Technology in knowledge management

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ISBN 1-84544-101-X

ISSN 1367-3270

Volume 9 Number 1 2005

Journal of

Knowledge Management Technology in knowledge management Guest Editor: Eric Tsui

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Table of contents Technology in knowledge management Guest Editor: Eric Tsui Volume 9 Number 1 2005

Access this journal online Guest editorial The role of IT in KM: where are we now and where are we heading?

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Eric Tsui

Note from the publisher

Articles Integrating knowledge management technologies in organizational business processes: getting real time enterprises to deliver real business performance

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Yogesh Malhotra

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Balancing business process with business practice for organizational advantage

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Laurence Lock Lee

The inseparability of modern knowledge management and computerbased technology

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Clyde W. Holsapple

Understanding computer-mediated interorganizational collaboration: a model and framework

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Lei Chi and Clyde W. Holsapple

Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations

76

Jay Liebowitz

A knowledge-based system to support procurement decision

87

H.C.W. Lau, A. Ning, K.F. Pun, K.S. Chin and W.H. Ip

The ‘‘global’’ and the ‘‘local’’ in knowledge management

101

Joseph G. Davis, Eswaran Subrahamanian and Arthur W. Westerberg

Knowledge management systems: finding a way with technology

113

John S. Edwards, Duncan Shaw and Paul M. Collier

Connected brains

126

Paul Iske and Willem Boersma

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Guest editorial The role of IT in KM: where are we now and where are we heading? Eric Tsui

Eric Tsui, Innovation Services, CSC Australia and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University ([email protected]).

Abstract Purpose – To provide a summary of the major trends in the evolution of knowledge management (KM) technologies in the last five years. Design/methodology/approach – Drawing from a range of literature published in the academic and industry arenas including the articles accepted in the special issue, the author also applied his own personal experience and practice knowledge in the field to summarize the three major trends in the use of KM technologies for the workplace and individual knowledge workers. Findings – First, KM is becoming more and more process-centric and relevant technologies are gradually being aligned to support process-based KM activities. Second, there is the emergence of personal networks and applications. Third, knowledge sharing and capturing are becoming more instantaneous (i.e. on-demand and just-in-time). Practical implications – KM is becoming more and more just-in-time. Large-scale KM programmes still prevail but, in future, the technical infrastructure and information content of these programmes also need to support ad hoc, spontaneous but intensive intra- and inter-organizational collaborations. Originality/value – While most articles on KM technologies tend to focus on individual technique(s)/system(s), this paper provides a succinct and high-level summary of the evolution of KM technologies from a commercial and practical perspective. Keywords Computers, Knowledge management, Social networks, Portals Paper type Research paper

1. Introduction

First and foremost, the Guest Editor would like to thank Rory Chase, Editor-in-Chief of the Journal of Knowledge Management for offering him the privilege of guest editing this special issue, the very first one on technology for the journal. He was also allowed a generous timeframe to compile this special issue, which he feels very grateful for. The following persons have acted as reviewers for papers submitted to this special issue and their assistance is deeply appreciated: Paul Iske, Frada Burstein, Karl Wiig, Robert Smith, John Debenham, Simeon Simoff, Jay Liebowitz, Bill Martin, Ralph Miller, Jeanette Bruno, John Gordon, Kevin Johnson, Patti Anklam, Yuefan Li, Ian Watson, Geoff Webb, Igor Hawryszkiewycz, Donmeng Zhu, John Edwards, Joseph Davis, Clyde Holsapple, Chris Lueg, Brian Garner, Zili Zhang, Yogesh Malholtra, Ingrid Slembek, and Dickson Lukose.

In the last five to six years, we have seen plenty of knowledge management (KM) projects come and go. Many of these projects were successful and organizations are still leveraging benefits from their KM systems. However, it is also fair to say that a considerable proportion of KM projects/initiatives have failed. In retrospect, many of the KM projects that commenced in the past are primarily driven by the adoption of technologies. Technologies such as search engines, retrieval and classification tools, e-collaboration tools, portals and content management systems. One of the lessons learnt from these failures is that technology alone should not be the primary driver for any KM projects/initiatives and that an appropriate balance of technology, process, people and content is instrumental to the continued success of any KM deployment. Technology, however, can act as a ‘‘catalyst’’ (i.e. an accelerator) for the introduction and initial buy-in of a KM program but, in order to be successful, this accelerated adoption has to be aligned with a defined KM strategy and supported by a change program. On the technologies for supporting KM, as mentioned above, during the 1990s, these technologies tend to be discrete, distinct from each other and not aligned with defined business processes. When implemented, a user may have to operate separate systems in order to accomplish his/her task (e.g. location of company procedures/methodologies, discussion with peers and sharing material with peers).

