Intelligent Support Systems Technology:
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Knowledge
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Management
Vijayan Sugumaran
IRM PRESS
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Intelligent Support Systems: Knowledge Management Vijayan Sugumaran, Ph.D. Oakland University, USA
IRM Press
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Intelligent Support Systems: Knowledge Management Table of Contents
Foreword ............................................................................................................ vii Vijayan Sugumaran Oakland University, USA Preface .................................................................................................................. x Chapter 1. Intelligent Agents and the World Wide Web: Fact or Fiction? ............................................................................................................ 1 Sudha Ram, University of Arizona, USA Chapter 2. Comparing U.S. and Japanese Companies on Competitive Intelligence, IS Support and Business Change ....................................... 4 Tor Guimaraes, Tennessee Technological University, USA Osamu Sato, Tokyo Keizai University, Japan Hideaki Kitanaka, Takushoku University, Japan Chapter 3. Knowledge Assets in the Global Economy: Assessment of National Intellectual Capital ..................................................................... 22 Yogesh Malhotra, @Brint.com and Syracuse University, USA Chapter 4. Knowledge-Based Systems as Database Design Tools: A Comparative Study ..................................................................................... 43 W. Amber Lo, Millersville University and Knowledge-Based Systems, Inc., USA Joobin Choobineh, Texas A&M University, USA Chapter 5. Policy-Agents to Support CSCW in the Case of HospitalScheduling ................................................................................................... 72 Hans Czap, University of Trier, Germany
Chapter 6. Building an Agent: By Example ................................................. 84 Paul Darbyshire, Victoria University of Technology, Australia Chapter 7. Intelligent Agents in a Trust Environment .............................. 98 Rahul Singh, University of North Carolina, Greensboro, USA Mark A. Gill, Arizona State University, USA Chapter 8. A Case Study on Forecasting of the Return of Scrapped Products through Simulation and Fuzzy Reasoning ............................109 Jorge Marx-Gómez and Claus Rautenstrauch Otto-von-Guericke-University, Magdeburg, Germany Chapter 9. Newshound Revisited: The Intelligent Agent That Retrieves News Postings ..........................................................................................124 Jeffrey L. Goldberg, Analytic Services Inc. (ANSER), USA Shijun S. Shen, Tygart Technology, Inc., USA Chapter 10. Investigation into Factors That Influence the Use of the Web in Knowledge-Intensive Environments ................................................135 Yong Jin Kim and H. Raghav Rao, SUNY at Buffalo, USA Abhijit Chaudhury, Bryant College, USA Chapter 11. A Study of Web Users’ Waiting Time ....................................145 Fiona Fui-Hoon Nah, University of Nebraska-Lincoln, USA Chapter 12. Stickiness: Implications for Web-Based Customer Loyalty Efforts .........................................................................................................153 Supawadee Ingsriswang and Guisseppi Forgionne University of Maryland, Baltimore, USA Chapter 13. “Not” is Not “Not” Comparisons of Negation in SQL and Negation in Logic Programming ............................................................164 James D. Jones, University of Arkansas at Little Rock, USA Chapter 14. Knowledge Management and New Organization Forms: A Framework for Business Model Innovation ........................................177 Yogesh Malhotra, @Brint.com, L.L.C. and Florida Atlantic University, USA Chapter 15. Implementing Virtual Organizing in Business Networks: A Method of Inter-Business Networking ................................................200 Roland Klueber, Rainer Alt and Hubert Osterle University of St. Gallen, Switzerland
Chapter 16. Managing Knowledge for Strategic Advantage in the Virtual Organization ..............................................................................................225 Janice M. Burn and Colin Ash, Edith Cowan University, Australia Chapter 17. Virtual Organizations That Cooperate and Compete: Managing the Risks of Knowledge Exchange .................................................248 Claudia Loebbecke, Copenhagen Business School, Denmark Paul C. van Fenema, Erasmus University, The Netherlands Chapter 18. Becoming Knowledge-Powered: Planning the Transformation ................................................................................................274 Dave Pollard, Ernst & Young, Canada About the Editor .............................................................................................296 Index .................................................................................................................297
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Foreword Organizations use a variety of computer-based systems such as management information systems, decision-support systems and executive information systems to support decision making. These systems deliver business data and information in a highly aggregated form. However, they have not been able to keep up with the new flood of information, particularly with the explosion in the amount of data being generated, stored, accessed and processed by the ubiquitous Internet technologies. This information overload coupled with competitive pressures signal the need for “intelligent support systems” that can minimize the cognitive load on the knowledge workers and decision makers. In addition, fierce competition, globalization, and the digital economy have forced organizations to search for new ways to improve customer satisfaction and competitive advantage. This has created tremendous pressure on businesses to minimize cost, increase quality, and reduce time-to-market for products to meet customer demand. In order to satisfy these objectives, businesses are reorganizing themselves into smaller and efficient units by pruning the organizational hierarchy and becoming decentralized. Consequently, there is great need for improving communication and information flow, and providing decision-making capabilities to sites that