation and data collection, data must be aggregated and analyzed. With the resulting quality indices, different types of analysis can be carried out, e.g., a prioritization of attributes for improvement. A comparison of perceived performance and surveyed importance of each single attribute leads to a classification of attributes (see Figure 3.8). Furthermore, a performance gap analysis can be Performance gap analysis

Priority setting

company information

performance

+

interactivity overkill

product information

well done!

performance gap logistics

o

-

must be improved

unimportant

o

1

2

3

4

5

6

speed

+

importance

FIGURE 3.8 Analysis of benchmarking results.

security

ease-of-use

reliability Own website Benchmarking partner A Benchmarking partner B


64


done to compare the perceived quality of each attribute for each Web site (see Figure 3.8). An attribute’s performance gap indicates a potential for improvement. Measure must be selected based on the prioritization of attributes.

3.5 CONCLUSIONS The Web has emerged as an innovative communication channel that: • • • •

Facilitates new ways to address and interact with customers Requires and offers new roles and extended functions for customers Yields unprecedented opportunities to collect detailed data Is a platform for Web-based services that have been designed as an integral part of companies’ offerings, e.g., Web-based customer self service and configuration options in the telecommunications industry

Consequently, companies require new ways to evaluate and control the quality of their Web-based activities. While Web evaluation is sometimes seen as just a method to test the quality and performance of Web applications, or a prerequisite of benchmarking and continuous improvement, this chapter discusses a comprehensive framework to structure objects, subjects, events and methods of evaluation, and classify possible approaches. The wealth and complexity of possible evaluation options raises the question of design. We suggest a goal-driven approach that takes contingency into account; the process of selecting, prioritizing and combining possible approaches should reflect the strategic intent and operational goals of companies’ Web applications. The timing of evaluations is another fundamental design option: evaluation approaches can be used in early phases of Web-application development, in a later stage to assess effects, and throughout the development, implementation and deployment process to facilitate feedback loops and accelerate learning processes. A careful assessment design reflects the attempt to capture dynamics and innovation of Web applications.

REFERENCES 1. Webster’s New World Dictionary. 2. Colorado State University: http://writing.colostate.edu/references/processes/evaluate/ 3. Morris, Ed: Toward a Process Framework for COTS Evaluation, Carnegie Mellon University, http://www.sei.cmu.edu/cbs/cbs_slides/98symposium/framework/tsld004.htm. 4. Splaine, Steven, Jaskiel, Stefan P. and Savoia, Alberto (2001): The Web Testing Handbook; Software Quality Engineering Pub., 2001. 5. Nguyen, Hung Quoc (2000): Testing Applications on the Web: Test Planning for Internet-Based Systems, New York 2000. 6. Meffert, Heribert (2001): Marketing – Grundlagen marktorientierter Unternehmensführung, 9. Auflage, Wiesbaden 2000.


Web Evaluation

65

7. Hermanns, Arnold and Sauter, Michael (1999) : Electronic Commerce – Grundlagen, Potentiale, Marktteilnehmer und Transaktionen, in: Hermanns, Arnold and Sauter, Michael (Eds.): Management-Handbuch Electronic Commerce, München 1999, p.1330. 8. Yates, JoAnne and Orlikowski, Wanda J. (1992): Genres of organizational Communication: A structural approach to studying communications and media, in: Academy of Management Review, Volume 17, 2 (1992), p. 299-326. 9. Crowston, Kevin and Williams, Marie (2000): Reproduced and emergent genres of communication on the World Wide Web, in: The Information Society, Volume 16, 3 (2000), p. 201-215. 10. Kroeber-Riel, Werner and Weinberg, Peter (1999): Konsumentenverhalten, 7. Auflage, München 1999. 11. Trommsdorf, Volker (1998): Konsumentenverhalten, 3. Auflage, Stuttgart et al. 1998. 12. Esch, Franz-Rudolf, Hardiman, Marco and Langner, Tobias (2000): Wirksame Gestaltung von Markenauftritten im Internet, in: Thexis, 3 (2000), p. 10-16. 13. Donabedian, Anthony (1980): The Definition of Quality and Approaches to its Assessment and Monitoring, Volume 1, Ann Arbor 1980. 14. Parasuraman, A., Zeithaml, Valerie A. and Berry, Leonard L. (1985): A conceptual model of service quality and its implications for future research, in: Journal of Marketing, Volume 49, 1 (1985), S. 4-50. 15. Meyer, Anton/Mattmüller, Roland (1987): Qualität von Dienstleistungen; Entwurf eines praxisorientierten Qualitätsmodells, in: Marketing – Zeitschrift für Forschung und Praxis, Volume 9, 3 (1987), p. 187-195. 16. Corsten, Hans (1990): Betriebswirtschaftslehre der Dienstleistungsunternehmen, 2. Auflage, München 1990. 17. Corsten, Hans (1997): Dienstleistungsmanagement, 3. Auflage, München 1997. 18. Meffert, Heribert and Bruhn, Manfred (2000): Dienstleistungsmarketing, 3. Auflage, Wiesbaden 2000. 19. Hofmaier, Katja and Walczuch, Rita M. (1999): Measuring Customer Satisfaction on the Internet, Research Symposion on emerging Electronic Markets (RSEEM), Münster, Germany, 1999. 20. Spiliopoulou, Myra (2000): Web usage mining for Web site evaluation, in: Communications of the ACM, Volume 43, 8 (2000), p. 127-135. 21. Hentschel, Bert (2000): Multiattributive Messung von Dienstleistungsqualität, in: Bruhn, Manfred and Stauss, Bernd (Eds.), Dienstleistungsqualität, S. 289-320, Wiesbaden 2000. 22. Selz, Dorian and Schubert, Petra (1997): Web Assessment — A model for the evaluation and the assessment of successful electronic commerce applications, in: Int. J. Electronic Mkts., 9, S. 46-48. 23. http://www.4allsites.com/linkchecker.html 24. http://www.hh-forum.de/Webchecker.htm 25. Bauer, Christian and Scharl, Arno (2000): Quantitive evaluation of Web site content and structure, Internet Res. J., Vol. 10, 1 (2000), p. 31-43. 26. Elliot, Stephen Ross, Schreiner Morup-Petersen, Anders and Bjorn-Andersen, Niels (2000): Toward a framework for evaluation of commercial Web sites, in: Klein, Stefan; et. al. (Eds.), Proc. 13th BLED Electronic Commerce Conference, S. 69-86, Bled: Slovenia, 2000. 27. Stauss, Bernd and Hentschel, Bert (1991): Dienstleistungsqualität, in: Wirtschaftswissenschaftliches Studium (WiSt), Volume 20, 5 (1991), S. 238-244.


66

The E-Business Handbook 28. Liu, Chang, Arnett, Kirk P. and Litecky, Chuck (2000): Design Quality of Web sites for Electronic Commerce: Fortune 1000 Webmasters’ Evaluations, in: Int. J. Electronic Mkts., Vol. 10, 2 (2000), p. 121-129. 29. Liu, Chang and Arnett, Kirk P. (2000): Exploring the factors accociated with Web site success in the Content of electronic commerce, Information & Management, (38) 2000, p. 23-33. 30. van der Heijden, Hans (2000): The Impact of perceived Web site Characteristics on Web site Traffic, in: Klein, Stefan; et al. (eds.), Proc. 13th BLED Electronic Commerce Conference, p. 414-425, Bled: Slovnia, 2000. 31. Ho, Chin-Fu and Wu, Wen-Hsiung (1999): Antecedents of customer satisfaction on the Internet: an empirical study of online shopping, in: Proc. 32nd Ann. Hawaii Int. Conf. System Sciences, Hawaii: IEEE Computer Society, 1999. 32. Parasuraman, A., Zeithaml, Valerie A. and Berry, Leonard L. (1988): SERVQUAL. A multi-item scale for measuring consumer perceptions of service quality, in: J. Retailing. 33. Selz, Dorian and Schubert, Petra (1998): Web Assessment — A Model for the Evaluation and Assessment of successful Electronic Commerce Applications, in: Proc. 32nd Ann. Hawaii Int. Conf. System Sciences, Hawaii, Internet and the Digital Economy Track, Vol. IV, 1998, p. 222-231. 34. Belz, Christian, et al. (1991): Erfolgreiche Leistungssysteme, Stuttgart 1991. 35. Barnes, Stuart J. and Vidgen, Richard (2000): WebQual: An Exploration of Web-Site Quality, in: Hansen, Hans Robert; et al. (Eds.), Proc. 8th Eur. Conf. Information Systems (ECIS), p. 298-306, Vienna: Austria, 2000. 36. Barnes, Stuart J. and Vidgen, Richard (2000): Information and interaction quality: evaluating Internet bookshop Web sites with WebQual, in: Klein, Stefan, et. al. (Eds.), Proc. 13th BLED Electronic Comm. Conf., p. 426-444, Bled: Slovenia, 2000. 37. Olsina, Luis (1999): Web-Site Quality Evaluation Method: a Case Study on Museums, ICSE 99 — 2nd Workshop on Software Engineering over the Internet, http://sern.ucalgary.ca/~maurer/ICSE99WS/Submissions/Santos/Santos.pdf. 38. Olsina, Luis, Gogoy, Daniela and Lafuente, Guillermo (1999): Assessing the Quality of Academic Web sites: a Case Study, in: New Review of Hypermedia and Multimedia (NRHM) Journal, http://www.euitio.uniovi.es/Actividades/cursos_verano_9900/Olsina_NRHM.pdf. 39. Olsina, Luis, Lafuente, Guillermo and Rossi, Gustavo (2000): E-commerce Site Evaluation: a Case Study, http://www.euitio.uniovi.es/Actividades/cursos_verano_9900/ Olsina_ECWeb2000_formatted.pdf. 40. Harms, Ilse and Schweibenz, Werner (2000): Testing Web Usability, in: Information Management & Consulting, Volume 15, 3 (2000), p. 61-66. 41. Bitner, M.J., Booms, B.H. and Tetreault, M.S. (1990): The Service Encounter. Diagnosing Favorable and Unfavorable Incidents, J. Mktg., Volume 54, 1 (1990), p. 71-84. 42. Bruhn, M. and Hennig, K. (1993): Selektion und Strukturierung von Qualitätsmerkmalen. Auf dem Weg zu einem umfassenden Qualitätsmanagement für Kreditinstitute, Teil 1 und 2, in: Jahrbuch der Absatz- und Verbraucherforschung, Volume 3. 43. Lederbogen, Kai (1998): Entwicklung eines Benchmarks für Business-to-Customer Electronic Commerce Applikationen; Masters Thesis, University of Muenster, 1998. 44. Misic, Mark M. and Johnson, Kelsey L. (1999): Benchmarking: a tool for Web site evaluation and improvement, Internet Res. J., Vol. 9, 5 (1999), p. 383-392. 45. Zairi, Mohamed and Leonard, Paul (1994): Practical Benchmarking: The Complete Guide, London, 1994. Chapman and Hall. 46. Camp, Robert C. (1995): Business Process Benchmarking, Milwaukee, 1995. ASQ Quality Press.


4

Role of Information Technology in Supply Chain Management G. Prem Premkumar

CONTENTS 4.1 Introduction ....................................................................................................67 4.2 Value Chain ....................................................................................................68 4.3 Analysis of Information and Product Flow in the Supply Chain .................70 4.4 Responsiveness and Efficiency — Push and Pull Chain ..............................71 4.5 Strategies for Improvement............................................................................73 4.6 Supply Chain Management — Evolution of Information Technologies ......75 4.7 Implementation of Supply Chain Management ............................................78 References................................................................................................................83

4.1 INTRODUCTION Supply Chain Management (SCM) has received considerable attention in the past few years as an area where information technology-enabled e-commerce initiatives can bring significant cost reduction and efficiency improvement. AMR, a leading research firm in this area, forecasts that Internet-based business-to-business (B2B) commerce will reach $5.7 trillion by 2004.1 Industries are predicted to move 60%–100% of their procurement transactions to the Internet by then. New technologies — XML, trading exchanges, enterprise application integration (EAI), application service providers (ASP), etc. — are anticipated to transform B2B interactions. Industries such as electronics, industrial and transportation equipment, wholesale trade, and financial services are expected to find great value in these new technologies and lead in the transformation of B2B commerce. Supply chain management has become important ever since electronic communication technologies first enabled sharing of information electronically among trading partners. The competitive marketplace has put significant pressure on improving the bottom line, and, given that direct costs account for 50%–70% of total expenses in most firms, there is a fertile ground for technology-enabled cost reductions in procurement and management of direct and indirect materials. While, in the past, firms have primarily focused on EDI as the enabling technology, the growth in B2B electronic marketplaces and supply chain software such as enterprise resource planning (ERP), advanced planning and scheduling (APS), and customer relationship management (CRM) systems have

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

67


68


opened up new opportunities for collaboration. Although some of the technologies have not lived up to the past year’s hype, the problems cannot be completely attributed to the technologies themselves. Successful implementation requires a change in the philosophy for B2B interaction, shifting from an adversarial relationship to a more collaborative relationship, and a reengineering of the supply chain business processes. This is a difficult exercise, because the change has to pervade the entire industry chain, which comprises multiple business entities with conflicting business objectives and intermediaries whose existence may be threatened by new initiatives. In the long run, firms must collaborate to survive in the competitive marketplace. AMR claims, “Collaboration will become increasingly important for users to handle demand–supply constraints and long run collaboration will evolve into a fundamental capability… .”1 This chapter will provide a framework to understand supply chain management, the problems and opportunities in the area, the evolution of technologies in the last two decades, and some guidelines for implementation.

4.2 VALUE CHAIN Porter and Millar2 developed the concept of the value chain to understand the various activities in an organization that create value, the cost drivers, and the role of IT in enabling the creation of value and, thereby, competitive advantage for the organization. Figure 4.1 shows the various activities that form the core of the value chain. Inbound Logistics

Operations

Outbound Logistics

Sales & Marketing

Service

Customer

FIGURE 4.1 Value chain.

Raw material is received and stored in inbound logistics, converted to a finished product in operations, shipped and distributed through outbound logistics to customers who are identified by the sales and marketing function, and serviced through after-sales service. The primary support activities are assisted by procurement, product design and R&D, human resources, and corporate services such as accounting, legal and other administrative services. Value is created at each stage as the material is converted from a simple raw material to a complex product with a brand image. Costs are incurred at each stage in the value creation process. Value can be defined as the customer’s perception of the worth or price for the end product. The difference in the product value as assessed by the market and the cost incurred in the creation of the product is the profit for the firm. Every firm is focused on two objectives — creation of value and minimization of cost. The value creation could occur through new features in the product, creation of brand image, or excellent customer service. Cost reduction could occur at every stage through increased automation, cheaper sourcing for materials, reduced inventory, etc. IT plays a critical role in achieving both these objectives. In the late ’80s to early ’90s, most firms focused on innovative IT solutions that enhanced value and created competitive advantage for firms.2 In the early-


Role of Information Technology in Supply Chain Management

69

to mid-’90s, firms focused on cost reduction and efficiency improvement as they went through extensive reengineering exercises to create new business processes that leveraged the potential capabilities of the IT infrastructure. Business process reengineering ensured information visibility to various procedures across the entire organization through extensive information sharing and breaking down information silos into different functional areas. In many firms, it changed the existing notions of information and power by breaking power structures created by information silos. In the late ’90s, the focus of process reengineering moved to interorganizational interfaces where significant opportunities existed for leveraging IT to improve efficiency, reduce costs, and enhance value. The Internet, providing network connectivity to all trading partners, led to the development to new technologies such as B2B e-marketplaces, SCM systems and e-procurement systems. The value chain concept can be expanded from an organizational context to an industry analysis. An industry value chain can be considered as a series of value chains of various participants interlinked.3 Figure 4.2 shows the various participants in an industry value chain. While the organization’s value chain is focused on the immediate customer, the industry value chain is focused on adding value at minimum cost to the ultimate end consumer. It takes a “global” rather than a “local” view of optimization. Sometimes, using a local optimum strategy may not be beneficial in obtaining a global optimum. This situation is no different from the view of an organization 20–30 years ago, where individual functional areas or divisions were independent cost or profit centers concerned more about their local optimum at the risk of a global optimum that would be beneficial to the entire firm. As the marketplace becomes increasingly competitive, the competition has shifted from individual firms to whole-industry value chains, one competing with another. In such an environment,it becomes important to take an industry level perspective and foster greater collaboration among the participants.

Financial Institutions Logistics Supplier

Manufacturer

Distributor

Information

Physical Goods Payment

FIGURE 4.2 Supply chain.

Retailer

Customer


70


Implementation of new processes and IT innovations at organizational level is relatively easy because top management can enforce certain strategies and guidelines. Implementation at the industry level is a more difficult exercise, because the participants are independent business entities with different business objectives, and sometimes could be competitors or competitors’ suppliers. The realization that all participants will lose business if the value chain is not competitive is perhaps the greatest motivator for collaboration. Power and dependence relationships, and equity in sharing of the benefits become important factors for successful implementation. Also, there are challenges from a technology and optimization perspective. Because most participants are members of multiple SCM, optimization across multiple networks becomes a nontrivial task. As shown in Figure 4.2, there are basically three flows in the industry value chain: 1. Product flow from supplier to customer through various intermediaries 2. Payment flow in the opposite direction 3. Information flow in both directions The flow of information and payment can occur electronically, while product flow, unless it is a digital product, is a physical flow. Logistics providers facilitate the physical flow of the product and financial institutions facilitate the flow of payments. The upstream is sometimes considered the supply chain and downstream the demand chain. The supply and demand chain changes depending on the member in the chain. A demand chain for one could become the supply chain for another. In practice, most people consider the entire industry value chain as a supply chain, and the same view will be used in this chapter.

4.3 ANALYSIS OF INFORMATION AND PRODUCT FLOW IN THE SUPPLY CHAIN The information flow in the value chain is dependent on the characteristics of the information such as its availability and the possibility of sharing it, and the speed of information flow among the different participants. The availability of electronic communication channels among the participants considerably increases the speed of information flow. For example, a 5- to 6-day paper-based mail system can be reduced to a few seconds in an electronic mail system. However, the characteristics of the information influence what flows freely and what does not, regardless of the communications technology. Lack of information flow creates uncertainty. Organizations attempt to reduce their environmental uncertainty both upstream (supplier) and downstream (customer). However, uncertainty also creates power, due to information asymmetry, that can be leveraged in negotiations and in interorganizational relationships. Hence, information flow is dependent on the willingness of members to share information, resultant loss of power, and equitable incentives for sharing information. The trigger for information flow is the order (or order forecast) from the customer. The uncertainty in customer needs creates a ripple effect along the entire value chain. The uncertainty could be caused by the customer’s not being aware of future needs,



71

or not willing to share that information due to commitment costs. However, most customers expect the firm to fulfill the order quickly. According to Christopher,4 “Most organizations face a fundamental problem: the time it takes to procure, make and deliver a finished product to a customer is longer than the time the customer is prepared to wait for it.” The lead-time gap varies from industry to industry. Organizations address the problem of lead-time gap by carrying inventory to meet the needs of the customer as shown in Figure 4.3. Inventory is carried at each stage in the supply chain because the demand uncertainty is transmitted right through the chain, sometimes with a magnifying effect. A small change in a customer order could cause a “bullwhip” effect of major changes at the other end of the chain, as most participants overreact to changes in their demand chain by making a greater change in their supply chain, greatly exaggerating the actual change in demand for the ultimate consumer (Hau, Padmanabhan, and Whang, 1997). Firms enjoy two benefits from carrying inventory: • •

Provides the customer with instantaneous or quick delivery of product. Also buffers the firm’s internal operations from external uncertainty.

The amount of inventory is dependent on the order cycle time. If shorter than the customer’s acceptable waiting time, then there is no need to carry any inventory.

Supplier

I n v

Manufactrer

I n v

Supply

I

Distributor n

Retailer

v

I n v

Customer

Demand Chain

FIGURE 4.3 Supply and demand chain.

4.4 RESPONSIVENESS AND EFFICIENCY — PUSH AND PULL CHAIN Inventory, while providing improved customer service, is an extra cost. The value of instantaneous or quick delivery must be matched with the cost of providing it. Most firms strive to balance responsiveness with cost and efficiency.5 Improved responsiveness is always at the expense of cost. The market determines the appropriate value for responsiveness and, therefore, different industries have different tradeoffs. While customers to a convenience or grocery store place a significant value on responsiveness, customers for large capital goods may be willing to wait longer. Some goods must be made to order because of product complexity or other factors. The production process could also influence the tradeoff, because some goods are most efficiently manufactured in large volume due to high setup costs, which may change the tradeoff between responsiveness and efficiency. A commodity


72


Supplier

I n v

Manufacturer

Push

Supplier

I n v

Manufacturer

Distributor

Retailer

Customer

Pull

I n v

Push

Distributor

I n v

Retailer

I n v

Customer

Pull

FIGURE 4.4 Push–pull chain.

product with a certain demand (e.g., steel pipes) is better produced by an efficient production process. A firm has to examine its product characteristics, production processes, and market demands, and determine a strategic fit among demand uncertainty, responsiveness, and cost.5 Once a firm has determined its strategic fit, all its functional strategies should mesh with it. For example, a firm focused on efficiency uses a business strategy of low manufacturing cost, high volume, and low margin, while a firm focused on responsiveness would use flexible manufacturing and high margin to offset the cost of responsiveness. The industry and firm characteristics determine the level of responsiveness and location of the inventory in the supply chain. Because inventory can be partially substituted with information, IT influences the location and volume of inventory stored at each of the interorganizational interfaces. The two types of supply chains — push and pull — are based on information flow and how the order is filled. Typically, most supply chains use a combination of both. Figure 4.4 shows the difference between the two chains. In the “push” chain, inventory is pushed through the chain in anticipation of customer orders. All actions are based on forecasts. This can be considered a responsive supply chain because inventory is located in every interface to swiftly meet customer demands. However, the chain also runs the occasional risk of obsolete inventory and of products in the chain that do not exactly match a customer’s specifications. In the “pull” chain, the customer’s order triggers the information flow and associated activities to fulfill the order. While this reduces the inventory in the chain, it is less responsive and puts pressure on reducing the manufacturing cycle time. In most practical cases, it is difficult to have a chain with no inventory in any of the stages. Typically, you will encounter some items where the lead-time gap is too large to slow down the supply chain. Natural raw materials and foreign outsourced parts that have long delivery times are typically stored as inventory. While industries such as consumables, consumer durables, and groceries are more likely to be push chains, commercial aircraft, ship building, large capital goods, etc. are invariably pull.



73

4.5 STRATEGIES FOR IMPROVEMENT An organization can use two strategies to reduce the inventory — (1) reduce demand uncertainty by improving the forecast or (2) reduce the order cycle time. While the first reduces obsolescence and unwanted inventory, the second makes it possible to carry smaller inventory. Forecasting is prone to errors and firms tend to be conservative, carrying more inventory than necessary. A whole field of optimization has developed models and methods to improve forecasting accuracy. Newer technologies such as customer relationship management (CRM), data mining methods, and decision support systems are attempting to better assess customer needs and reduce forecasting errors in estimating product volume and options, characteristics, and order timing. Developing close relationships with a few suppliers and providing realtime information visibility on customer demands to them will enable substituting inventory with information. Supply Chain

Supply Time

Firm

Manufacturing Time

Demand Chain

Delivery Time

FIGURE 4.5 Time

The second strategy is to reduce the order cycle time and thereby the lead-time gap. If the lead-time gap can be reduced or eliminated, the firm does not need to carry any inventory. As shown in Figure 4.5, order-cycle time has three components: demand chain, manufacturing, and supply chain. Hence, any strategic exercise in reducing the lead-time gap must examine time reduction strategies in each of the three areas and potential costs for each strategy. In the demand chain, various strategies are employed to address the lead-time gap.6 Figure 4.6 shows some of the options. A firm could use a “ship-to-order” strategy by maintaining finished goods inventory to service the order immediately and thereby reduce the lead-time gap. Another strategy is the “pack-to-order” strategy, where the final assembly is postponed and is packed or assembled from a inventory of modules after the order is received. It reduces the need to carry all the different option combinations in finished goods and thereby reduces total inventory. This requires a product design strategy of modularization of the product assembly. Because this approach requires packing or assembly, the packing time should not be greater than the customer’s acceptable wait time. Additional capacity for packing may also be required. The third strategy is the “make-to-order” approach, where an order triggers the manufacturing of the product. This, however, requires a manufacturing system that is very responsive to changing customer orders. The firm should manage customers’ expectations based on their needs and acceptable wait time. The


74


Manufacture

I n v

Pack

I n v

Distribute

Ship to Order Pack to Order Make to Order

Consume

I n v

Plan

I n v

Procure

Buyer - Purchase Order Automatic Replenishment - CRP End user - E-Procurement

FIGURE 4.6 Order strategies.

firm also needs to reduce its order cycle time through improved manufacturing and delivery logistics. In the manufacturing stage, a common strategy to reduce time is to have surplus manufacturing capacity to meet variations in demand. Some firms move away from assembly line manufacturing to make their systems more responsive, which could be at the cost of efficiency. A firm could modify the product design to reduce the number of product options so that variance in product orders can be reduced, facilitating more efficient manufacturing and reducing the manufacturing time. Efficient manufacturing processes that reduce the delays between stations reduce the total manufacturing time. In the supply chain, a firm could reduce the supply time by requiring its major suppliers to maintain inventory in its premises. Vendor-managed inventory (VMI) was a popular approach that used this method. However, this is a local optimum solution, essentially moving inventory from one location to another, and it does not benefit the whole chain. Modern innovative approaches attempt to reduce the supply chain time by moving the ordering point earlier into the procurement life cycle. A procurement cycle can be considered to consist of three stages: 1. End users or manufacturing processes consuming inventory 2. Planners requesting replenishment of inventory 3. Buyers evaluating suppliers and placing purchase orders Typically, the purchase order is triggered at the last stage by the buyer. If suppliers are allowed to monitor the inventory and capture the order information at the planning stage, they can be more responsive, because they can save the time spent on purchase-order processing. An additional improvement would be to access consumption information of end-users and capture the order directly from them. This would save the time spent on planning and purchase order processing. An eprocurement system that is integrated into a firm’s intranet to directly receive orders



75

from end-users is an example of such an approach. Dell has partnership with a few customers where it allows end-users in these firms to order directly from customized Web pages, thereby saving internal planning and purchase-order processing time. A firm may have to use different procurement strategies, depending on the product characteristics, vendors, and procurement practices. Some indirect materials and office supplies can be directly ordered by the end-users, while other items may require more formal planning and ordering by the purchasing department. Firms have been trying to reduce the order cycle time as well as changing customers’ expectations on acceptable wait time. For instance, in the PC industry, online purchase of computers has become very common. For the customer, this involves moving from the traditional approach of going to a retail store and picking up a computer (order cycle time of 0) to ordering and waiting for 5–10 days to receive the product. By carefully modifying the needs of the customer, the industry was able to make a change in the procurement practice for a majority of customers. In other industries, such as grocery supply, where instantaneous delivery is normally a requirement, firms along the value chain are tightly integrating their operations through efficient consumer response (ECR) systems and other new initiatives to reduce the order cycle time and inventory in the chain. Although it is a push process right through, the industry has significantly reduced the inventory at each stage. New entrants in the grocery industry, such as Webvan, have tried, to some extent, to modify the behavior of the customer, thereby reducing uncertainty. For example, they monitor the purchases of individual customers and suggest a list of items that they will need at least a week in advance. This way, they have reduced the order cycle time by a week, and, to that extent, have converted some parts of the chain to a pull cycle or reduced the inventory storage at each stage. If the order cycle time for a product in the chain is less than a week, they have eliminated inventory for the product in the chain.

4.6 SUPPLY CHAIN MANAGEMENT — EVOLUTION OF INFORMATION TECHNOLOGIES Organizational interactions have gone through significant changes in the last 50 years. In the ’50s and ’60s, manufacturing firms were highly vertically integrated, where many of the first-tier suppliers were either divisions or subsidiaries of the firms. A classic example of such an organization was General Motors, whose philosophy dictated that all the critical parts of an automobile be engineered and made by the company. Other large manufacturing firms during that period had similar philosophies. Supply chain coordination was much easier, because all the parts belonged to the same company. In the ’70s and ’80s, lack of economies of scale, labor issues, globalization, etc., forced firms to focus on their core competency and outsource manufacturing of some of their products to low-cost subcontractors, both inside and outside the country. The availability of cheap labor in developing countries led to globalization and greater outsourcing, which significantly increased the coordination problems caused by distance, time zone variations, cultural differences, lack of communication-technology infrastructure, and other factors. It also brought in significant competition from foreign firms that had a different cost structure. In the


76


Synchronization

Collaboration Level of Integration

Coordination

Communication

Time

FIGURE 4.7 Evolution of IOS Technology.

’90s, growth of the Internet and developments in communication technologies have brought significant changes to SCM practices. Hence, there has been significant pressure in the SCM area to reengineer business processes, reduce inefficiencies and costs, and improve SCM performance. IT has played a key role in the evolution of SCM. The availability of cheap electronic communications has considerably reduced coordination cost and enabled organizations to move from vertically integrated firms to horizontally networked organizations. Figure 4.7 shows the various phases in the evolution of SCM with time in the x-axis and level of integration in the y-axis. As is evident from the figure, the level of coordination in the past 20 years has continued to increase. We can identify four broad phases in the evolution of communications technology: 1. 2. 3. 4.

Communication Coordination Collaboration Synchronization

The first phase of evolution can be termed the “communication phase,” where IT essentially enabled faster communication of transaction information between trading partners. A paper-based purchase order mailed to a supplier took 3 to 6 days to arrive. The use of IT reduced it to a few hours. Electronic Data Interchange (EDI) was extensively used for communication. It provided a structured format for transmitting different types of transaction information. The information was transmitted through either direct dial-in connections or through third party value-added networks (VAN). Early pioneers in the retail industry such as Wal-Mart, Sears, K-Mart, etc. used proprietary standards and formats for communication. Suppliers had multiple



77

systems to interact with each of their major customers. In most cases, it was a batchoriented process where the trading partner would check its mailbox with the VAN two or three times a day to download or upload its EDI transactions. Typically, the information went through multiple media changes. A purchase order created by an information system or filled out on a paper form was transcribed into EDI format and then electronically transmitted to the other end, where it was either printed out and used as the basis for triggering internal processing, or reentered into an appropriate information system. There was very little integration with the internal system. In this phase, technology essentially made the transmission of information faster, replacing the paper-based mail system. While large firms benefited from directly feeding the electronic transaction information to their internal information systems, the value proposition was very limited for many firms, especially those that used FAX extensively for communication. The next phase in the evolution was the “coordination” phase. This involved the expansion of communication to a wide variety of transactions and greater integration with the systems that generate or consume the EDI information. Many firms used a middleware to interface EDI with existing legacy systems for purchase order, sales invoices, accounting, etc. The movement from proprietary formats to standard formats and the strong push for standardization to ANSI X.12 or EDIFACT helped to alleviate the problems in communicating with multiple partners. VANs also provided translation software and middleware to convert flat file information from legacy systems to approved EDI formats. The expansion in the range of transactions brought in both internal and external challenges. The difficulty in coordinating with multiple functional areas, interfacing with multiple internal information systems, and reluctance to share information both internally and externally slowed implementation in many firms. While there was little resistance to transmitting traditional transaction information such as purchase orders, sales invoices, bills of lading, etc., there was significant reluctance to share other information such as demand forecast, warehouse inventory, and other information that firms perceived could impact their competitive advantage. Integration with internal systems brought significant improvement to efficiency and cost reduction. Prior to introduction of IT, an average purchase order cost $50–$100 for creation and transmission. The cost has been reduced by 50%–75% through savings in labor and coordination cost. The third phase in evolution is the “collaboration phase,” where a firm moves from simplistic information sharing to true collaboration. Technologies for collaboration include group support systems, teleconferencing, e-mail, discussion forums, and other communication technologies that reduce the constraints of space and time and enable global collaboration. Two trading partners could collaborate at different levels between multiple functional areas. Collaboration ideally starts at the strategic level and works its way to the tactical and operational level. For example, a firm could collaborate on new product development, sharing information on market research, product designs, customer preferences, and process technologies. This would enable the firm to design better products in a competitive manner. Collaboration can occur in the operations stage in the value chain. Visibility of information on demand, inventory, production schedules, etc., greatly improves the efficiency by reducing the information uncertainty and associated bullwhip effect.


78


Coordinated production, with changes being communicated instantaneously, can bring stability to the chain. The potential for collaboration in the logistics stage is highlighted by the SCM practices in the electronics and PC industries. Dell shares its order and customer information with its monitor supplier (Sony) and logistics provider so that monitors can be shipped to customers directly without having to be transported to Dell’s warehouse, stored, and then shipped to customers with the rest of their computer orders. While collaboration eliminates double handling, it requires extensive coordination and working closely with a trading partner. It is based on trust in the capabilities of the trading partner. Micron technology goes one step further in a collaborative relationship. It collaborates with UPS to enable the package-delivery service to assemble all the packages in a computer order that it maintains in its warehouse. Collaboration in the marketing stage of the value chain could involve joint programs for product promotion with the retailer, market research on customer preferences, sharing retail sales data for better promotion and pricing, and joint demand forecasting leading to reduced inventory in the demand chain. In the after-sales service area, firms could outsource to third-party providers, but could provide integration through IT. For example, although Dell outsources its after-sales service, customers and outsourcers use Dell’s information systems for troubleshooting and reporting problems. Hence, the extensive expertise of Dell is provided to the outsourcer to provide high quality service. Dell also benefits from direct receipt of information on problems, which enables the company to improve its products. The last phase can be termed the “synchronization” phase, where collaboration between trading partners is taken to the next level, moving from a dyadic collaboration to synchronization and collaboration across the entire supply chain. The entire chain functions as a single unit, with all partners striving for a common business goal. It requires information visibility across the entire chain and close collaboration with at least two levels of trading partner. New initiatives such as collaboration planning, forecasting, and replenishment (CPFR), are influenced by the availability of technology for sharing real-time information and facilitating collaborative demand planning, what-if analysis, and various other collaborative activities. Synchronization could involve redesigning the entire supply chain so that the firms, through strategic partnerships, evaluate supply chain flows and redesign business processes, identify storage and distribution points, determine benchmarks for performance measurement, recognize the risks and rewards, and develop equitable methods for sharing the benefits among the partners.