2. Evolution of KM technologies Over the last five years, there have been two significant changes in landscape of KM technologies. First, due to advancements in open standards, these technologies have

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become far more interoperable and less platform dependent. As a consequence, many of these technologies are now componentized and can be embedded seamlessly into other enterprise applications. For example, a search engine can be incorporated as part of an e-collaboration suite and a portal usually provides a document management component. The second change is the bundling of the market offerings by the vendors of commercial KM technologies. KM solutions in the marketplace today are likely to be a collection of complementary technologies that aim at execution of a specific process (e.g. collaborative product development), a solution (e.g. problem resolution and service support by a contact center) or a particular industry (e.g. wealth management portal in financial services). This change is brought about by the consolidation of vendors in the market as well as the realization that embedding knowledge in processes is a critical success factor in nearly all KM initiatives (Eppler et al., 1999; Seely, 2002). So where is KM technologies heading towards in the next five years? There can be three generalizations. First, given the enormous focus on business process management (BPM) these few years and the increasing focus of process-based knowledge management, we should expect an increasing alignment of KM technologies/solutions with process management tools. The location of relevant information, re-useable assets, stakeholders (e.g. subject matter experts, sponsor, partners etc.) should be automatic when a process is initiated (Lewis, 2002). Furthermore, business processes will become less and less structured in the future, they may only last a few weeks, spread across organizations, and users will be given powerful tools to create, adjust and dismantle their processes (Seely, 2002). Second, there is the emergence of personal networks in the society. These personal networks manifest in the form of personal knowledge grids (where an individual can coordinate an array of resources to support the capturing and sharing of knowledge at the personal level), social networks (which there are already abundant tools to help identify the concentration and flow of knowledge), and personal applications (software applications developed/selected by an individual to support his/her daily work tasks yet these applications can also operate independently as well as in conjunction with enterprise KM applications) (Tsui, 2002). Third, KM will become more and more ‘‘on-demand’’ (or ‘‘just-in-time’’). Large-scale long-term KM programs still exist but, at the same time, organizations realize that they need to become more agile and adaptive in order to capitalize on strategic opportunities (Lewis, 2002; Morey, 2001; Davenport and Glaser, 2002; Snowden, 2002; Snowden, 2003). Increasingly, KM technologies will operate on infrastructures, both technical and content-wise, that support the rapid deployment of relevant tools and systems for ad hoc, intensive and inter-organizational collaborations. Some of these tools are now available and gaining popularity in the market, notably peer-to-peer (P2P) collaboration tools (Tsui 2002), information retrieval and filtering tools, personal voice over IP (VoIP) communication systems, and taxonomy tools that groups relevant e-mail messages, documents and contact names on desktops.

3. Papers in the special issue Altogether nine papers have been accepted for publication in this special issue. The collective themes of these papers certainly reflect and reinforce the observations on the past and current trend of KM technologies. These papers critically explore the changing role of technologies for KM, the embodiment of knowledge in business process management and execution, social network analyses and artificial intelligence reasoning systems, the nature of inter-organizational collaborations, and the fusion between local and global knowledge in multi-national corporations. Chi and Holsapple presented a comprehensive framework that identifies the various types of inter-organizational collaboration systems. While most existing work address the technological and relationship aspects, their model focuses on the types of knowledge sharing, participative decision-making and conflict governance that underpin inter-organizational collaborations. The findings of the paper shed light on a deeper understanding of the processes and learning involved in such collaborations and, ultimately, led to better alignment and choice of technologies to support different types of inter-organizational collaborations.