have to respond quickly to market changes. Organizations are increasingly turning to technologies to support their problem-solving and decision-making activities. To gain dramatic improvement in organizational productivity, emerging information technologies (such as intelligent agents) are being applied to create a cooperative and group-based work environment. Although artificial intelligence (AI) technologies such as expert systems and neural networks have been successfully used in aerospace, communication, medicine, finance, etc., they have not made a significant impact on improving overall productivity due to their narrow scope. In contrast, the new breed of “intelligent support system technologies” hold greater potential in that they can be applied to a large number of domains and a diverse set of problems. For example, a generic intelligent agent-based application can be customized for different domains and a variety of problem scenarios. Intelligent support systems are generally characterized as systems that help users in carrying out difficult tasks by minimizing complexity and, hence, the users’ cognitive load. These systems have a learning component and gain “experience” over time. They respond to changes in the environment and new situations with minimal human intervention. They are context sensitive and capable of making sense out of ambiguous or contradictory information. They also maintain user profiles
viii including user preferences and previous actions, and serve as a tutor, critic, consultant or advisor by providing suggestions and/or courses of action to take. These systems exhibit “intelligent” behavior by dealing with complex situations and applying their knowledge to manipulate the environment by recognizing the relative importance of different elements within a problem scenario. The following is a partial list of enabling technologies that are used in creating intelligent support systems: a) intelligent agents, b) data mining and knowledge discovery, c) data warehousing, d) fuzzy computing, e) neural networks, f) machine learning, g) client-server and web technologies, h) business components, i) java and XML technologies, and j) evolutionary algorithms. This book discusses the various aspects of designing and implementing intelligent support systems using one or more of the afore-mentioned technologies. Intelligent agent technology is finding its way into many new systems, including decision-support systems, where it performs many of the necessary decision-support tasks formerly assigned to humans. Agents are loosely defined as “software entities that have been given sufficient autonomy and intelligence to enable them to carry out specified tasks with little or no human supervision.” Software agents are useful in automating laborious and repetitive tasks, such as locating and accessing necessary information, filtering away irrelevant and unwanted information, intelligently summarizing complex data, and integrating information from heterogeneous information sources. Like their human counterparts, intelligent agents can have the capability to learn from their managers and even make recommendations to them regarding a particular course of action. Generally, agents are designed to be goal driven, i.e., they are capable of creating an agenda of goals to be satisfied. Organizations are investing heavily in systems that help capture and manage Business Intelligence (BI). One technology used to generate BI is data mining and knowledge discovery. Data mining applications are coming to the forefront of business data analysis and decision making. However, to successfully execute these applications, a significant amount of a priori knowledge is required about data mining techniques, their applicability to different scenarios, relevant data selection and transformation, etc. Hence, for a casual user interested in deciphering trends and buying behaviors from customer “digital footprint” data, shielding some of the nuances of normal data mining operations would be a welcome change. Intelligent agent technology can play a major role in the design and development of such data mining systems, particularly in hiding the complexity and implementing a scaleable system. For example, the “interface agent” can assist decision makers (users) to perform actions on a data warehouse that they cannot, or prefer not, to do themselves. Thus, intelligent agent technology is emerging as one of the most important, and rapidly advancing areas in support system technologies. A number of agentbased applications and multi-agent systems are being developed in a variety of fields, such as electronic commerce, supply chain management, resource allocation, intelligent manufacturing, mass customization, industrial control, information retrieval
ix and filtering, collaborative work, mobile commerce, decision support, and computer games. While research on various aspects of intelligent agent technology and its application is progressing at a very fast pace, there are still a number of issues that have to be explored in terms of agent design, implementation, integration, and deployment. For example, identifying salient characteristics of agents in different domains, developing formal approaches for agent-oriented modeling, designing and implementing agent-oriented information systems, collaborating and coordinating multi-agent systems, and analyzing the organizational impact of agent-based systems are some of the areas in need of further research. Intelligent support system technologies will attain a permanent place in industry and will be deployed for the purpose of increasing industrial productivity in many roles, such as assistants to human operators and autonomous decision-making components of complex systems. One can easily envision a world filled with millions of knowledge agents where the boundary between human knowledge agents and machine agents is invisible. Intelligent agents have the potential to radically change the way organizational work is currently performed. Human agents can delegate a range of tasks to personalized software agents that can not only make decisions based on the criteria provided by their human counterparts, but also model the reasoning, action, communication, and collaboration skills involved in performing human job functions. Capturing organizational knowledge in a reusable form, and designing intelligent agents having access to this corporate knowledge, is going to revolutionize organizational work environment in the near future.