4.7 IMPLEMENTATION OF SUPPLY CHAIN MANAGEMENT SCM implementation is a difficult task that requires the cooperation of all the participating firms. Because an organization deals with multiple suppliers and has different procurement needs for different products, it is necessary to develop a comprehensive plan for SCM. Major strategies and policies should flow from the top to ensure commitment among the internal units. SCM planning should address the strategic, tactical, and operational level issues. It is important that



79

the coordination between the two firms happen at all the three levels. One without the other is always a recipe for failure. For example, top management at the strategic level may agree on a partnership, but if it is not followed through at the tactical or operational level, or if there is significant resistance at the lower level, then SCM will exist only as an idea. Similarly, if people at the operational level see the tremendous opportunities for collaboration based on their experience with EDI, but it is not in alignment with the firm’s strategic vision, or the supplier is not a strategic trading partner, then they will hit a roadblock in implementation. Ideally, SCM is implemented as a top-to-bottom approach, with strategy driving execution. Sufficient communication and buy-in by the planning and execution team is required in both firms, because SCM may involve a change in culture, job redesign, and restructuring of some functions. A brief analysis of the issues at the three levels is discussed below. Strategic Analysis — Strategic planning for SCM is critical to allow the organization to ensure that SCM initiatives are in alignment with corporate business strategies and goals, as well as with the strategies and plans of individual units. It is not only important for the planners to evaluate the strategies within the organization, but also to work with planners in the participating firms. It is very critical for an organization to realize that SCM implementation, unlike other initiatives, is an interorganizational exercise that requires strategic commitment from the participating units. Top management involvement sends a strong signal of commitment to the trading partners (see Figure 4.8).

Strategic Alignment of Business & SCM strategies Identification of strategic business partners Partnership Agreements

Tactical Supply chain planning Technology planning Change management Benchmarking

Operational Supply chain execution Technology Facilitation Performance Monitoring Relationship facilitation

FIGURE 4.8 Supply chanin management implementation .


80


Some of the activities to focus on at the strategic level are: • Evaluation of business strategies and development of SCM strategies • Identification of strategic partners for SCM • Development of strategic partnership agreements An important exercise at the strategic level is to evaluate business plans and strategies and develop an SCM strategy that is in alignment with them. Lack of alignment in functional strategies can lead to problems. For example, difficulties could arise if marketing promises a variety of product options and quick delivery while SCM is developing a low-cost strategy, trading responsiveness for cost and efficiency. This happened recently with many online firms, where their SCM strategies did not match their marketing commitments on the Web, leaving many customers dissatisfied. As discussed in the earlier section, a firm must determine the strategic fit between responsiveness and efficiency, and then align functional strategies to match that fit. For example, a firm focused on cost and efficiency would use a pricing strategy of low cost, high volume and low margin, while a firm focused on responsiveness would use a high-margin strategy to offset the cost of responsiveness. The manufacturing strategy must focus on efficient utilization for firms with cost-efficiency strategy and on flexibility and surplus capacity for firms with responsiveness strategy. Similarly, in product design, while cost-efficient firms would focus on designs for low-cost manufacturing, responsive firms would focus on creating modular designs that allow postponement of assembly and product differentiation. Identification of strategic partners is key to initial success in implementation. As it is not possible for a firm to have a close relationship with every trading partner, an important strategic exercise is to determine which segments of trading partners are most critical for business, and the potential opportunities and benefits from forming a supply chain partnership with them. Various factors that could influence the decision include: • • • • •

Criticality to business — strategic fit and competitive advantage Potential opportunities and benefits from SCM collaboration Flexibility — responsiveness vs. efficiency Willingness of the partner Strategic dependence and power structure

Strategic partnership agreement is required to signal commitment to partnership and provide direction for lower-level plans and actions. The agreement could identify the broad areas for collaboration, ranging from a narrow focus such as logistics to a very broad focus including joint product development, manufacturing process design, product promotion, collaborative demand forecasting and planning, etc. Leadership is critical for successful implementation. At the strategic level, top management needs to identify the task force in charge of execution. Various mechanisms for conflict resolution and legal issues can also be addressed in the partnership agreement.



81

Tactical Analysis — At the tactical level, the focus is on planning. Some of the major areas to address at this level are: • • • •

Supply chain planning Technology planning Change management Benchmarking

Supply chain planning provides detailed plans for collaboration between two firms. It identifies the areas and levels of collaboration in different functional areas, SCM initiatives with different partners, information that will be shared and timing of information flow, and cross-functional teams in charge of planning and execution of SCM in the two firms. Supply chain collaboration, although predominantly focused on logistics and procurement, could involve all the functional areas. Joint planning sessions with strategic partners will identify the functional areas for collaboration, and then explore the various initiatives that need to be launched in each area. For example, two firms may decide to move to continuous replenishment planning (CRP). This would require more detailed planning on how it will be implemented, the items that will use CRP, the policies for replenishment, location of inventory, and technology interfaces between the systems of the two teams. Technology planning is a critical activity because it provides the basic infrastructure for SCM implementation. However, firms must discard the faulty notion that once technology is in place, SCM implementation is complete. Technology is merely a facilitator and an infrastructure over which business processes must be implemented. Technology architecture provides a broad framework for interfacing with multiple suppliers with possibly incompatible systems. It typically includes the communications and applications architecture. SCM plans and strategies from the strategic exercise and information from tactical supply chain planning will influence the architecture. The communications architecture develops guidelines for the transmission method, protocols, security requirements, and alternate communication channels. A firm could use multiple transmission methods for information flow, including Internet, direct dial-in, value-added networks (VAN) providing EDI service, and virtual private networks (VPN). Firms are moving away from proprietary formats and protocols toward open standards for easy connectivity across multiple partners. Network security is a major concern, especially if the data is being transmitted on a public network. The architecture must provide guidelines and standards for secure communication, digital certificates for authentication, and procedures for security audit. Alternate communication channels such as e-mail, discussion forums, Web conferencing, virtual meetings, etc., are very useful to enhance the communication among firms. The applications architecture identifies the various information flows among firms, the applications that generate or consume the information, the interfaces of these applications to the communication channel, the access mode, and applications level security. The information flow could be an arm’s-length relationship between systems as in EDI, or could be as tightly integrated as in two ERP systems that are directly interfaced to share data. New SCM applications provide Web-based interface


82


to a partner’s information systems and databases, and provide ability to download data into standard desktop applications. The systems could communicate in a batch mode, in an exception-based message mode, or in real time. One of the biggest bottlenecks in sharing data is the lack of standardization of data. Definition of data items varies from organization to organization. For example, the unit of measure for the same item could be different among firms, or lead time could include delivery time in one firm but not in another. An important exercise prior to sharing information is developing uniform definitions for commonly used data. Change management is critical for successful implementation because any new initiative is bound to encounter resistance. The cross-functional project teams need to become champions for the new initiative and actively market it within their functional areas. They must be trained to address the issues that arise from implementation, including staff relocation, replacement, job redesign, and various other personnel issues. In interorganizational initiatives such as SCM, relationship building and trust development between firms is very important (Kumar, 1996). Sometimes, firms enter into these relationships wary of each other’s motives. Firms could develop trust by ensuring that all actions are taken in the best interest of both firms, and that all issues are openly discussed and resolved. Game playing can rapidly destroy the relationship and lead to failure in implementation. Firms with a procurement philosophy based on distrust and pitting one supplier against another may have to undergo cultural change to operate in the new environment. Benchmarking helps a firm identify the important goals to be achieved and the performance measures to strive for, and to evaluate its performance as well as its partners’ progress. Partners in SCM relationships expect equitable sharing of benefits resulting from collaboration. Performance measurement based on wellestablished metrics and associated rewards systems help to create a performanceoriented culture. Operational Analysis — At the operational level, the focus is on execution and measurement. Some of the activities at this level are: • • • •

Execution Technology facilitation Relationship facilitation and monitoring Performance monitoring

Supply chain execution of coordinating day-to-day transactions is perhaps the most difficult task. The relentless quest for cost reduction and efficiency improvement has, in many cases, created a supply chain with very little slack. Any small problem in the supply chain can create major ripple effects across the chain. Hence, task forces need to be always on the alert to solve problems before they become very big. Technology support at the operational level facilitates small suppliers that may not have the expertise to integrate complex communication and application systems. The dependence on IT in SCM has reached a level where a small outage could shut down the entire supply chain. Hence, close coordination between IT staffs in the two firms is critical.



83

The relationship between partners must be continuously evaluated and improved, as it is key to SCM success. The SCM task force must meet regularly to sort out any major problems with relationships. New areas to collaborate must be identified to foster the growth of the relationship. Performance monitoring helps to provide critical feedback for the partners. Areas for improvement can be identified and strategies for improvement planned. Firms that do not meet performance measures can be evaluated for continuance in the SCM program.

REFERENCES 1. AMR. 2000. B2B E-Commerce Report — 1999-2000. 2. Porter, M.E., and Millar, V.E. 1985. How information gives you competitive advantage, Harvard Business Review, 63(4), 149-161. 3. Premkumar, G. 2000. Interorganizational systems and supply chain management — an information processing perspective, Information Systems Management, 17(3), 5669. 4. Christopher, M. 2000. Creating the Agile Supply Chain, www.ascet.com/ascet/wp/ wpchristopher.html. 5. Chopra, S. and Meindl, P. 2000. Supply Chain Management — Strategy, Planning, and Operation. Prentice Hall, Upper Saddle River, NJ. 6. Holmstrom, J., Hoover, W.E., Louhiluoto, P., Vasara, A. 2000. The other end of supply chain, Mckinsey Quarterly, No. 1, 63-71.

SEE

ALSO:

Anderson, D.L. and Lee, H. 2000. Synchronized Supply Chains: The New Frontier, www.ascet.com/ascet/wp/wpanderson.html. Fisher, M.L. 1997. What is the right supply chain for your product?, Harvard Business Review, March-April, 105-116.



5

Online Auctions: A Closer Look Alok Gupta and Ravi Bapna

CONTENTS 5.1 5.2

Introduction to Online Auctions ....................................................................85 A Review of Major Online Auction Mechanisms.........................................87 5.2.1 Single-Item Auctions..........................................................................87 5.2.2 Multi-Unit Auctions ...........................................................................89 5.2.3 Combinational Auctions.....................................................................91 5.2.4 Multidimensional Auctions, Reverse Auctions..................................92 5.3 Consumer Bidding Strategies ........................................................................92 5.4 Opportunism and Trust in Online Auctions ..................................................93 5.5 Simultaneous Substitutable Mechanisms: Auctions vs. Posted Price...........95 5.6 Conclusions and Future Trends .....................................................................96 References................................................................................................................97

5.1 INTRODUCTION TO ONLINE AUCTIONS Online auctions represent a model for the way the Internet is shaping the new economy. In the absence of spatial, temporal and geographic constraints, these mechanisms provide many benefits to both buyers and sellers. They are now an important component of the portfolio of mercantile processes that are transforming the economy from traditionally hierarchical to market-oriented structures (see Ref. 16 for an exhaustive review). A broad and deep body of economics literature exists that investigates the theoretical properties of traditional auctions. However, significant differences in the cost structures, to both buyers and sellers participating in online auctions, have resulted in a need to revisit much of the existing theory. This chapter provides a broad context, derived from an overview of the current research and practice in this field, and provides insights into this interesting sphere of economic activity. Online auctions fall under the ambit of Web-based dynamic pricing mechanisms. In these mechanisms, consumers become involved in the price-setting process. Consumers can now experience the thrill of “winning” a product, potentially at a bargain, as opposed to the typically relatively tedious notion of “buying” it. For sellers, these mechanisms are likely to bring access to newer markets, help 1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

85


86


clear aging or perishable inventory, and provide experiential and, at times, vital marketing capabilities. Nowhere are these trends so visible as in the hugely popular online auction site eBay. Among other things, eBay has resulted in dramatically improving the efficiency of secondary markets that were typically associated with garage sales and flea markets. EBay’s legion of 10 million monthly visitors provides the necessary critical mass of buyers and sellers to set market prices for their goods. The more bids that come in, the more competition there is, and, chances are, the higher the price. In retrospect, eBay had, and continues to have, both the positive network externality effect of a growing user base as well as the first mover advantage, necessary conditions for success in today’s economy.35 The impact is even more dramatic in business-to-business (B2B) markets, where Forrester Research predicts an increase in sales from $19.3 billion last year to $52.6 billion by 2002. A full suite of dynamic pricing mechanisms is in use in B2B markets, including standard auctions where there is a single seller and multiple buyers, reverse auctions where a single buyer receives bids from multiple sellers and multiple buyers, and multiple seller exchanges that resemble the bid–ask markets for stocks and commodities. Ref. 25 presents an overview of the top-performing B2B auctions. Beginning with the dot-com euphoria of 1999, one can observe the emergence of a myriad of price-setting processes, such as traditional first-price auctions for single items (e.g., eBay.com), multi-item auctions selling multiple identical units (e.g., Onsale.com and eBay’s Dutch auction), reverse auctions for goods and services (e.g., eLance.com and FreeAgent.com), name-your-price mechanisms (e.g., Priceline.com), quantity discounters (e.g., Mercata.com), and methods that used derivative-based pricing for consumer goods (e.g., Iderive.com). Not surprisingly, some continue to flourish (e.g., eBay and Onsale) while others have floundered (Mercata and Iderive). Despite the originality of these pricing approaches and the initial dotcom buzz surrounding them, little attention has been paid to their effectiveness. Instead, directionless entrepreneurship, at times fueled by overzealous venture capitalists, replaced scientific enquiry and rigor when it came to examining the efficacy and viability of candidate mechanisms. We contend that significant research is still needed in designing new and better mechanisms, as well as in examining the efficacy of existing ones in the contexts of the markets they serve. In this chapter, we touch upon issues of mechanism design, secondary market creation, bidding costs and strategies, incentive compatibility, bid-taker cheating (shilling), simultaneous substitutability, and associated research methodologies. Interestingly, the advent of auctions over open protocol-based networks such as the Internet has also facilitated the pursuance of a richer set of empirically derived methodologies by today’s researchers. Most pre-Internet-based auction research was either purely theoretical in nature22–24 or it involved laboratory experimentation.14 Empirical research was rare due to the lack of meaningful data sets, which in turn could be attributed to the lack of mainstream appeal of auctions. Lucking-Reiley18 refers to the difficulty in obtaining field data for testing long-standing hypotheses, such as the supposed revenue equivalence between the basic auction formats. The best data set available prior to the arrival of Web-based auctions covered U.S. Forest


Online Auctions: A Closer Look

87

Service sales of contracts for harvesting timber in the Pacific Northwest during 1977.12 The earlier lack of empirical or realistic experimental test environments is increasingly disappearing with the technological advancements in online auction technology. The widespread popularity of online auctions, coupled with the open computing paradigm upon which Internet applications are built, together present a golden opportunity for researchers to revisit the various branches of auction theory in a setting that is more realistic and has higher inductive value. In the remainder of this chapter, we share the insights of these recent research developments in online auctions.

5.2 A REVIEW OF MAJOR ONLINE AUCTION MECHANISMS A key factor that makes electronic markets such as online auctions interesting is the potential for achieving higher efficiency. On the surface, e-markets such as eBay, with millions of registered users, would appear to be a close approximation to an economist’s idealization of a frictionless, efficient market. One thing for certain is that using information technology has brought this sphere of economic activity out of the domain of specialists to that of the average person. The success of eBay notwithstanding, we contend that the frictionless efficient market is still an ideal to strive for. We review the popular types of online auctions, with the caution that this is by no means an exhaustive list of current online auctions. Our objective here is to isolate mechanisms that are interesting, are being currently researched, and are those in which the online environment influences the strategic spaces of the participants. We begin with the classic single-item, open, ascending, English auction.

5.2.1 SINGLE-ITEM AUCTIONS This asset-exchange mechanism has been extensively studied theoretically, beginning with the seminal article by Nobel laureate William Vickrey.36 More-recent coverage can be found in Ref. 29. Researchers studying this auction commonly make use of assumptions, such as the independent private values (IPV) assumption to derive its equilibrium characterization. Such an assumption implies that a single indivisible object is to be sold to one of several bidders. Each bidder is risk-neutral and knows the value of the object to himself, but does not know the value of the object to other bidders. It also implies that there is a finite population of bidders, each of whom draws valuation independently from some given continuous distribution.23 Consider the applicability of this assumption to common single-item online auctions such as those conducted on eBay (eBay’s multi-item Dutch auctions are discussed in the next subsection). For most goods being auctioned, the IPV assumption is robust. However, for collectibles, a popular category on eBay, it is reasonable to assume that an individual’s valuation will be dependent on the valuations of fellow bidders. Presumably, a collector will have the objective of at least recovering the cost of the item purchased and thus will implicitly base valuation on that of other bidders.


88


The presence of “winner’s curse” in auctions, namely that bidders win items only because they pay too much, has long been of interest to researchers. In an empirical study of eBay auctions.,2 it was found that a bidder’s surplus falls by 3.2% when the expected number of bidders increases by one. In Section 5.6, we describe two recent studies that have compared online auctions with posted price mechanisms selling the same goods (using matching SKUs). The evidence of winner’s curse from those two studies is mixed. A common assumption related to IPV, especially popular in experimental studies in the lab, is that the number of bidders in an auction is exogenously determined. That is, somehow this population is known a priori and the design of the auction itself does not influence the number of participants. Several recent empirical studies challenge this notion and point to interesting issues regarding the choice of a reserve price by the seller. On eBay, the seller can set an open minimum bid that serves as the starting price for the auction. Additionally, sellers also have the option of setting a hidden reserve price below which they are not obligated to sell. Once the bidding level exceeds the reserve, an indication of “reserve met” is displayed to the bidders. The questions are: • Should the seller use the hidden reserve at all? • Does setting a low minimum bid attract bidders to the auction, thereby increasing competition? • Is there an optimal mixed strategy that can be employed by sellers with respect to these two parameters? In Ref. 2, it was found that items with higher book value tend to be sold using a secret, as opposed to a posted, reserve price with a low minimum bid. These researchers also found that the minimum bid is the most significant determinant of whether a bidder enters an auction. Lucking-Reiley20 describes controlled experiments conducted on the Internet to verify a variety of theoretical properties of electronic auctions by manipulating the reserve prices in these auctions as an experimental treatment variable. This researcher’s findings indicate that bidders consider their bid submission to be costly and that bidder participation is indeed a subjective decision. Additionally, the data shows that a zero reserve price provides higher expected profits than a reserve price greater than or equal to the auctioneer’s salvage value for the goods. In contrast, Riley and Samuelson28 show that, for an optimal highest-clearing-price English auction, the reserve price is a function of the seller’s valuation of the product. Perhaps the most cited property of single item auctions is the Theorem of Revenue Equivalence.9,24,36 The idea is that, under a set of restrictive assumptions (IPV and risk-neutral bidders), the expected revenue from a variety of auction types — English, Dutch, first-price and second-price sealed bid auctions — is equivalent. Revenue equivalence results are known not to be robust with respect to the slightest deviation from the restrictive assumptions of the independent private values model24 or bidder risk preferences,21 which are notoriously difficult to observe. LuckingReiley18 tests revenue equivalence through field experiments auctioning magic game cards. It was found that the Dutch auction produces 30% higher revenues than the



89

first-price auction format, a contradiction to the theoretical prediction and a reversal of previous laboratory results that the English and second-price formats produce roughly equivalent revenues. It will be interesting to see whether similar trends are observable in online auctions of a more general nature. Future research in this area promises to bring interesting new insights into these age-old topics of interest.

5.2.2 MULTI-UNIT AUCTIONS The online environment has spawned a variety of auctions that sell multiple identical units of the same item. These range from auctions of consumer goods, mostly aging hardware and electronics through sites such as Onsale.com and Ubid.com, to auctions of fixed-income and equity securities by Muniauction.com, to OpenIPO.com — which allows individual and institutional investors to bid online for shares of an IPO — giving both types of investors a level playing field in the IPO market for the first time in history. A cursory examination of the above mentioned sites reveals that what was historically a sealed-bid-dominated market, as in the auction of Treasury bonds, now supports a wide range of auction mechanisms, namely ascending English, descending Dutch, and eBay’s so-called “Dutch” — which is really an ascending open uniform-price auction. Rothkopf and Harstad29 point out that single-item results do not carry over into multiple-item settings and that this has been a vastly neglected area of auction theory research. Of late, there is evidence of research spawning in multi-item auctions. Lucking-Reiley and List19 examine the case when consumers are allowed to bid for more than one item under two different types of two-unit, two-person sealed-bid auctions. When consumers are allowed to bid for more than one item in an m-item auction, Vickrey’s original proposition — full demand revelation occurs in a sealedbid auction — does not hold. Instead, the rule has to be modified such that for an m-item Vickrey auction bidders can submit as many individual unit bids as they like. Further, the top m bids are declared winners and, for the jth unit won by a bidder, she pays an amount equal to the jth highest of the rejected bids submitted by others. Hence, this revised mechanism offers discriminating prices in contrast to the original mechanism’s uniform pricing. Importantly, this mechanism is incentive compatible. Bidders gain nothing by not revealing their true valuations, as they never have to pay what they bid. In the two-item case, Reiley and List19 indicate that there is evidence of demand reduction, i.e., lowering of the second bid below the true valuation, when the uniform pricing rule is applied. This is a cause for concern and leads to lower allocative efficiency. In the case of real-world B2C online multi-item auctions, consumers are allowed to bid for more than one item, but these bids cannot be discriminating, i.e., they all must be of the same amount. For instance, a given individual can bid for three items at $100 each, but cannot bid for two items at $110 and one item for $80. Whether this constraint is designed to prevent demand reduction in auctions that sell multiple (far more than two units) is an open and interesting research question. Bapna et al.4,5 present an analytical modeling approach that analyzes multi-item progressive English auctions, also known as Yankee auctions, such as those con-


90


ducted by Onsale.com. The analytical modeling is subsequently validated by empirical investigation using data collected by automated agents that track real-world Web auctions, adding a new methodological dimension to auction theory research. These researchers focus on the hitherto undescribed discrete and sequential nature of the revenue realization process of such auctions, caused by the presence of the bid increment. In such auctions, say, of ten Palm V PDAs, the ten highest bidders win and the price they pay is equivalent to their highest bids, technically making this a discriminatory auction. Usually, a very low opening bid such as $1 is set by the auctioneer as a way to attract Web traffic. In addition, all auctions have a bid increment that defines the minimum step size for bidders. Bids that fall in between bid increments are automatically rounded down to the nearest step. This discretization of the process challenges the common auction theory assumption that individuals’ valuations can be drawn from a known, continuous distribution. The bid increment also helps determine the minimum required bid at any time during the auction. This is equal to the lowest winning bid plus the bid increment. The list of current winning bidders, the bid increment, the minimum required bid, and the auction closing time are all continuously updated on the Web. Auction durations typically range from 1-hour express auctions to day-long regular auctions. Bids are ranked by amount and by time within amount. Unlike traditional single-item English auctions, a new bidder’s high bid does not automatically displace the existing winner from the winner’s list. In fact, if the current number of bidders is less than the lot size, then the new high bid does not affect any of the existing winners. An interesting observation revealed by the data collection process of Refs.4 and 5 is that online auctioneers experiment with the design parameters they can control for such auctions. For instance, they sell the same goods using a $20 bid increment one day, followed by using a $15 bid increment on another day. There exists a great opportunity for researchers in determining how to optimally set the control factors such as the bid increment, lot size and the opening bid that influence the efficiency of such auctions. There have been other attempts to compare the efficiency of different auction mechanisms both theoretically and empirically. The focus has been on comparing single-item sealed-bid competitive auctions with sealed-bid discriminatory auctions. In the former, the highest bidder wins, but the price paid is the secondhighest bid; whereas, in the latter, the highest bidder wins with the price being the highest bid. Competitive auctions were first suggested in the seminal article in Ref. 36; the special property of this mechanism is that all bidders have incentive to bid their true valuation. Given the growing theoretical and empirical interest in multi-item auctions, it will be interesting to see work that examines the relative efficacy of mechanisms that are available to consumers today. Does eBay’s so called “Dutch” mechanism, which has little theoretical basis and unexplored incentive characteristics, lead to higher clearing prices than, say, an equivalent Yankee auction? The extension of the single-item results, such as revenue equivalence, to the online setting of multi-item auctions is an interesting area of research. This will help us understand which mechanism should be adopted under what circumstances. For instance, if there is a



91

predominance of risk-averse bidders who prefer a certain outcome to an uncertain one, than would a descending Dutch auction yield higher expected revenue? Of course, this analysis is not trivial even with the most simplistic of assumptions regarding the consumer type.

5.2.3 COMBINATIONAL AUCTIONS If we allow multiple units of nonidentical goods to be sold through online auctions, then we get into the realm of combinational auctions. Such auctioning schemes are desirable to sell complementary goods that can be “bundled” together. A good example is the FCC spectrum auction for different regional licenses, where the value of having, say, Boston increases if the bidder can also acquire neighboring New York. These auctions present many interesting challenges to practitioners, both sellers and bidders, as well as theoreticians. In general, the auctioneer’s problem of determining an optimal set of bids in a combinational auction is an NP-Complete problem, problems that are tough to solve in a reasonable amount of time.30 Additional issues are the (1) exposure problem: an unsuccessful attempt to acquire a collection of assets, when combinational bidding is not allowed, may lead to paying more for some individual assets than they are worth, and (2) threshold problem: a bidder on item A and a bidder on item B may not be able to coordinate to displace a bid on package AB in the presence of unequal economies of scale. Several interesting approaches are being proposed to overcome some of the above-mentioned computational and mechanism-design difficulties in combinatorial auctions. One such approach is the iBundle mechanism.26 The basic idea of the iBundle mechanism is to use software to calculate the maximal allocation of products among various users who can bid on bundles. Each bidder can bid for any number of bundles, so can offer $10 for A, $20 for A and B. The iBundle software then calculates the combination of bundles that maximize total transaction value and notifies the bidders of the provisional winners. The bidders are then able to make higher bids, and the process repeats until bidders are satisfied. The contribution of iBundle is to use IT to quickly solve an optimal allocation problem that would become computationally infeasible for human agents in real time. Another interesting combinational auction mechanism being developed13 accepts incompletely specified bids that provide a framework to guide, rather than dictate, the choice of goods that satisfy bidder needs. Its incompletely specified combinational auction mechanism is designed to facilitate large, complex problems commonly relegated to negotiated sales, where the allocation of goods requires solving a complex combinational problem. A representative example is the complex multidimensional process of media buying, specifically, the sale of television-advertising airtime. Allowing a bid in the form of high-level rules relieves the buyer from the burden of enumerating all possible acceptable bundles. Further research is needed to harness the enormous computational power available to us to make combinational auctions mainstream for corporations keen to optimize their complex logistical decisions, such as determining optimal freight patterns for moving goods from manufacturing sites to wholesale sites and on to retail sites.


92


5.2.4 MULTIDIMENSIONAL AUCTIONS, REVERSE AUCTIONS In certain cases, for example, in many procurement situations, it is not sufficient to conduct auctions where only the price dimension matters. Often, price and quality go hand in hand and jointly determine the winning bid — not necessarily the lowest. Most of the literature on auction theory has focused on the analysis of auctions of a well-defined object or contract so that the price to be paid is the unique strategic dimension with the exception of Refs.7, 10 and 33. For example, in the auction for a department of defense contract, say for the construction of an aircraft, the specification of its characteristics is as important as the price. In such multidimensional auctions bidders submit bids with relevant characteristics of the project, price being just one such characteristic, and the procurement agency uses a scoring mechanism to select among the bids. Branco,7 and, to some extent, Theil,33 recommend a two-stage auction mechanism where, in the first stage, the procurer selects one firm and, in the second stage, bargains to readjust the level of the quality to be provided. Multiple dimensions also exist in the reverse auctioning of service contracts, such as those conducted by Elance.com and Freeagent.com, where price is not the only differentiating factor. For instance, a client who posts a requirement for a Web design project could receive bids from all over the world, ranging from Bangalore, India to Yugoslavia to the U.S., with hourly rates of $24, $10 and $50 respectively, as we casually observed on Freeagent.com. It would be naive to think that the client would necessarily go with the lowest bid in this case, as service quality may be widely varying, and perhaps even difficult to assess. Ref. 32 studies Internet-based spot markets for service contracts. Its analysis confirms the fact that the transaction costs of posting a project, bidding on it, and evaluating bids are all significant.

5.3 CONSUMER BIDDING STRATEGIES The global scope and reach of the online environment makes it feasible for “armchair” bidders16 to be active players in auction markets. While much of the theoretical development assumes buyers and sellers to be rational, profit-maximizing individuals —a tenuous assumption to begin with —the reality of the online-markets landscape indicates a wide disparity in user experience and information levels. Researchers have been quick to capitalize on the availability of real auction data, obtained through field experiments and automated agent-based real-time tracking of online auctions respectively, for examining consumer bidding strategies on the Internet.4,5,19 Among other things, they test the behavior of uninformed bidders, who exhibit behavior that is different from what theory predicts. Lucking-Reiley and List19 auctioned four types of trading cards ranging from $3 to $70. They examined whether dealers of such cards would bid more rationally than nondealer, less experienced, individual card collectors. Their findings suggest that dealers exhibit more of the predicted strategic behavior than do non-dealers, for both lower- and higher-priced cards. Additionally, predicted strategic behavior is considerably greater when the auctioned sports cards have higher values, confirming



93

TABLE 5.1 Bidder Classification Evaluators

Participators

Opportunists

Early one-time-high bidders who have a clear idea of their valuation Bids are, usually, significantly greater than the minimum required bid at that time. Rare in traditional auction settings — high fixed cost of making a single bid. Violates the assumption of rational participatory behavior described earlier. Derive some utility (incur a time cost) from the process of participating in the auction itself. Make a low initial bid equal to the minimum required bid. Progressively monitor the progress of the auction and make ascending bids, never bidding higher than minimum required. Bargain hunters. Place minimum required bids just before the auction closes.

prior theories31 that suggest that rationality is more likely to be exhibited when the stakes are higher. Additional support for the notion that rationality becomes more evident as the expected payoff is higher is found in Refs. 4 and 5. The empirical investigation of 90 such auctions identified three distinct types of bidders. They are summarized in Table 5.1.3 To compare the performance of these strategies, Bapna et al.4,5 introduced a metric based on loss of surplus. This is the difference between an individual’s winning bid and the minimum winning bid. Loss of surplus evaluates the performance of an individual or a group with respect to the bidder who had the minimum winning bid in a given auction. The relative performance of these three groups, with respect to loss of surplus, indicates that the evaluators as a group fared worst, the participators were best off, and the opportunists lay in between.4 Much like Lucking-Reiley and List,19 Bapna et al.4 also finds evidence for the fact that, as the stakes get higher, a larger percentage of the population behaves strategically. This is evident in Figure 5.1, where the percentage of rational, nonevaluator-type bidders is plotted against the dollar values of the auctions tracked. It is easy to see an increasing trend in the percentage of bidders who behave in a nonevaluator mode as the auction stakes get higher. Evaluators, as we know, were found to be the worst-off of the three bidder categories.

5.4 OPPORTUNISM AND TRUST IN ONLINE AUCTIONS Typically, ignorance of what price to post is a reason for negotiating or holding an auction. Pavlou and Ba29 provide a behavioral reason for holding auctions. They assert that one of the critical reasons for the use of bidding is that the formality of the auction process provides legitimacy in a way that other economic means cannot.


94


% Rational Bidders 120% 100% 80% 60% 40% 20% 0% -20%

0

500

1000

1500

2000

$ Value of Auction

FIGURE 5.1 Rationality increases with the dollar value of the auctions.

Contradicting this belief from academia is the practical notion that the lnternet makes the likelihood of fraud detection and punishment low. Thus, it is not surprising that opportunistic behavior in online auctions, by both sellers and buyers, is constantly reported in the media. According to Internet Fraud Watch, online auction fraud has become the number-one type of Internet fraud over the last 2 years. Brandabur and Saunders-Watson8 discuss this issue in depth. For instance, they talk about an auction for a painting that was claimed to by the late Bay Area painter Richard Diebenkorn. The auction soared to $135,805, with the winning bid coming from Holland. However, the painting turned out to be a fake. The seller gave the appearance of being a novice, never actually mentioning the artist by name. In reality, it turned out this seller, along with two accomplices, often used more than 20 eBay screen names for the purpose of shilling — a strategy of placing phony bids to run up the closing prices of auctions. Recently, researchers have begun to attempt to model important aspects of trust and reputation in online auctions. Much of this stream relies on analytical modeling backed up by empirical investigation using automated agents to capture data from real-world auctions. This is an important area of research, as it addresses many of the issues raised by the critics of online auctions and helps formalize the concept and measurement of trust and reputation of sellers. There is increasing concrete evidence of shilling by bid-takers in online auctions. Kauffman and Wood15 present an analytical model and provide empirical evidence of “questionable bidding behavior (QBB)” by sellers on eBay. They define QBB as bidding on an item when the same or a lower bid could have been made on the exact same item in a concurrent auction ending before the bid-upon auction. QBB can be considered irrational, because buyers have a greater level of utility if they were to bid on another item for the same or lower cost. The research highlights the difficulties associated with identifying opportunistic sellers using QBB in online environments, when many auctions are going on in parallel:



95

1. Nonreputable sellers try to remain anonymous. Because they are attempting to hide their identity, it is difficult to identify them. 2. It is difficult to track multiple Internet auction identities and tie them together. 3. QBB needs to be reviewed over time in multiple auctions. For instance, if a bidder (using the same name) consistently rates a particular seller higher and exhibits active bidding behavior (for shilling purposes) while never winning many of the items, it would be easy to identify suspicious behavior; however, in practice, finding such behavior is difficult. Using intelligent data-gathering agents, Kauffman and Wood15 track a number of eBay auctions of coins, and the initial findings suggest that, indeed, a significant amount of QBB is evident. Opportunism can also be exhibited by the bidders in the form of collusion through fictitious identities. Wang et al.37 bring out the importance of appropriate mechanism design to counter such undesirable behavior. They focus on sealed-bid auctions and propose an alternative mechanism to the Generalized Vickrey Auction (GVA), called the Sealed-bid Multi-Round Auction Protocol (S-MAP), and give instances when the GVA is no longer incentive compatible. This group’s examples show that, in GVA under collusion, truth-telling is no longer a dominant strategy, and illustrate how bidders can reduce their payment by submitting bids under false names. This limitation is overcome in S-MAP, but the mechanism itself induces a higher cognitive load on the players and it remains to be seen whether it can become mainstream. Pavlou and Ba27 recognize that trust is an essential component of online auctions, and that buyers pay a price premium to transact with reputable sellers, particularly for expensive products. Their results show a significant correlation between trust and price premiums for all products. Moreover, this correlation becomes increasingly more significant for more expensive products. Another interesting empirical study11 examines the economic value of trust in electronic markets, based on a comparison of prices across generalist (eBay) and specialty sites (Michael Rogers, Inc.) in the arena of person-to-person online auctions. Generally, the two types of sites have very different mechanisms for providing trust in the marketplace — whereas generalist sites do not inspect the merchandise and rely instead on a reputation-reporting system, specialty sites typically take possession of auction items and provide a variety of value-added services directed at reducing information asymmetry and other sources of transaction risk. The study’s empirical findings appear to confirm that observed price differences between eBay and the specialist are driven primarily by the relative effectiveness of trust mechanisms in the two markets.