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Through the use of a case study on the project management and R&D efforts of a large multi-national corporation, Davis explores the challenges and solutions involved in fostering knowledge sharing and management aspects at the global and local levels of the organization. Some of the key issues include the generation of knowledge at the local level, the verification and passing on such knowledge from the local to the global level, and maintaining the local-global divide yet ensuring that the knowledge is up to date and not duplicated. In his study, he has found four generic roles in the organization that are instrumental to the success in maintaining the local-global divide of knowledge. Edwards, Shaw and Collier conducted workshops with ten organizations to ascertain their KM initiatives/systems. Their key findings from these workshops are that only three of the ten organizations have a KM program that is technology-driven and even so, organizations are utilizing general IT tools (e.g. e-mail, bulletin boards, information databases) to support KM initiatives rather than KM-specific technologies. Other issues unveiled include the tension between decentralization and centralization of IT decisions and the contrast between providing (pushing) information to users and users requesting (pulling) information from databases and repositories. The role of technologies in KM has always been a debatable topic, both in academia and industry. As mentioned earlier, the general perception is that technology was a driver in many of the KM projects in the late 1990s but nowadays organizations are treating the process and people aspects as critical success factors in any KM initiatives. Holsapple, again, presented another paper examining the critical role of computer-based technology (CBT) in KM. He argued that both the inclusive and exclusive perspectives of separating knowledge from information completely ignored or under-estimated the contributions of CBT to KM. He further proposed a third perspective that is to subdivide the representation and processing of various types of knowledge by a computer system. Through this new perspective, which is further substantiated by observations with several renowned e-business/commerce systems, one can gain a stronger appreciation of how CBT can add value to KM. The majority of KM systems in implementation are on capturing, searching, and distributing knowledge (e.g. search engines, portals, collaboration systems, intellectual capital reporting tools). Tools/systems that foster the accumulation of social capital are rare. Iske and Boersma remedies this imbalance by outlining a proprietary-developed question-answering system that facilitates not only the encapsulation of core knowledge into a repository but also linking up users with subject matter experts and thereby reducing ongoing help desk support costs and time. Furthermore, the authors have also discussed the cultural issues and impact on the deployment of their system as well as developed a detail quantitative model that computes and ranks the importance of knowledge in the entire knowledge value chain of an organization. Knowledge audit is a technique that is often applied by organizations to ascertain what knowledge the organization already has what else is needed to accomplish corporate objectives. Social network analysis (SNA) is a key step in any knowledge audit. Liebowitz defined the analytic process hierarchy (AHP) technique that can be used to measure the requirements and preferences of an individual or department in the network. Findings from using the APH technique can, for example, be used to better channel relevant information to individuals, help understand decision-making in an organization, and contribute to better process design and management. A comprehensive list of SNA tools is also outlined in his paper. Information technology can accomplish a lot more than mere storing and retrieving data. Over the decades, advancements in artificial intelligence and other information processing techniques lead to the verification and generalization of stored data, as well as the discovery of new actionable knowledge. Lau et al. demonstrated, via a research prototype, the use of a hybrid neural network and online analytical processing (OLAP) algorithm to capture, process procurement data and generate recommendation for suitable supplier(s) in an online supply chain network. This application clearly demonstrates and reinforces the trend

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of embedding data and discovering new knowledge in business processes that spread across multiple organizations. In the pursuit to become a real time enterprise (RTE), many organizations have invested heavily on KM and related technologies. Unfortunately, based on experience gained in the last ten years, most of these organizations failed and very few become a truly RTE. Malholtra critically explored the dichotomy between the technology-push and strategy-pull approaches in organizational investments in KM technologies. With justifications and case studies, the paper outlined the reasons that the form and function of a RTE, and the contrast between the ends and means of achieving performance outcomes hold the key to understand why some organizations are so successful in harnessing embedded knowledge in their business model and processes, both at the intra and inter levels. More on business processes, Lock Lee focused on the possible synergy between business processes and business practice in an organizational setting. Business processes are normally centrally defined and structured. Business practice, on the other hand, occurs at the operational level and involves a lot of tacit knowledge. Furthermore, on many occasions, complex decisions are also needed when executing a process. Lock Lee proposed a two cycle model of interaction based on communities of practice to foster an appropriate mix of process and practice to achieve optimal organizational performance. Once again, this paper reinforces the importance of embedding and sharing knowledge in business processes as well as shed light on how to align existing KM tools to support and enhance decision-making in organizations at the process level.

References Davenport, T.H. and Glaser, J. (2002), ‘‘Just-in-time delivery comes to knowledge management’’, Harvard Business Review, Vol. 80 No. 7, pp. 5-9. Eppler, M.J., Siefried, P.M. and Ropnack, A. (1999), ‘‘Improving knowledge-intensive processes through enterprise knowledge medium’’, SIGCPR ’99, pp. 222-30. Lewis, B. (2002), ‘‘On demand KM: a two-tier architecture’’, IT Professional, Vol. 4 No. 1, pp. 27-33. Morey, D. (2001), ‘‘High-speed knowledge management: integrating operations theory and knowledge management for rapid results’’, Journal of Knowledge Management, Vol. 5 No. 4, pp. 322-8. Seely, C.P. (2002), ‘‘Igniting knowledge in your business processes’’, KM Review, Vol. 5 No. 4, pp. 12-15. Snowden, D. (2002), ‘‘Just-in-time knowledge management: part 1’’, KM Review, Vol. 5 No. 5, pp. 14-17. Snowden, D. (2003), ‘‘The knowledge you need, right when you need it’’, KM Review, Vol. 5 No. 6, pp. 24-7. Tsui, E. (2002), ‘‘Technologies for personal and peer-to-peer knowledge management’’, CSC Leading Edge Forum Technology Grant Report, May, available at: www.csc.com/aboutus/lef/mds67_off /index.shtml#grants