Vijayan Sugumaran Department of DIS School of Business Administration Oakland University Rochester, MI 48309
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Preface
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The Internet and associated technologies are playing an ever-increasing role in the lives of businesses and academic organizations. As these technologies grow in use, organizations are striving to improve their use within the organization. Intelligent Support Systems play an important role in developing competitive technologies in the Internet world. Additionally, knowledge capture, use and sharing are extremely timely issues for businesses as they deal with virtual communities and virtual organizations. In order to get the most from these emerging technologies and understand how to best manage knowledge, academics, researchers and practitioners must have access to latest information describing the most current research and best practices in the use and development of these technologies. This book provides just that. The chapters in this timely new book are a compilation of research on how to develop and implement information systems. Additionally, the authors tackle the difficult topics of defining virtual organizations and utilizing them to gain advantage. Furthermore, the chapters describe the optimal knowledge management techniques and practices. The authors represent a wide variety of organizational and cultural backgrounds and share their insights in the following chapters. Chapter 1, “Intelligent Agents and the World Wide Web: Fact or Fiction” by Sudha Ram of University of Arizona (USA), proposes that collaborative multi-agent systems are a very promising approach for managing information overload. The author indicates that it will be necessary to move beyond the current Web interaction paradigm of direct manipulation to indirect management of the Web. The author describes how multi-agent systems have the capabilities to make the transition from the current paradigm more smoothly. Chapter 2, “Comparing U.S. and Japanese Companies on Competitive Intelligence, IS Support and Business Change” by Tor Guimaraes of Tennessee Technological University (USA), Osamu Sato of Tokyo Keizai University and Kideaki Kitanaka of Takushoku University (Japan), reports on the findings of a field test of how effectively U.S. and Japanese business organizations are identifying strategic problems and opportunities, and how effectively they implement business changes and use IS technology to do so. Chapter 3, “Knowledge Assests in the Global Economy: Assessment of National Intellectual Capital” by Yogesh Malhotra of Syracuse University (USA), discusses the developing need for assessing knowledge capital at the national economic level. The chapter further reviews a national case study of how intellectual capital assessment was undertaken, suggests implications of such assessment methods, and offers areas needing advancement.
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xi Chapter 4, “Knowledge-Bases Systems as Database Design Tools: A Comparative Study” by W. Amber Lo of Millersville University and KnowledgeBased Systems, Inc. and Joobin Choobinch of Texas A & M University (USA), surveys tools used in prototype database design and compares these tools with respect to four aspects: database design support, tool flexibility, expert system features and implementation characteristics. The results of the study indicate that, in general, there is a lack of support for all phases of design, for group database design, for graphic support, for empirical verification of the tools’ effectiveness, for long-term maintenance of the tools, and for specialized knowledge representation. Chapter 5, “Policy Agents to Support CSCW in the Case of Hospital Scheduling” by Hans Czap of University of Trier, demonstrates the concept of a policy agent used in hospital scheduling. This agent is able to represent individual preferences and goals, and thus may act as a personal assistant to support solving standard problems like operating room scheduling. The chapter demonstrates the representation of preferences and goals in order to make adaptations to changes in the environment and shows how the interaction works. Chapter 6, “Building an Agent: By Example” by Paul Darbyshire of Victoria University of Technology (Australia), is written in response to the growing need of people who are interested in the emerging Web-based technologies and desire to build their own agents. This chapter demonstrates the problems of building an agent using the example of an email helper. Chapter 7, “Intelligent Agents in a Trust Environment” by Rahul Singh of University of North Carolina, Greensboro, and Mark Gill of Arizona State University (USA), addresses the need for intelligent agents to include a mechanism for handling trust. The chapter then discusses how the agents can be used as intermediaries in electronic commerce. This work responds to the growing use of the Internet for commerce and banking activities and acknowledges the important role trust plays in online transactions. Chapter 8, “A Case Study on Forecasting of Scrapped Products through Simulation and Fuzzy Reasoning” by Jorge Marx-Gómez and Claus Raustenstrauch of Otto-von Guericke University, Magdeburg (Germany), suggests a method to forecast the timing and quantities of scrapped products. The method combines a simulation approach with fuzzy reasoning. The prediction model presented is based on life-cycle data, such as sales figures and failures and impact factors such as lifetime wear and tear. The chapter presents the results of an empirical study wherein the model was to use life-cycle data of photocopiers to forecast the returns. Chapter 9, “Newshound Revisited: The Intelligent Agents That Retrieves News Postings” by Jeffrey Goldberg of Analytic Services (ANSER)and Shijun Shen of Tygart Technology (USA), reports on the authors’ experiences implementing an Intelligent Internet Agent, Newshound. Newshound can be trained to recognize a desired topic and scan Usenet newsgroups looking for new examples of that topic. The chapter also introduces two additional intelligent agents: Chathound and Webhound. Finally, the authors discuss the inter-agent communication layer, the facilitator for cooperation between ANSER’s intelligent agents.