5.5 SIMULTANEOUS SUBSTITUTABLE MECHANISMS: AUCTIONS VS. POSTED PRICE Another promising stream of IS research in the area of dynamic pricing deals with comparing auctions with posted price mechanisms for the sale of identical goods.


96


Vakrat and Seidmann34 compare online catalog prices with online auction prices. They obtained data from 473 online auctions, such as SurplusAuction (www.surplusauction.com) and OnSale.Com (www.onsale.com), and compared prices received in these auctions with prices from Internet catalog sellers such as Egghead (www.egghead.com) and PriceScan.Com (www.pricescan.com). The study’s data analysis revealed that consumers expect greater discounts for more expensive items. These researchers employed Internet agents as a data collection tool. Using a similar methodological approach, Lee and Mehta17 investigated the existence of winner’s curse using theoretical modeling and empirical validation. Their preliminary results confirm the existence of winner’s curse in electronic auctions. The amount overbid is especially pronounced for items where potential information asymmetries exist as a result of the nature of the product, and it is further augmented in cases where the product is relatively new, and not much information regarding it exists in the retail channels. Interesting current research in this area is being carried out both from the buyers’ and the sellers’ perspectives. One study6 is examining under what conditions buyers should use auctions in contrast to posted price mechanism. Aron et al.1 investigates when auctions should be used by sellers instead of posted prices. Their study is focused on developing a theoretical model of markets with uncertain demand, in an attempt to understand what types of demand uncertainty make it worthwhile for a seller to consider investing in an auction mechanism to gain more price flexibility.

5.6 CONCLUSIONS AND FUTURE TRENDS The above review indicates that interesting developments are happening in both the practice and research of online auctions. The current dot-com shakeout notwithstanding, dynamic pricing mechanisms such as online auctions will continue to be an important component in the portfolio of mercantile processes that deployed by businesses to transact with their customers and suppliers, and for consumers to transact with other consumers. We call for greater interaction between practitioners and researchers in this area. In many cases. practitioners, fueled by overzealous venture capitalists, would do well to resist carrying out costly field experiments in the name of innovation. They can enlist the research community to examine the design of the dynamic pricing mechanisms they propose to adopt in a given market. Is the mechanism suitable for the targeted market? Would an alternative mechanism fit the bill? Will it achieve the desired liquidity to sustain itself, and will it achieve higher allocative efficiency than its current counterparts? The large numbers of failed real-life experiments in dynamic pricing (mercata.com, priceline.com for groceries) and the associated loss of social capital could have been avoided if interactions between practitioners and researchers were de rigueur. We look forward to more research that examines the relevant issues in online auctions without the baggage of the traditional assumptions made in earlier auction theory. In the absence of the physical constraints of traditional auctions, the behavior of the different economic agents in auctions is heavily influenced by the (online) context in which they take place. For instance, the presence of simultaneous substitutable online auctions — which allows an individual shopping for, say, a computer, to simultaneously



97

bid at Onsale.com or Yahoo.com — impacts the efficiency of not just the isolated auction under consideration, but also the external market in which it takes place. Auction portals such as www.biddersedge.com are specifically designed to make tracking such simultaneous substitutable online auctions easy for the consumer. We firmly believe that the emerging practice and research in this area has high inductive value and will lead to a significant enhancement of the body of knowledge dealing with dynamic pricing and auctions.

ACKNOWLEDGMENTS Dr. Gupta’s research is supported by NSF CAREER grant #IIS-00 92780, but does not necessarily reflect the views of the NSF. This research was supported in part by TECI —the Treibich Electronic ccommerce Initiative, OPIM/SBA, University of Connecticut.

REFERENCES 1. Aron, R., Croson, D. and Lucking-Reiley, D., Auctions Versus Posted Prices, Workshop on Information Systems and Economics, Brisbane, 2000. 2. Bajari, P. and Hortascu, A., Winner’s Curse, Reserve Prices and Endogenous Entry: Empirical Insights from eBay Auctions, Working Paper, 2000, Department of Economics, Stanford University, CA. 3. Bapna, R., Economic and Experimental Analysis and Design of Auction Based Online Mercantile Processes, Ph.D. Dissertation 1999, Department of Operations and Information Management, University of Connecticut, Storrs. 4. Bapna, R., Goes, P. and Gupta, A., A theoretical and empirical investigation of multiitem on-line auctions, Information Technology and Management, Jan 2000, 1(1), 1-23. 5. Bapna, R., Goes, P. and Gupta, A., Online Auctions, (in press) Communications of the ACM, 2001. 6. Barua, A. and Tomak, K., Workshop on Information Systems and Economics, Brisbane, 2000. 7. Branco, F., The design of multidimensional auctions, RAND J. Econ., 28, 1, Spring 1997, 63-81. 8. Brandabur S. and Saunders-Watson C., Fraud online and off, AuctionWatch.com article, May 2001 9. Bulow, J. and Roberts, J., The simple economics of optimal auctions, J.Political Econ., 1989, 7, 5, 1060-1090. 10. Che, Y-K, Design competition through multidimensional auctions, RAND J. Econ., 24, 4, Winter 1993, 668-680. 11. Dewan, S. and Hsu, V., Trust in Electronic Markets: Price Discovery in Generalist Versus Specialty Online Auctions, Working paper – University of Washington, Seattle, 2001. 12. Hansen, R.G., Empirical testing of auction theory, Am. Econ. Rev. 75( 2), May 1985, 156-159 . 13. Jones. J.L. and Koehler, G.J., Incompletely Specified Combinatorial Auctions, University of Michigan working paper, 2001.


98

The E-Business Handbook 14. Kagel, J.H. and Roth, A.E., The Handbook of Experimental Economics, Princeton University Press, Fall 1997. 15. Kauffman, R. and Wood, C., Running Up the Bid: Modeling Seller Opportunism in Internet Auctions, Proc. AMCIS 2000 Conf., Long Beach, CA. 16. Kauffman, R. and Walden, E., Economics and electronic commerce: survey and research directions, submitted to Int. J. Electron. Comm., 2001. 17. Lee, B. and Mehta, K., Efficiency Comparison in Electronic Market Mechanisms: Posted Price Versus Auction Market, WISE Conference 1999, Charlotte, NC. 18. Lucking-Reiley, D., Using field experiments to test equivalence between auction formats: magic on the Internet, 1999, Am. Econ. Rev. December 1999a, vol. 89, no. 5, 1063-1080 19. Lucking-Reiley, D. and List, J.A., Bidding Behavior and Decision Costs in Field Experiments, Working Paper – Vanderbilt University, revised: March 2000. 20. Lucking-Reiley, D., Experimental Evidence on the Endogenous Entry of Bidders in Internet Auctions. Working Paper – Vanderbilt University, revised: May 1999b. 21. Maskin, E. and Riley, J. Optimal auctions with risk averse buyers. Econometrica, November 1984, 52 (6), 1473-1518. 22. McAfee, R.P. and McMillan, J., Auctions and bidding J. Econ. Lit., 25 (1987), 699738. 23. Milgrom, P., Auctions and bidding: a priori, J. Econ. Perspect., 3 (1989), 3-22. 24. Myerson, R.B., Optimal auction design, Math. Operations Res. 6, (1981), 58-73. 25. Mollman, S., 2000, http://www.zdnet.com/pccomp/stories/all/0,6605,2431978,00.html. 26. Parkes, D.C. iBundle: An Efficient Ascending Price Bundle Auction. Proc. 1999 ACM Conference on Electronic Commerce (EC-99), Denver, CO, ACM Press, New York, NY. 27. Pavlou, P.A. and Ba, S., Does Online Reputation Matter? – An Empirical Investigation of Reputation and Trust in Online Auction Markets, Proc. AMCIS 2000 Conf., Long Beach CA. 28. Riley, J. and Samuelson, W.F., Optimal Auctions, Am. Econ. Rev., Vol. 71, No. 3, 1981, 381 - 392. 29. Rothkopf, M.H. and Harstad, R.M., Modeling competitive bidding: a critical essay, Mgmnt. Sci., 40 (3), 1994, 364-384. 30. Rothkopf M.H., Aleksandar P. and Harstad, R.M. Computationally manageable combinatorial auctions, Mgmnt. Sci.. 44, 8, 1131 –1147. 31. Smith, V.L. and Walker J M., Monetary rewards and decision cost in experimental economics, Econ, Inq., 1993, 31, 245-261. 32. Snir E.M, (2000) Designing Internet Spot Markets for IT Services, Working PaperWharton School, U Penn. 33. Theil, S.E., Multidimensional auctions, Econ. Lett., 28, 1998, 37-40. 34. Vakrat, Y. and Seidmann, A. Can Online Auctions Beat Online Catalogs?, In, P. DeGross and J. DeGross (Eds.), Proc. 20th Int. Conf. Info. Syst. (ICI ’99), Charlotte, NC, December, 1999. 35. Varian, H., Miles and miles of flexible track, Forbes ASAP, 10/02/2000, http://www.forbes.com/asap/2000/1002/071.html 36. Vickrey, W., Counter-speculation, auctions, and competitive sealed tenders, J. Fin. 41, 1961, 8-37. 37. Wang W., Hidvegi Z. and Whinston, A.B., Economic mechanism design for securing online auctions, In Proc. 21th Int. Conf. Info. Sys. (ICI ’00), Brisbane, Australia, December, 2000.


6

Bid Together, Buy Together: On the Efficacy of Group-Buying Business Models in Internet-Based Selling Robert J. Kauffman and Bin Wang

CONTENTS 6.1 6.2

Introduction ..................................................................................................100 The Basics of Group-Buying Models in E-Commerce...............................102 6.2.1 The Market Mechanisms .................................................................103 6.2.2 The Value Proposition for Group Buying on the Internet...............103 6.3 Buyer Behavior and Market Competition in Group Buying ......................104 6.3.1 Buyer Behavior under the Group-Buying Market Microstructure..104 6.3.2 Competition in the Group-Buying Market ......................................105 6.4 A Framework for Comparing Group-Buying Web Sites ............................106 6.4.1 Mercata.com .....................................................................................108 6.4.2 MobShop.com ..................................................................................111 6.4.3 LetsBuyIt.com ..................................................................................116 6.4.4 Other Group-Buying Web Sites.......................................................121 6.5 Analyzing Group-Buying Business Models ................................................126 6.5.1 Some Dimensions for Comparing Group-Buying Business Models ..............................................................................126 6.5.2 How Do Prices on Group-Buying Web Sites Compare with Other Firms’ Prices? ................................................................128 6.5.3 Comparing Rivals: Mercata and MobShop .....................................129 6.5.4 Discussion ........................................................................................130 6.6 Conclusion....................................................................................................132 Acknowledgements................................................................................................133 References..............................................................................................................134

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

99


100


6.1 INTRODUCTION As a channel that is characterized by convenience, wide product selection, and easy comparison shopping, the Web has enjoyed tremendous growth in consumer spending in recent years. Despite the ongoing market shakeout and yet-to-come profits on the retailer side, Forrester Research, a Cambridge, Massachusetts-based marketing research firm, predicts that U.S. consumer online spending will be on the rise to $74 billion in 2001, a 64% increase from the $45 billion of 2000.5 Paralleling the proliferation of online consumer spending is the emergence of many innovative business models, including dynamic pricing mechanisms. According to San Francisco-based Vernon Keenan, a consultant and publisher of an industry newsletter called The Keenan Vision (available at www.keenanvision.com), in 2004 some $561 billion worth of transactions will be conducted online via dynamic pricing mechanisms, accounting for approximately 40% of all online transactions.6 Such market mechanisms are what Spulber7,8 and O’Hara9 refer to as market microstructure, a term that is often associated with the operational mechanics of the financial markets.* Andrews11 identifies three kinds of mechanisms, including group-buying models, price-reduction models and traditional auction models. We add to Andrews’ list the nontraditional auction models, including such recent phenomena as reverse auctions and other “name-your-own-price” mechanisms. (See Table 6.1). The online auctions by eBay (www.ebay.com) and Amazon.com (www.amazon.com), and the “name-your-own-price” mechanism by Priceline.com (www.priceline.com), are among the most well-recognized of the dynamic price mechanism brand names. In addition, other less-known players have appeared that have used variations on a “seek-out-a-seller” mechanism. These include, for example, the Colorado-based TravelBids (www.travelbids.com) and the Canadian Mortgage Centre (www.mortgagecentre.com). Still others, such as First Auction (www.firstauction.com), have found ways to cut the time-to-transaction for timeconscious bidders, by developing instant auctions and flash auction call markets of very short 30-minute duration for consumer goods. At these Web sites, buyers are no longer passive recipients of the sellers’ asking prices. Instead, they can actively engage in price discovery along with the sellers. Mimicking the general approach of traditional “discount shopping clubs,” group-buying discounts represent a dynamic pricing mechanism that allows consumers to aggregate their purchasing power and to obtain lower prices than they otherwise would be able to get individually. Introduced in 1999, group-buying business models have been employed by quite a few companies to date, not all of which have been very successful. They include Mercata.com, Accompany.com (now MobShop.com, www.MobShop.com), actBIG.com (now Etrana.com, www.etrana.com), CoShopper.com (www.coshopper.com), C-Tribe.com, DemandLine.com (www.demandline.com), Let’s Buy It (www.letsbuyit.com), * More recently, Weber 10 has referred to market microstructure in the context of Internet-based electronic marketplaces as market technostructure, implying the range of new possibilities that the Internet opens up for innovative market mechanism designs.


Bid Together, Buy Together

101

TABLE 6.1 Four Types of Dynamic Pricing Models for Internet Marketplaces Model Types Traditional auctions

Nontraditional auctions

Price-reduction models

Group-buying models

Key Concept Apply long-standing concepts associated with real-world auctions. These include: the single-item open-outcry ascending-price English auction; the single-item open-outcry descending-price Dutch auctions; the single-item first-price sealed-bid auction; the single-item second-price sealed-bid Vickrey auction;* the multiple-item, open-outcry call market; and the open- and closed-bid double auctions, in which both buyers and sellers simultaneously update their bids and offers. Apply variations on the auction approaches mentioned above. Examples include: reverse auctions, in which buyers either state an interest in purchasing a sale item or a bundle of items and sellers indicate their offers; 3-D auctions, in which price-quantity is supplemented by utility reflecting willingness to trade; among others. Enabls buyers to obtain lower prices, but based only on a pre-announced time schedule for price drops from a higher starting price. Operate without consideration given to the number of participants in the marketplace. Similar to Dutch auction. Enable buyers to obtain lower prices, as more people indicate a willingness to buy from the Internet-based seller’s Web site. There are two varieties, involving group-buying with a fixed time period to completion of an auction, and group-buying with a fixed price that is achieved only when enough buyers participate

Note: For a description and comparison of the first four types of traditional auctions, see McAfee and McMillan.12

OnlineChoice.com, PointSpeed.com, SHOP2gether (www.shop2gether.com), VolumeBuy.com (www.volumebuy.com), and Zwirl.com (www.zwirl.com). All the firms are based in the United States, except CoShopper and Let’s Buy It, which were founded in Norway and Sweden, respectively, and have operated in multiple European countries. Among the group-buying Web sites, Mercata and MobShop have been widely recognized as the international market leaders.13–16* O’Brien2 characterizes Internet-based group buying as one of a number of means of “cooperative commerce.” Viewed this way, consumers benefit from additional buyers who join a buying group and, thus, will have an incentive to recruit additional buyers. As a result, one can predict that group-buying business models may lower customer acquisition costs for retailers. They can also simultaneously help manu* The going has been tough among group-buying firms since the third quarter of 2000, as we observe from their announcements of closure, reorganization and revisions to their strategy. For example, C-Tribe and Mercata failed, MobShop and SHOP2gether reoriented themselves from a B2C to a B2B emphasis, Let’s Buy It is reorganizing after bankruptcy, and several of the others (including MobShop) have reoriented themselves toward the licensing and sale of demand aggregation and group-buying software solutions, in lieu of operating an electronic marketplace. OnlineChoice.com also recently shut its doors.


102


facturers to offload excess inventories. In addition, they can deliver lower price benefits to consumers.17 However, with the recent closing of Mercata and the change in strategic direction at MobShop, the future of group-buying discount business models in Internet-based selling is no longer clear.3,4,18 We will discuss these at greater length later in this chapter. The challenges faced by group-buying Web sites come from consumers, competitors, and suppliers. As group buying is still a new concept to many consumers, retailers have to develop a critical mass presence in the marketplace to realize a savings and be able to pass them on to consumers. In a discussion on the recent closure of Mercata, Cook4 points out that the group-buying business model is too difficult for the general consumer to understand. The author also argues that the mechanics of group buying on the Internet also prevent impulse buying, due to the lengthy periods consumers have to wait until the end of the auction cycles that characterize group-buying market mechanisms. More importantly, he argues that the transaction volume on group-buying sites is much smaller than those of the traditional discount stores, which makes it difficult for group-buying sites to compete with retail giants such as Wal-Mart and Target. This smaller volume also makes it unlikely for group-buying sites to secure better wholesale prices with their suppliers because of the latter’s risk of losing bigger clients.19 As a result, with the low prices and nearly instant gratification offered by discount offline and online retailers, consumers may not wait to see how the final prices turn out on the group-buying sites.* As a promising business model that has many obstacles to overcome, how, in retrospect, should we understand the efficacy of group-buying discounts on the Internet? With this overall concern in mind, we address the following four questions: 1. What are the innovations associated with group-buying business models in Internet-based selling? 2. How can we characterize the operational aspects of dynamic pricing mechanisms for group buying? To what extent do different firms illustrate the various approaches? 3. What is the overall quality of these firms’ business models relative to other new business models for Internet-based selling? 4. What conclusions can we draw about their competitive sustainability in light of competitive forces that exist in the marketplace?

6.2 THE BASICS OF GROUP-BUYING MODELS IN E-COMMERCE We next discuss group-buying models in Internet-based selling in greater detail. We first describe the market mechanisms, and then consider the core value proposition they offer to their participants. * For a perceptive and prescient discussion of the likely shakeout in the group-buying marketplace, the interested reader should see the occasional columns by the staff writers at Gomez Advisors, including Margaret Juergens,20 Mark Gambale,13,21,22 and Martin DeBono.23



103

6.2.1 THE MARKET MECHANISMS Pricing to match buyers and sellers is an important function of a market.7 In the bricks-and-mortar world, posted pricing mechanisms, where retailers display the prices they ask for the merchandise and consumers decide whether they would accept the prices, have been the dominant pricing strategies. Under dynamic pricing mechanisms, however, buyers are no longer left with this take-it-or-leaveit decision. They can actively negotiate with the sellers to reach a satisfactory price. For example, buyers in online auctions, such as we have seen at eBay, place their bids and the final transaction price is the highest price offered at the end of the auction. At Priceline, consumers can name their own price for airline tickets and hotel rooms. Based on the demand from consumers and their assessment of their own supply of perishable assets, sellers then can decide whether they will accept the prices. What makes dynamic pricing different from posted pricing strategies is that, with the wide network connection and lower operation costs enabled by technology, consumers become more active in the price discovery process, resulting in a greater likelihood for transactions to occur and for higher market efficiency and effectiveness. As a special instance of dynamic pricing mechanisms, group-buying discounts allow buyers to aggregately determine transaction prices and take advantage of the savings that a seller can offer in the presence of many buyers of the same product. This is how it usually works on group-buying Web sites. A product is put on sale on the company’s Web site with a specified starting and ending time for what they typically call an auction cycle. As more buyers join the group to purchase this product, the price drops from the starting price according to a predetermined price change trajectory. Some Web sites reveal this price change trajectory and some do not. The auction cycle can also end before the specified ending time if a maximum number of units have been sold. At the end, everyone who participated in the cycle will be charged the same final low price, even if some of them indicated a willingness to buy earlier at a higher price.

6.2.2 THE VALUE PROPOSITION ON THE INTERNET

FOR

GROUP BUYING

The primary value proposition of group-buying business models to consumers is the lower prices they can provide, due to the buyers’ collective bargaining power. By accumulating a large number of orders in a short time, group-buying Web sites claim they can negotiate low prices with manufacturers and suppliers and then pass these savings on to their customers. On these Web sites, the starting prices are typically higher than the prices charged by discount posted-price merchants, reflecting their lack of purchasing power with suppliers in the face of small order quantities. However, prices drop as more people buy. When enough people join an auction cycle, it is possible that the final price will be lower than the lowest price charged elsewhere. In fact, just as the name “group-buying” tells us, if purchasing does not reach a critical mass, the preconditions for the success of this kind of business model will not be in place.


104


6.3 BUYER BEHAVIOR AND MARKET COMPETITION IN GROUP BUYING We now turn to a discussion of how buyer behavior works in group-buying marketplaces, and the extent of the competition observed among firms that apply the various kinds of business models that characterize this kind of buyer services on the Internet.

6.3.1 BUYER BEHAVIOR UNDER THE GROUP-BUYING MARKET MICROSTRUCTURE Group-buying business models are representative of innovation in the context of Internet-based selling. But they also give rise to the following five interesting aspects of consumer behavior: anticipation of falling prices, a group-buying mentality, the potential presence of a price threshold effect, a reservation price effect, and wordof-mouth-induced behavior. The Anticipation of a Price Drop. The starting prices on group-buying sites are usually higher than the prices of online discount stores, such as Buy.com (www.buy.com). However, we need to ask why people still place orders at these higher prices. Is this behavior irrational? Or is it the result of asymmetric information, indicating that the consumer is at some kind of informational disadvantage, despite the power of the new technologies for search support that have become ? Or what else? We believe that potential buyers anticipate that prices for a given product offered during a Mercata or MobShop auction cycle will drop in the near future. And it is this anticipation of a price drop that motivates them to make their purchase so as to facilitate the process in which prices drop from discount price tier to discount price tier. The Group-Buying Mentality. The second aspect of consumer behavior is the group-buying mentality. Research on network externalities indicates that the utility a technology adopter obtains tends to increase as the number of network adopters increases.24–27 This higher utility, in turn, shifts the demand curve for a technologybased product, resulting in the adopter’s (or buyer’s) higher willingness-to-pay.28 This phenomenon is called a demand-side network externality. Due to the size of a group of buyers and the price-demand relationship inherent in this market microstructure, buyers will indirectly benefit from other people’s participation in the market, another indication of the presence of a beneficial externality. Previous research has used current installed base or expected installed base to quantify the impact of network externalities on a potential adopter’s decision to adopt.29,30 As a result, in the presence of group-buying market microstructure, ceteris paribus, potential buyers will be more likely to place their orders as the size of the buying group increases. The Price Threshold Effect. A third aspect of consumer behavior is what we call the price threshold effect, an expectation on the part of potential buyers that we observed in our empirical research on MobShop’s market microstructure and performance.31 At MobShop, the current price was displayed along with a price drop scheme that was determined by current order quantity. We expect more orders to be placed right before and after each price drop, as shown in Figure 6.1.



105

Oi Price Threshold

# New Orders

Oi

Oi+1 Price Threshold

Oi+1

Time

FIGURE 6.1 Price threshold effect. Note: Oi and Oi+1 are the starting and ending total order quantities for discount price-tier i. We expect more new orders right before and after price changes at Oi and Oi+1 and fewer new orders to arrive in between, as shown in the figure. (Adapted from Kauffman and Wang, 2001.)

We explain this phenomenon as follows. When the number of orders needed to reach the next price tier is smaller than some threshold number of bids, potential shoppers will form the expectation that price will decrease in the near future. At this time, if these consumers’ reserve price is no less than the next lower price and they are not risk-averse, they would simply place their orders to facilitate the price drop process. On the other hand, if these consumers are risk averse, they may wish to wait — unless the price actually changes — to place their orders. Using a timeseries of data collected from auction cycles on an Olympus digital camera at MobShop during the second quarter of 2000, we found a significant before-price-drop effect, but no significant after-price-drop effect.31 The “Save-a-Spot” feature at MobShop allowed shoppers to place conditional bids at lower price tiers if they were dissatisfied with the current price. This created a win-win situation for both the firm and its customers. By placing conditional bids, consumers would be added to the group only when the price actually dropped to their specified reserve prices. Thus, they did not incur the risk of buying at prices higher than they were willing to pay. However, with conditional bids at the next lower price tier, the number of orders needed to reach the next price drop decreased. This way, if there were a sufficient number of conditional bids arriving, this would also have the effect of driving the price down — even if no new orders were placed at the current price level. This tended to create a basis for higher market efficiency and effectiveness. And so we see that, under the group-buying market microstructure, consumers have the opportunity to collaborate with each other to get lower prices, instead of simply bidding against each other in auctions. Hence, consumers have the incentive to recruit other shoppers.

6.3.2 COMPETITION

IN THE

GROUP-BUYING MARKET

To realize the low price value proposition to their customers, group-buying firms face challenges from four different quadrants: suppliers, competitors, consumers and the marketplace for e-commerce investment opportunities. To provide shoppers with low prices, group-buying sites first need to negotiate low prices with manu-


106


facturers and distributors, or retailers. As Gottlieb32 points out, because of the low transaction volume on these sites compared with bricks-and-mortar retail chains, it is unlikely that they can get lower wholesale prices than other low-price-all-thetime sellers off the Internet. He argues that group-buying sites such as Mercata and MobShop were able to offer lower prices because of their lower overhead costs by operating only on the Web and by piggybacking on the manufacturers’ strategy to use these sites to price discriminate. However, the lower overhead for online merchants is not an exclusive advantage to group-buying sites. Web sites such as Amazon.com and Buy.com also have lower operational costs than bricksand-mortar stores, and they represent significant competition, albeit with different selling strategies on the Internet in mind. Moreover, buying at these Web sites involves no extensive waiting time, providing consumers an additional advantage in terms of immediate purchase.22,23 With these considerations in mind, then how should group-buying sites compete with offline and online retailers? MobShop’s answer to this question was to play the role of a pure intermediary and ask its suppliers to fulfill customers’ orders. It made money by taking a certain percentage cut of each sale. However, even with their innovative and attractive business models for Internet-based selling, group-buying sites still face the problem of obtaining market attention. In the competition with other online merchants for market participation, group-buying sites need not only a critical mass of consumer patronage and interest, but also a significant amount of transaction volume so as to be able to profitably deliver on their low price guarantee. If they cannot reach a critical mass of users and sales volume, then it will be difficult for the group-buying business model to bring the customers of the firms that adopt it the savings they expect.

6.4 A FRAMEWORK FOR COMPARING GROUP-BUYING WEB SITES In this section, we examine a number of companies that have implemented groupbuying business models on the Internet. They are actBIG.com, CoShopper, C-Tribe, DemandLine, Let’sBuyIt.com, Mercata, MobShop, OnlineChoice.com, PointSpeed.com, SHOP2gether.com, VolumeBuy.com, and Zwirl.com. Rugullies17 compared Mercata, MobShop, actBIG, and Zwirl using two different criteria: the auction cycle initiator, and the demand aggregation mechanism. We add an additional dimension for customers targeted and compare the group-buying Web sites in Table 6.2. Based on our survey of the group-buying Web sites listed above, we identified two primary kinds of customer-targeting strategies: business-to-consumer (B2C) and business-to-business (B2B). The B2B category is actually somewhat broader than how we have defined it, as the table shows, because, in addition to including small to medium-sized businesses, it also includes education and government procurement (e.g., SHOP2gether and MobShop). We also distinguish whether a buyer or a seller initiates the auction cycle. For seller-driven sites such as Mercata and VolumeBuy, suppliers maintained full discretion to decide on the products that were offered for



107

TABLE 6.2 A Comparison of Group-Buying Web Sites Customers Targeted Group-Buying Site Name ActBIG.com

B2C Emphasis ■ (past)

CoShopper

■

C-Tribe.com DemandLine.com LetsBuyIt.com Mercata.com

■

MobShop

Online Choice

Small business, software Small to midsize business, software

SellerDriven

■ ■

■ (past)

■

SHOP2gether ■ ■ (past)

BuyerDriven

■

Destination Site

■

■

■ ■

■ ■

■

■

■

■

Distributed Service ■

■

■

Small to midsize business, gov’t Small to midsize business, gov’t (present) Small to midsize business Small to midsize business Education, gov’t Software Software (present)

Aggregation Method

■

Small to midsize business

PointSpeed

Volume Buy Zwirl

B2B Emphasis

Auction Cycle Initiation

■ ■

■

■

■

■

■

■

■

■ ■ ■

■

■

Note: Mercata launched its Mercata Marketplace in November 2000, permitting sellers to initiate their own PowerBuys by listing their own merchandise for resale. Although one may think otherwise, we still categorize this firm as being seller-driven, because the Mercata Marketplace existed for only a couple of months, and never really had a chance to gain critical mass. In addition, Online Choice entered into an operating alliance with Mercata during September 2000, to share group-buying members across their distinct product bases.33

sale. At buyer-driven sites such as actBIG and Zwirl, however, consumers could propose the products they wanted but that were not available. Suppliers then decided whether they wished to provide the merchandise. Still other sites were or are now hybrids, in some cases, permitting sellers, and in other cases, buyers to initiate auction cycles and select what is to be bid upon (e.g., OnlineChoice, SHOP2gether and PointSpeed).


108


Finally, we note that the demand aggregation approach can be determined by whether the Web site is a destination site or a site with a distributed-service model. Destination sites are able to attract business and users by themselves, without having to be embedded in other Web sites to ensure that a sufficient number of users materialize. For sites such as MobShop and actBIG that used a distributedservice model, they offer group-buying services on their partners’ Web sites and their own. Using a distributed-service model, group-buying sites can increase their exposure to traffic on partner Web sites, obtaining higher transaction volume. The simpler of the two approaches is simply to list and transact in the context of a single Web site. We recognize these variations among the group-buying firms that we examined as well. We now turn to a more in-depth discussion of several firms, to provide answers to some of the research questions that we laid out at the outset in this chapter.

6.4.1 MERCATA.COM Mercata, a Bellevue, Washington-based group-buying company was 54.7% owned by Microsoft co-founder Paul Allen’s Vulcan Ventures. It was started up in September 1998, and its Web site went live in May 1999. Along with MobShop, Mercata was among the first Web sites to provide the innovative and new groupbuying service to Internet users. At the height of its success, the company offered consumer electronics, computers, home appliances and household goods, jewelry and other merchandise using both fixed and dynamic prices. Business Model. Using the slogan “the more people who buy, the lower the price,” Mercata allowed its buyers to aggregate their purchase volumes and to obtain lower prices using a mechanism they trademarked as a “PowerBuy” auction cycle. For products listed in PowerBuy auction cycles, prices dropped in small decrements as more orders were placed. Figure 6.2 shows a PowerBuy auction cycle for a Nikon digital camera on Mercata. The only product information that we found on the page was the brand name, model and pixel resolution of the camera, along with a picture. In the PowerBuy Summary box, three prices were listed: the list price (usually the MSRP), the starting price and the current price. The difference between the list and the current prices gives a potential buyer an idea of what savings, below list price, are available. In addition to the price information, visitors to the page also could view the starting and ending time for the current auction cycle. If buyers were not satisfied with the current price, they could also use the feature “make an offer” to specify their reserve price. This way, they would be added to the buying group only when the price actually dropped to their reservation price. The product-offering page on Mercata was lean in terms of the information it provided. Potential buyers were unable to get detailed product features, specifications and warranty information from the Web site, and were required to go elsewhere for this. The company also appeared to have designed the price change process as a “black box” for buyers. As a result, people visiting the site would not be able to tell how the price was going to change according to the number of units sold and how low the final price would be. Products not listed in a PowerBuy auction cycle could be purchased only at their posted prices.



109

FIGURE 6.2 A PowerBuy Web page at Mercata.com. Mercata endows the buyer with relatively little information. Its Web page provides only information about the starting price for an auction cycle, and what the savings level is for the current price tier that has been reached. The potential buyer obtains no information about the number of price tiers, and the lowest possible price that may be reached. The starting price for the auction, in this case, is clearly shown to be less than the list price. Whether the real savings level is $95.03 is debatable, because many other Internet-based sellers probably offered the same product for less than list price during the same time frame. The reader should also note that the end of the auction occurs either at the posted date and time “or when the supply runs out.”

Mercata’s group-buying business model caused quite a stir in the business community, because it offered a new way of freeing up the long-standard reliance on posted prices for consumer purchases of commodity products. Pricing in Internetbased selling would never be the same after Mercata. The company’s business model was featured in magazines such as Forbes.34 Newsweek,19 and BusinessWeek,35 and the popular business press for the most part applauded the group-buying site as innovative and promising.* Mercata pursued a multimillion dollar advertising campaign to turn its group buying into a well-known brand identity. It also employed a slogan, “Down is Good,” in an ad campaign that won the firm two “Telly Awards.”37 This helped educate consumers on the collective bargaining power of Internet users. By aggregating their purchase power, according to Mercata, consumers could obtain the volume discounts that were traditionally the privilege of bulk buyers.* Mercata experienced considerable growth in 1999 and early 2000. Randy Nargi, the company’s vice president of marketing, reported more than 10,000 participants in some of their largest buying groups.38 By eliminating suppliers and dealing with manufacturers directly, Mercata was also able to negotiate lower prices and pass these savings on to the consumers. The market perceived the company to be per* To get a sense of what the counter arguments were regarding the success of the group-buying Web sites, the interested reader should see Mullaney36 in BusinessWeek and Patsuris34 in Forbes.com. For example, Patsuris comments: “Experts predict that when group buying does catch on it won’t be on a destination site. They expect the group shopping process to be adopted by portals and other sites that are already up and running, spreading the same way that auctions did.”