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Integrating knowledge management technologies in organizational business processes: getting real time enterprises to deliver real business performance Yogesh Malhotra

Dr Yogesh Malhotra serves on the Faculty of Management Information Systems at the Syracuse University and has taught in the executive education programs at Kellogg School of Management and Carnegie Mellon University. He is the founding chairman of BRINT Institute, LLC, the New York based internationally recognized research and advisory company. His corporate and national knowledge management advisory engagements include organizations such as Philips (The Netherlands), United Nations (New York City Headquarters), Intel Corporation (USA), National Science Foundation (USA), British Telecom (UK), Conference Board (USA), Maeil Business Newspaper and TV Network (South Korea), Ziff Davis, Government of Mexico, Government of The Netherlands, and Federal Government of the USA. He can be contacted at: www.yogeshmalhotra.com e-mail: yogesh.malhotra@brintcom

Constructive comments offered by the special issue Editor Eric Tsui and the two anonymous reviewers are gratefully acknowledged.

DOI 10.1108/13673270510582938

Abstract Purpose – To provide executives and scholars with pragmatic understanding about integrating knowledge management strategy and technologies in business processes for successful performance. Design/methodology/approach – A comprehensive review of theory, research, and practices on knowledge management develops a framework that contrasts existing technology-push models with proposed strategy-pull models. The framework explains how the ‘‘critical gaps’’ between technology inputs, related knowledge processes, and business performance outcomes can be bridged for the two types of models. Illustrative case studies of real-time enterprise (RTE) business model designs for both successful and unsuccessful companies are used to provide real world understanding of the proposed framework. Findings – Suggests superiority of strategy-pull models made feasible by new ‘‘plug-and-play’’ information and communication technologies over the traditional technology-push models. Critical importance of strategic execution in guiding the design of enterprise knowledge processes as well as selection and implementation of related technologies is explained. Research limitations/implications – Given the limited number of cases, the framework is based on real world evidence about companies most popularized for real time technologies by some technology analysts. This limited sample helps understand the caveats in analysts’ advice by highlighting the critical importance of strategic execution over selection of specific technologies. However, the framework needs to be tested with multiple enterprises to determine the contingencies that may be relevant to its application. Originality/value – The first comprehensive analysis relating knowledge management and its integration into enterprise business processes for achieving agility and adaptability often associated with the ‘‘real time enterprise’’ business models. It constitutes critical knowledge for organizations that must depend on information and communication technologies for increasing strategic agility and adaptability. Keywords Knowledge management, Real time scheduling, Business performance, Return on investment Paper type Research paper

Introduction Technologists never evangelize without a disclaimer: ‘‘Technology is just an enabler.’’ True enough – and the disclaimer discloses part of the problem: enabling what? One flaw in knowledge management is that it often neglects to ask what knowledge to manage and toward what end. Knowledge management activities are all over the map: building databases, measuring intellectual capital, establishing corporate libraries, building intranets, sharing best practices, installing groupware, leading training programs, leading cultural change, fostering collaboration, creating virtual organizations – all of these are knowledge management, and every functional and staff leader can lay claim to it. But no one claims the big question: why? (Tom Stewart in The Case Against Knowledge Management, Business 2.0, February 2002).

The recent summit on knowledge management (KM) at the pre-eminent ASIST conference opened on a rather upbeat note. The preface noted that KM has evolved into a mature reality from what was merely a blip on the ‘‘good idea’’ radar only a few years ago. Growing

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‘‘ One can see the impact of knowledge management everywhere but in the KM technology-performance statistics. ’’