xii Chapter 10, “Investigation into Factors That Influence the use of Web in Knowledge-Intensive Environments” by Yong Jin Kim and H. Raghav Rao of SUNY, Buffalo and Abhijit Chaudhury of Bryant College (USA), develops a set of hypotheses regarding the relationship between the Technology Acceptance Model (TAM) constructs and external variables The study reported here give insights regarding the questions of when to implement a new technology and who is an eager user of new technologies to learn. The chapter also is one of the first papers to use TAM in the context of knowledge-management systems. Chapter 11, “A Study of Web Users’ Waiting Time” by Fiona Fui-Hoon Nah of University of Nebraska—Lincoln (USA), evaluates Nielsen’s hypothesis of 15 seconds as the maximum waiting time of Web users and provides approximate distributions of waiting time for Web users. The chapter discusses the literature on waiting time and reports on a study conducted by the author. The chapter recommends that researchers and practitioners understand users’ waiting time behavior, propose and evaluate techniques to reduce users’ perception of waiting time, and recommend a trade-off between aestheticism of Web page design and download/access time. Chapter 12, “Stickiness: Implications for Web-Based Customer Loyalty Efforts” by Supawadee Ingsriswang and Guisseppi Forgionne of University of Maryland (USA), applies the concept of customer loyalty in traditional businesses to digital products or services in order to describe a conceptual model of online stickiness. Using the conceptual model, the authors identify the measures that determine the stickiness of the Website and describe the applications of the stickiness value. Chapter 13, “’No’ is Not ‘Not’ Comparisons of Negation in SQL and Negation in Logic Programming” by James Jones of University of Arkansas at Little Rock (USA), focuses on the expressive power of weak negation in logic programming. Weak negation is not presently well understood and is easily confused with negation in SQL. The author describes weak negation and, to a lesser extent, discusses strong negation in logic programming. Chapter 14, “Knowledge Management and New Organization Forms: A Framework for Business Model Innovation” by Yogesh Malhotra of Syracuse University, proposes a sense-making model of knowledge management for new business environments. The chapter then applies this framework in order to facilitate business model innovations necessary for sustainable competitive advantage in the new business environment, characterized by dynamic, discontinuous and radical pace of change. Chapter 15, “Implementing Virtual Organizations in Business Networks: A Method of Inter-Business Networking” by Roland Klueber, Rainer Alt and Hubert Österle of University of St. Gallen (Switzerland), describes a method that addresses the need for a holistic view and methods that support implementation for business networks. The method described includes the dimensions of strategy, process and IS required for establishing and managing business networks. The authors describe
xiii a project implementing a business-networking solution for electronic procurement. The scenario described shows how a structured approach helps to identify scenarios, aids in implementation, and applies previously created and newly created knowledge. Chapter 16, “Managing Knowledge for Strategic Advantage in Virtual Organization” by Janice Burn and Colin Ash of Edith Cowan University (Australia), looks at the virtual organization and suggests that the basic concepts of virtual management are so poorly understood that there are likely to be very few such organizations gaining strategic advantage from their virtuality. The authors provide clear definitions of virtual organizations and different models of virtuality that can exist. The chapter presents six virtual models with a dynamic framework of change and offers specific examples applying the models to organizations. Chapter 17, “Virtual Organizations That Cooperate and Compete: Managing the Risks of Knowledge Exchange” by Claudia Loebbecke of Copenhagen Business School (Denmark) and Paul van Fenema of Erasmus University (The Netherlands), explores the art of controlling knowledge flows in cooperative relationships. The chapter conceptualizes types of knowledge flows and dependencies, resulting in four configurations. The authors propose control strategies that allow companies engaged in cooperation to anticipate deviant trajectories and define adequate responses. Chapter 18, “Becoming Knowledge Powered: Planning the Transformation” by Dave Pollard of Ernst & Young (Canada), identifies possible strategies, leading practices and pitfalls to avoid in each phase of his award-winning process to transform the company from a knowledge-hoarding to knowledge-sharing enterprise. The chapter describes the challenges involved in identifying and measuring intellectual capital, encouraging knowledge creation, capturing human knowledge in structural form, and enabling virtual workgroup collaboration. The role of intelligent agents in optimizing Website performance and development and in enhancing security of Websites, as well as knowledge management’s place in the virtual organization and in establishing and maintaining competitive business advantage are just some of the timely topics contained in this important new book. The information contained herein will be useful to academics as they attempt to understand the theory of intelligent agent systems, to researchers as they attempt to evaluate the efficacy of these systems and understand the intricacies of the emerging field of virtual organizations, and to business people and practitioners as they strive to implement the most current, best practices in knowledge management, intelligent systems and virtual organizations. This book is a “must have” for all those who want to understand how to achieve and maintain competitive advantage in this increasingly virtual world. IRM Press January 2002
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Chapter 1
Intelligent Agents and the World Wide Web: Fact or Fiction? Sudha Ram University of Arizona
We are fortunate to be experiencing an explosive growth and advancement in the Internet and the World Wide Web (WWW). In 1999, the global online population was estimated to be 250 million WWW users worldwide, while the “/images/spacer_white.gif”number of pages on the Web was estimated at 800 million (http://www.internetindicators.com/facts.html). The bright side of this kind of growth is that information is available to almost anyone with access to a computer and a phone line. However, the dark side of this explosion is that we are now squarely in the midst of the “Age of Information Overload”!!! The staggering amount of information has made it extremely difficult for users to locate and retrieve information that is actually relevant to their task at hand. Given the bewildering array of resources being generated and posted on the WWW, the task of finding exactly what a user wants is rather daunting. Although many search engines currently exist to assist in information retrieval, much of the burden of searching is on the end-user. A typical search results in millions of hit, many of which are outdated, irrelevant, or duplicated. One promising approach to managing the information overload problem is to use “intelligent agents” for search and retrieval. This editorial explores the current status of intelligent agents and points out some challenges in the development of intelligent agents based systems. An intelligent agent is a piece of software that performs a given task using information from its environment and acts in such a way that it can complete the given task successfully. Some desirable properties for such Previously Published in the Journal of Database Management, vol.12, no.1, Copyright © 2001, Idea Group Publishing.