110


forming well during its first year of operation on the Internet, enabling Mercata to file for an initial public offering of stock totaling $100 million in March 2000.15 By the late third and early fourth quarters of 2000, however, there were rumors of increasing difficulties at Mercata. Chief among the difficulties reported was the “burn rate” of the firm’s capital. During the year, the firm managed to go through about $89 million in capital, largely to support its advertising and operating infrastructure investment and to pay its growing workforce.39 Analysis. Although Mercata would subsequently fail, as we reported earlier in this chapter, and a lack of additional funding was cited by the firm’s senior management as an important reason for the failure,3 closer examination of the groupbuying business model implemented by Mercata reveals problems that needed to be recognized sooner. 1. Group buying, as Mercata learned, is a complex buying mechanism from a consumer perspective, and, as a result, it takes group-buying Web sites time, effort and financial resources to educate their customers and develop a large following. Because the formation of a large group — what economists often call an installed base of users or critical mass in the network — is essential to the success of this business model, customer acceptance and participation in the market become even more important. 2. Mercata also faced the dilemma of choosing between emphasizing the development of a wide product selection and the formation of large group sizes. Unlike posted-price online merchants such as Buy.com (www.buy.com), where consumers can find just about any product they are hoping to purchase in the popular categories, Mercata tended to offer only limited product selection within each category. Right from the start, this constrained its capability to cater to the diverse preferences of consumers. On the other hand, broader selection of products also meant a smattering of orders across the various products in the same category. This would have the impact of diminishing the number of buyers attracted to any given sale item, resulting in smaller buying group sizes and less savings for consumers. Mercata’s challenge, then, was to achieve an appropriate balance, not an easy task. 3. It was difficult for a potential buyer to make a purchase decision based only on the limited product information that was available on Mercata’s Web site. To get information such as product features and specifications, and to do a good job of comparison shopping, a consumer was forced to either visit the manufacturer’s Web site or go to another online merchant. If the other online retailer offered the same or better products at reasonably competitive prices, Mercata bore the risk of losing sales. 5. Mercata’s marketing strategy also created a significant financial burden. The firm purchased full-page advertisements in the Wall Street Journal and New York Times, and issued $100 in shopping vouchers for purchases on Mercata to attract new customers. Both proved to be very costly for the firm.



111

FIGURE 6.3 Mercata’s shutdown announcement, January 2001.

Looking for ways to succeed, Mercata launched the Mercata Marketplace in November 2000. The intent was to allow sellers to initiate their own group-buying auction cycles by offering their own products to Mercata’s installed base of users. The end came in January 2001, when Mercata posted a message on its Web site, indicating that, in spite of its prior status as a high-flying DotCom company, it was unable to raise additional capital or reorganize itself in a way that gave investors sufficient confidence to take it forward. (See Figure 6.3.) The former market leader in group buying on the Internet was suddenly a player no more.

6.4.2 MOBSHOP.COM MobShop is a San Francisco-based, group-buying service provider. The firm was founded in October 1998 as Accompany.com, and launched its initial group-buying Web site in March 1999. In March 2000, the firm changed its name to MobShop.com.14 Business Model. When it initially positioned itself as a B2C group-buying Web site, MobShop offered computers, PDAs, consumer electronics, software, movies and computer games using an approach for group buying that was called “buying cycles.” A buying cycle essentially operated as a call market, with a fixed time period for buyers to make bids and some flexibility as to how many units of a specific sale item would be sold. The bulk of the sale items was the typical commodity products that one might associate with the core of Internet-based selling activities over the past several years (with the exception of books). Similar to Mercata, prices at MobShop dropped as more people bought. However, the difference, as we pointed out earlier, lay in the buyer’s information endowment. MobShop offered more information on its buying cycle pages for the products it sold, assisting buyers to make more informed purchase decisions. Figure 6.4 shows an auction cycle page for a Kodak digital camera. Along with the product name and model number, a brief description about the product features was also available. The most interesting aspect of the MobShop version of group buying was its price drop trajectory histogram. With four price

112

FIGURE 6.4 A group-buying auction cycle at MobShop. MobShop’s Web page endows the buyer with more information. It shows MSRP, the starting price, the price levels for the different discount price tiers, the number of buyer bids that must be received at a price level to drop down a price tier, the number of buyers in cycle, the current price in the auction and the savings so far. Notice that a MobShop auction cycle also may close before the listed time if the maximum number of buyers has been reached. Finally, we also see the typical “four-step-to-maximum-discount” approach that MobShop used.





113

tiers below MSRP, the histogram told buyers how the price was going to change according to the number of units sold. In the case of the Kodak DC290 digital camera depicted above, the initial listing was $899. However, the bidding started at $689.95, and not the MSRP. The price remained the same until the number of orders reached 26, which, in turn, drove the price down by another $10 to $679.95. Thereafter, the price would be $679.95 for 26 to 200 orders, $659.95 for 201 to 500 orders, and $649.95 for 501 to 1001 orders. Combining this price change histogram and the number of orders placed, potential buyers were able to gauge the progress of the auction cycle. Even though MobShop specified the starting and ending time for its product offerings, it still was possible that an auction cycle might actually end earlier if the maximum number of orders was reached. In addition to this kind of basic product and auction cycle information, MobShop also offered features such as “save-a-spot,” “buyer flash” and “click-and-tell.” (See Figure 6.5.) If consumers were not satisfied with the current price, they could save a spot at any of the lower price tiers. This conditional bid guaranteed that the consumer would not be added to the buyer group unless the price reached their reserve price. The “Buyer Flash” service provided consumers with an e-mail reminder when the price

FIGURE 6.5 MobShop Marketplace’s “save-a-spot” and “click-and-tell” features. When a potential buyer clicked on the “click-and-tell” feature button, it provided access to a screen with blanks for the e-mail addresses of people known to the potential buyer who could be contacted by MobShop via e-mail to notify them of the opportunity to buy a given product during the current auction cycle. The “save-a-spot” feature provided the potential buyer with a mechanism to represent a reserve price for the sale item.


114


reached a specified level, or when a certain amount of time was left in the cycle. Because of the opportunity to leverage the “word-of-mouth” effect with its groupbuying business model, MobShop also provided a “click-and-tell” facility, so that visitors could easily send e-mail to their friends. Because of its innovative group-buying business model, MobShop was named as a finalist for the MIT Sloan School of Management’s E-Business Award in March 2000.40 In the same month, the firm also was nominated for having the best e-commerce Web site at the Webby Awards.41 Then, in July 2000, MobShop was ranked fourth among the top 50 private e-commerce companies by a WIT SoundView corporate study.42 In addition to the attention MobShop received from the business community, consumers also showed a tremendous interest in MobShop’s group-buying service. For example, in the 3-month period from January to April 2000, the number of registered users at MobShop increased from 37,000 to 132,000. When the company started accepting orders, there were just about 20 to 25 orders for a product. But by March 2000, they were accumulating 1000 orders for Palm V PDAs in just 36 hours. The average buying-group size for their top selling products also increased from less than 100 to several hundred, a clear sign of growing critical mass. Analysis. MobShop’s early success can be attributed to its careful design of its business model. 1. By illustrating the relationship between order quantity and price using the price trajectory, MobShop gave potential buyers more information about how their orders would facilitate the price drop, creating the impetus for additional orders. 2. Due to the demand externalities in the group-buying setting, a large group size shown on the product cycle page indicates high market interest and possibly more savings by the end of the auction cycle. This tends to motivate potential buyers to place their orders. Additional orders placed would further attract new shoppers, resulting in a positive feedback loop.27 3. The “save-a-spot” feature allowed buyers to place conditional bids without incurring any risk, which increased the perceived effectiveness of the market. After a buyer saved an additional spot at the next lower price tier, the number of orders needed for the next price drop decreased by one even though the group size did not change. This way, if enough spots were saved, it could drive the price down even if no additional orders were placed. 4. The “click-and-tell” feature on the MobShop Web site leveraged the wordof-mouth effect and let potential buyers recruit new customers for the company. Mark Melville, MobShop’s Senior Director of Corporate Development, indicated that, unlike some of its competitors, MobShop did not spend large sums of money on advertising. The “click-and-tell” feature worked well for the company in terms of acquiring new customers. 5. MobShop saved money by not maintaining a physical warehouse to fulfill the orders. The company took a 3 to 10% cut of each sale and did not



115

need to maintain a warehouse. Instead, MobShop prenegotiated prices at volume discounts with its suppliers and passed the orders on to its suppliers for fulfillment. This way, the firm carved much of its supply chain out of its own business process. But, beginning in the summer of 2000 and paralleling the expectations of industry analysts and e-commerce journalists, MobShop started to lose steam. Participation in its auction cycles was down. For example, group sizes for popular products dropped from several hundred back to fewer than 100. The firm’s product variety available to buyers also decreased. We believe the following three factors were a force: 1. Even though the strategy of not having a warehouse saved money for MobShop, it also posed constraints on what the company had to offer its customers. Melville referred to this as a “chicken and egg” problem: “Suppliers aren’t willing to give aggressive pricing until you can show there is the demand there. Buyers won’t come until they see there’s a good pricing scheme, a value proposition for them.”* When MobShop was growing rapidly, the positive feedback loop between volume and price gave the company greater leverage for success. But, once new demand was no longer there, a negative feedback loop began to occur. This eventually left the MobShop’s Web site with just a handful of orders and much less evidence for potential buyers that any critical mass remained. Moreover, requesting retailers and distributors for order fulfillment prevented MobShop from having full control in product deliveries. As a result, they were more prone to delayed and damaged shipments. Things became even worse when it came to returns and exchanges of products purchased, because MobShop also outsourced its 1-800 customer support. The consumer complaints about MobShop at Gomez.com evidenced the frustrations consumers had in their dealings with MobShop.43 In a market that had fierce competition and high customer acquisition cost, MobShop apparently was at a disadvantage when it came to transforming first-time buyers into repeat customers. Just how did MobShop go from being in “high gear” to running “out of gas”? This is primarily explained by a second factor: competition coming from other online merchants. As Melville pointed out when we interviewed him, price competition on the Internet has become fierce and many companies have used setting low prices as a customer acquisition strategy. As a result, MobShop was no longer able to attract shoppers using prices that permitted it to earn an acceptable margin. 2. An outdated product selection at MobShop also appeared to play a role in the diminished interest from the market. According to our observations of new order arrivals at MobShop’s Web site, the most popular items were products that were in the prime of their growth in the market, including * From a telephone interview by the authors with Mark Melville, February 1, 2001.


116


PDAs, MP3 players, digital cameras, DVD players, and newly released DVD movies and video games. After a new product in these categories was put on MobShop’s Web site, we saw a surge in the buying-group size. However, manufacturers continually introduced new models of the same basic product, so that a once-popular item might garner less and less interest over time. Based on what we observed, it is clear that a requirement for growing consumer interest in group buying is to be selling the right products. Ultimately, it became increasingly hard for MobShop’s senior management to grow the business on the B2C side, due to all the other competitors in the marketplace and the growing recognition among consumers of the frustrations associated with transacting in group-buying electronic markets. Along the way, MobShop did try out a few innovations on the B2C side, as it developed its new strategies for software licensing and the B2B side of group buying. For example, in spring 2000, MobShop sold ten new Toyota Camrys for two California automobile dealerships using the group-buying approach.44 But this was not enough. Finally, faced with higher costs and declining chances for success, in January 2001, the company ceased its B2C operations, and fully repositioned itself as a group-buying software licensing firm.3,4,18

6.4.3 LETSBUYIT.COM One of the two European firms among those we examined in the group-buying sector on the Internet is LetsBuyIt.com, which was founded in Sweden in January 1999. Its Web site went live in Sweden in April that year and, by August, became available in Denmark, Finland and Norway. In October, the LetsBuyIt.com network expanded to Germany and the United Kingdom, and then, in the first half of 2000, to Austria, Netherlands, Spain, Belgium, Italy and Switzerland. As of the end of late 2000, 14 national Web sites had been established (although only four of them are now active due to a restructuring of the firm to achieve profitability), and there are almost 1.2 million registered users. LetsBuyIt.com acts as a vendor of 150 internationally known brands, including Pentax, Pioneer, Nikon, Logitech, Aiwa, and LG, among others. The firm’s product diversity is greater than what we saw with MobShop and Mercata, including some 50,000 products and services, and the emphasis is on the sale of high-quality and medium- to high-priced goods having known brands. Business Model. LetsBuyIt.com calls its group-buying model the “co-buying” approach, and it is very similar to what we have seen with the other firms whose dynamic pricing models we have already reviewed. Figure 6.6, which shows a cobuying auction cycle for a Universal Full Suspension Stinger Mountain Bike, illustrates the basics of how it works. The co-buy auction cycle Web page shows a range of useful information. For this mountain bike, the lowest price tier is £115, which requires no fewer than five buyers. The posted comparison is to an average retail price of £140, and there are three steps to the deepest discount. The cycle has almost 1 week left to run, and delivery is indicated to occur within 10 days of when the transaction is finalized. Of special



117

interest to us is the presence of two choices for the terms that buyers set for their purchase. The marked choice is, “I want to buy at the best price only,” in other words, the lowest price reached in the final price tier. Checking this box makes the bid act as a limit order or conditional bid, because it will only be filled if five or more people join the co-buy group. The second choice, “I will buy at the price reached when the co-buy closes,” indicates that the buyer will choose to transact no matter how low the price goes, and that the price at which the bid occurred is an acceptable one. The LetsBuyIt.com co-buy facility also includes a “Click-and-Tell” feature that is similar to the one used by MobShop. Note the words in the figure above: “Tip your friends and lower the price.” It turns out that this enables the potential buyer to provide information about ten friends who may be interested in the co-buy opportunity, creating some positive likelihood that the co-buy price will decline to the next price tier. Figure 6.7 shows the “Tip-Your-Friends” tool in LetsBuyIt.com that is obtained in the context of an auction cycle for a Canon MV3000 Camcorder. Another interesting aspect of the capabilities that LetsBuyIt.com offers is its “Suggest-a-product” feature. (See Figure 6.8.) This feature emphasizes the approach that the firm has taken to balancing buyer and seller initiation for the transaction of sale items. “Suggest-a-product” enables potential buyers of an unlisted product to initially search to determine whether the product they had in mind to suggest has been suggested by other shoppers via the “Search suggestions” box. If the product is already listed as a suggestion, similar to what is shown in the “Top suggestions” box that accompanies the product suggestion screen, the next step is to click on the item (in this case, we demonstrate with the Canon Ixus II APS Camera) and cast a vote. (See Figure 6.9.) Based on what we saw with respect to MobShop and Mercata, this group-buying Web site appears to offer both enhanced functionality and better balance and responsiveness to buyer interests, as opposed to a primary emphasis on the sellers’ interests only. Next, we analyze how well this firm did in the marketplace, with the experiences and analysis we conducted of other American firms in mind. Analysis. In comparison with the American firms we have discussed, LetsBuyIt.com was in a relatively enviable position. It ranked among the top ten Internetbased sellers in Europe during 2000, and grew a very large installed base of cobuying participants through the first half of the year. (See Table 6.3 and Figure 6.10.) In addition, LetsBuyIt.com was able to increase the value of the average co-buy purchase on its Web site from EUR 64.00 to EUR 167.00 from January to June in 2000. (See Figure 6.11.) But, similar to some of the other firms we have discussed, the company also ran into trouble in the third quarter as the novelty of its service began to wear off, and consumers became more knowledgeable about how to efficiently shop on the Internet. The costs LetsBuyIt.com was incurring to migrate its operations to 14 European countries grew rapidly. The growth in installed base of users required the costly localization of software for a number of languages (e.g., English, German, Spanish, French, Swedish, Norwegian, Danish, etc.) The firm had some difficulties doing an initial public offering of stock at an attractive price. When it finally did issue stock, it turned out that the EUR 62.3 million in capital that it netted in the marketplace was insufficient to see it through to the end of the year.46 In December 2000, LetsBuyIt.com notified its investors and customers that it was halting operations,



FIGURE 6.6 The co-buying mechanism at LetsBuyIt.com.

118


119

FIGURE 6.7 The “Tip-your-friends” feature for co-buying at LetsBuyIt.com.




FIGURE 6.8 LetsBuyIt.com’s “Suggest a product” feature.

120



121

FIGURE 6. 9 The LetsBuyIt “Top Suggestions” box and “Suggestion Vote” Web page. The screen that permits the user to cast a vote for having LetsBuyIt.com offer the product also provides a response box that enables the company to sample how long a potential buyer is willing to wait (e.g., 2 weeks, 1 or 2 months) for the product to be sourced and offered as a co-buy. This provides a useful source of information to enable the firm to be responsive to its customers. The implication is that LetsBuyIt.com would source the most demanded products with the shortest willingness to wait on the part of the potential buyers.

pending bankruptcy reorganization.47 The company’s restructuring plan enabled it to bring in an additional EUR 53 million however, the new board required that the firm’s operations be scaled back significantly.48

6.4.4 OTHER GROUP-BUYING WEB SITES In addition to those discussed above, a number of other group-buying Web sites are worthy of discussion. They include actBIG.com, DemandLine.com, OnlineChoice.com, PointSpeed.com, SHOP2gether.com, VolumeBuy.com and Zwirl.com, which once provided or are still providing group-buying services on the B2B and B2C sides. As we show below, the products and services offered by these group-buying Web sites differed considerably, and not all of them have fared well in the marketplace.


122


TABLE 6.3 The Top Ten Internet-Based Sellers in Europe, August 2000 Rank 1 2 3 4 5 6 7 8 9 10 -----

Firm Name Amazon.de Amazon.com Amazon.co.uk Jungle.com Bol.de FNAC.com LetsBuyIt.com Handy.de ShopSmart.com Quell.de All WWW Sites All retail sites

Unique Visitors 1,253,000 1,213,000 1,086,000 495,000 475,000 419,000 404,000 380,000 347,000 326,000 23,497,000 10,400,000

Source: www.LetsBuyIt.com, March 2001.

FIGURE 6.10 Number of LetsBuyIt.com Co-Buy purchasing members (000s). (Source: www.letsbuyit.com, March 2001.)

ActBIG.com (Etrana Inc.) New York City-based actBIG.com was founded in 1999 to provide group-buying and reverse auction services in many product categories, including airfare, hospitality services and vacation packages. Compared with MobShop and Mercata, actBIG.com was more buyer-driven and allowed buyerinitiated group-buying cycles. If shoppers were not able to find the products they wanted, they could submit a proposal to the suppliers with the prices they were willing to pay. The merchants would then decide to either offer the products or make



123

FIGURE 6.11 Average transaction value at LetsBuyIt.com (first half of 2000, in Euros).

a counter offer. Even though actBIG had a wide variety of product categories, there were few sales in many of the categories.16 Unable to sustain its B2C business model, actBIG changed its name to Entrana.com (www.entrana.com) and is now providing franchise systems services and group-buying technologies to businesses. CoShopper.com. Norway-based CoShopper.com (www.coshopper.com) is LetBuyIt’s competitor in the European group-buying B2C market. Founded in 1999, CoShopper now maintains a presence in six European countries (United Kingdom, Norway, Spain, Germany, Sweden and Portugal), two South American countries (Brazil and Chile), three Asian countries (Singapore, Japan and Malaysia), as well as Australia. By participating in “CoShop cycles” that usually last for 1 to 2 weeks, buyers at the site can obtain collective savings in product categories such as consumer electronics, computer hardware, home and garden, and sports and leisure. Maintaining a similar information structure to LetsBuyIt, CoShopper displays the price drop trajectory and the total number of orders placed. Shoppers can also place conditional bids at lower prices if they are not satisfied with the current offering. A feature called “Ti -Friends” allows consumers to send e-mails to friends informing them about CoShop cycles going on at the site. Employing a zero inventory policy, CoShopper outsources its order fulfillment through two European logistics firms, iForce and Parcel Force, and charges shoppers a fixed delivery fee. Expanding into the B2B market, CoShopper now offers group-buying services in France, Sweden and Norway in the business procurement sector. However, it has been unable to maintain its B2C operations in Singapore, Malaysia and Chile, so CoShopper now provides only e-commerce solutions in these three countries. C-Tribe.com. Founded in 1998, San Francisco-based C-Tribe.com focused solely on the group buying of offline retail gift certificates, making it unique among all the group-buying firms we examined. C-Tribe illustrates the case that group-buying sites may not always be in competition with offline retailers.49 Based on the same concept, “The more people who buy, the lower the price,” C-Tribe promoted bricks-andmortar retail stores, such as Barnes and Noble, Blockbuster, KB Toys, Foot Locker and Darden Restaurants, by offering their gift certificates at volume discounts. It enticed shoppers back to the physical shopping experience.50,51 Following a redesign


124


of its Web site in June 2000, C-Tribe provided its customers with access to its services through multiple channels via the Internet, telephone, and hand-held wireless devices.50 In addition, the company’s newsletter, The C-Scribes, gave shoppers up-to-date information on product offerings at the site. Similar to a loyalty program, customers could also earn cash-back rewards by purchasing gift certificates of CTribe’s partner retail stores. Unable to maintain a critical mass of shoppers, C-Tribe appears to have folded in late 2000. DemandLine.com. Targeted at small businesses, San Bruno, California-based DemandLine.com (www.demandline.com) provides group-buying opportunities and reverse auction services on telecommunications, Internet, software support, credit card processing, and human resources services.52 By aggregating the buying power of small businesses, DemandLine helps companies obtain the low prices that are available to large companies.53 However, the mechanism the firm uses does not report prices in real time. Instead, the mechanism is more like a reverse auction in that a potential buyer submits a request for a price and DemandLine acts as an intermediary in the presentation of attractive prices on applicable services. The potential buyer then can decide to arrange for the purchase of the service online, or pursue further discussions with the vendor directly. OnlineChoice.com. Founded in Pittsburgh as ElectricityChoice.com, OnlineChoice was the nation’s first Internet-based buying pool for electricity.54 As the Web site expanded its services, it focused primarily on demand aggregation for services, such as local and long distance phone service, and pure commodity essentials, such as natural gas, electricity and gasoline.55 Participants could join “buying pools,” and, in lieu of observing the operation of an electronic market, OnlineChoice.com periodically approached suppliers to negotiate deals to obtain discounts on behalf of buyers. Although consumers joined a pool, they were not required to purchase the services or products that were offered at a negotiated price. If a deal went through, OnlineChoice.com earned a referral fee by acting as the matchmaker. The range of services offered for group buying and the installed base of consumer and corporate participants shown in Table 6.4. OnlineChoice.com’s positioning from the beginning was in an area that the major brand name competitors, MobShop and Mercata, did not target. However, there have been other companies, especially those that utilized a reverse auction model, that appeared to have been competing in the categories that OnlineChoice.com selected for its emphasis. However, OnlineChoice was unable to build toward profitability in its business, and closed its doors in late April 2001. PointSpeed.com. PointSpeed.com (www.pointspeed.com) was a San Mateo, California-based group-buying purchasing center for small businesses. Functioning as a back-end service provider, PointSpeed differed from the other group-buying Web sites we examined in that its service was available only through its partners. They included such firms as Bizography.com (www.bizography.com) and Wells Fargo (biz.wellsfargo.com). Through its partners’ Web sites, PointSpeed also provided small businesses access to more than 30 retailers and manufacturers such as AT&T, Barnes and Noble, NECX, and Prudential.56 Similar to MobShop, PointSpeed’s profits came from fees charged to suppliers based on transaction volume. However, a lack of market interest caused PointSpeed to discontinue its service in March 2001.



125

TABLE 6.4 Participants in the Services Group-Buying Pools at OnlineChoice.com Category of Service

Number of Consumer Buyers

Number of Corporate Buyers

Electricity Gasoline Health Insurance Home Security Internet Local Phone Long Distance Natural Gas Term Life Insurance Wireless

90,006 106,165 9,759 12,827 12,216 22,327 105,269 39,654 2,563 45,996

242 177 304 — — — 198 179 — 250

Source: OnlineChoice.com. The data for buying-group participation are as of March 20, 2001. This company branded its consumer-side services as OnlineChoice, and its business side services as BusinessChoice. Overall, it reported that it had over 460,000 unique customers.

SHOP2gether.com. Founded in early 1999, San Jose, California-based SHOP2gether (www.shop2gether.com) was among the first to provide group-buying services to small businesses by catering to their needs for office furniture and equipment, travel, employee benefits and corporate gifts. Similar to actBIG, SHOP2gether also functioned as an intermediary to facilitate the product request flow from buyers to sellers. However, different from MobShop’s take-a-sales-cutfrom-the-supplier approach, SHOP2gether assessed a fixed fee from buyers whenever a transaction was made. To seize opportunities in the education procurement market, SHOP2gether introduced the first education-focused group-buying platform in April 2000, partnering with more than 400 vendors with a total sale of $74 billion in the estimated $618 billion education market.57 Now, with a focus on educational institutions and local government, SHOP2gether supports collective purchasing in product categories such as furniture, education technology, office equipment and supplies, and transportation. VolumeBuy.com. Encino, California-based VolumeBuy.com (www.volumebuy.com) is a software company that licenses its group-buying technologies to small and medium-sized businesses. In addition, the company provides group-buying services to consumers on its Web site using three types of buying pools. When they participate in a “Time Power Pool,” consumers are guaranteed a low price based on the amount of time they are willing to wait to make a purchase. The company’s “Flex Power Pool” works like Mercata’s and MobShop’s group-buying business models, in which prices decrease as more people join the pool. Finally, the “Group Power Pool” does not close until a prespecified buying group size has been reached. Then, everyone in the pool will receive the same low group price. The product selection at VolumeBuy


126


includes computers and office products, consumer electronics, communication products and services, and vacation packages. Although VolumeBuy’s various types of buying pools offer consumers the flexibility in choosing the group in which they want to participate, Figure 6.12 illustrates the additional effort on the consumer side that is required to understand the differences among the pool types. Zwirl.com. Zwirl.com (www.zwirl.com) is a New York-based e-commerce startup that was founded in July 1999. The company provided group-buying services in product categories such as computing equipment and software, office supplies, baby and household goods, and sporting goods. At Zwirl, shoppers also could initiate auction cycles if the products they wanted were not in cycles. Moreover, Zwirl also provided a “wish list” service to shoppers similar to LetsBuyIt.com that would let the company know what products consumers wanted at what prices. Zwirl.com was designed as a destination site, but later partnered with other Web sites to provide group-buying e-commerce solutions. In 2000, Zwirl ceased its own B2C operation and is now focusing on group-buying technology provision.

6.5 ANALYZING GROUP-BUYING BUSINESS MODELS We now turn to a comparison and analysis of these business models, building upon our discussion of the individual group-buying sites, and taking advantage of other assessments.

6.5.1 SOME DIMENSIONS BUSINESS MODELS

FOR

COMPARING GROUP-BUYING

Two industry studies offer useful input on how to compare group-buying firm effectiveness. 1. In 2000, Gomez Advisors (www.gomez.com), a well-known e-business consultancy, compared three group-buying Web sites: Mercata, MobShop, and VolumeBuy, together with descending-price Web site OutletZoo.com (www.outletzoo.com). (See Figure 6.13). Using criteria such as ease of use, customer confidence, on-site resources, relationship services, and overall cost, Gomez ranked Mercata the top site among the four firms, followed by MobShop. Mercata had advantages over the other firms in customer confidence, on-site resources, relationship services, and overall cost. MobShop featured high ease of use, but was deemed to be weaker in on-site resources and relationship services. 2. Rugullies17 compares four group-buying sites — Mercata, MobShop, actBIG, and Zwirl — based on site features and usability, business model, corporate infrastructure, and Web site foundations. Because Mercata dealt with manufacturers directly, Rugullies argues it could achieve higher margins than the other three firms, resulting in the strongest business model. Consistent with Gomez, MobShop received the highest score in site features and usability. Mercata also was rated higher than the other three firms in terms of corporate infrastructure and Web site foundations.



127

❑

Time Power Pool: Length of time to the close of the pool determines the price.

❑

Group Power Pool : Low prices are achieved if group size meets a pre-specified level.

❑

Flex Power Pool: Prices fall over a fixed time, but do not reach a pre-specified level.

FIGURE 6.12 VolumeBuy.com’s three types of pools. (Source: www.volumebuy.com, March 20, 2001.)


128


FIGURE 6.13 Gomez Advisors’ comparison of four group-buying Web sites.58 Outlet Zoo, apparently included incorrectly in this assessment, is a descending-price auction site, where product prices drop as time progresses until all the units have been sold. MobShop ceased its B2C operations on January 13, 2001. Mercata ceased its entire operations on January 31, 2001.

6.5.2 HOW DO PRICES ON GROUP-BUYING WEB SITES COMPARE WITH OTHER FIRMS’ PRICES? Because e-commerce industry observers have considered group-buying Web sites to be an innovative use of the Internet, such sites also have drawn a lot of scrutiny related to their performance. Many comparisons have been made between the final prices charged by group-buying Web sites and posted-pricing retailers. Is it the case that the group-buying Web sites truly are cheaper for consumers? Or is the opposite true? Overall, the results we have examined seem to be mixed. Consider the following anecdotal evidence on variable cost savings that has been documented by industry observers: Palm Pilots. Dodge59 compared the prices charged for a Palm V by groupbu y i n g s i t e M o b S h o p a n d t wo p o s t e d - p r i c e s i t e s , Pa l m G e a r. c o m (www.palmgear.com) and eCost.com (www.ecost.com). MobShop’s final price was $224.95. At PalmGear the same product was priced at $309.95. At the same time, eCost, a site ranked “number one” on overall cost for computer purchase among a set of the top 20 Web sites, based on a Gomez Advisors (www.gomez.com) ranking, was asking $255.99. In addition, Gambale13 reported that eTown (www.etown.com) listed a Palm V $319.91 on two successive days in early April 2000, while Outpost.com (www.outpost.com) listed it for $274.95. Both days, the product was unavailable at both MobShop and Mercata. DVDs and DVD Players. Jidoun60 also noticed the considerable savings offered by MobShop. She noted, for example, that one cycle on The Matrix DVD accumu-



129

lated 400 orders and resulted in a final price of $9.95. This was 60% off the list price and $5 less than the lowest price that two shopping bots could find on the Web. However, Jidoun also cautioned against buying expensive items on groupbuying Web sites. For example, she found that MobShop charged $650 or more for a Hewlett-Packard notebook computer, when it could be bought elsewhere on the Internet for about $600. Gambale13 offers additional indications of the mixed results in this product area. Again, on successive days in April 2000, he found that eTown charged the fixed price of $291.35 for a Toshiba 3109 DVD Player that Outpost.com and MobShop did not list. However, the prices listed on Mercata each of those days were $289.40 and $293.29, respectively, indicating that Mercata had the low price edge. However, for a Toshiba SD1200 DVD Player, the story was different. ETown and Outpost.com listed this item at $245.60 and $249.99, respectively, while MobShop did not list it. On the first of the two days, Mercata’s price was $159.95, but on the second day (the beginning of an auction cycle, we guess) it was $249.99, just matching Outpost.com’s price. VCRs. The final evidence that we have seen is again from Gambale,13 this time for VCRs. He reports mixed pricing between the fixed-price and group-buying sellers. For example, a JVC HRVP770 VCR priced at $163.17 by eTown, and unavailable at Mercata and Outpost.com was priced at $175.95 at MobShop both days in early April 2000, giving eTown the low price lead. Just the opposite was true with a Toshiba M685 VCR. eTown listed this second VCR at $152.64 both days, while MobShop and Outpost.com did not offer it. However, Mercata’s prices were better by more than $25 both days, at $128.84 and $124.69. Clearly, the evidence we have pulled together suggests that the value proposition with respect to retail group-buying discounts still requires the buyer to beware.

6.5.3 COMPARING RIVALS: MERCATA

AND

MOBSHOP

Based on our mini-case analyses of the various group-buying Web sites, as well as the comparisons made by Gomez Advisors58 and Rugullies,17 we identify the following five overall aspects that differentiate the two former market leaders, Mercata and MobShop, in group-buying services: pricing strategy, information endowment, site features, product emphasis, and pretrade and posttrade logistics. 1. Pricing Strategy. The two firms used different pricing strategies. Mercata offered both fixed and dynamic pricing. For products involved in a Mercata PowerBuy auction cycle, the prices changed frequently but in small decrements. In contrast, MobShop generally took a four-tier approach to price changes, and their prices changed less frequently. 2. Information Endowment. The information endowment for buyers that is available on the two sites clearly was different. Consumers were unaware of the price-quantity relationship at Mercata. They observed only the dropping prices resulting from increasing order volume, but not the driving force: the buying group. At MobShop, consumers knew the internal mechanisms that drove price changes. Potential buyers were not able to locate detailed product features and specifications at Mercata. MobShop provided brief product


130


specifications. But compared with Web sites such as Amazon, the details of product descriptions about products at both sites were modest at best. 3. Site Features. The many additional features such as “buyer flash” and “click-and-tell” provided by MobShop gave it advantages over Mercata in ease of use. 4. Product Emphasis. The two firms had different product emphases. Compared with MobShop, Mercata had a wider product selection and focused on consumer electronics and household goods. In contrast, MobShop offered high-tech commodities that were relatively new to the market. 5. Pretrade and Posttrade Logistics. The logistics before and after sales were made were also different. Mercata dealt with manufacturers directly for procurement, enabling it to achieve higher margins. MobShop dealt with retailers and distributors, however, making its margins slimmer. MobShop also eliminated inventory costs by acting as an intermediary that did not physically fulfill its own orders. Mercata, on the other hand, bought from manufacturers and had to maintain a warehouse and fulfill the orders itself. Table 6.5 summarizes these key differences between Mercata and MobShop.