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Intelligent Agents and the World Wide Web: Fact or Fiction?
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agents are: autonomy, adaptability, mobility, and communication ability. To deal with complex real world problems, it is desirable to have different type of agents specializing in different types of tasks to collaborate with others to solve a problem. Given the number of sources of information on the web, using a network of collaborating agents is bound to ease the task of information discovery and retrieval and therefore appears to be very promising. The Virtual Enterprise Model of collaborating agents (see Figure 1) uses software agents of three different kinds — demand, supply, and broker agents who interact with each other to supply answers to users. Such systems are also known as Multi-agent systems. Demand agents interact with end-users to determine their background and understand their information needs. Supply agents understand specific sources of information and advertise their “information wares”. Broker agents interact with demand and supply agents to match the needs of end-users with what is available. For the virtual enterprise model to be successfully deployed, it is essential for the agents to understand and communicate with each other. This requires a common ontology that the agents can use to facilitate interaction. An ontology is a set of terms or vocabulary that describes a subject area. It includes a description of how the terms are related to each other. A number of systems based on the virtual enterprise model are currently being designed and tested to handle the “information overload” probFigure 1: Virtual Enterprise Model lem. Infosleuth, and Warren are two such systems, that provide inforDemand Agents mation finding, filtering and integration functions ▼ in the context of Broker Agents helping a user manage his financial portfolio. Such systems ▼ consist of agents Supply Agents that cooperatively self organize to monitor stock quotes, financial news, financial ▼
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analysis reports and company earnings reports. The agents also continuously filter incoming news flashes to alert users about events that may affect his portfolio. While such systems exist as prototypes, I believe a number of key research challenges need to be addressed to make them truly useful in the real world . These include: 1. Semantic Heterogeneity: As stated earlier, there is a staggering number of sources of information available on the web. Most of these are textual or unstructured sources. The semantic heterogeneity problem has been addressed successfully in the context of structured data sources such as relational or object relational databases. However, the web poses a new problem. We need solutions to automatically detect and resolve semantic heterogeneity in an unstructured environment. Mediators (a type of broker agent) may be one way to tackle this challenge. 2. Support for Dynamic Evolution of Information Sources: A major problem with the Web is that it is continuously evolving i.e. new information sources are being added and existing ones removed. This evolution is exacerbated by the problem of the sources themselves changing over time. A comprehensive set of techniques to keep track of information sources and their changes (via brokers or supply agents) needs to be developed. 3. Scalability and Performance: The web presents an unprecedented scale because of its sheer size and number of sources. For a multi-agent system to be effective in light of this large scale, we need to address important questions such as: (a) How many types of collaborating agents are necessary? (b) How many instances of each type of agent will be necessary to provide quick responses? (c) How do we minimize the amount of communication to provide adequate response times to users? 4. Generalization across application domains: Current prototype multiagent systems are built to address specific domains such as financial portfolio analysis, and technology tracking. However, it remains to be seen how these systems can be adapted and reused for application domains other than the ones for which they were originally designed. In conclusion, I believe collaborative multi-agent systems are a very promising approach for managing the information overload problem. However, given the rate of the WWW, we have to move beyond our current dominant web interaction paradigm of “direct manipulation” to “indirect management” of the WWW. Multi-agent systems provide us with the capability to make this transition, provided we can tackle the challenges presented in this article. I exhort the information systems research community to respond to these challenges and help eradicate the information overload problem.
4 Comparing U.S. and Japanese Companies
Chapter 2
Comparing U.S. & Japanese Companies on Competitive Intelligence, IS Support, and Business Change Tor Guimaraes Tennessee Technological University, USA Osamu Sato Tokyo Keizai University, Japan Hideaki Kitanaka Takushoku University, Japan
The increase in business competitiveness forces companies to adopt new technologies to redesign business processes, improve products, and support organizational changes necessary for better performance. The literature on Competitive Intelligence (CI) touts its importance in providing corporate strategic vision to improve company competitiveness and success. To implement their strategic vision companies have to implement changes to their business processes, products, and/or to the organization itself. The voluminous body of literature on the management of change, including subareas such as Business Process Reengineering (BPR), Total Quality Management (TQM), and product improvement, implicitly or explicitly propose that company strategic intelligence is a pre-requisite for change, and that effective Information Systems (IS) support is a critical requirement for Previously Published in the Journal of Global Information Management, vol.7, no.3, Copyright © 1999, Idea Group Publishing.
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implementing change. There is some empirical evidence supporting these two hypotheses based on U.S. business organizations and there is little reason to believe that the relationships do not hold for Japanese companies. Whether or not U.S. and Japanese organizations are different in any way along these important variables is an interesting question. A field test of how effectively U.S. and Japanese business organizations are identifying strategic problems and opportunities, how effectively they implement business changes, and use IS technology to do so, was undertaken to empirically explore any differences. Despite the relatively small sample size, the results corroborate the importance of competitive intelligence and IS support for effectively implementing business change in U.S. and Japanese companies. The findings indicate, on the average, American companies are more effective in providing IS support for business change and Japanese companies are more effective in CI activities. Increasing business competition has forced managers to recognize the importance of business innovation. American business organizations have derived substantial benefits from widespread changes to the old business ways. For example, the American manufacturing sector is thought to have become more productive and the erosion of our manufacturing base and the loss of initiative to Japan and Europe has been reversed [Howard, 1994]. In the process of exploring the basic differences between the Japanese and American manufacturing management approaches and applying a host of new methods and techniques, many U.S. firms are redefining the very nature of their businesses [Patterson & Harmel, 1992]. On the other hand, success implementing the required changes is far from assured, with many organizations reporting very disappointing results, given the cost and turmoil caused by the changes [Guimaraes and Bond, 1996]. Two primary approaches for implementing organization change worldwide are known as Total Quality Management (TQM) and Business Process Reengineering (BPR). BPR differs from TQM in two important respects. First, TQM focuses on continuous improvement (an incremental performance improvement approach), while reengineering is founded on the premise that significant corporate performance improvement requires discontinuous improvement (breaking away from the outdated rules and fundamental assumptions that underlie operations). With BPR, rather than simply eliminating steps or tasks in a process, the value of the whole process itself is questioned [Gotlieb, 1993]. In conformance with TQM principles, the focus of change is also market driven [Guimaraes and Bond, 1996]. Second, reengineering makes a significant break with previous performance improve-
6 Comparing U.S. and Japanese Companies
TE
AM
FL Y
ment approaches by requiring a high level of state-of-the-art information technology awareness among the entire reengineering team prior to, rather than after, the definition of process changes or improvements [Cypress, 1994]. Some technologies (i.e., imaging systems and expert systems) can provide substantial opportunities for the redesign of business processes [Guimaraes, 1993; Guimaraes, Yoon and Clevenson, 1998]. Regardless of the change methodology being employed (i.e., BPR or TQM) the factors important to innovation success or failure are many, but most authors would agree that strategic awareness or competitive intelligence is an important prerequisite for success. This is deemed particularly important in highly competitive industries [Luecal & Dahl, 1995; Cartwright, Boughton & Miller, 1995]. Competitive intelligence (CI) is the process by which organizations gather and use information about products, customers, and competitors, for their short and long term strategic planning [Ettorre, 1995]. It is the first step guiding the planning and redesign of processes, products, and organization structure. Without this strategic vision, business changes will be conducted in haphazard fashion and are less likely to produce significant results. To implement their strategic vision, take advantage of strategic opportunities, and address problems, companies have to implement changes to their business processes, products, and/or organization. It is reasonable to assume that knowledge about their markets (customers, competitors, etc.) is a prerequisite for effective change, and effective Information Systems (IS) support is a critical requirement for implementing business change. There is some empirical evidence supporting these two hypotheses based on U.S. business organizations [Guimaraes & Armstrong, 1998], and little reason to believe the relationships do not hold for Japanese companies. However, an interesting question is whether U.S. and Japanese organizations are different in any way along these important variables. If any differences can be detected, managerial attention can be focused on the impact of strengths or weaknesses on company performance in the two nations. Also, any differences may provide further motivation to explore these important issues from different perspectives, addressing other theoretical constructs, and using improved measures. A field test of how effectively U.S. and Japanese business organizations are identifying strategic problems and opportunities, how effectively they implement business changes, and use IS technology to do so, was undertaken to explore any differences.