6.5.4 DISCUSSION Although they are still in their infancy, group-buying business models on the Internet face the challenges of accumulating critical mass and establishing themselves in

TABLE 6.5 A Comparison of Mercata and MobShop Comparison Dimension Pricing strategy

Information available on Web site

Site features, Ease of use Product focus

Logistics

Mercata Both fixed and dynamic pricing Frequent price change, small decrements Internal pricing mechanism unavailable No product features and specifications No Wider product selection Consumer electronics and household goods Dealt directly with manufacturers Provided fulfillment of orders

MobShop Dynamic pricing only Four-tier pricing Price drop trajectory and group size info available Brief product specifications Buyer flash Click and tell Fewer products offering High-tech products that were relatively new to market Dealt with retailers and distributors Pure intermediary, did not engage in fulfillment of orders



131

competition with posted-pricing discount Web sites. Now that we have examined the bases of the initial successes enjoyed by these firms and the subsequent shakeout of the group-buying sites in the marketplace, we offer the following questions and preliminary answers: In which market does the group-buying business model work best, B2C or B2B? Compared with offline and online discount merchants, group-buying Web sites are at disadvantage in the B2C market because of the small transaction volume they can aggregate, which makes it difficult for them to realize their low price proposition to their customers. The recent closure of several B2C group-buying service providers suggests the nature of the obstacles that exist in the consumer market. On the other hand, because of the large number of small businesses and government agencies, as well as the high transaction amount, the B2B market provides companies with group-buying business models ample opportunities for making their mark in the marketplace. How should firms focusing on the B2C market compete with other business models for limited customer resources? The last 2 years of experience of the group-buying Web sites that we examined suggest the use of a distributed-service model instead of being a destination site. The high customer acquisition cost and limited traffic to group-buying sites make it difficult for a destination site to obtain the critical mass it needs to become truly effective. By partnering with other sites that enjoy higher traffic, group-buying sites can provide their services on portal sites and aggregate orders across sites, increasing their chances of marketplace viability. What do we learn about the composition of effective product offerings at groupbuying Web sites? As we illustrated in the case of Mercata, group-buying firms face the dilemma of selecting between developing a wide product selection and a large buying-group size. A variety of goods in each category can satisfy consumers’ diverse preferences. However, too many products in each category make it difficult to achieve large buying-group sizes because of the scattered orders across different products. As a result, group-buying Web sites need to maintain a balance between product variety and order volume for each product. In the selection of each product, it is also important to consider its potential of market interest. From MobShop, we observed that the top sellers were either high-tech big-ticket items, such as PDAs, digital cameras, DVD players, MP3 players, or small items, such as DVD movies and video games. A characteristic shared by all the big-ticket items is that they are still in the growth stage of their product life cycle, and demand for them in the marketplace is still increasing. At this stage, buyers tend to be early adopters with higher income and education levels. This seems to match the profile of online shoppers. Hence, these products are best positioned for group buying. In our observation of order arrivals at MobShop,31 there was often a surge of orders after the introduction of a new model of a particular product in the product categories that we discussed. But, some time later, market interest tended to decline, signifying the importance of maintaining fresh and interesting product offerings. After it has been on sale for a while, even a previously hot seller may become relatively unattractive, with the result that it will fail to capture the interest of a sufficient number of buyers to create a basis for critical mass usage. The reasons


132


are simply that repeated visitors who want the product will have purchased it and that the number of newly acquired customers will not be large enough to result in a large buying-group size. To what extent are group-buying Web sites at a disadvantage when it comes to the use of shopbots for comparison shopping? The Web as a shopping channel has been characterized by low search cost. Shopbots on the Internet (e.g., www.dealtime.com or www.mysimon.com) make it even easier to do comparison shopping. Using these services, a savvy shopper can easily locate the best deal among a large number of online merchants. This puts group-buying sites at disadvantage for two reasons. First, many group-buying sites are still not well recognized by shopping bots. Thus, it is impossible for the group-buying Web site to leverage these shopping bots in some way to acquire new customers. Second, even if they were listed by a shopbot, the dynamic nature of the prices also makes it less likely that the information that is captured represents the best prices the sites have to offer. For example, when buyers conduct a search at the beginning of an auction cycle, they will observe only the high starting price, which is not likely to be the final one. The information clearly will be misleading. Group buying poses both opportunities and challenges for Web sites employing this business model. With the estimated $2.4 trillion worth of transactions projected to materialize by 2004,61 the B2B market gives group-buying companies potential opportunities for success. To enhance their viability, group-buying firms focusing on the B2C market can take advantage of the high traffic on portal sites to increase shoppers’ exposure to this relatively new buying concept. In addition, DealTime’s effort to add group-buying merchants to its search capabilities illustrates that as the group-buying business model matures, the merchants will get recognition from shopping bots to overcome the disadvantage they have when it comes to comparison shopping.62

6.6 CONCLUSION In this chapter, we examined group-buying business models in Internet-based selling. Based on a series of mini-case studies and related analysis, we illustrated the innovative use of technologies by group-buying sites in providing their customers with volume discounts that had traditionally been privileges of bulk buyers. However, even though group buying on the Web represents an interesting business model, this alone is not sufficient to justify its viability. Group buying still is a new concept to many shoppers, and so group-buying firms face the challenges of educating the consumers and achieving critical mass. Moreover, the competition coming from posted-price merchants, especially offline and online discount retailers, makes it difficult for group-buying sites to deliver low prices base on their limited transaction volumes. In consideration of the successes and the failures of group-buying business models in the past couple of years, we believe group-buying firms oriented toward the B2B market are better positioned for future growth. In addition, for sites that focus on the B2C market, a distributed-service model has advantages over a destination site. Furthermore, it is also crucial to maintain a balance between a wide product offering and large group sizes.



133

In this chapter, we also intended to make a contribution to senior management thinking about group buying on the World Wide Web. First, our research highlights the unique features presented on many group-buying Web sites that facilitate the collective purchasing process. Companies interested in site redesign and improvement can add functionalities, such as the “save a spot” feature at MobShop and the “Email Friends” function supported by many sites, that will improve market efficiency and effectiveness. Second, following an analysis of the competition coming from posted-price discount retailers and the small transaction volume on groupbuying sites, we identified strategies taken by group-buying companies to obtain competitive cost savings. Examples include dealing directly with manufacturers and maintaining zero inventory. At the same time, we also noted the problems resulting from these strategies, such as delayed shipments and difficult product returns and exchanges. By illustrating the pros and cons of group-buying companies, we provide a more holistic view of the problem space that relates to corporate strategy in the presence of this variety of Internet technology. Third, our framework for comparing group-buying business models and our analysis of the opportunities and challenges in the market can help interested parties to critically evaluate the most promising market approaches and business models. For researchers in IS and e-commerce, our research offers other contributions. First, by cross-examining several group-buying Web sites, we identified the key aspects of consumer behavior under the group-buying market microstructure. This will provide the motivation for future research to more thoroughly understand and test the efficacy of these group-buying models in the presence of different kinds of consumer behavior. Second, group-buying business models are relatively new, and our research is the first to critically examine the efficacy of this type of business model. Third, with the ongoing market shakeout in e-commerce, our research illustrates how the academic world can go about helping the business community learn from its successes and failures. With the new opportunities that are becoming available in the B2B market, we believe that group-buying business models actually can help small businesses obtain volume discounts. Future research can examine the efficacy of group-buying business models in the B2B market. Moreover, buyer behavior in the B2C market, such as the anticipation of falling prices, the group-buying mentality, the price threshold effect, the reserve price effect, and word-of-mouth behavior might also be mapped to the B2B market and examined more closely. Finally, comparisons can also be made on the efficacy of group-buying business models for different product types in different stages of their life cycles.

ACKNOWLEDGEMENTS We acknowledge the special input offered on group-buying on the Internet by Becky Porter, director of public relations, and Mark Melville, senior manager of corporate development, at MobShop, Inc., in San Francisco. The authors wish to thank Eric Clemons, Rajiv Dewan, Baba Prasad and Andrew Whinston for useful input on research that is related to what we discuss in this chapter. We also appreciate related


134


comments and suggestions from the participants in our presentations at the 2001 Hawaii International Conference on Systems Science, and the Information and Decision Sciences Workshop and the Executive Development Center at the Carlson School of Management of the University of Minnesota. Any errors are the responsibility of the authors.

REFERENCES 1. Kane, M., Web startup pushes volume shopping, ZDNews, Internet, March 3, 1999. www.zdnet.com/zdnn/stories/news/0,4586,2219279,00.html. 2. O’Brien, J., Cooperative commerce: Group-buying engines promise consumers strength in numbers, Computer Shopper, 78, May 2000. Also dated April 10, 2000, www.zdnet.com/computershopper/stories/reviews/0,7171,2487711,00.html. 3. Sandoval, G. and Kawamoto, D., Group-buying site Mercata to shut its doors, CNet News.com, January 4, 2001. news.cnet.com/news/0-1007-200-4372403.html. 4. Cook, J., Venture capital: Where Mercata led, consumers were unwilling to follow, Seattle Post-Intelligencer, January 12, 2001. seattlep-i.nwsource.com/business/ vc122.shtml. 5. Scheraga, D., 2001: A sales odyssey, Chain Store Age, 77, 96, January 2001. 6. Andrews, W., The new laws of dynamic pricing, Internet World, 5, 27, December 15, 1999. www.internetworld.com/121599/12.15commerce.jsp. 7. Spulber, D.F., Market microstructure and intermediation, J. Economic Perspectives, 10, 135, 1996. 8. Spulber, D. F., Market Microstructure: Intermediaries and the Theory of the Firm, Cambridge University Press, Cambridge, UK, 1999. 9. O’Hara, M., Market Microstructure Theory, Blackwell, Malden, MA, 1997. 10. Weber, B., Elements of market structure for online commerce, in Future Markets: How Information Technology Shapes Competition, Kemerer, C. F., Ed., Kluwer Academic, New York, 15, 1998. 11. Andrews, W., Dynamic pricing 101, Internet World, 6, 42, May 15, 2000. www.internetworld.com/051500/5.15ebusiness2.jsp. 12. McAfee, R.P. and McMillan, J., Auctions and bidding, J. Econ. Lit., 25, 699, 1987. 13. Gambale, M., Group buying space needs more than name changes to demonstrate consumer value, Gomez Wire, News Analysis, April 7, 2000. www.gomez.com/ features/article.asp?id=4996&col=79&topcat_id=0. 14. Olsen, S., A company changes its name, CNET News.com, March 27, 2000. news.cnet.com/news/0-1007-200-1591013.html. 15. Van Horn, T., Mercata files for $100 million IPO, CNet News.com, March 9, 2000. news.cnet.com/news/0-1007-200-1568987.html. 16. Wimpsett, K., The buy brigade: CNet’s ultimate guide to group buying on the Internet, CNET Internet, July 20, 2000. cnet.com/internet/0-3821-7-2259973.html. 17. Rugullies, E., Power to the buyer with group buying sites, e-Business Advisor, 10, 10, February 2000. www.advisor.com/articles.nsf/aid/rugue05. 18. Clark, D., MobShop, a pioneer in group buying on the Web, discontinues consumer service, Wall St. J., January 15, 2001. www.MobShop.com/ar011501d. 19. Tanaka, J., The never-ending search for the lowest price, Newsweek, June 7, 1999. www.newsweek.com/nw-srv/focus/ty/foty0223_1.htm.



135

20. Juergens, M., Making sense of buying services, Gomez Wire, Shopping Tips, August 16, 2000. www.gomez.com/features/article.asp?id=6003&col=70 &topcat_id=0. 21. Gambale, M., Tips for getting the most from Internet buying services, Gomez Wire, Auction Tips, March 3, 2000. www.gomez.com/features/article.asp?id=4702 &col=69&topcat_id=0. 22. Gambale, M., Dynamic pricing: A beast in prince’s clothes, Gomez Wire, News Analysis, February24, 2000. www.gomez.com/features/article.asp? id=4627&col=67 &topcat_id=0. 23. DeBono, M., Playing the price is right: The Internet edition, Gomez Wire, Shopping Tips, January 17, 2000. www.gomez.com/features/article.asp? id=4113&col=70&topcat_id=0. 24. Katz, M.L. and Shapiro, C., Network externalities, competition, and compatibility, Am. Econ. Rev., 75, 422, 1985. 25. Katz, M.L. and Shapiro, C., Technology adoption in the presence of network externalities, J. Political Economy, 94, 822, 1986. 26. Katz, M.L. and Shapiro, C., Systems competition and network effects, J.Economic Perspectives, 8, 93, 1994. 27. Shapiro, C. and Varian, H.R., Information Rules: A Strategic Guide to the Network Economy, Harvard Business School Press, Cambridge, 1999. 28. Economides, N., The economics of networks, Int. J. Indust. Org. 14, 673, 1996. 29. Dybvig, P.H. and Spatt, C., Adoption externalities as public goods, J. Public Economics, 20, 231, 1983. 30. Kauffman, R.J., McAndrews, J., and Wang, Y.M., Opening the “black box” of network externalities in network adoption, Info. Sys. Res. 11, 61, 2000. 31. Kauffman, R.J. and Wang, B., New buyers’ arrival under dynamic pricing market microstructure: The case of group-buying discounts on the Internet, J. Mgmt. Info. Syst., 15, 157, 2001. 32. Gottlieb, B., Does group shopping work? The economics of Mercata and MobShop, Slate Mag., July 25, 2000. slate.msn.com/Features/groupshop/groupshop.asp. 33. Guzzo, M., OnlineChoice.com, Mercata creates buying pool, Pittsburgh Business Times, September 6, 2000. pittsburgh.bcentral.com/pittsburgh/stories/ 2000/09/04/daily11.html. 34. Patsuris, P., Group-buying boom, Forbes.com, March 3, 2000. www.forbes.com/2000 /03/29/mu1.html. 35. Browder, S., Paul Allen’s e-commerce play: Bring the buying club to the net, BusinessWeek Online, May 13, 1999. www.businessweek.com/bwdaily/dnflash/may1999/ nf90513c.htm. 36. Mullaney, T., Online shopping: Bargaining power, BusinessWeek Online, December 13, 1999. www.businessweek.com/1999/99_50/b3659033.htm. 37. Business Wire, Mercata’s television commercials honored in 21st Annual Telly Awards; Group-buying e-commerce company takes home silver and bronze awards for outstanding creative achievements, April 11, 2000. www.findarticles.com/m0EIN/ 2000_April_11/61423048/p1/article.jhtml. 38. Mara, J., Good buys, Adweek, 41, 58, March 6, 2000. 39. Sullivan, B., Paul Allen’s Mercata.com to close, MSNBC, January 4, 2001. www.zdnet.com/zdnn/stories/news/0,4586,2670749,00.html?chkpt=zdhpnews01. 40. Business Wire, MIT Sloan School of Management announces finalists for the 2nd Annual MIT Sloan eBusiness Awards; Monster.com, Ebay, iCanBuy, Freeserve and Red Hat among finalists, March 9, 2000. www.findarticles.com/cf_0/m0EIN/2000_ March_9/60017608/p1 /article.jhtml.


136


41. WebbyAwards.com, 2000 nominees and winners, The Webby Awards, May 11, 2000. www.webbyawards.com/nominees/2000/commerce_win.html. 42. PR Newswire, MobShop named fourth among top 50 private e-commerce companies, July 7, 2000. www.findarticles.com/m4PRN/2000_July_7/63190578/ p1/article.jhtml. 43. Gomez Advisors, Reviews of MobShop, Inc. (ceased operations), March 18, 2001. www.gomez.com/reviews/index.asp?topcat_id=65&firm_id=2431&x=14&y=7. 44. Eldridge, E., Web sites offer group car buying: Plans to use old-economy way of lowering prices, USA Today, Auto Track, June 22, 2000. www.usatoday.com/life/cyber /tech/cti135.htm. 45. Stelin, S., Going abroad, ZDNet InteractiveWeek, November 27, 2000. www.zdnet.com/intweek/stories/news/0,4164,2659144,00.html. 46. LetsBuyIt.com, Offer price set at EUR 3.50, Corporate press release, June 2, 2000. investor.letsbuyit.com/investor/en/news/press/00176/. 47. LetsBuyIt.com, LetsBuyIt.com applies for moratorium, Corporate press release, December 28, 2000. investor.letsbuyit.com/investor/en/news/press/00156/. 48. LetsBuyIt.com, LetsBuyIt.com back in control. Corporate press release, February 21, 2001. investor.letsbuyit.com/investor/en/news/press/00180/. 49. Apparel Industry Magazine, Web to help, not hurt traditional sales, 61, 14, 2000. 50. Business Wire, C-Tribe, Inc. unveils the next generation of its Web venture, CTribe.com, offering the complete clicks and mortar solution, June 1, 2000. www.findarticles.com /cf_0/m0EIN/2000_June_1/62414978/p1/article.jhtml. 51. PR Newswire, Prandium announces alliance with C-Tribe.com; Koo Koo Roo and El Torito Gift Certificates to be marketed in cyberspace, January 11, 2000. www.findarticles.com/cf_0/m4PRN/2000_Jan_11/58520838/p1/article.jhtml. 52. DeCeglie, P., It’s negotiable: How to negotiate discounts on products and services, Business Startups Magazine, July 2000. www.entrepreneur.com/Your_Business /YB_SegArticle/0,1314,276126,00.html. 53. King, C., Small businesses get online help, InternetNews.com, August 11, 2000. www.internetnews.com/ec-news/article/0,,4_436061,00.html. 54. United States Department of Energy, In Pennsylvania, ElectricityChoice.com becomes first in the nation to form Internet buying pools for electricity, Weekly Update, December 3, 1999. 55. Belsie, A., A power shift on energy prices, Christian Science Monitor Electronic Edition, September 25, 2000. 56. Business Wire, PointSpeed launches online purchasing centers for small business; Announces key partnerships with Concentric Network, Bizography.com, May 23, 2000. www.findarticles.com/cf_0/m0EIN/2000_May_23/62257715/p1/article.jhtml. 57. PR Newswire, SHOP2gether.com kicks off nationwide program to pool local vendors to serve education market, April 18, 2000. www.findarticles.com/cf_0/m4PRN /2000_April_18/61721849/p1/article.jhtml. 58. Gomez Advisors. Buying Services: Group Buyer, December 6, 2000. . www.gomez.com/scorecards/index.asp?topcat_id=65&item_id=312&itemclass=SCORECARDPROFILE. 59. Dodge, J., Strength in numbers? A look at what group-buying sites have to offer consumers in search of computer gear, Inc. Magazine, 3, 135, September 15, 2000. www.inc.com/articles/details/0,3532,CID20262_REG14,00.html. 60. Jidoun, G., Online shopping: When teaming up to buy pays off, Money, 29, 180, June 2000. www.money.com/money/depts/websmart/webwatch/archive/ 000524.html.



137

61. Kafka, S.J et al., EMarketplaces boost B2B trade, Forrester Research, February 2000. Excerpt www.forrester.com /ER/Research/Report/Excerpt/0,1338,8919,00.html. 62. Business Wire, DealTime.com expands with new shopping categories and buying groups, October 14, 1999. www.findarticles.com/cf_0/m0EIN/1999_Oct_14/56283870/p1 /article.jhtml.



7

The Great Experiment: Pricing on the Internet Karen Clay, Ramayya Krishnan, and Michael Smith

CONTENTS 7.1 7.2

Introduction ..................................................................................................139 Market Efficiency.........................................................................................140 7.2.1 Price Levels ......................................................................................140 7.2.2 Price Elasticity .................................................................................141 7.2.3 Price Dispersion ...............................................................................141 7.2.4 Menu Costs ......................................................................................142 7.2.5 Future Research................................................................................143 7.3 Retailer Behavior .........................................................................................143 7.3.1 Books................................................................................................143 7.3.2 Other Products..................................................................................145 7.3.3 Future Research................................................................................146 7.4 Consumer Behavior......................................................................................146 7.4.1 Retailer Data ....................................................................................146 7.4.2 Shopbot Data....................................................................................147 7.4.3 Aggregation ......................................................................................148 7.4.4 Experiments......................................................................................148 7.4.5 Future Research................................................................................149 7.5 Conclusions ..................................................................................................149 References..............................................................................................................150

7.1 INTRODUCTION The rise of business-to-consumer (B2C) electronic commerce represents one of the greatest economics experiments in history. In its initial conception, anyone could start a B2C firm. By shipping goods directly from wholesalers, firms would run extremely efficient operations. They would spend only on the Web site and earn money from sales, possibly from advertising, and from interest earned because goods were sold before suppliers had to be paid. Customers would use comparisonshopping engines to choose the lowest cost suppliers for relatively standardized goods. Thus, prices for these goods would fall to cost and price dispersion would fall to zero, because no firm could charge a price above market. 1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

139


140


The reality, as of 2001, is that B2C electronic commerce firms look a lot like physical retailers and many operate in both channels. Most customers do not use comparison-shopping engines, although usage is slowly increasing. Those who comparison shop often do not choose the lowest-priced vendor. Thus, price has not fallen — and almost certainly never will fall — to cost. Further, price dispersion is significant, online as well as offline. The fact that the evolution of business-toconsumer electronic commerce did not conform to the initial rather far-fetched plan should not obscure the fact that the rise of business-to-consumer electronic commerce still represents a significant economic achievement. Despite the importance of the experiment, we know remarkably little about how the rise of electronic commerce has affected retail prices both online and offline. In this chapter, we survey the academic empirical literature as of mid-2001 on the impact of the Internet on retail prices. The survey examines three issues: market efficiency, retailer behavior, and consumer behavior. The market efficiency section examines four aggregate measures that are related to efficiency: price, elasticity of demand, price dispersion, and menu costs (the costs of changing prices). The next two sections examine more disaggregate measures of pricing on the Internet: how firms set prices and how consumers respond to prices. Each of the three sections ends with suggestions for future research.

7.2 MARKET EFFICIENCY Market efficiency occurs when all welfare-enhancing trades are executed. In a perfectly competitive market for homogenous goods, efficiency implies that price will fall to marginal cost, the price elasticity of demand that any one firm faces will be infinite, price dispersion will be zero, and prices will change instantaneously as supply and demand conditions change. Conventional wisdom suggests that, by increasing competition, lowering menu costs, and lowering search costs for prices and product information, Internet markets should be more efficient than comparable physical world markets. What happens in practice is potentially quite another matter. In this section we review the empirical literature regarding the level of efficiency in Internet markets.

7.2.1 PRICE LEVELS Bailey2,3 conducted the seminal research to compare price levels in physical markets with price levels in Internet markets. In 1996 and 1997, Bailey compared prices for a matched set of books, CDs, software titles collected from the Internet and physical stores. For this period, he found higher prices on the Internet than in physical stores. He hypothesized that this finding could be due to a lack of competition in Internet markets during this period of time. There were few well-known retailers in the market and thus customer search intensity across retailers may have been low. This hypothesis is supported by Bailey’s observation of Barnesandnoble.com’s entry into the Internet market in March 1997. Bailey observes that Barnesandnoble.com entered by undercutting Amazon.com’s prices by 10% and that Amazon quickly matched these lower prices during the following months.


The Great Experiment: Pricing on the Internet

141

In a later study, Brynjolfsson and Smith8 examined prices for books and CDs gathered from Internet and physical retailers in 1998 and 1999. In contrast to Bailey, their study found that prices were lower on the Internet than in physical stores: 16% lower when just considering the item prices and 6–10% lower when taxes and shipping and handling charges were included. As noted above, a possible source for the differences between Bailey’s and Brynjolfsson and Smith’s results was increasing competition among retailers during this time period. Both of the previous studies raise questions about the nature of competition over time. Using data collected between August 1999 and January 2000 covering 399 books and 32 online bookstores, Clay, Krishnan, and Wolff10 found that prices were stable or rising over the sample period. Separate regressions that controlled for the number of bookstores selling a particular book indicated that prices were effectively stable and that prices were lower for books sold by larger numbers of stores. A related question concerns how prices change with changes in Internet penetration when retailers set a single price for both physical and Internet markets. Brown and Goolsbee7 examined how the proportion of individuals using the Internet affects online prices for life insurance. Their data included regional Internet usage figures for customers in the United States and microdata on individual life insurance prices. They found that a 10% increase in the proportion of individuals using the Internet in a particular geographic area is associated with a 5% reduction in average insurance prices in those areas. Researchers have also used Internet and physical retailer price comparisons to examine indirect characteristics of market efficiency. Png, Lee, and Yan,37 for example, collected prices for books and CDs from physical and Internet retailers to examine the degree of competitiveness in the two markets. They found that their data is consistent with lower buyer search costs in Internet markets.

7.2.2 PRICE ELASTICITY Increased price elasticity is a signal of increased customers search intensity in markets for homogeneous goods. Two recent studies analyzed firm-level elasticity in the context of Internet markets. Ellison and Ellison17 analyze price elasticity for three computer hardware products at a retailer that gains most of its sales through shopbot referrals. Their data exhibited extremely high price elasticity for motherboards and low-quality memory modules. However, they also found negative crossprice elasticity between low- and medium-quality memory modules, suggesting that the retailer was using a price obfuscation strategy to soften price competition. Similarly, Smith and Brynjolfsson45 found that shopbot customers for books were very sensitive to the ordinal position of offers in a comparison table sorted by price.

7.2.3 PRICE DISPERSION In contrast to the results for price levels above, the empirical results for price dispersion are nearly unanimous. Almost all studies show a high degree of price dispersion in Internet markets for seemingly homogeneous physical goods. For data collected in 1996 and 1997, Bailey2,3 found more dispersion in Internet markets for


142


books, CDs, and software than for these same products sold in physical markets. For data collected in 1998 and 1999, Brynjolfsson and Smith8 also found that dispersion is higher for books and CDs on the Internet than in physical stores. These high levels of dispersion have been confirmed in more-recent studies for the Internet book markets10,44 and DVD markets.48 Recent work on the international online textbook market indicates that cross-country dispersion is also very large.11 Other researchers have studied dispersion in Internet markets for differentiated goods by using hedonic techniques to control for observable differences in products. Clemons, Hann, and Hitt12 analyzed markets for airline tickets sold by online travel agencies. They controlled for observable difference in the tickets (times, layovers, connections, airlines) and found that prices differed by as much as 20% across the online travel agents they surveyed. Bakos et al.5 also found significant dispersion in trading costs for online retailer brokerage services. A variety of possible explanations can be suggested for these findings. One possibility is that there are other unobservable sources of product differentiation that were not accounted for in the price analyses. For example, it may be that, while consumers have good information about prices across retailers, they have poor information about service quality across retailers. Because the Internet imposes a spatial and temporal separation between consumers, retailers, and products, it may increase the importance of service quality and decrease service quality cues available to customers.43 It is also interesting to analyze the change in dispersion over time as markets change. Brown and Goolsbee7 found that the initial introduction of shopbots for life insurance initially increased the dispersion in life insurance prices. However, price dispersion fell as the share of customers using these shopbots increased further. Clay, Krishnan, and Wolff10 found dispersion did not change between August 1999 and January 2000 in the online book market.

7.2.4 MENU COSTS Although menu costs are an important aspect of market efficiency, they are difficult to measure directly. In physical stores, studies typically quantify menu costs by documenting the exact process retailers go through to change prices and assigning costs to each step.27 Absent such information, another approach is to look for indirect signals of the size of menu costs. In this regard, Bailey2,3 found the Internet retailers undertake more price changes than physical stores for the same books in the same time period. Brynjolfsson and Smith8 observed that, over the course of their 15-month study, Internet retailers regularly made price changes between $0.01 and 0.05, physical retailers never made price changes smaller than $0.35 and rarely made price changes smaller than $1.00. Since these are indirect measures, they are not conclusive. It is possible that smaller price changes are more common on the Internet because of higher price elasticity, and it is possible that more price changes are caused by strategic pricing behavior on the part of Internet retailers. However, taken together, these findings seem to confirm the conventional wisdom that Internet retailers should have lower costs to change prices in response to shifts in market supply and demand.



143

7.2.5 FUTURE RESEARCH A variety of ways extend the empirical results mentioned in this section. Most of the studies to date have focused on low-priced consumer products such as books, CDs, and software. Markets for more expensive products may have different search intensities and therefore different efficiency characteristics.* Also of interest are services that have “perishable” capacity such as logistics services and products such as airline seats and telecommunications bandwidth. Consider the case of airline tickets. To fill seats that would otherwise be empty, airlines developed yield management techniques. Using these techniques, airlines track inventory and iteratively modify the set of seats allocated to different price structures as flight time approaches.6,46 As yield management techniques migrate to other domains and begin to incorporate new distribution channels such as online auctions, empirical research is needed to understand pricing in this important domain. Price levels and price dispersion may also change over time as markets mature. It would be interesting to document how changes in markets over time affect measures of efficiency. Markets with price discrimination and auction markets may have different efficiency metrics from the consumer markets discussed above. It would be interesting to quantify and analyze appropriate efficiency metrics in these markets and compare them with the efficiency characteristics discussed above. Finally, it would be interesting to analyze price elasticity among customers who do not use shopbots, both as a point of reference and to increase the ability of current studies to be generalized.

7.3 RETAILER BEHAVIOR Predictions about retailer behavior have ranged from perfect competition to some form of tacit collusion to differentiation. As of this writing, empirical evidence to test these hypotheses is limited to a very small number of categories — books, travel, perfume, DVD players, and computer components. With the exception of the study of computer components, all of the retailers are selling branded commodity products, so it is easy to compare prices across vendors. The discussion that follows summarizes some stylized facts from the empirical literature and then outlines areas for future research.

7.3.1 BOOKS The online book industry has received the most intense scrutiny from academics and nonacademics, so we know the most about retailer behavior in this segment. Internet retailers post significantly different prices for the same books. One comprehensive study is that of Clay, Krishnan, and Wolff.10 Their data cover the weekly minimum prices of 399 books at 32 online bookstores for the period August 1999 to January 2000. The books include a mix of New York Times * However, Smith44 observes that, for books priced from $1 to several thousand dollars, there is an increase in dispersion as price increases.


144


bestsellers, Amazon bestsellers, and random books selected from Books in Print. The lowest-priced store in the sample is Buy.com, which had the lowest price 35% of the time. No other store had the lowest price more than 5% of the time. The “big three” offered very similar prices. Across all books and stores, the average difference between Amazon and the lowest-price store ranged from a low of 10% for current New York Times bestsellers to a high of 25% for books that had moved off the list. Another comprehensive study is by Smith.44 His data cover prices for 23,000 unique titles and 33 retailers for the period August 25, 1999 to November 1, 1999. In each case, the prices were returned in response to searches for books initiated by customers visiting Dealtime.com, a prominent shopbot for books. An advantage of his data set relative to Clay, Krishnan, Wolff10 is the much broader range of titles included. A disadvantage is that prices are usually observed only on a single day. His data set, which does not include Buy.com, found that Kingbooks and A1Books returned the lowest price on 15.1% and 12.5% of the products respectively. One question of interest is whether observed differences in prices reflect strategic decisions, cost differentials, or both. Buy.com’s financial documents make it clear that its price leadership in this category is a strategic decision. Other retailers’ marginal undercutting of Amazon, presumably to get better placement on shopbots, suggests that strategic decisions are important. Discussions with industry sources, case studies, and media reports suggest that — with the exception of the big three — all bookstores face the same wholesale costs for books. The big three appear to receive additional discounts of about 5%–7% of publishers’ recommended prices in return for co-op advertising, warehousing books, and, in some cases, waiving the right to returns. According to recent financial documents, Amazon’s fulfillment costs, including all fixed and marginal costs associated with distribution and call centers, is roughly 15% of net sales, and the costs for Barnesandnoble.com appear to be quite similar. According to industry sources, differences in fulfillment costs across online vendors are not large. Thus, observed price differences seem to be primarily a result of strategic differences. Another question of interest is how multichannel vendors price across the two channels. Ten of the 32 vendors in the Clay, Krishnan and Wolff sample were multichannel. Seven of the ten were on-line branches of independent bookstores or small chains, and the remaining three were on-line branches of large chains. The large chains — Barnes and Noble, Borders, and Books-aMillion — offered lower prices in the Internet channel than in their physical stores, whereas the remaining stores appear to have offered the same prices in the two channels. Finally, the issue of collusion has arisen because of the similarity in prices among the big three and possible leader-follower behavior, with Amazon acting as an industry leader. Smith44 and Clay, and Krishnan and Wolff10 document the similarity in prices among the big three and note that several other stores post prices that are very similar to Amazon’s. Kaufmann and Wood25 explicitly examine the issue of leader-follower behavior using a dataset comprising bestsell-



145

ing books and compact disks. Unfortunately, the nature of their dataset does not allow them to differentiate between exogenous changes associated with items going off the bestseller list and endogenous changes associated with leader-follower behavior.

7.3.2 OTHER PRODUCTS Clemons, Hitt, and Hann12 compared the prices of 939 unique tickets for 436 unique combinations of departure and return cities of eight online travel agents on 4 days in 1998.* Although the price of an identical ticket did not differ across the eight online travel agents, differences in the way they processed search queries led to different return sets. These differences arose because different search algorithms had different implicit weights on price, match to specified departure and return times, and connecting vs. direct flights. Even accounting for differences in the quality of the tickets, Clemons, Hitt, and Hann still found substantial variation in the lowest-price ticket offered by an online travel agent that met specific criteria. Of particular concern was that online travel agents offered tickets that were strictly dominated by tickets offered by other online travel agents from 3% to 28% of the time. Their evidence suggests that online travel agents were differentiated both in the quality of the tickets returned and in the quality of interface and search algorithms they offered. Carleton and Chevalier9 examined the prices of 1106 unique fragrances and 201 DVD players sold online and in physical stores in June 2000. Although their primary focus was on manufacturers’ decisions about distribution, they provided some evidence on individual retailer behavior. Seventy-six percent of the fragrances in their sample could be purchased from an Internet retailer. Prices for fragrances at Internet stores varied systematically across stores, with online branches of department stores and some high-end beauty sites charging the manufacturer’s suggested price, and perfume sites and drugstores charging substantially lower prices on average. All of the DVD players in their sample could be purchased from an Internet retailer. Prices for DVD players were higher at retailers that also had physical stores, retailers that were authorized resellers, and manufacturer’s Web sites. This fits with evidence from online books that there is significant price variation across retailers and that multichannel stores tend to charge the same price in both channels. One interesting difference between perfumes and DVD players, and books and travel was that not all manufacturers of perfumes and DVDs chose to sell all products through all sites. Ellison and Ellison17 examine the prices of two types of computer memory upgrades and one type of computer motherboard as reported by Pricewatch.com from May 2000 to January 2001. Unlike books, where the variation in prices, even within the first page of a comparison shopping engine, can be 30%, they find that the difference between the first- and the tenth-lowest price was just 4%. This suggests that competition in prices across unbranded items of apparently similar quality may * The online travel agents were not identified by name. The eight were generated by studying four online travel sites and one proproprietary site. Three of the five sites allowed individuals to specify whether they were more interested in price or quality of match, generating the equivalent of three additional sites for a total of eight.