Team-Fly®
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Figure 1: The Main Conceptual Model CI Effectiveness
Effectiveness Implementing Business
IS Support Effectiveness
Innovation
CONCEPTUAL FRAMEWORK AND PROPOSED HYPOTHESES The basic conceptual model for this study is graphically represented in Figure 1. It proposes that effectiveness in competitive intelligence and in using IS technology to support business change will be directly related to company effectiveness innovating in the areas of products, processes, organization structure and culture. An extensive survey of the literature reveals that academics have neglected to address some of these constructs and their relationships from a practical perspective. For example, there is very little work in theory building in the competitive intelligence area, and there is practically nothing in this area regarding intercultural differences. Most of the discussion on these extremely important constructs and their relationships come from the practitioner literature. This situation provides a rich opportunity for rigorous academic research attempting to build theory useful in practice. Implementing Business Change To take advantage of strategic opportunities and address problems, companies worldwide have to implement changes to their business processes, products, and/or to the organization itself. Similar to the earlier study by Guimaraes and Armstrong [1998], the dependent variable in this case is the degree of company effectiveness in implementing business change. As business competitiveness increases, many business organizations have reacted to expand the value of their products and services to customers by redesigning their business processes to increase efficiency, deliver new products and services, and improve quality of their offerings [Tsang, 1993].
8 Comparing U.S. and Japanese Companies
The literature contains considerable evidence showing U.S. and Japanese management differ substantially in many ways [Badawy, 1991; Sherman, 1996; Billings & Yaprak, 1995; Herbig & Jacobs, 1996]. However, there is no evidence organizations in one culture are better managers of innovation than in the other. Quite to the contrary, effectiveness in innovation seems to be a shared gift with neither East nor West excelling at sustained innovation [Sherman, 1996]. Thus we propose: H1: There is no difference in effectiveness implementing business change between U.S. and Japanese companies. Company Competitive Intelligence Again, the importance of competitive intelligence and knowledge as a key asset is increasingly recognized by managers [Darling, 1996]. Even though most of the necessary operational knowledge within a company is in the employees’ minds [Sawka, 1996], with the increase in business competition, company survival and success is increasingly determined by its rate of learning. If learning is faster than external changes, the organization will experience long term success; otherwise, it is at risk [Darling, 1996]. The antecedents and consequences of CI dissemination were studied by Maltz and Kohli [1996]. Competitor Analysis (CA) was proposed by Ghoshal & Westney [1991], and approaches useful for companies to collect information from competitors were addressed by Heil and Robertson [1991]. The importance of organization intelligence to financial performance has also been demonstrated. Companies with well established CI programs on the average showed earnings per share of $1.24, compared to those without CI programs which lost 7 cents per share [King, 1997]. The literature contains many examples of benefits that can be derived from CI. Among these are improved competitive edge [McCune, 1996; Sawka, 1996; Westervelt, 1996] and improved overall company performance [Babbar and Rai, 1993], two essential company goals that can be brought about with effective application of competitive intelligence. More specific benefits of CI include: Uncovering business opportunities and problems that will enable proactive strategies [Ellis, 1993; Westervelt, 1996]; providing the basis for continuous improvement [Babbar and Rai, 1993]; shedding light on competitor strategies [Harkleroad, 1993; Westervelt, 1996]; improving speed to markets and supporting rapid globalization [Baatz, 1994; Ettorre, 1995]; improving the likelihood of company survival [Westervelt, 1996]; increasing business volume [Darling, 1996]; providing better customer assessment [Darling, 1996]; and aiding in the understanding of external influences
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[Sawka, 1996]. Benefits like these provide the basis for firms to better understand the potential impact of the proposed changes and the means by which they can be infused into the company’s fabric. Based on the above discussion, we propose the following hypothesis: H2a: Regardless of nationality, company CI effectiveness is directly related to effectiveness implementing business change. CI effectiveness is proposed as an important requirement for effective implementation of business change. Ironically, even though as much as 68% of U.S. companies have an organized approach to providing information to decision makers [Westervelt, 1996], “probably less than 10% of U.S. corporations know their way around the CI process and effectively integrate the information into their strategic plans...” [Ettorre, 1995]. Japanese organizations are known for greater dedication to this area. Perhaps as a necessary requirement for shifting from imitation to innovation, leading Japanese companies developed CI as part of their research and development [Kokubo, 1992]. Business organizations in the U.S. are moving slowly to develop intelligence about competitors, markets, and important technologies [Shermach, 1995; Anonymous, 1996] and, in general, CI has been considered low priority [Herring, 1991]. U.S. companies “have not relied on CI as much as they should or as much as non-U.S. companies do.” [Bertrand, 1990]. Based on this discussion, we propose: H2b: On the average, Japanese companies have greater CI effectiveness than U.S. counterparts. Using IS Technology To Support Business Change Also as discussed by Guimaraes and Armstrong [1998], the effects of IS technology on organization design, intelligence and decision making have been studied by Huber [1990]. Many authors have proposed the importance of a wide variety of IS technologies to support business change. Computer Telephony Integration has been touted as a powerful tool to improve the relationship with customers [McCarthy, 1996]. The use of IS for data mining and warehousing is seen as essential for decision support [Anonymous, 1995]. Friedenberg and Rice [1994] and Guimaraes, Yoon and Clevenson [1998] have proposed Expert Systems as viable implementation vehicles for business change because they are effective in capturing and distributing knowledge and knowledge processing capability across an organization. IS technologies available to support the necessary business changes are endless:
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DSS, Group DSS, EDI, Client Server Systems, Imaging Systems, the Internet, and Intranets. Without effective IS support the change implementation processes would be severely hindered, and in many cases rendered impossible. Based on the above discussion we propose: H3a: Regardless of nationality, company effectiveness using IS technology to support business change is directly related to effectiveness implementing business change. Western counterparts in the implementation of IS technology [Davenport, 1996], and the latter has received considerable credit for driving the latest renewal of US business competitiveness. Correspondingly, U.S. companies have increased their spending in IS an average of 14% in 1996, compared with 8% by Japan [Moshella, 1997]. In other areas, such as Internet use, similar to American organizations, Japanese business use has lately increased substantially as a tool to reduce communication costs [Sasaki, 1998]. Nevertheless, the time lag is significant. “Nine years after the network revolution swept into American offices, the wiring of corporate Japan has begun in earnest,” [Anonymous, 1997]. Further, Japanese companies are expected to speed their adoption of IS technologies, but face obstacles such as more rigid corporate culture and tighter state control over electronic communication links, leading to greater costs and equipment obsolescence [Anonymous, 1997]. In many areas which are heavily dependent on electronic communications, Japanese companies also have lagged behind their U.S. counterparts to a large degree [Patton, 1995]. Based on the above discussion, we propose a final hypothesis: H3b: On the average, U.S. companies will be more effective in the use of IS technology to support business change than Japanese companies.
STUDY METHODOLOGY Data Collection Procedure A questionnaire was used to collect data from a convenience sample of 52 top managers from companies in the U.S. and 39 from Japanese companies headquartered in Japan. A cover letter described the purpose of the study and provided instructions for the respondents. Much of the data was collected through personal interviews with top managers (VP or higher.) A similar questionnaire was used in a previous study which included only U.S.
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Table 1: Sample Demographics A) Industry Sectors - Manufacturing - Communications - Health Care - Retail - Banking - Other
Japan 24 ( 62%) 2 ( 6%) 0 4 ( 9%) 2 ( 6%) 7 ( 17%) 39 (100%)
US 26 7 4 3 2 10 52
(51%) (13%) ( 8%) ( 5%) ( 4%) (19%) (100%)
B)
Japan 12 ( 30%) 19 ( 48%) 9 ( 22%) 39 (100%)
US 19 23 10 52
(37%) (44%) (19%) (100%)
Gross revenues (in dollars) - $50 million or less - $51 to $500 million - Above $500 million
companies [Guimaraes & Armstrong, 1998]. As discussed later, the questionnaire content and readability were extensively tested through several meetings and phone conversations with U.S. and Japanese managers and employees. These managers are known personally to the researchers and have expressed their personal opinions about their company’s processes and activities for identifying strategic problems and opportunities, business changes, and IS support to business activities. Sample Description The companies represented in the sample range widely in terms of their industry sector and size. Among the U.S. organizations, 51% of the firms identified their primary business as manufacturing, with the remaining companies distributed fairly evenly across other sectors. In terms of gross revenues, among the U.S. and Japanese companies there was also broad representation. Table 1 presents these sample demographics in more detail. Validity of the Measures Several precautions were taken to ensure the validity of the measures used, and many of the recommendations by Carmines and Zeller (1979) were followed. To ensure content validity, and that any important dimension of each of the constructs would not be neglected [Guimaraes & Armstrong, 1998], a thorough survey of the relevant literature was undertaken to understand the important aspects of each major variable and its components. As discussed earlier, the theoretical underpinnings of this study are quite intuitive but have not been empirically well established. A previous study used the same questions to collect data from U.S. companies [Guimaraes
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& Armstrong, 1998]. To reduce the possibility of any non-random error (the main source of invalidity) several managers and employees in the areas of CI, IS management, and management of change reviewed the questionnaire for validity, completeness, and readability. This validation process occurred before the data collection process in the U.S. and Japan. A few questions were reworded to improve readability; otherwise, the items composing each major variable remained as in Appendix A. Reliability of the Measures The earlier study on U.S. companies was based on a sample too small to assess the psychometric qualities of the measures [Guimaraes & Armstrong, 1998]. In this case the U.S. and Japanese data were combined. Exploratory factor analysis produced 3 factors and showed the items for each scale loading unambiguously (>.50 into one factor and 15 Sec.) > 15 Sec.) > 15 Sec.) 37.6 Sec. 17 Sec. 6.7 Sec. (27 out [or 79%] of (7 out [or 21%) of (4 out [or 12%] of 34 accesses > 15 Sec.) 34 accesses > 15 Sec.) 34 accesses > 15 Sec.) p