146


be intense. One of the reasons, however, to offer low prices is to attract customers and then “upsell” them to a higher quality. This hypothesis is supported by evidence on the cross elasticities of demand, which were large and negative. The fact that retailers engage in such loss-leader behavior is, in part, a reflection of the difficulty of using comparison-shopping engines to search for unbranded goods.

7.3.3 FUTURE RESEARCH The studies cited above are an important start in understanding retailer pricing in online markets. Collectively, however, they cover relatively few markets and fairly short time periods, suggesting that much more work is needed to understand how Internet retailer behavior varies within and across markets and over time. Specifically, future work should address questions of whether certain stores are systematically cheaper or more expensive; whether price differences reflect cost advantages or strategic decisions; the extent of differentiation; the relationship among price, differentiation and sales; and whether the behavior of multichannel retailers differs from Internet-only retailers. It should also address dynamic issues such as how individual retailers’ prices and nonprice attributes are changing over time and whether observed changes are driven by exogenous changes in cost or demand, or factors internal to the market.

7.4 CONSUMER BEHAVIOR The Internet raises many interesting questions about consumer behavior: • How will consumer behavior change in electronically mediated markets from what is observed in physical markets? • How does Web page design impact consumer choices? • How do consumers respond to asymmetric information regarding retailer quality? We survey selected empirical research on these questions. Discussion is organized based on the source of the data: retailer data, shopbot data, aggregator data, and experimental data. In each case, we discuss the advantages and disadvantages of the particular type of data. The section closes with a discussion of areas for future research.

7.4.1 RETAILER DATA While it is easy to gather price and other product data directly from retailers, data on consumer behavior is typically harder to come by. In most cases, data on customer choice must be gathered with the cooperation of the retailer in question. For example, Degeratu, Rangaswamy, and Wu15 used data obtained from Peapod to compare the behavior of online grocery shoppers with physical-world shoppers. They found that price sensitivity can be lower and brand sensitivity can be higher among online customers. Fader and Hardie18 and Moe and Fader33 used data provided by CDNOW to model customer purchase and visit behavior over time. Fader and Hardie found



147

that standard marketing models can do a surprisingly good job of predicting trial and repeat purchases. Moe and Fader found that retailers should take into account changes in customer behavior over time — not just total number of visits — when forecasting which customers are most likely to buy. Other studies correlate offline survey data with online customer behavior. ScottMorton, Zettelmeyer, and Risso41 correlated data from car referrals made through Autobytel to physical sales reports obtained through J.D. Powers and Associates. They found that Autobytel customers typically pay 2% less for their cars than typical physical world customers. Shankar, Rangaswamy, and Pusateri42 used customer survey data to show that Internet customers who had prior positive experience with a hotel brand were not as price sensitive as other Internet customers. Finally, a number of studies use bid data collected directly from Internet auction sites to analyze customer bidding behavior. Resnick and Zeckhauser38 found that reputation ratings have a positive effect on prices in eBay auctions. Wilcox51 found that more-experienced bidders tend to bid more rationally than less-experienced bidders in eBay auctions. Data gathered directly from retailers typically excels in both richness and accuracy. However, this does come at a cost. Gathering such data typically involves the cooperation of the retailer. This is true even in some cases where consumer data are available directly from Web pages, such as the eBay bidding data mentioned above. EBay carefully monitors its site to block automated data-gathering spiders. Obtaining cooperation from retailers may come at a cost, either in terms of the time to negotiate with the retailer for access or in the form of limitations placed on publication of results.

7.4.2 SHOPBOT DATA Data from Internet shopbots allow researchers to analyze the response of customers comparing products offered by different retailers. Shopbots are Internet tools that allow customers to search for prices from numerous Internet retailers with just a few clicks. The prices are typically presented to the customer in an easy-to-read comparison table that lists price, delivery time, and other salient characteristics of the available products. Studies in this category include Brynjolfsson and Smith8 and Smith and Brynjolfsson,45 who used a multinomial logit model to analyze the choice behavior among customers to DealTime.com. These authors found that shopbot customers, while price sensitive, were also very sensitive to retailer brand name and to retailers they had visited before. In a hybrid study, Ellison and Ellison17 combined shopbot price data with data on customer behavior at one of the retailers listed at the shopbot. They used this data to infer price elasticity among the retailer’s customers. They found extraordinary high price elasticities, but also evidence of obfuscation strategies practiced by the retailer. The advantage of shopbot data is that it documents the competing prices available to the user at the time of their decision and thus can provide an accurate picture into their evaluation of different offers. Disadvantages of this data include the need to obtain an agreement from the site to observe customer choices, the difficulty of


148


associating customer clicks with purchases, and the fact that shopbot customers are likely to be systematically different from the typical Internet customer.

7.4.3 AGGREGATION Customer behavior can also be obtained from aggregation data, which can come from html request logs maintained by Internet backbone companies, through consumer panels from companies such as Media Metrix, or private studies such as Carnegie Mellon University’s HomeNet study. In the category of html request logs, Adamic and Huberman1 used logs obtained from AOL tracking customer html requests. They found the number of Web-page hits was highly concentrated among a few sites: the top 1% of sites capture more than half of the total number of customer visits. In Internet consumer panel data, a representative set of customers have their surfing behavior tracked over time.28,33 These data typically include the self-reported demographic data of the household, the family member making the visits, the sites visited, and the timing of the visits. Montgomery34 used data provided by Media Metrix to forecast the future behavior of customers from past behavior. Johnson et al.23 used Media Metrix data to show that the depth of customer search was surprisingly low in Internet markets. Johnson, Bellman, and Lohse22 used this data to analyze how learning to use a retailer’s site can create “cognitive lock-in” among customers. Goldfarb19 used data gathered from Plurimus to study the portal choice among Internet households. Similar data is available through private studies such as HomeNet at Carnegie Mellon University.26 HomeNet collected data from new users of residential Internet service in the Pittsburgh area starting in 1995. In addition to information comparable to the Media Metrix data documented above, the project collected newsgroup messages, logs of help requests, and data from customers through home interviews and periodic questionnaires. Christ et al.13 and Christ14 used this data to study the depth of customer search behavior over time, and found that customers appeared to visit fewer sites as they gained more experience with the Internet. Aggregation data combines many of the advantages of retailer and shopbot data. Like retailer data, it provides a very rich picture of customer behavior. Like shopbot data, it provides measures of customer behavior across multiple sites. Aggregation data, in many cases, also provides additional demographic data that can be associated with individual customers.

7.4.4 EXPERIMENTS Experiments offer researchers the opportunity to control and manipulate the shopping environments and observe subsequent customer response. Such experiments have been used in the context of Internet markets to analyze how customers respond to different aspects of a site design,31,32 Web site response time,16 and product information.30,49 Other experiments simulate the results of auctions over an electronic channel.29 In each case, the primary advantage of the experimental approach is that researchers can control the shopping environment in ways that would not be possible



149

in most “working” markets. The art of this technique is designing experiments that produce the same outcomes as would be observed in the working markets.24

7.4.5 FUTURE RESEARCH Much work remains to both understand customer behavior in current Internet markets and how this behavior is likely to change over time. It would be interesting to see more studies partnering with Internet sites to conduct online experiments using actual Web site customers. Such experiments would combine the control possible in experimental research with the authenticity of retailer data. In the category of shopbot data, it would be interesting to analyze customer response to different shopbot interface designs to explore how shopbots can use customer preferences to improve their customer interface (see Montgomery et al.)36 In the context of aggregation data, it would be interesting for researchers to make more use of the timing and sequencing of Web requests to predict customer characteristics.

7.5 CONCLUSIONS We do not yet have definitive answers to the questions of whether Internet markets are more efficient than their physical counterparts or whether Internet markets are themselves becoming more efficient over time. The primary barrier to addressing these questions is obtaining data that covers a wide range of products sold in both Internet and physical stores over a reasonably long time span. Early studies suggest that prices are lower on average online. At least for some products, lower online prices appear to put pressure on offline prices, narrowing differences between the two channels. Estimates of elasticity indicate that individuals who use shopbots are extremely price sensitive. We know almost nothing about elasticity for other customers either offline or online. Price dispersion appears to be substantial, both on- and offline. More specific inferences are limited by the fact that virtually no online measures of dispersion are weighted by actual sales and by the fact that it is extremely difficult to get appropriate comparison data for physical stores. Finally, vendors do seem to take advantage of the lower cost of changing prices online to make smaller, more frequent changes. Retailers continue to experiment with pricing on the Web. One of the highestprofile experiments was Amazon’s experiment with DVD prices. More broadly, vendors continue to adjust prices, offer coupons or individual specific discounts, and provide free shipping to attract new customers and retain existing ones. Four patterns emerge from investigations of retailer behavior. First, highly branded retailers are generally more expensive than less well-known retailers. One puzzle is the vendors that do not seem to fit into either of these categories, particularly those that charge higher prices than highly branded retailers. Second, multichannel retailers are typically more expensive than Internet-only retailers. Higher prices may be a reflection of channel conflict, and customers’ ability to return goods to a physical store. Third, manufacturers, if they sell online at all, almost always sell at full price to minimize conflict with their retailers. Fourth, less-well-known vendors dynamically adjust their prices to ensure that they appear in the first few entries of a shopbot. More


150


generally, patterns of prices over time suggest that firms monitor one another carefully and that they may be engaging in leader-follower behavior. Consumers appear to be both more and less price sensitive on the Internet relative to physical channels. Customers who are already price sensitive can more easily search for low prices online than offline, leading to extremely high elasticities. At the same time, customers who are already price insensitive because of high income or limited time may appear extremely price insensitive. Part of their apparent insensitivity may be the limited information available to them in the Internet context. For instance, customers who shop for groceries online may be less able to evaluate quality than if they were actually in the store. Thus, they may rely more on brand, appearing to be extremely price insensitive. A similar effect shows up in eBay auctions, where buyers pay higher prices for identical goods sold by sellers with better reputations (effectively more highly branded sellers). Overall, we have only begun to learn about the effects of the great experiment that the rise of Internet sales represents. Much more work remains to be done.

REFERENCES 1. Adamic, Lada A. andBernardo A. Huberman. 1999. The nature of markets in the World Wide Web. Quart. J. Electron. Comm.1(1) 5-12. 2. Bailey, Joseph P. 1998a. Intermediation and electronic markets: Aggregation and pricing in Internet commerce. Ph.D thesis., Technology, Management and Policy, Massachusetts Institute of Technology, Cambridge, MA. 3. Bailey, Joseph P. 1998b. Electronic commerce: Prices and consumer issues for three products: books, compact discs, and software, Org. Econ. Co-op. Devel., OCDE/GD 98(4). 4. Bakos, J. Yannis. 1997. Reducing buyer search costs: Implications for electronic marketplaces. Mgmnt. Sci. 43(12) 1613-1630. 5. Bakos, J. Yannis et al. 2000. The Impact of Electronic Commerce on the Retail Brokerage Industry. Working Paper, Stern School of Business, New York University, New York, NY. July. 6. Belobaba, Peter P. 1987, Airline yield management: An overview of seat inventory control, Trans. Sci., Vol. 21, No. 2 (May), pp. 63-73. 7. Brown, Jeffrey R. and Austan Goolsbee. 2000. Does the Internet Make Markets More Competitive? Evidence from the life insurance industry. National Bureau of Economic Research Working Paper #7996, October. 8. Brynjolfsson, Erik, Michael Smith. 2000. Frictionless commerce? A comparison of Internet and conventional retailers. Mgmnt. Sci. 46(4) 563-585. 9. Carleton,D.W. and J.A. Chevalier. 2001. Free Riding and Sales Strategies for the Internet. National Bureau of Economic Research Working Paper #8067, January. 10. Christ, Mario et al. 2000. Developmental Trajectories of Individual WWW usage: Implications for Electronic Commerce. Working Paper, Carnegie Mellon University, Pittsburgh, PA. 11. Christ, Mario. 2001. On Web Site Loyalty and Web Site Stickiness in the World Wide Web. Working Paper, Carnegie Mellon University, Pittsburgh, PA.



151

12. Clay, K., R. Krishnan, and E. Wolff. 2001. Prices and Price Dispersion on the Web: Evidence from the Online Book Industry. National Bureau of Economic Research Working Paper #8271, May. 13. Clay, K. and C.H. Tay. 2001. Cross-Country Price Differentials in the Online Textbook Market. Working Paper, Carnegie Mellon University, May. 14. Clemons, Eric K., Il-Horn Hann, Lorin M. Hitt. 1998. The nature of Competition in Electronic Markets: an Empirical Investigation of Online Travel Agent Offerings. Working Paper, The Wharton School of the University of Pennsylvania, Philadelphia, PA. 15. Degeratu, Alexandru, Arvind Rangaswamy and Jeremy Wu. 2000. Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. Int. J. Res. Mktg. 17(1) 55-78. 16. Dellaert, Benedict G.C. and Barbara E. Kahn, 1999. How Tolerable is Delay? Consumers’ Evaluations of Internet Web Sites after Waiting. Working Paper, The Wharton School, University of Pennsylvania, Philadelphia, PA. 17. Ellison, Glen and Sara Fisher Ellison. 2001. Search, Obfuscation, and Price Elasticities on the Internet. Working Paper, MIT, Cambridge, MA, January. 18. Fader, Peter S.G. Bruce and S. Hardie. 1999. Forecasting Repeat Sales at CDNOW: A Case Study. Wharton Marketing Department Working Paper #99-023, Philadelphia, PA. 19. Goldfarb, Avi. 2001. Analyzing Website choice using click-stream data. Working Paper, Northwestern University, Evanston, IL. 20. Goolsbee, Austan. 2000a. In a world without borders: The impact of taxes on Internet commerce. Quart. J. Electron. Comm. 115(2) 561-576. 21. Goolsbee, Austan. 2000b. Competition in the Computer Industry: Online versus Retail. Working paper, University of Chicago. 22. Johnson, Eric J., Steven Bellman and Gerald L. Lohse. 2000. What makes a Web Site “Sticky”? Cognitive Lockin and the Power Law of Practice. Working Paper, Columbia School of Business, Columbia University, New York. 23. Johnson, Eric J. et al. 2000. On the Depth and Dynamics of Online Search Behavior. Wharton Marketing Department Working Paper #00-019, Philadelphia, PA. 24. Kagel, John H. and Alvin E. Roth, Eds. 1995. The Handbook of Experimental Economics. Princeton University Press, Princeton, NJ. 25. Kauffman, Robert J. and Charles A. Wood. 2000. Follow the Leader? Strategic Pricing in E-Commerce. Proc. Int. Conf. Info. Syst. 2000, Brisbane, Australia. 26. Kraut, Robert E.et al. 1996 HomeNet: A field trial of residential Internet services. Communications of the ACM 39(12) 55-63. 27. Levy, Daniel et al. 1997. The magnitude of menu costs: Direct evidence from large U.S. supermarket chains. Quart. J. Electron. Comm.112(3) 791-825. 28. Lohse, Gerald L., Steven Bellma and Eric Johnson. 2000. Consumer buying behavior on the Internet: findings from panel data. J. Interactive Mktg. 14(1) 15-29. 29. Lucking-Reiley, David. 1999. Using field experiments to test equivalence between auction formats: magic on the Internet. Am. Econ. Rev. 89(5) 1063-1080. 30. Lynch, John G., Jr. and Dan Ariely. 2000. Wine online: Search cost and competition on price, quality, and distribution. Mktg. Sci. 19(1) 83-103. 31. Mandel, Naomi and Eric Johnson. 1998. Constructing Preferences Online: Can Web Pages Change What You Want? Working Paper, University of Pennsylvania, Philadelphia. 32. Menon, Satya; Kahn, Barbara E. 2000. Cross-Category Effects of Stimulation on the Shopping Experience: An Application to Internet Shopping. Working Paper, The Wharton School, University of Pennsylvania, Philadelphia, PA.


152


33. Moe, Wendy, Peter S. Fader. 2000. Capturing Evolving Visit Behavior in Clickstream Data. Wharton Marketing Department Working Paper #00-003, Philadelphia, PA. 34. Montgomery, Alan L. 1999. Using clickstream data to predict WWW usage. Working Paper, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA. 35. Montgomery, Alan L. 2001. Applying quantitative marketing techniques to the Internet. Interfaces. 31(2) 90-108. 36. Montgomery, Alan L.et al. 2001. Designing a better shopbot. Working Paper, Carnegie Mellon University, Pittsburgh, PA. 37. Png, Ivan et al. 2000. The Competitiveness of On-line vis-à-vis Conventional Retailing. Working Paper, National University of Singapore, Singapore. 38. Resnick, Paul, Richard Zeckhauser. 2001. Trust among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System. Working paper, University of Michigan School of Information, Ann Arbor, Michigan. 39. Salop, S., J.E. Stiglitz. 1982. The theory of sales: A simple model of equilibrium price dispersion with identical agents. The American Economic Review 72(5) 11211130. 40. Schmalensee, Richard. 1981. Output and welfare implications of monopolistic thirddegree price discrimination. The American Economic Review 71(1) 242-247. 41. Scott-Morton, Fiona, Florian Zettelmeyer, Jorge Silva Risso. 2001. Internet car retailing. Working Paper, Yale University, New Haven, CT. 42. Shankar, Venkatesh, Arvind Rangaswamy, Michael Pusateri. 1998. The impact of Internet marketing on price sensitivity and price competition. Marketing Science and the Internet, INFORM College on Marketing Mini-Conference. Cambridge, MA. 43. Smith, Michael, Joseph Bailey, Erik Brynjolfsson. 2000. Understanding digital markets. E. Brynjolfsson, B. Kahin, eds. Understanding the Digital Economy MIT Press, Cambridge, MA. 44. Smith, Michael. 2001. The Law of One Price? Price Dispersion and Parallel Pricing in Internet Markets. Working Paper, Carnegie Mellon University, Pittsburgh, PA. 45. Smith, Michael and Erik Brynjolfsson. 2001. Customer Decision-Making at an Internet Shopbot. Working Paper, Carnegie Mellon University, Pittsburgh, PA. 46. Smith, Barry et al. (2001), “e-Commerce and Operations Research in Airline Planning, Marketing, and Distribution”, Interfaces, ,” Interfaces, Vol. 30, No. 2 (MarchApril). 47. Stigler, George. 1961. The economics of information. Journal of Political Economy. 69(3) 213-225. 48. Tang, Fang-Fang and Xiaolin Xing. 2000. An Empirical Study on Pricing Differences between Dot Coms and Hybrids in the Online Video Market. Working paper, Nanyang Business School, Singapore. 49. Urban, Glen L., Fareena Sultan, William Qualls. 1998. Trust-Based Marketing on the Internet. Working Paper #4035-98, MIT Sloan School of Management, Cambridge, MA. 50. Varian, Hal R. 1980. A model of sales. The American Economic Review. 70(4) 651659. 51. Wilcox, Ronald T. 2000. Experts and amateurs: The Role of Experience in Internet Auctions. Working Paper, Carnegie Mellon University, Pittsburgh, Pennsylvania.


8

Virtual Trade Fairs: An Emerging Internet Application Ramesh Sharda and Ramesh Sambasivan

CONTENTS 8.1 8.2

Introduction ..................................................................................................153 Virtual Trade Fairs: Definition and Purpose................................................154 8.2.1 Virtual Trade Fair Spectrum ............................................................156 8.2.2 Working Effectively with Virtual Trade Fairs .................................157 8.3 Developing a Virtual Trade Fair ..................................................................158 8.3.1 Process View ....................................................................................158 8.3.1.1 Content Creation...............................................................158 8.3.1.2 Showtime ..........................................................................159 8.3.1.3 Reporting...........................................................................159 8.3.2 Technology Overview ......................................................................160 8.3.2.1 Booth Builder System ......................................................160 8.3.2.2 Attendee Personalization System .....................................160 8.3.2.3 Virtual Show Run-Time System.......................................160 8.3.2.4 Online Reporting System .................................................161 8.4 Virtual Trade Fair Technology Providers ....................................................161 8.5 Case Studies in Virtual Trade Fairs .............................................................162 8.6 Conclusions ..................................................................................................164 References..............................................................................................................164

8.1 INTRODUCTION What the Internet brings us is convenience at our fingertips at negligible costs. It is clear that the power and global reach of the Internet are transforming many aspects of commerce. Several applications have been developed to support and Web-enable activities in the business value chains: advertising, marketing, transactions, supply chain management, customer relationship management including technical support, etc. Technologies available today, and being developed for tomorrow, will result in creating even further efficiencies in connecting buyers and sellers, and extending

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

153


154


their reach. This chapter describes a new class of such applications, namely virtual trade shows. All organizations must address the problem of finding qualified leads that can be converted into customers or clients. Many traditional approaches are available to reach the potential audience: advertising in various media, sponsorships, giveaways, participation in trade shows, etc. Trade shows represent a major segment of advertising and marketing expenses for the industry. According to the research conducted by the Center for Exhibition Industry Research,1 exhibition at trade shows has jumped to third place as the major marketing focus. According to CEIR research, 88% of the exhibitors reach new leads at a tradeshow. More than 12,000 exhibitions are held each year with more than 1.5 million exhibiting companies and in excess of 75 million attendees.2 However, this industry has not been studied as extensively as its size would suggest.3 The Internet has created additional opportunities for reaching the audience through Web sites, Web-based advertising, participation in vertical portals for a specific vertical industry segment, and so on. The existence of a Web site alone is not enough to generate an audience, hence the need for vertical communities. With each of these facing the uphill task of creating and sustaining audience interest, the need for constant reinventing of a vertical community becomes critical. An online interactive event venue is neither like a static Web site nor a vertical portal. As the numbers of corporate Web sites keep growing, businesses are faced with two challenges: to achieve a Web presence that will be dynamic, not just an online catalog; and to attract the attention of a desired audience and draw its members to the Web site. The drawing power of an event and the global reach of the Internet can be combined into an online event or a virtual trade fair to create value for the Website visitors (trade show attendees), Website owners (trade show exhibitors), and the event organizers (community builders). An online event avoids the pitfalls of a degenerative vertical portal. The Internet, by its inherent nature, facilitates the meeting of people and exchange of information. This power can be used to offer attendees and exhibitors the convenience of flexibility, low cost, and ease of use — a combination that only the Internet can make possible. Tradeshow producers can lease virtual trade fair space just as they lease space at a convention center. The next section further defines a virtual trade fair.

8.2 VIRTUAL TRADE FAIRS: DEFINITION AND PURPOSE Mellinger4 provides a sound overview of the virtual tradeshow application. He defines a virtual trade show as “a collection of exhibitor-related information on products and services for a specific show.” From our perspective, a truly virtual trade show encompasses significantly more than the above description implies. We define a virtual trade fair as an event that replicates (and enhances) many of the information exchange, communication, and community gathering aspects of a physical event. A virtual trade fair thus has an event orientation and includes the capabilities of supporting information exchange among various exhibitors and numerous attendees, including live interaction.


Virtual Trade Fairs: An Emerging Internet Application

155

Event producers can use a unique interactive online venue to create trade shows online. It is akin to a convention center with exhibition facilities, except that the virtual event venue is located on the Internet. The virtual event venue is open online and staffed for a specific period of time or season, marketed to the appropriate audience, and, after a short peak of traffic and attention, this temporary venue is disbanded. A virtual trade fair is made possible by the two-way-communication capabilities (interactive features) of the Internet. By being able to congregate vendors and customers of particular industries at a set virtual location at a given time, virtual trade fairs create temporary event-based portals. During such an event, exhibitors have booth staffers available online, and the portal provides special resources to enable attendees and exhibitors to have real-time interaction, chat online, exchange business cards, work with special discounts, and receive giveaways and online documents or catalogs from the exhibitors. After the duration of the event, it can be archived in digital form to be reconvened at the next best marketplace or in the next best season. Thus, a virtual trade fair is a perfect example of a vibrant event-based portal that reinvents itself as necessary. The online booths can be adapted to suit the changing audience preferences even as an event is in progress. Physical trade shows present enormous monetary challenges for smaller enterprises that cannot afford the huge costs involved in publicizing and marketing them. If marketed through a virtual trade fair, the Internet presence of a business is greatly enhanced by attracting a universal audience at minimal costs. In comparison with the time, expense, and human effort required to participate in a physical, in-person trade fair, virtual trade fairs are more effective and inexpensive, generating about 80% of the benefits of physical trade fairs at 20% of the costs. Thus, the return on investment (ROI) for trade-show-based marketing can increase substantially An online trade fair provides a great advantage by delivering the trade shows to participants right at their personal computers. Virtual trade fairs also allow dynamic feedback of booth traffic that helps vendors assess the effectiveness of their participation. Using this feedback, exhibitors can reconfigure their booths during the event to optimize booth traffic, which is generally not possible in brick-and-mortar trade fairs once the event has commenced. Overall, virtual trade fairs create a win-win situation for exhibitors, attendees and trade show producers alike. Industries that can benefit from virtual trade fairs include: • Industries in which knowledge obsolescence is high, such as information technology (IT) • Industries with seasonal products, such as the fashion industry • Industries with a Web-savvy audience • Businesses that market products through catalogs or television shopping guide networks • Industries that manufacture heavy machinery that presents transportation challenges or those such as real estate that deal with immovables • International companies that have the U.S. as a target market • Industry trade associations with limited market reach


156


• Industries dealing with products that possess universal appeal • Industries dealing with small products that can be handled in bulk such as spices, coffee, and auto parts, whose samples can be shipped easily • Specialty stores or industries that face a need to clear inventory quickly • Industries handling products targeted at niche audiences, such as manufacturers of fitness equipment reaching out to the readers of a fitness magazine

8.2.1 VIRTUAL TRADE FAIR SPECTRUM Virtual trade fairs or trade shows have taken many forms. Table 8.1 lists the many observed forms of virtual trade shows and an example in each category. It is clear that the definition of a virtual trade show varies by provider. Many event producers provide only a link to the Web sites of exhibitors. Others provide some additional product or corporate contact information as well as physical booth location. Yet others include attractive booth images or pictures of a physical booth (panoramic, virtual reality-oriented images) in a virtual exhibit hall. Online presentation providers such as WebEx, MyWebcast, and Placeware allow a single company to make presentations with audio (and sometimes video) included. However, this service is usually centered on the presenter company. The purpose of a trade show is not only to broadcast the exhibitor information, but also to provide a mechanism for attendees and exhibitors to come together and exchange information. In a physical trade show, this happens at the same time and place. In a virtual trade show, this should be possible from different places. Thus, a virtual trade show should permit numerous exhibitors to interact with many attendees and exchange information live from several different places. This level of interactivity is possible using today’s technologies.

TABLE 8.1 Virtual Trade Fair Examples VTF Style

Examples

Only links to exhibitors’ Web sites

www.farmmechshow.com/links.htm

A Web page that gives exhibitor contact information and booth location information at the physical show

www.pentonevents.com/spring2001 or

A Web page that contains a booth image, but little interactivity (graphical or picture)

www.railroadcastnetwork.com/worldrailexpo/wr e/virtualexhibithall.htm

Single company presentations

www.webex.com, www.placeware.com, or www.mywebcast.com

An interactive trade show

www.itradefair.com

www.techxny.com/pc-expo/virtualframeset.html



8.2.2 WORKING EFFECTIVELY

WITH

157

VIRTUAL TRADE FAIRS

Virtual trade fairs cannot replace the need for humans to interact face to face. The experience of interpreting body language, gathering competitive intelligence through overhearing conversation at a trade show, the rush of being in crowded exhibition halls, and touching products on display cannot be replaced by the Internet. However, Internet trade fairs add value in a unique way as described below. “Add-on” virtual trade fairs are virtual events run in conjunction with a legacy physical trade event. Combining virtual and legacy trade fair events maximizes profitability when they are used to complement each other. Figure 8.1 illustrates some proposed combinations of physical and virtual trade fairs. The momentum created in the legacy event can be sustained and enhanced through follow-up add-on events. Subsequently, stand-alone virtual trade fairs can extend existing brand recognition by providing increased exposure of vendors to the target audience. It is easier to create repeat events as well as upgrade events on the Internet without facing logistical nightmares, and it can be done for an incremental cost. For instance, the gaming industry has traditionally hosted a physical event twice a year in the months of January and July. A virtual complement for this event can be added using the Internet in two ways: first, an add-on event can be run in tandem with the regular events; second, two stand-alone virtual events can be organized in April and October. These standalone events can help sustain the momentum generated by the physical events. A stand-alone virtual complement enables industry representatives to share ideas and keep abreast of developments without having to wait 6 months for the next event to occur. Thus, the virtual event becomes a powerful source of information for the industry for constituents anywhere in the world. Optimal combinations of legacy and virtual trade fair events can provide increased reach to a now universal audience. The frequency and timing of these combinations would depend upon the rate of change of information in the specific industry and the need for localization. In the IT area, trade shows are held frequently. There are also traveling trade shows that hold localized versions of a trade show (e.g., www.itec.com or www.dmashows.com). Virtual trade shows can be the ultimate in localization of a trade show.

JAN

FEB MAR

APR

MAY

JUN

JUL

Present

Legacy Event

Legacy Event

Proposed

Legacy Event Add-on Virtual


Proposed

Legacy Event

AUG SEP

Legacy Event Stand-alone Virtual

Proposed


OCT

Stand-alone Virtual

Stand-alone Virtual Legacy Event Add-on Virtual

FIGURE 8.1 Combinations of physical and virtual trade fairs.

Stand-alone Virtual

NOV

DEC


158


8.3 DEVELOPING A VIRTUAL TRADE FAIR 8.3.1 PROCESS VIEW Typically, a virtual trade show organizer requests a VTF technology provider to furnish the technology for a virtual show. The event producer must promote the virtual show in the same way as a physical show. This promotion would involve print, mail, or e-mail advertising to build attendee traffic and convince exhibitors to buy virtual booth space. A virtual event should also include keynote speeches and other presentations. These may be archived from a physical meeting or produced specifically for a virtual event. These Web-casts may be live or streamed from an archive. 8.3.1.1 Content Creation Virtual trade fair booths can be created online when the exhibitors select booth specifications within those stipulated by the show’s producers. Figure 8.2 illustrates the complete process of a virtual trade show. The exhibitor visits a Web site location assigned by the trade fair producer and fills out an online form of specifications required to build a booth. An exhibitor can choose from booth image templates that are made available for this purpose. The booth is then

Producer

VTF

Promote show

Technology and Service Provider

Request virtual trade show

Create keynote events

2

1

Create virtual booth online

· Provides all

Exhibitor

3

technical and customer service support to event producer, exhibitors, and attendees

Receive leads in real time Remotely operate booth

1

3

2

· Provides show

promotion support

to producers and exhibitors

Visit booths mingle with attendees/ exhibitors

Register and receive digital ID

· Provides reports

to producers and exhibitors Attendee

1

Attend keynote events 3

FIGURE 8.2 Process overview of VTF generation.

2

Collect materials and/or gifts 4



159

created digitally, using the document files, image files, and video clips of the exhibitor’s choice. Additionally, trade fair producers can have virtual booths customized in accordance with the preferences of the exhibitor over and above available standard templates. 8.3.1.2 Showtime Attendees log on to the Internet through a personal computer, sign up to create an online badge, and participate in the trade fair directly from the comfort of their own home, office, laptop or handheld computer. They can choose the booths to attend, click on various buttons or icons on the screen to exchange business cards (this transmits the leads to the exhibitor’s database in real time), download promotional literature and catalogs of the exhibitor, or have such information mailed to them. Attendees can also click on different icons to visit the exhibitor’s Web site, watch an exhibitor’s video, chat online with the exhibitor’s booth staffers, or request the virtual booth to automatically dial and create an instant phone connection. They can also click on other buttons or icons to qualify for prizes and giveaways. Attendees will also be able to view messages posted by other participants on the online bulletin board, leave comments for others to read, and chat with others in the common online lounge. Several other technologies to create a realistic interactive experience for booth staffers and attendees are under development and can be incorporated as bandwidth makes it practical. Figure 8.3 displays a sample virtual booth offering icons for the functionality described above. 8.3.1.3 Reporting Lead-generation reports delivered to the exhibitors allow them to tune their booth offerings and display services as close to real time as possible. It also allows them

FIGURE 8.3 A sample virtual booth.


160


to follow up with qualified leads immediately. These reports, which provide the exhibitor considerable information about the attendees, can be far more extensive than what is gathered at the physical show.

8.3.2 TECHNOLOGY OVERVIEW The software platform should consist of at least the following four components. 8.3.2.1 Booth Builder System The booth builder system allows companies to dynamically create and update the contents of their online exhibits. A booth builder system is a necessary, critical element to allow many booths to be developed simultaneously and in scale. Otherwise, hoards of HTML and graphics programmers would be needed to produce each virtual show. For example, one provider of VTFs, iTradeFair.com, has developed its Booth Builder System, which puts control of the booth’s contents in the exhibitor’s hands in real time. Through a very user-friendly browser-based interface, the exhibitor can create, edit, and delete all the booth’s contents. Even during the event, they can change the welcome messages on the booth, something not easy to do at a physical show. Several of the features are automatically configured and require no setup from the exhibitor. Many others are configured via menu selections. Even the exhibit’s “booth skin” (graphic image) is selected from a menu. The exhibitor does not require knowledge of HTML or any other programming language. It is a truly self-administered booth for the exhibitor. 8.3.2.2 Attendee Personalization System The attendee personalization system collects attendee name and address information, and any other custom demographic information that the producer has requested to deliver meaningful leads, while making for relevant personalized visits each time the attendee returns to the online event. 8.3.2.3 Virtual Show Run-Time System This system mimics a physical trade show with coffee rooms, media rooms, exhibit halls, and booths that offer all the services that attendees expect at a physical show. Attendees find booths offering lucky draws, online chats, gifts, and fax-back services, along with other information presented in multimedia format. This system must include the functionality to enable all the features that mimic the physical show experience. These functions must appear in a seamless, easy-to-understand interface. Further, as is the case in a physical show, a digital badge should allow access to various booths and interaction capabilities with those booths without requiring filling another form. Technologies that enable these features include chat, Web callback, FAX server, e-mail (SMTP), and the usual database and Web application programming languages.



161

8.3.2.4 Online Reporting System This system allows the exhibitors to view and download their lead information in real time. A generic attendee report, similar to traditional show attendee lists, is available to all exhibitors through the event producer. Each exhibitor can access detailed traffic reports outlining all registered attendees who visit the virtual booth. For easy customer database administration, all reports are available for electronic download in .csv format. No special software is required. For obvious security purposes, all reports are maintained under controlled access through the exhibitor’s booth identification and password.

8.4 VIRTUAL TRADE FAIR TECHNOLOGY PROVIDERS As described in a previous section, many vendors offer technology services that are sold as virtual trade shows. Almost any Web site developer can design a Web page for a show, provide links to the exhibitor Web sites, and call it a virtual trade show. Other providers offer specific technologies to develop virtual trade shows. A Web site at tsnn.com includes a list of virtual trade show providers. Table 8.2 offers a list of virtual trade fair technology providers and a brief

TABLE 8.2 Representative VTF Technology Providers Category

Companies

Show site developers that include A2Z Inc. links to exhibitor Web sites or Bluedot.com exhibitor information page Cyber Centre Excel-Online, Inc. ExpoExchange EXPOVenture iConvention.com Momentix.com PNBC.net Show Experience, Inc. Vertical portal sites that use a Globalbizshows.com booth metaphor for displaying IndustryShows.com vendor information, but no iVTS.com event orientation WebXpos 3-D booth navigation providers Expocentric.com Expomaker.com RMR plc Presentation conference Centra providers Placeware WebEx Live interaction trade show event iTradeFair.com providers UnisFair, Inc.

Web sites www.a2zshow.com www.bluedot.com www.cyber-centre.com www.excel-online.com www.ExpoExchance.com www.expoventure.com www.iConvention.com www.momentix.com www.pnbc.net www.showexperience.com www.globalbizshows.com www.industryshows.com www.ivts.com www.Webxpos.com www.showcase.expocentric.com www.expomaker.com www.rmrplc.com www.centra.com www.placeware.com www.Webex.com www.itradefair.com www.unisfair.com


162


description. This list is incomplete at best, but no single source other than the Web site mentioned above and our efforts have compiled this information. As seen in Table 8.2, virtual trade shows come in many flavors. The true same-time, different-place gathering of several exhibitors and many attendees is currently offered by three providers: Expocentric, Unisfair, and iTradeFair. Expocentric offers a 3-D VRML-based virtual reality booth, but the bandwidth limitations could hamper its acceptance. Unisfair offers Flash-based booths, providing nice visuals but limiting the number of attendees who can experience the virtual trade fair. Neither of these integrates the phone callback to allow exhibitors and attendees to talk to each other through traditional phone lines. iTradeFair’s system, on the other hand, is designed with the focus on same-time, different-place gathering. This allows for a more complete experience of the booth metaphor including “pick up goodies,” “enter door prize drawing,” “instant callback,” etc. The modular design of the system can easily integrate other features such as VRML or 3-D video tours, product demonstrations, PowerPoint presentations, etc. This list of providers is representative. Other vendors offer features that can be marketed as virtual trade fairs. The technology to enable higher interactivity among Web visitors is developing, so the booth metaphor will continue to grow to present live interaction among exhibitors and attendees.

8.5 CASE STUDIES IN VIRTUAL TRADE FAIRS This section describes some virtual events that have taken place using iTradefair.com’s technology. These events range from pure virtual shows to complementary online events. OKLAHOMA INVESTMENT FORUM’S SOUTHWEST CAPITAL CONFERENCE 2000 (TULSA AND ONLINE) Established in 1989, the Oklahoma Investment Forum (OIF) is a broad-based, statewide, not-for-profit organization formed to educate, encourage, and promote entrepreneurship and small-business development. Membership consists of business professionals with a direct interest in small business, economic development, entrepreneurship, venture and angel capital, and technology commercialization. For the past 11 years, OIF (www.ok-investmentforum.org) has organized the Southwest Capital Conference. The presenters are startup companies that, by invitation, present their businesses to the attendees (private investors, VCs and related service industries, along with a series of speakers). As an adjunct to the October 2000 event, OIF ran an online site called Southwest Capital Conference Online 2000 that showcased all the presenting companies via virtual booths. The actual conference with presenters and exhibitors was in late September 2000, and, after a 3-week break, the online event was held for 4 hours. Each virtual booth contained static information such as product and corporate specifics, business cards, press releases, etc. Various virtual booths also included multimedia presentations, documents available through an online fax-on-demand service, and the capability for chat, instant-messenger or phone-callback services.



163

The virtual show was very much oriented toward self service. The exhibiting companies completely controlled the content of their booths from browsers at their offices. The participating companies had access to online tools, with which they constructed their own online booths, customizable at will for an unlimited number of times at the click of a mouse. ITradeFair.com supported the event with pre-event publicity, e-mail blitzes, and post event follow-up e-mails to drive traffic. Visitors such as investors and investment bankers who would have otherwise not visited the event in Tulsa, were able to attend online. Visitors from as far away as New York and California came online to look at the investment opportunities in Oklahoma via the Internet. The Internet-based extension of the event tripled the audience to the Southwest Capital Conference 2000. Several presenters received inquiries from interested venture financiers in the Midwest and Northeast. In summary, according to Paul White, executive director of Oklahoma Investment Forum, “The online component was critical to the success of Southwest Capital Conference 2000.” TECHNOXPO

AT

CONVEX 2000 (SAN DIEGO CALIFORNIA,

AND

ONLINE)

PEI is the international trade association for more than 1600 distributors, manufacturers, and installers of equipment used in petroleum marketing and liquid-handling operations. Users of the equipment include service station and convenience store owners, terminals, bulk plants, and airport refueling operations. TechnoXpo is a high-tech add-on to Convex, a large trade show held in San Diego in 2000 — its 50th anniversary. Convex Online was introduced in 2000 as an online adjunct to the event in San Diego. The online event was publicized through online methods and had strong support from the event organizer. A message appearing on the online bulletin board of Convex Online summarizes the event’s sentiment: “This is the only view of Convex that I will have since I am not going to San Diego. Keep up the good work.” AECT SHOW

OF THE

FUTURE

AECT (Association for Educational Communications Technology) is a leader in the educational communications and technology industry and interfaces among professionals with a common interest in the use of educational technology and its application to the learning process. The online event, called AECT’s Show of the Future, was accessible over the Internet for 45 days during October–November 2000. ASCP SENIOR CARE PHARMACY ONLINE 2000 The American Society of Consultant Pharmacists (ASCP) is the international professional association that provides leadership, education, advocacy, and resources to advance the practice of senior care pharmacy. ASCP’s Senior Care Pharmacy show was brought online for a 45-day period after the physical event was over.


164


PENNWELL PUBLISHING’S FIRE DEPARTMENT INSTRUCTORS’ CONFERENCE (FDIC ONLINE) In May 2001, Pennwell Publishing brought two events online at one Web site with the idea of leveraging the traffic of both through one site long after the events are over in the physical convention center. Over 800 exhibits were brought online through electronic import of data to create standard virtual booths, upgradable with interactivity by the exhibitors independently. Responses from exhibitors have been very enthusiastic.

8.6 CONCLUSIONS Virtual trade fairs make it possible for an exhibitor to obtain additional leads with minimal cost and effort while allowing attendees to come to the event, but in the convenience of their home or office. By saving tremendously on obtaining qualified leads through a virtual trade fair, companies can reallocate resources toward followup on those quality leads. Virtual trade fairs thus save time, money, and human effort. Virtual trade fairs also empower small and medium-sized businesses by leveling the playing field in trade fair representation. Applications of virtual trade fairs present new technology and management research issues. A virtual booth is a platform for launching many different Web applications including audio, video, text display, e-mail exchange, etc. Development of user interfaces that make this interaction as intuitive and easy as in a physical show remains a challenge and a major research issue. There are also numerous opportunities to study the patterns of interactions among attendees and exhibitors. Virtual trade fairs will offer a rich dataset of logs to begin studying the online behaviors of community members in a B2B environment. There are also many business changes to study. Given that the trade-show industry is so cash-rich and growing, the technology acceptance model for adoption of innovative ideas is probably different from other industries. Resistance to change is common, but it is even stronger when the established players are in a comfortable situation. The technology adoption curve for this industry is worth further study.5 Current and rapidly available technologies as well as the growing bandwidth make virtual trade fairs an emerging Internet application to facilitate commerce. The application not only can enhance existing same-time, same-place gatherings to include same-time, different-place components, it can also trigger new categories of online events (e.g., a book launch on the Web). We believe that it is a high-growth industry in terms of its size, and also of much research interest in terms of technologies, user interfaces, and technology acceptance models.

REFERENCES 1. Center for Exhibition Industry Research,. www.ceir.org/forms/CAEM May01.ppt, June 02, 2001.



165

2. Center for Exhibition Industry Research, www.ceir.org/forms/Trends.ppt, June 02, 2001. 3. Gopalakrishna, S. and G.L. Lilien, A three-stage model of industrial tradeshow performance, Marketing Science, Vol. 14, No.1, pp. 22-42, 1995. 4. Mellinger, B.. (2000, March) Virtual shows: definitions and trends, Magazine for Trade Show and Event Marketing Management, http://www.exhibitornet.com/exhibitormagazine/articles/mar00.asp. 5. Christensen, C., The Drivers of Vertical Disintegration, in Innovation and the General Manager, C.M. Christensen, Ed. Irwin McGraw-Hill, New York, 1999, pp. 429-461.

SEE

ALSO:

Axelson, B.. A Trade Show With No Sore Feet: Online Events Offer Good Value, but It’s Still a Long Leap from a Physical Hall to Cyberspace,” viewed at http://www.ecommercebusinessdaily.com/archive/ECBPA91200-104210.asp, in May 2001. Putting Your Trade Show Online: Virtual Trade shows, http://www2.tsnn.com/html/osp/nohf/ospvirtualts.html.


SL3054_frame_C09 Page 167 Wednesday, November 28, 2001 10:31 AM

9

Planning Business-toBusiness E-Procurement Marketplaces Jae Kyu Lee and Efraim Turban

CONTENTS 9.1 9.2

9.3

9.4

9.5

9.6

9.7

Introduction ..................................................................................................168 Taxonomy of the E-Procurement Marketplace............................................168 9.2.1 E-Marketplace and Exchanges.........................................................168 9.2.2 Definitions by Kaplan and Sawhney................................................169 9.2.3 Vertical and Horizontal E-Marketplaces..........................................169 9.2.4 External and Internal E-Marketplaces .............................................169 9.2.5 Public and Private Exchanges..........................................................170 9.2.6 Supply Chain Management..............................................................171 9.2.7 Converging Exchanges and SCM to E-Hub....................................171 Characteristics of B2B E-Procurement Marketplaces.................................172 9.3.1 Characteristics of Public Exchanges to Buyers...............................172 9.3.2 Characteristics of Private Exchanges to Buyers..............................172 9.3.3 Characteristics of Internal E-Marketplace to Buyers ......................174 Selection of Public Exchanges ....................................................................174 9.4.1 Activities in Public Exchanges ........................................................174 9.4.2 Vertical E-Marketplaces ...................................................................174 9.4.3 Horizontal E-Marketplaces ..............................................................175 9.4.4 Outside-In System Integration Policy..............................................176 Planning Supply Chain Management Using Private Exchange ..................176 9.5.1 Activities in Private Exchange.........................................................176 9.5.2 Business Models of B2B SCM........................................................176 9.5.3 Inside-Out Integration with Extended ERP.....................................179 Internal E-Marketplaces and Desktop Purchasing ......................................180 9.6.1 Desktop Purchasing with Internal E-Marketplaces .........................180 9.6.2 MS Market in Microsoft ..................................................................180 9.6.3 Eliminating the Procurement Department .......................................181 Considerations in Planning B2B Procurement Systems .............................181 9.7.1 Connecting Service to SMEs...........................................................182 9.7.2 B2B Payment ...................................................................................182

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

167


168


9.7.3 Implementation of Outsourced Solutions ........................................183 9.7.4 XML Standard for B2B Message Exchanges .................................184 9.8 Summary and Conclusion............................................................................185 References..............................................................................................................186

9.1 INTRODUCTION Planning a business-to-business (B2B) e-procurement system is a complex process because several types of e-marketplaces exist. To develop a framework of planning a B2B procurement system, we need to classify the types of e-marketplaces with the e-procurement perspective. Three types of e-marketplaces we adopt here are public exchange, private exchange, and internal e-marketplace. To start with, let us define the characteristics of these types of marketplaces, then identify the circumstances under which each of the marketplaces can effectively be adopted. The important factors selected for planning are: • • • •

Items to purchase Managerial objective Supplier relationship System integration policy

Eventually, we need to come up with B2B planning as a combination of different types of marketplaces.1–3 The additional issues to be resolved for the planning of B2B procurement systems are: • • • •

Securing the connectivity of small and medium enterprises (SMEs) Adopting secure and cost-justifiable B2B payment systems Implementing optimal outsourcing and implementation strategies Adopting standard B2B messages in XML

These issues will be dealt in section 9.7. Because the same terms in B2B mean different things to different authors, let us first review the current literature and define the taxonomy.

9.2 TAXONOMY OF THE E-PROCUREMENT MARKETPLACE 9.2.1 E-MARKETPLACE

AND

EXCHANGES

In general, e-marketplace means any type of electronic site that helps and executes trading online. It is not necessarily a B2B site. Therefore, e-marketplace is an umbrella term. Similarly, the term exchange can mean different things, depending on the author’s purpose.4,5 A popular definition of exchange implies a B2B marketplace, usually a neutral one, with many buyers and many sellers. However, in the


Planning Business-to-Business E-Procurement Marketplaces

169

Purchasing Items

SYSTEMATIC SPOT

Procurement Type

Indirect (Horizontal)

Direct (Vertical)

MRO Hubs

Catalog Hubs

Yield Managers

Exchanges

FIGURE 9.1 Purchasing items and types.

real world, the service scope of exchange sites is evolving without changing their terms. In this case, the exchange might mean a facilitator of any B2B trades. Without clear definition of terms, planning B2B can be very ambiguous. So let us review the terms used in the literature and define the terms for B2B planning purpose. In this chapter, we adopt the broad definition, so an exchange is defined as an e-marketplace where business buyer(s) and seller(s) meet to trade and share information.

9.2.2 DEFINITIONS

BY

KAPLAN

AND

SAWHNEY

E-marketplaces can be classified from various angles. Kaplan and Sawhney6 classified four types of e-marketplaces, depending on the purchasing items (direct or indirect material) and procurement type (systematic or spot purchase) (see Figure 9.1): 1. 2. 3. 4.

MRO hub Catalog hubs Yield managers Exchange

Other classifications are point-to-point, purchasing e-hub, selling e-hub, and neutral e-hub, depending on the number of buyers and sellers (see Figure 9.2).

9.2.3 VERTICAL

AND

HORIZONTAL E-MARKETPLACES

Marketplaces are classified as vertical or horizontal marketplaces, depending on whether items are purchased mainly by one industry or by many industries. Some well-known vertical marketplaces in various industries are described in section 9.4.

9.2.4 EXTERNAL

AND INTERNAL

E-MARKETPLACES

E-marketplaces imply external e-marketplaces. Because a multitude of external marketplaces are not well organized for the buyer’s procurement, some buyers make


170


Number of Buyers Small Large Number of Sellers

Small

Large

Point-to-Point

Purchasing e-Hub (Reverse Aggregator)

Selling e-Hub (Forward Aggregator)

Neutral e-Hub (Catalog hub, Exchange)

FIGURE 9.2 Number of buyers and sellers.

their own internal e-marketplace that can be tightly integrated with their internal procurement systems. The main objective is to eliminate the unnecessary purchase approval process for low-end items. This means that the decision between internal or external marketplaces has become an important planning factor.

9.2.5 PUBLIC

AND

PRIVATE EXCHANGES.

The external exchange can be classified as public or private depending on whether the exchange is open to the public or not. To define the public and private exchanges more precisely, we can classify them into four categories (see Figure 9.3): 1. Third Party Exchange — open to both many buyers and many sellers. In this case, the market maker tends to be neutral to all buyers and sellers. 2. Buyer-Centric Exchange — open only to sellers. Usually established by a buyers consortium. A buyer’s own reverse auction site also belongs to this category. 3. Seller-Centric Exchange —open only to buyers, which may be chosen by one or a few sellers. A seller’s direct-marketing site belongs to this category. 4. Private Exchange —open only to a small number of invited sellers and buyers. The private exchange emerged in 1999 to implement tight integration among important partners, so it is usually adequate for supply chain management (SCM). Openness to Buyers

Closed Open Openness to Sellers

Closed (Private to Buyer)

Open (Public to Buyer)

Private Exchange

Seller-centric Exchange

Buyer-centric Exchange

Third Party Exchange

h

FIGURE 9.3 Taxonomy of exchanges.



171

Because the terms “seller-centric” and “buyer-centric” are semi-public, the private exachange can be classified as either public or private, depending on the study’s purpose. In planning the corporate procurement system, a key concern to buyers is whether a priori commitment to an exchange is required. The private exchange, buyer-centric exchange, and internalized marketplace usually require a buyer’s agreement and commitment with the partnered suppliers, while the public exchange does not. In this regard, whether a private exchange or buyer-centric exchange is used makes no difference. So, in this chapter, the term private exchange can also be regarded as the buyer-centric exchange from the procurement’s point of view. On the other hand, buying from either third-party exchange or sellercentric exchange does not require the buyer’s a priori commitment. So public exchange in the study of procurement can include both third-party exchange and seller-centric exchange. The buyer’s concern is when to use public and when to use private exchanges

9.2.6 SUPPLY CHAIN MANAGEMENT SCM has been around for several decades.7,8 It deals with demand forecasting, inventory management, purchase planning, warehousing, delivery management and so forth. In the early stage, its main focus was intra-enterprise SCM, but it evolved to information (such as sales and inventory) sharing among the inter-enterprise partners along the supply chain. If we adopt a wider definition of SCM that encompasses any type of service along the value chain of procurement, sales, and collaboration, its coverage becomes even wider than B2B. This definition covers not only the inter-enterprise value chain, but also the intra-enterprise one, which is usually implemented by ERP. However, originally the primary goal of SCM was reduction of inventory among the aligned partners. The original definition could have a contrast with exchanges that seek to find the lowest prices. Here, SCM is defined as the management of supply chain among a small number of key partners. With this definition, SCM is adequate for the systematic purchase under a long-term contract with the agreed quality and price. Thus, the main goal of SCM, unlike that of public exchanges, is a reduction of inventory cost and inter-enterprise transaction cost. Business models of SCM are explained in section 9.5.

9.2.7 CONVERGING EXCHANGES

AND

SCM

TO

E-HUB.

Even though we distinguish the concept of exchange from SCM, these services are merging to electronic hubs (e-hubs). A hub may provide both exchange and SCM services, so the solution and service providers in both applications have begun to converge and compete. For the planning of B2B procurement, we must select the classification that has a sufficient distinction for the procurement strategy formation, but that is as simple as possible by aggregating the terms with virtually the same implication. In this regard, we concentrate here on three types of B2B procurement e-marketplaces: public exchange, private exchange and internal e-marketplaces.


172


9.3 CHARACTERISTICS OF B2B E-PROCUREMENT MARKETPLACES To characterize these marketplaces, we consider five factors for analysis: 1. 2. 3. 4. 5.

Definition Purchased items Managerial objectives Supplier relationship System integration (as summarized in Table 9.1)

9.3.1 CHARACTERISTICS

OF

PUBLIC EXCHANGES

TO

BUYERS

Public exchange means to purchase from either a third party or seller-centric exchange that is open to any buyers. It is usually the most economical implementation for spot purchase. Goods usually purchased from the public exchanges are many items with low value. It belongs to the rule of 80% of items with 20% of value. Most indirect materials for maintenance, repair, and operations (MRO) belong to this category. However, even some maverick and small quantity of direct material can be purchased from the public vertical exchanges as well. These days, most commercial public exchange sites also support private rooms for negotiation between buyer and seller. The primary managerial objective the buying company can pursue in the public exchange is the cost reduction through comparison purchase and reverse auctions. Buyers can, of course, pursue lowering administrative cost and enhancing speed, but these factors can be sought in any type of e-marketplace. Discovering new vendors is also a big opportunity. The public exchange is appropriate for the spot purchase of market-proven branded items. As a buying company deals with multiple public exchanges, tight integration with the buyer’s back-end system is difficult to implement. Therefore, adherence to the standard interface offered by public exchanges is a reasonable strategy to adopt. A variational application of the public exchange is using it as if it were a buyercentric exchange. Because the major hurdle to buying in the public exchange is the absence of some existing suppliers, a buyer may establish an agreement with existing suppliers to trade at a designated public exchange.


OF

PRIVATE EXCHANGES

TO

BUYERS

Private exchange here refers to purchasing from either a private exchange or buyer-centric exchange that consists of only invited buyers. The items best fitted to purchase from the private exchange are a small number of items with high value. It belongs to the rule of 20% of items with 80% of value. Most direct materials purchased from the partnered suppliers belong to this category. When a material can be purchased only from limited qualified suppliers, the buying company needs to assure the quality and make a long-term contract for systematic purchase. Therefore, a major concern is connecting key existing suppliers online.



173

TABLE 9.1 Characteristics of Procurement Marketplaces Public Exchange

Private Exchange

Definition

Third Party Exchange; Seller-centric Exchange

Private Exchange Buyer-centric Exchange

Purchased Items

Large number of items with low volume each; Tends to be indirect material (MRO); Direct material from vertical marketplaces; Quality is assured by most vendors without buyer’s verification Price Cut

Small number of items with high volume each; Direct material from committed suppliers; Quality assured suppliers are invited

Managerial Objectives

Supplier Relationship

Spot purchase; Discover new suppliers

System Integration

Loosely couple the marketplaces and suppliers with the buyer’s back-end system; Outside-in approach is adequate

Inventory reduction; Information sharing along the supply chain Systematic purchase from partnered suppliers; Connect with existing suppliers Tight integration of private exchanges and partners who may use the compatible ERP software; Inside-out approach is adequate

Internal EMarketplace Internal e-catalog based marketplace integrated with eprocurement system Low-end items; Tends to be indirect material; Quality assured suppliers are invited

Reduce the approval process; Desk-top purchasing; Reduce the procurement department Either spot or systematic purchase from the partnered suppliers; Tight integration of procurement system with the internalized e-marketplace

The primary managerial objectives are inventory reduction, transactional efficiency, and information sharing with suppliers about sales and inventory. These goals are exactly the same as those of SCM, so private exchange can be regarded as a platform for efficient and effective SCM. By using a private exchange, the buying company’s back-end system, which might have implemented the use of ERP, can be tightly integrated with the exchange. Thus, the supplier’s information systems can be streamlined along the supply chain. A most efficient implementation among ERP-using companies is to adopt the ERPcompliant exchange software such as MarketSet. SAP has developed the solution jointly with Commerce One, and, in June 2001, invested an additional $225 million to Commerce One, bringing its stake to 20%.9


174



OF INTERNAL

E-MARKETPLACE

TO

BUYERS

An internal e-marketplace can be established by making a buyer’s customized ecatalog of qualified suppliers with prenegotiated prices. Therefore, the internal emarketplace can be tightly integrated with the e-procurement systems in a buyer’s intranet. By adopting this approach for the purchase of low-end items, the prepurchase approval process can be waived, so the requisitioners can purchase online directly from the suppliers without the assistance of a procurement department. This can result in a slimmer procurement department. This approach is adopted by Microsoft, Kosa, Lockheed Martin, Cisco, among others, as illustrated in section 9.6.

9.4 SELECTION OF PUBLIC EXCHANGES Sections 9.4 to 9.6 describe the activities of current markets, successful case studies, and integration approaches in public exchanges, private exchanges and internal emarketplaces, respectively. In this section, you also will see how to select the right public exchanges.

9.4.1 ACTIVITIES

IN

PUBLIC EXCHANGES

Key features of the public exchange are the representation of buyers’ requirement and specification of sellers’ products. Sellers can display their e-catalogs with posted prices if the products are commodities. However, because the standardized representation of a buyer’s requirement is not easy, few public exchanges can automatically match the buyer’s requirement with the seller’s product specification unless a buyer is willing to specify the requirement in the context of product specification. Instead, a keyword-based search engine supports the search on the buyers’ wish list and sellers’ inventory. This means that the major role of current public exchanges is to help find the relevant buy or sell requests just like a portal, along with the order fulfillment service. For the followon negotiation, interested buyers and sellers can use the private room facility. The auction and reverse auction can support the competitive price determination process. A managerial problem with public exchanges is that only 16% of professional buyers had joined the third-party exchanges by early 2001, and only 22% plan to use one in the future. Therefore, 50% of procurement personnel see little value, mainly because their key existing suppliers are not there yet. Purchasing personnel are more interested in automated trading with existing partners. Overcapacities cause a significant shakeout of public exchanges. It is reported that 80% of registered participating companies had not used the system at all in October 2001. Nevertheless, only 4% of exchanges went out of business or merged by early 2001. The years 2001–2002 are expected to be make-or-break years. However, after the shakeout, a few large-scale public exchanges will survive in industries that have sufficient liquidity.

9.4.2 VERTICAL E-MARKETPLACES The most popular type of public exchange is a vertically integrated e-marketplace. Active industries and highly ranked exchanges are listed below:11



175

• Aerospace and Defense: Exostar (Consortium of BAE Systems, Boeing, Lockheed Martin, and Raytheon). • Automotive and Heavy Equipment: Covisint (Consortium of Ford, GM, and Daimler Chrysler). • Chemicals and Plastics: Chematch (Independent exchanges). • Consumer Products: Transora (55 investment members of $250 million). • High Tech and Electronics: Converge (Consortium of HP, Compaq, etc; Purchased NECX for offline business, and provide online and offline business complementarily). • Metal: e-Steel (U.S. Steel, Ford, and steel makers for commodity trading; also offers private trading service). • Oil and Gas: FuelQuest (has changed its business to technology provider). • Paper: Paper Exchange (increased its trade transactions by 575% in 2000). • Retail: GlobalNeteXchange (GNX: Established by Sears, and Who’s Who in Retailing. It also supports collaborative forecasting service.) • Utilities: Altra Energy Technology (its transaction volume is $1 billion a month, and it is profitable). A ranking of the B2B Web sites for 15 industries is provided by BtoBonline.com.12 The key industries included are agricultural and food processing; automotive; construction and construction equipment; financial services and insurance; health care and pharmaceuticals; manufacturing (high-tech); manufacturing (industrial); outsourcing data processing; petroleum, chemicals and mining;professional services; software; telecom service; transportation and shipping; utilities; and wholesale and retail distributors.

9.4.3 HORIZONTAL E-MARKETPLACES Horizontal e-marketplaces that deal with MRO are MRO.com, Grainger.com, and FindMRO.com. Grainger.com (www.grainger.com) is a leading MRO procurement site. The items in various areas are posted at fixed prices. It also uses the domain name www.totalmro.com. MRO.COM™ exchange is the real-time reverse auction portion of the MRO.COM™ Web site. In a reverse auction, the price is driven down as competing suppliers bid lower in order to win business. Many RFQs can be viewed on the exchange screen even if the viewer is not registered. Registered users can bid on RFQs and post them online. MRO Software (MRO.com) provides solutions for maintenance optimization, industrial supply chain planning, and supplier enablement. FindMRO.com supplies services for strategic sourcing and dynamic pricing for hard-to-find MRO products. It provides two types of services: Assisted Find and Fast Find. Assisted Find locates within 24 hours where to buy a particular product by searching multiple catalogs on behalf of clients. Fast Find searches the company’s own database for instant information on millions of products. Customers can also order online and get delivery directly from the suppliers.


176


9.4.4 OUTSIDE-IN SYSTEM INTEGRATION POLICY To integrate multiple external public exchanges with a buyer’s procurement system, the Outside-In approach is appropriate. Here, instead of extending the reach of ERPbased business processes through a Web server, software called e-commerce application server integrates multiple back-end systems with an e-marketplace solution (see Figure 9.4). The Outside-In architecture is better suited for complex e-business with multiple back-end and front-end applications. In the Outside-In approach, the e-business application resides within the application server, rather than within the individual back-end systems. Typical software with the function of e-commerce application servers include Application Server (Netscape), Enterprise Server (Microsoft), Domino (Lotus), Websphere (IBM), and Enterprise Server (Sun). However, the effectiveness of the Outside-In approach is limited by the capabilities of the application server platforms on which they are built.13,14

9.5 PLANNING SUPPLY CHAIN MANAGEMENT USING PRIVATE EXCHANGE A primary role of private exchanges is improved SCM. Here, we examine the activities of private exchanges and review the business models of SCM.

9.5.1 ACTIVITIES

IN

PRIVATE EXCHANGE

A third of U.S. companies over $1 billion in annual sales are expected to implement private exchanges by 2005. For instance, the private exchange of PrintNation.com is built for PIP Printing, Inc.’s 450 franchises. Daimler Chrysler AG, Toyota Motor Corp. and Volkswagen are a few of the companies that started to build a private exchange together in 2001. Many public-exchange sites also offer some private-exchange services to key customers by adding SCM functions to public exchanges, e.g., Covisint provides SupplyConnect Service along with the other functions of exchanges like auction, catalogs, and quotation management for procurement. The boundary of pubic and private is blurred, as we have seen in the buyer-centric exchange. How many suppliers can be called “many,” and how much public can be called “public” are not clear.

9.5.2 BUSINESS MODELS

OF

B2B SCM

SCM consists of a combination of transaction processing and planning decision, making optimization and data mining models useful in the planning. Early focus of SCM was the application of such models for a company. In the B2B context, we explore inter-enterprise SCM. For B2B planning, let us review seven B2B SCM models that can be implemented in private exchanges. However, B2B SCM, of course, is not disconnected from intra-organizational SCM. 1. Collaborative Forecasting and Planning — Statistical models and neural network models have been used for demand forecasting. To allow supplies to reduce uncertainty, buyers share with suppliers their information about



ERP-based Catalog …...

Seller

Web-based Catalog …...

Web Browser

Web Browser

Buyer

Web Server

Application n Server

Sales Force Automation Mainframe

ERP System

(a) Inside-Out Approach

177

Supply Chain Planning Data Mark

ERP System

(b) Outside-In Approach

FIGURE 9.4 Architectures of integrating e-marketplaces with ERP.

demand and inventory, and collaborate for forecasting. A well-known example is RetailLink, implemented by Wal-Mart for its major suppliers such as Warner-Lambert (see Figure 9.5). Wal-Mart can ask a reduced price in return for reduced cost to suppliers. The solutions MarketSet 2.0 and Enterprise Buyer 2.0, developed by Commerce One and SAP, enable trading partners to swap product plans, streamline e-procurement, content management, and related buying and selling functions. 2. Vendor Managed Inventory Replenishment — Traditional inventory models dealt with optimizing order quantities, lot sizing, and determining the reorder points. But to reduce uncertainty for suppliers, buyers may allow suppliers to monitor the inventory on the buyer’s shelves, and to replenish continuously and autonomously. This scheme is called vendor managed inventory, and is usually combined with collaborative forecasting and planning. Together, they are called collaborative planning, forecasting, and replenishment (CPFR). A comprehensive scenario between WarnerLambert and CVS is described by Bresnahan.15 3. Multi-Factory Planning and Scheduling — Coordinated scheduling over multiple factories can enhance the level of on-time delivery. According to Copperweld Corporation, which produces copper tubing parts and sells them to Ford Motor and Caterpillar, coordinated scheduling could increase the on-time-delivery level from 62% to 95% by adopting i2’s factory planning module. (I2 is a multi-billion-dollar solutions company founded by Sajib Sithu.) 4. Outsourced Warehouse Planning and Control — Outsourcing the warehouse service has become popular in e-commerce. The manufacturer needs to track the progress of logistics as if it were handled in its own warehouse. Managing the warehouse efficiently means checking the

Internet

FIGURE 9.5 Collaborative planning, foredcasting and replenishment: retail link case.

Planner

Forecast

Sales data about W-L Products

Retail Link

Data warehouse

Operational System

Review and Comments

WWW

EDI

Planner

Manufacturing Plan

ERP

Supplier:Warner-Lambert

178

Inventory Plan

Wal-Mart





179

incoming shipment, assigning storage location, routing inventory to stores and pick, assigning gates to trucks and picked items, and loading to the right trucks. UPS provides for SCM and collaborative planning along with order management, warehouse, pick, pack and ship, and value-added service. EXE’s solution, Exceed eFulfillment System, supports such functions. HPShopping.com — a subsidiary of HP — keeps its inventory in a FedEx warehouse, so delivery within 24 hours is possible. 5. Multi-Enterprise Trucking Plan and Control — This business model attempts to plan and control the routing of trucks in alliance with partners and eliminate empty backhauls. For instance, General Mills16 has 40 distribution centers and manufacturing plants across the United States. The shipping alone cost $400 million which makes up 60% of the cost of the goods. The rate of empty-truck trips in the U.S. is 12–18% (lost revenue of $30 billion a year). To reduce the empty backhaul, General Mills shares shipping information over the Web with cereal-box papermaker Fort James Corp. to fill empty backhauled trucks. This increased the percentage of full load to 97% (from 85% before) and saved $700,000 for General Mills. By expanding the program to 19 partners and 200 carriers, General Mills expects to save $24 million a year. 6. Collaborative Design and Product Lifecycle Management — This is another application, expected to grow very rapidly, of collaborative commerce designed to reduce the time to market of new products. For instance, Ingram Micro receives customized PC orders from resellers and transfer the orders to one of nine manufacturers owned by Solectron or Ingram Micro. Solectron buys chips from Intel for these orders. The participating companies must share supply level, manufacturing capacity, inventory and logistics data. The fulfilled orders are delivered to sellers or end customers. This kind of service may be handled by a private exchange. 7. Aftermarket Hub — An e-hub can be used to support the aftermarket. For example, Toyota spends billions of dollars each year on maintenance service parts. So Toyota, with 1,500 U.S. dealers, created a joint venture with i2 called the iStar-Xchange to share the order and inventory information with top 30 suppliers. $100 million is expected to be saved over 3 years. U.S. auto makers are working on similar projects.17 Another example is AviationX, which supports the aftermarket for aircraft. AviationX takes orders and collaboratively designs them with its partners through the hub. It reduces inventory by sharing information about parts, services, technical manuals, and other data needed to keep planes in the air.

9.5.3 INSIDE-OUT INTEGRATION

WITH

EXTENDED ERP

The leading ERP vendors offer a way to extend their solutions so that they are usable with external e-marketplaces. This approach is called the Inside-Out approach (see Figure 9.4). One method of implementing this approach is to have an ERP solution maker also provide an e-marketplace solution that is compatible with the ERP package. For example, the solution MySAP (www.mysap.com) is developed by SAP


180


for this purpose as an Extended ERP.18 Another scheme for more general interface can be achieved by building a strategic alignment with the e-marketplace solution providers, e.g., SAP has a strategic partnership with Commerce One.,19 and has jointly developed a SAP-compliant e-market building solution known as MarketSet. When the e-marketplace solution requires a simple mapping of ERP functionality with a Web interface, the Inside-Out architecture can be highly effective. It allows companies to distribute ERP transaction capabilities to a wide audience of Web users, without requiring that they load any specific client software on their PCs. However, from the e-marketplace point of view, this approach is applicable only when all participating companies install the ERP system. A limitation of this approach is that many companies still use legacy systems.

9.6 INTERNAL E-MARKETPLACES AND DESKTOP PURCHASING When an internal e-marketplace is implemented, the requisitioners can bypass the procurement department. Let us review some cases of desktop purchase and the phenomenon of reducing the procurement department. Such reduction means the emergence of outside procurement service providers.

9.6.1 DESKTOP PURCHASING

WITH INTERNAL

E-MARKETPLACES

Desktop purchase means that ultimate users order items at their desks without the assistance of the requisition department and without going through an approval process. Typical examples can be found in the following cases of Microsoft, KoSa, Lockheed Martin and Cisco. 1.

MS Market in Microsoft: In Microsoft, small purchases represent about 70% of total volume, but only 3% of accounts payable. In the past, many employees were wasting time turning requisitions into purchase orders, trying to follow business rules and processes. To handle this problem, in 1996, Microsoft Corporate Procurement Group implemented MS Market, an online ordering system that works on Microsoft’s intranet. The system is integrated with SAP R/3. MS Market is interfaced with key procurement partners such as Barnes and Noble, Boise Cascade and Marriott. Upon request, MS Market immediately identifies requisitioners’ preference and approval codes through their login IDs. Then the buyer can select the supplier to display the items and prices that have been pre-negotiated between Microsoft and the suppliers. An employee can initiate an order after verifying his group’s cost center number and the manager’s name. The order is immediately transmitted to suppliers, and to accounting for payment. MS Market reduces the personnel required to manage low-cost requisitions and gives 6,600 employees worldwide a quick, easy way to order material without being burdened with paperwork and bureaucratic approval process. The company invested $1.1 million and expects that



181

more than $3 billion will be purchased through MS Market annually. It handles 1,000 orders per day, and the purchase cycle time is reduced from 8 to 3 days.20 2. KoSa is headquartered in Houston and has a main office in Frankfurt, as well as 10,000 employees worldwide. The company develops and sells polyester-based intermediate products and polymers. The company deployed an internal e-catalog for more than 56,000 products for one-stop procurement via desktops. Through strategic sourcing and desktop purchasing, 80% of orders are handled by six selected and qualified suppliers instead of the 450 previous suppliers. 3. Other Cases: Lockheed Martin created an e-catalog for small-dollar (less than $10,000) procurements, moving 30%–35% of purchase decisions to end-users. Cisco also developed the same type of system to let low-end items go without the traditional approval process. More cases can be found on the mySAP case site.21

9.6.3 ELIMINATING

THE

PROCUREMENT DEPARTMENT

Because desktop purchasing processes bypass the procurement department, the role of the department is changing from procurement transaction processing to strategic sourcing. Consequently, some companies have eliminated the traditional type of procurement department. Following are examples of an eliminated procurement department and the emergence of outside procurement service. 1. Avendra LLC: Procurement Department Goes Outside As the desktop becomes more popular, the role of procurement departments shrinks, and eventually the role may be completely outsourced. For instance, Hyatt Hotel and Marriott International Hotel have completely eliminated their procurement departments. Instead, the former procurement staffs in these and three other lodging companies formed a joint venture, Avendra LLC, to procure for these hotels. 2. WorldCrest: A Specialized Procurement Service Provider Another example of a specialized procurement service provider is WorldCrest, which specializes in negotiating office supplies, furniture, gas, chemicals, and bargains for group purchasing. For example, the members of Pantellos, a group of 20 large energy companies, access WorldCrest to purchase indirect goods and services.

9.7 CONSIDERATIONS IN PLANNING B2B PROCUREMENT SYSTEMS The other hurdles in implementing B2B procurement systems are securing small and medium enterprise (SME) connectivity, inexpensive B2B payments, outsourcing and implementation strategy, and a B2B communication standard.


182


9.7.1 CONNECTING SERVICE

TO

SMES

Most manufacturers use e-marketplaces only with their largest 20 suppliers, so the connectivity of SME suppliers is a must for effective B2B. According to Sears, among 3,000 small and medium-sized suppliers, a large number of its SME suppliers have no Internet linkage as yet. Besides, SMEs need training about the concept and systems of B2B. Therefore, exchanges start to provide training in addition to the online service. To fill the missing link, intermediate hub services to SMEs are emerging. For example, SPS Commerce provides linkage service between Sears and its SME suppliers (see Figure 9.6). SPS Commerce receives fax, telephone, and mail messages from SMEs and forwards them to Sears via the Internet, and vice versa.22 Another example is The 21st Supplier. The company’s name reflects it support to small suppliers beyond the largest 20 suppliers. This company is a business unit of Ingersoll-Rand, which allows SME suppliers to use the Web site to connect to the manufacturer’s ERP. The 21st Supplier allows checking of the shipment schedule, order processing, dispatching and payment to 9,300 suppliers.23

9.7.2 B2B PAYMENT In exchanges, 90% of B2B deals are still sealed offline. Unlike business-to-customer (B2C) payment, the B2B payment is tightly associated with the procurement processes, including quotation, negotiation, approval, ordering, billing, financing, invoicing, dispute resolution, and settlement. Therefore, the payment system should be tightly integrated with corporate e-procurement systems, external exchanges and external payment organizations such as banks. To come up with a standard protocol among these entities, several XML-based communication standards are under development. For instance, OBI (Open Business Interface) — organized by eCommerce SME

Secure Internet EDI

SPS Internet

Commerce

Fax Phone

Sears

Internet SCM System

FIGURE 9.6 Connecting services for SMEs.



183

Supplier Catalog Mgmt. Requisitioner

Price Information Mgmt.

3a

Order Entry & Inventory Mgmt 1

3b

3 5

Buying Organization Supplier Search 7

Requisitioner Profile Mgmt.

6

Payment Authority

Approval. 1. Connect to BO's Web Server and Select a Hyperlink to SO's catalog. 2. Authenticate Requisitioner using Digital Certificate 3. OBI Order Request 4. Add Administrative Information 5. OBI Order 6. Obtain Credit Authorization 7. Issue Invoice and Receive Payment

FIGURE 9.7 Procedures of OBI (open buying on the Internet) protocol.

organizations including CommerceOne, Ariba, SupplyWorks, EPIC Systems, Netfish, and iPlanet — encompasses a payment process as depicted in Figure 9.7.24 Finding B2B exchanges that can provide inexpensive payment systems is necessary to justify their use. For instance, World Outdoor Product Exchange and FinancialSettlementMatrix.com support inexpensive payments through participating banks and financial institutions. They also support the necessary administration for crossborder trades, including letters of credit, buyer credit guarantees, and financing.25

9.7.3 IMPLEMENTATION

OF

OUTSOURCED SOLUTIONS

Implementing B2B solutions is an essential step in planning, which usually involves outsourcing. Not all companies have been successful in this process. Rumor has it that Nike did not have a good experience with i2 in this respect.26 The lessons we can learn from Nike’s painful experience is basically the same as what we must consider for software implementation, namely: • The manufacturer should explain its manufacturing process well to SCM suppliers. • Confirm that solutions match well with the company’s problem. • Monitor the system development process closely to ensure the outcome is what the company needs. • Verify the system before field deployment. • For large comprehensive systems, take a pilot and a parallel conversion process.


184


So far, only the most advanced organizations have reengineered their e-procurement process. Two-thirds of the purchasing communities are still in experimental stages. AMR Research concludes, “Big bangs don’t work. Iterative steps work.” This is because the “big-bang” projects require too much integration, take too much time, and need quality data all at once.27

9.7.4 XML STANDARD

FOR

B2B MESSAGE EXCHANGES

EDI has been the major standard platform for B2B communications in the past. Many EDI systems are moving from value-added networks to the Internet, and Webbased XML/EDI becomes the standard platform. In the beginning, Hypertext Markup Language (HTML) was developed to display text for human comprehension. To allow software agents to comprehend the global HTML files, the agents should be equipped with natural language-processing capability. However, natural languageprocessing capability is limited only to cases with a small amount of vocabulary and fairly standard grammar. Because Web sites cover all sorts of information, it is impossible to effectively extract the relevant data from the HTML files. So eXtensible Markup Language (XML) was developed to define the semantic data items in tags. Then the software agents were able to read out the values in the tags. XML files are also transformable to HTML files to display on browsers. To complement the XML files, Document Type Definition (DTD) defines the structure between data elements, and eXtensible Stylesheet Language (XSL) defines the transformation of XML and DTD to HTML files for human comprehension. XML has become popular as the second generation of Web technology. For B2B EC, XML can be observed from the view of EDI and agents. From EDI’s point of view, XML is a tool to implement EDI on the Web. Users can just download the XML-based Web page, browse the transformed HTML files, and retrieve the values of embedded data items. If the purpose of XML is just a message exchange, it can be regarded as the XML/EDI. However, the EDI community does not limit the function of XML to message exchange, as the traditional EDI did. EDI task forces, mainly led by industrial experts, set up business protocols implemented on XML. The main issue here is the establishment of standard protocols, including business entities, procedure, message format, and product specification. For instance, the protocol open business interface (OBI) adopts the requisitioner’s buying organizations, suppliers, and payment authorities as the entities of B2B protocol. It is very difficult for a single protocol to meet the needs in all circumstances. So, establishing standards is essential for capturing a strategically beneficial position in B2B EC. Several task forces attempt to establish a number of XML-based business protocols. Some of them are: • OTP (Open Trading Protocol): Proposed by a 30-company consortium for handling remote electronic purchase regardless of the payment mechanism. • OFX (Open Financial Exchange): Proposed by Microsoft, Intuit, and Checkfree for exchanging financial transaction documents.



185

• OSD (Open Software Description): Proposed by Marimba and Microsoft for describing software package components to use in automated software distribution environments. • OBI (Open Buying on the Internet): Proposed by e-commerce organizations and major buying and selling organizations (www.openbuy.com). • CBL (Common Business Language): Supports the messages for the basic business forms used in ANSI X12 EDI (www.xmledi.com) transactions as well as those in OTP and OBI. • RossetaNet: Developed specifically for the PC industry (www.rosettanet.org). Software vendors also attempt to establish protocols and then develop solutions that can implement the standard protocols. Biz Talk, developed by Microsoft, is one of these. A concern at this point is that there are too many standards, so companies are confused as to which will really become the de facto standard in the market. To overcome this chaos, UN/CEFACT and OASIS developed and approved ebXML specifications at a meeting in Vienna, Austria on 11 May 2001. A bright side of this standard is that major solution vendors announced that they would support ebXML when it is established. So ebXML may become the de facto industry standard.28

9.8 SUMMARY AND CONCLUSION The taxonomy of e-marketplaces is reviewed and defined from various angles. From the procurement point of view, an adequate classification is public exchange, private exchange, and internal e-marketplace. The public exchange is appropriate for the spot purchase of large numbers of low-value items, to reduce the item cost without heavy investment on implementation. The tight integration with a buyer’s back-end system is not economically feasible. However, it turns out that the main goal of e-procurement, which is to reduce inventory of key items by sharing information along the supply chain with major suppliers, cannot be handled by public exchanges. The private exchange for invited partners is emerging as the best solution, so the private exchange can be regarded as the platform for B2B SCM. The third approach is the internal e-marketplace, which can handle the purchase of low-end items without the bureaucratic approval process and intervention of a procurement department. Because most companies need effective spot purchasing, systematic SCM, and efficient desktop purchasing for low-end items, planning B2B procurement is actually planning a portfolio of the e-marketplaces. An integration policy should be established depending upon the type of emarketplaces to be adopted. The planning should also encompass the connectivity of existing SME suppliers, inexpensive B2B e-payment platforms, XML-based message standards, and cautious deployment strategy. Since this area is changing so rapidly in its business models and technologies,29,30 it is necessary to monitor changes closely.


186


REFERENCES 1. Turban, E., J.K. Lee, D. King and M. Chung, Electronic Commerce 2002: A Managerial Perspective, Prentice Hall, 2002. 2. Turban, E., E. McLean, and J. Wetherbe, Information Technology for Management, 3rd ed., NY: John Wiley & Sons, Inc., 2002. 3. Cunningham, M.J., B2B: How to Build a Profitable E-Commerce Strategy, Perseus Publishing, Cambridge, MA. 2000. 4. Sculley, A.B. and W.W. Woods, B2B Exchanges, ISI Publications, 1999. 5. Silverstein, B., Business-to-Business Internet Marketing, Gulf Breeze, FL: Maximum Press, 1999. 6. Kaplan S. and M. Sawhney, e-hubs: the new B2B marketplaces, Harvard Business Review, 78, 3, May-June 2000, pp. 97-103. 7. Fisher, M.,What is the right supply-chain for your product, Harvard Business Review, March 1997, p.105. 8. Handfield, R. and E. Nicols, Supply Chain Management, Upper Saddle River, NJ: Prentice Hall, 1999 9. Weston, R., SAP ups its Commerce One investment, AMR Research Alert, June29, 2001, www.amrresearch.com/preview/010629alert78.htm. 10. Morneau, J., Who's using exchanges?, InternetWeek, Feb 14, 2001. 11. Harbin, J. and J. Fontanella, Top trading exchange services for industry verticals, AMR Research, April 17, 2001. 12. The 2000 NetMarketing 200 best B-To-B Web sites by category, BtoBonline.com, www.netb2b.com/netMarketing200, 2001. 13. Sullivan, D., Extending E-business to ERP, e-business Advisor, Jan 1999a, pp.18-23. 14. Sullivan, D., Take ERP on the road, e-business Advisor, Jan 1999b, pp. 24-27. 15. Bresnahan, J., The incredible journey, CIO Enterprise Magazine, August 15, 1998, www.cio.com/archive/enterprise/081598_jour_content.html. 16. Moozakis, C., No-slack supply chain, InternetWeek, January 30, 2001, www.internetweek.com/newslead01/lead013001.htm. 17. Pryweller, J., Toyota gears up aftermarket hub, BtoBonline.com, March 5, 2001, www.btobonline.com/cgi-bin/article.pl?id=5241. 18. Nelson, M., SAP adds module for I-Commerce, InfoWorld, Vol. 21, No. 27, Jul 6, 1998. 19. Weston, W., Commerce One debuts SAP-oriented tools, News.com, June 12, 1998 www.news.com/News/Item/0,4,23566,00.html. 20. Kalakota, R. and M. Robinson, e-Business: Roadmap for Success, Reading, MA, Addison Wesley, 1999, pp.241-243. 21. MySAP.com™, E-procurement — customer successes, www.mysap.com/solutions/eprocurement/customersuccesses.htm, 2001. 22. Kemp, T., Small suppliers: weak retail link, InternetWeek, February 20, 2001, www.internetweek.com/newslead01/lead022001.htm. 23. Moozakis, C., Ingersoll’s big tent, InternetWeek, April 9, 2001 www.internetweek.com/newslead01/lead040901.htm. 24. The OBI Consortium, OBITM Technical Specifications: Open Buying on the Internet, 1999, www.openbuy.org/obi/specs/OBIv210.pdf. 25. Moozakis, C., E-settlement: sealing deals online, InternetWeek, December 6, 2000, content.techWeb.com//wire/story/TWB20001206S008. 26. Karpinski, R., Don’t get Nike-ed, InternetWeek, March 7, 2001 www.internetweek.com/transtoday01/ttoday030701.htm.



187

27. O’Marah, K. and K. Murphy, Supply chain initiatives require an iterative approach, AMR Research Alert, June 29, 2001, www.amrresearch.com/preview/010629alert79.htm. 28. Lee, J.K., Business-to-Business Electronic Commerce, Encyclopedia of Information Systems, New York, Academic Press, 2001. 29. Lim, G. and J.K. Lee, Buyer-Carts for B2B EC: The b-Cart Approach, Proceedings of the International Conference on Electronic Commerce, Seoul, pp. 54-63, 2000 30. Joh, Y.H. and J.K. Lee, Buyer’s Customized Directory Management over Sellers’ eCatalogs: Logic Programming Approach, Decision Support Systems, 2001(forthcoming).

WEB RESOURCES

ON

B2B EC

BtoB Magazine: www.netb2b.com InternetWeek: www.internetwk.com InformationWeek: www.informationweek.com TechWeb: www.techWeb.com CIO: www.cio.com McKinsey Quarterly: www.mckinseyquarterly.com AMR Research: www.amrresearch.com Forrester Research: www.forrester.com Gartner: www4.gartner.com eMarketer: www.emarketer.com eCommerce Times: www.ecommercetimes.com InfoWorld: www.infoworld.com Computer World: www.computerworld.com Internet: www.internet.com News.com: news.cnet.com International Center for Electronic Commerce: icec.net



10

Internet Evolution and Social Impact* Sara Kiesler, Robert Kraut, Jonathon Cummings, Bonka Boneva, Vicki Helgeson, and Anne Crawford

CONTENTS 10.1 Internet Evolution and Social Impact..........................................................189 10.1.1 Study 1: Follow-Up of the Original HomeNet Sample ..................191 10.1.2 Study 2: A Panel Study of Computer and Television Purchasers...192 10.2 Study Method...............................................................................................193 10.2.1 Results ..............................................................................................194 10.3 Outcomes of Using the Internet ..................................................................195 10.3.1 Entire Sample...................................................................................195 10.3.2 Extraverts vs. Introverts ...................................................................195 10.3.3 For Those High and Low in Social Support ...................................196 10.3.4 Teens and Adults ..............................................................................196 10.4 Implications ..................................................................................................197 References..............................................................................................................198

10.1 INTERNET EVOLUTION AND SOCIAL IMPACT With the rapidly expanding reach of the Internet into most aspects of everyday life, we need to understand its social impact and the behaviors leading to this impact. Since 1995, our studies have consistently pointed to communication as a principal reason people use the Internet. From the early days of networked mainframe computers to the present, interpersonal communication has been the technology’s most frequent use.1,2 More than 90% of people who used the Internet in a typical day during 2000 sent or received e-mail.3 Using e-mail leads people to spend more time online, encourages their use of the Internet for information, entertainment, and shopping, and discourages them from dropping Internet service.4 As new Internet communication services arise — instant messaging, chat rooms, multiple-person games, auctions, and myriad support groups — they become instantly popular. * This chapter is adapted from Internet Paradox Revisited, a forthcoming article in the Journal of Social Issues.

1-57444-305-4/02/$0.00+$1.50 © 2002 by CRC Press LLC

189


190


If communication is so important to Internet users, there is good reason to expect that the Internet will have positive social impact. Communication, including contact with neighbors, friends, and family, and participation in social groups, improves people’s level of social support, their probability of having fulfilling personal relationships, their sense of meaning in life, their commitment to social norms and to their communities, and their psychological and physical well being. 5–10 Through its use for communication, the Internet could have important positive social effects on individuals,11,12 groups, organizations,1 communities,13 and society at large.14,15 Broad social access could increase people’s social involvement, as the telephone did.16,17 It also could facilitate the formation of new relationships,11 social identity and commitment among otherwise isolated persons,18 participation in groups and organizations by distant or marginal members,1 and political mobilization.19 Whether the Internet has positive or negative social impact, however, may depend upon the quality of people’s online activities and what they give up to spend time online. Stronger social ties generally lead to better social outcomes than do weaker ties.20,21 Many writers have worried that the ease of Internet communication might encourage people to spend more time alone, talking online with strangers, or forming superficial “drive-by” relationships, at the expense of deeper face-to-face discussion and companionship with friends and family.22 Further, even if people use the Internet to talk with close ties, these online discussions might displace higher quality faceto-face and telephone conversation.23 Research has not yet led to consensus on either the nature of social interaction online or its effects on social involvement and personal well-being. Some survey research indicates that online social relationships are weaker than off-line relationships,24 that people who use e-mail regard it as less valuable than other modes of communication for maintaining social relationships,23,25 that people who use e-mail heavily have weaker social relationships than those who do not,26 and that people who use the Internet heavily report spending less time communicating with their families.27,28 In contrast, other survey research shows that people who use the Internet heavily report more social support and more in-person visits with family and friends than those who use it less,3 and that people use the Internet to bolster existing community.29 Because this research has been conducted with different samples in different years, it is difficult to identify central tendencies and changes in these tendencies with time. Further, the cross-sectional nature of the research makes it impossible to distinguish self-selection (in which socially engaged and disengaged people use the Internet differently) from causation (in which use of the Internet encourages or discourages social engagement). In a longitudinal study by Kraut et al.,30 the authors attempted to assess causal direction. The HomeNet field trial followed 93 households in their first 12–18 months online. Although the sample as a whole reported high well being at the start of the study, those participants who used the Internet more became reliably less socially involved and more lonely and showed an increase in depressive symptoms. These changes occurred even though participants’ dominant use of the Internet was communication. These findings were controversial. One problem in the original HomeNet study is the unknown generalizability of the results over people and time. The participants


Internet Evolution and Social Impact

191

in the original study were an opportunity sample of families in Pittsburgh with high social involvement and strong social ties initially, compared with the population as a whole. In 1995 and 1996, when they began the study, few of their family and friends had Internet access. Using the Internet might have disrupted this group’s existing social relationships. Had the study begun with a more socially deprived sample or when more of the population was online, their use of the Internet for social interaction might have led to more positive effects. This chapter addresses these issues of generalizability through a follow-up of the original HomeNet sample and a new longitudinal study. We first examine the longer-term impact of Internet use on those in the original study. Although following the same participants over time does not allow us to distinguish the effects of changes in the sample (e.g., acquisition of more online experience) from effects of changes in the Internet (e.g., more of one’s social circle being online), this analysis provides a second look at a group for whom initial Internet use had negative effects. We next follow a 1998 sample of people in the Pittsburgh area for a year. All had recently purchased a new computer or television set. This study addresses the effects of Internet use in a more recent era. The sample was sufficiently large to permit an analysis of the impact of individual differences in extraversion, social support, and age on outcomes, and of the possible differences in use of the Internet that could explain different outcomes.

10.1.1 STUDY 1: FOLLOW-UP HOMENET SAMPLE

OF THE

ORIGINAL

We examined data from 208 members of 93 Pittsburgh families, to whom we provided a computer and access to the Internet in 1995 or 1996. The families were recruited through four high-school journalism programs and four community-development organizations in eight Pittsburgh neighborhoods. The sample was more demographically diverse than was typical of Internet users at the time. Details of the sampling and research protocol are described in Kraut et al.31 The analyses of social impact reported in Kraut et al.30 were drawn from Internet usage records, from surveys given just before participants began the study, and again in May 1997. Server software recorded participants’ use of the Internet — hours online, e-mail volume, and Web sites visited per week. The surveys assessed demographic characteristics and measures of social involvement and psychological well being. In our re-analysis, we looked at the impact of the use of the Internet using a third survey administered in February 1998. For about half the participants, the final survey came nearly 3 years after they first used the Internet; for the other half, 2 years later. We limited analysis to the 208 participants who completed two out of three surveys. To assess changes in social and psychological outcomes, we used a longitudinal panel design to evaluate changes in social involvement and psychological well being from pretest to first posttest, and from first posttest to second posttest. We statistically controlled for the prior level of social involvement and psychological well being by including a lagged form of the dependent variable as an independent variable in the model. For example, when examining the effect of Internet use on loneliness at the


192


TABLE 10.1 Summary of Outcomes of Internet Use: Original vs. Follow-Up Study

Outcome variable

Original study report on first 12-18 months of Internet use (N = 169)

Local social circle Distant social circle Family communication Stress Depressive symptoms

Declined with more Internet use Declined with more Internet use Declined with more Internet use Increased with more Internet use Increased with more Internet use

Loneliness

Increased with more Internet use

Follow-up study, results over 2-3 years (N = 208) No overall effect No overall effect No overall effect Increased with more Internet use Increased with Internet use in first 12-18 months, decreased with Internet use after 12-18 months Increased with Internet use in first 12–18 months, no effect after 12–18 months

Note: Summary results from Ref. 29 and Kraut et al. (in press, Table 1).

second posttest, we included the lagged variable for loneliness at the first posttest to control for the effects of prior loneliness on Internet use. Table 10.1 summarizes our later findings and compares them with those reported by Kraut et al.30 Except for the increase in stress with more Internet use, the effects reported earlier were not maintained over the longer period. Two Internet-use times period statistical interactions reflect different trends at different periods. Depressive symptoms significantly increased with Internet use during the first period but significantly declined with Internet use during the second period (p < .05). Loneliness significantly increased with Internet use during the first period but was not associated with Internet use during the second period (p < .01). When we tested effects of age, the only effect of note was that adults’ stress increased more than teens’ stress with more Internet use (p < .10).

10.1.2 STUDY 2: A PANEL STUDY OF COMPUTER AND TELEVISION PURCHASERS In this study, we attempted to replicate the original HomeNet research design in a sample of households that had recently purchased a new personal computer or television set. We added controls to the design and new measures. First, we attempted to manipulate Internet use to create a true randomized experiment. We randomly offered free Internet service to half of those households purchasing a computer and arranged with the Internet service provider to monitor their usage of the Internet; households in the control condition received an equivalent amount of money ($225) to participate. Unfortunately, by the end of 6 months, 84% of the control households obtained Internet access on their own (vs. 95% of the experimental group). Because of this failed attempt to conduct a true experiment, we combined the groups for analyses of the effects of using the Internet.



193

The addition of the television buyer comparison group (of whom 29% had Internet access after 6 months) helps us to rule out explanations of changes over time based on sample selection. Previous research generally shows that heavy, as compared with light, television viewers stay at home more, are less socially involved, and experience poorer intellectual, physical, and psychological outcomes.32–36 In our analyses of Internet use, we included participants from the television purchaser group, but controlled for sample selection bias by creating a statistical dummy variable indicating whether participants were in the television or computer purchaser group. In this new study, we also examined the differential effects of individual differences in extraversion and perceived social support on the effects of Internet use. Extraversion is the tendency to like people, to be outgoing, and to enjoy social interaction; it is highly consistent over the life course,37 and it is predictive of social support, social integration, well being, and positive life events.38,39 The perception of social support refers to feelings that others are available to provide comfort, esteem, assistance, and information or advice; perceived social support buffers the effects of stress.5,40,41 Two opposing hypotheses predict different relationships between extraversion or social support and Internet use. A “rich-get-richer” hypothesis predicts that those who are highly sociable and have existing social support will get more social benefit from using the Internet. Highly sociable people would reach out to others on the Internet and use it especially to foster relationships. Highly supported people would use the Internet to reinforce their support networks. Both groups may have the social skills necessary to elicit social benefits from using the Internet. If so, these groups would gain more social involvement and well-being from using the Internet than those who are introverted or have poor network relations. By contrast, a “social compensation” hypothesis predicts that those who are introverted or lack social support would profit most from using the Internet. They might use the new communication opportunities to form connections and obtain supportive communications and useful information that they do not have locally.18 At the same time, for those who already have satisfactory relationships, using the Internet could interfere with their real-world relationships if they swap strong ties for weaker ones. Analogous to the finding that cancer patients with emotionally supportive spouses can be harmed by participation in peer-discussion support groups,42 it is possible that people with strong local relationships might turn away from family and friends if they used the Internet for social interaction.

10.2 STUDY METHOD We recruited 446 participants through local newspaper advertisements for a study of household technology, soliciting people who had purchased a new computer or new television within the past 6 months. We obtained agreement from adults and children in the family above age 10 to complete surveys. After the initial telephone contact, we mailed consent forms and pretest surveys with return envelopes. Unlike the procedures used in Study 1, we did not encourage Internet use or provide technology support.


194


We administered surveys three times during the study — in February 1998, 6 months later, and a year later, February 1999. We used an index of self-reported Internet use (alpha = .86) from all participants rather than automated measures of usage as in Study 1. (Automated usage records were available for the computerexperimental group but not for participants in the computer-control group and for TV purchasers. The self-report index of Internet use and the automated count of sessions logged into the Internet over 8 previous weeks was correlated r = .55 at Time 2.n = 114. and r = .42 at Time 3n = 104) We used self-report measures to assess demographic characteristics of the participants, and measures from the original HomeNet study, including perceived social support, size of local and distant social circles, and time talking with other family members. We used the same measure of extraversion.43 We added new measures of anomie, trust in people, community involvement, and intentions to stay in the Pittsburgh area. We also assessed respondents’ peer relationships with 10 specific family and friends by asking them to identify family members or friends (five living in the Pittsburgh area and five living outside of the area) who were closest to them in age. Participants described their feelings of closeness to each nominee at each time period on a five-point scale. To assess well being, we again measured depressive symptoms with a widely used scale.44 We also used the daily life stresses scale45 and the UCLA Loneliness Scale.46 from the original HomeNet study. We added measures of self-esteem, positive and negative affect, perceived time pressure and physical health. Finally, because the Internet is a source of skill and information as well as communication, we included a self-report measure of skill using computers and a test of knowledge, including multiple-choice items on national current events, Pittsburgh current events, and general knowledge from a high-school equivalency test (GED). References for published measures and a list of all unpublished measures are available in Kraut et al.47

10.2.1 RESULTS Of the 446 members of 216 households who were eligible to be in the sample, 96% completed survey 1, 83% completed survey 2 and 83.2% completed survey 3. Figure 10.1 shows self-reported time usage data for the sample at the beginning and end of the study. It shows that, as participants gained Internet access over the year, the only home activity that declined overall was watching television. Almost all of this decline happened among adults. Our analyses of social impact were similar to those performed for Study 1. In the Study 2 models, social involvement, well being, and knowledge outcomes at the second and third time period were regressed on self-reported Internet use during that period, controlling for demographic characteristics and the lagged dependent variables. The models controlled for whether the respondent came from the TV purchaser or computer purchaser subsample and whether the dependent variables were collected at the second or third time period. We also tested whether extraversion, social support, or age moderated the effects of using the Internet. In the models we included the main effects for the measure of extraversion43 and Cohen et al.’s.48 measure of social support and the interaction of these variables with Internet use.



195

Household Time on Activities at Home 3.5

Hours in recent weekday

3.0 2.5

1998 1999

2.0 1.5 1.0 0.5 0.0 Talk w. friends

Talk w. family

Alone

Read books

Read Watch TV magazines

Use Internet

FIGURE 10.1 Time reported on different activities (standardized scores).

10.3 OUTCOMES OF USING THE INTERNET 10.3.1 ENTIRE SAMPLE Generally, the effects of using the Internet on interpersonal communication, community involvement, well being, and knowledge were negligible or positive. Participants who used the Internet more had larger increases in the sizes of their local (β = .12, p < .01 ), distant social circle (β = .15, p < .01), and face-to-face interaction with friends and family (β = .09, p

The E-Business Handbook

eBusiness and Workplace Redesign

Marketing the eBusiness: An Introduction (Routledge Textbooks in Ebusiness, 2)

eBusiness Essentials, 2nd Edition

eBusiness Fundamentals (Routledge Textbooks Inebusiness)

Aufbau einer eBusiness Marketing-Datenbank

Corporate Portals and eBusiness Integration

Enabling eBusiness - Integrating Technologies Architectures & Applications

The Electronic Packaging Handbook (Electronics Handbook Series)

The Roofing Handbook,

The handbook of integration

The Poisoner's Handbook

The J2EE Architect's Handbook

The Handbook of Dramatherapy

The ocean engineering handbook

The Cattle Health Handbook

The Image Processing Handbook

The Nearest Relative Handbook

The Accessibility Handbook

The Linguistics Student's Handbook

The Home Security Handbook

The Data Journalism Handbook

The handbook of sailing

The Pension Trustee's Handbook

The Fortran 2003 handbook

The Intercultural Performance Handbook

The modernism handbook

The Personal Security Handbook

The Exoplanet Handbook

The E-Business Handbook

eBusiness and Workplace Redesign

Marketing the eBusiness: An Introduction (Routledge Textbooks in Ebusiness, 2)

eBusiness Essentials, 2nd Edition

eBusiness Fundamentals (Routledge Textbooks Inebusiness)

Aufbau einer eBusiness Marketing-Datenbank

Corporate Portals and eBusiness Integration

Enabling eBusiness - Integrating Technologies Architectures & Applications

The Electronic Packaging Handbook (Electronics Handbook Series)

The Roofing Handbook,

The handbook of integration

The Poisoner's Handbook

The J2EE Architect's Handbook

The Handbook of Dramatherapy

The ocean engineering handbook

The Cattle Health Handbook

The Image Processing Handbook

The Nearest Relative Handbook

The Accessibility Handbook

The Linguistics Student's Handbook

The Home Security Handbook

The Data Journalism Handbook

The handbook of sailing

The Pension Trustee's Handbook

The Fortran 2003 handbook

The Intercultural Performance Handbook

The modernism handbook

The Personal Security Handbook

The Exoplanet Handbook

Recommend Documents