pervasiveness of KM in worldwide industries, organizations, and institutions marks a watershed event for what was called a fad just a few years ago. KM has become embedded in the policy, strategy, and implementation processes of worldwide corporations, governments, and institutions. Doubling in size from 2001, the global KM market has been projected to reach US$8.8 billion during this year. Likewise, the market for KM business application capabilities such as CRM (Malhotra, 2004a) is expected to grow to $148 billion by the next year. KM is also expected to help save $31 billion in annual re-invention costs at Fortune 500 companies. The broader application context of KM, which includes learning, education, and training industries, offers similarly sanguine forecasts. Annual public K-12 education is estimated at $373 billion dollars in US alone, with higher education accounting for $247 billion dollars. In addition, the annual corporate and government training expenditures in the US alone are projected at over $70 billion dollars. One can see the impact of knowledge management everywhere but in the KM technology-performance statistics (Malhotra, 2003). This seems like a contradiction of sorts given the pervasive role of information and communication technologies in most KM applications. Some industry estimates have pegged the failure rate of technology implementations for business process reengineering efforts at 70 percent. Recent industry data suggest a similar failure rate of KM related technology implementations and related applications (Darrell et al., 2002). Significant failure rates persist despite tremendous improvements in sophistication of technologies and major gains in related price-performance ratios. At the time of writing, technology executives are facing a renewed credibility crisis resulting from cost overruns and performance problems for major implementations (Anthes and Hoffman, 2003). In a recent survey by Hackett Group, 45 percent CIOs attribute these problems to technology implementations being too slow and too expensive. Interestingly, just a few months ago, some research studies had found negative correlation between tech investments and business performance (Alinean, 2002; Hoffman, 2002). Financial performance analysis of 7,500 companies relative to their IT spending and individual surveys of more than 200 companies had revealed that: B

companies with best-performing IT investments are often most frugal IT spenders;

B

top 25 performers invested 0.8 percent of their revenues on IT in contrast to overall average of 3.7 percent; and

B

highest IT spenders typically under-performed by up to 50 percent compared with best-in-class peers.

Based upon multi-year macroeconomic analysis of hundreds of corporations, Strassmann (1997) had emphasized that it is not computers but what people do with them that matters. He had further emphasized the role of users’ motivation and commitment in IT performance[1]. Relatively recent research on implementation of enterprise level KMS (Malhotra, 1998a; Malhotra and Galletta, 1999; Malhotra and Galletta, 2003; Malhotra and Galletta, n.d. a; Malhotra and Galletta, n.d. b) has found empirical support for such socio-psychological factors in determining IT and KMS performance. An earlier study by Forrester Research had similarly determined that the top-performing companies in terms of revenue, return on assets, and cash-flow growth spend less on IT on average than other companies. Surprisingly, some of these high performance ‘‘benchmark’’ companies have the lowest tech investments and are recognized laggards in adoption of leading-edge

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‘‘ Despite increasing sophistication of KM technologies, we are observing increasing failures of KM technology implementations. ’’

technologies. Research on best performing US companies over the last 30 years (Collins, 2001) has discovered similar ‘‘findings’’. The above findings may seem contrarian given persistent and long-term depiction of technology as enabler of business productivity (cf. Brynjolfsson, 1993; Brynjolfsson and Hitt, 1996; Brynjolfsson and Hitt, 1998; Kraemer, 2001). Despite increasing sophistication of KM technologies, we are observing increasing failures of KM technology implementations (Malhotra, 2004b). The following sections discuss how such failures result from the knowledge gaps between technology inputs, knowledge processes, and business performance. Drawing upon theory, prior research, and industry case studies, we also explain why some companies that spend less on technology and are not leaders in adoption of most hyped RTE technologies succeed where others fail. The specific focus of our analyses is on the application of KM technologies in organizational business processes for enabling real time enterprise business models. The RTE enterprise is considered the epitome of the agile adaptive and responsive enterprise capable of anticipating surprise; hence our attempt to reconcile its sense making and information processing capabilities is all the more interesting. However, our theoretical generalizations and their practical implications are relevant to IT and KM systems in most enterprises traversing through changing business environments.

Disconnects between disruptive information technologies and relevant knowledge Organizations have managed knowledge for centuries. However, the popular interest in digitizing business enterprises and knowledge embedded in business processes dates back to 1993[2]. Around this time, the Business Week cover story on virtual corporations (Byrne, 1993) heralded the emergence of the new model of the business enterprise. The new enterprise business model was expected to make it possible to deliver anything, anytime, and, anywhere to potential customers. It would be realized by digitally connecting distributed capabilities across organizational and geographical boundaries. Subsequently, the vision of the virtual, distributed, and digitized business enterprise became a pragmatic reality with the mainstream adoption of the internet and web. Incidentally, the distribution and digitization of enterprise business processes was expedited by the evolution of technology architectures beyond mainframe to client-server to the internet and the web and more recently to web services. Simultaneously, the software and hardware paradigms have evolved to integrated hosted services and more recently to utility computing and on demand computing (Greenemeier, 2003a, b; Hapgood, 2003; Sawhney, 2003; Thickins, 2003) models. Organizations with legacy enterprise business applications trying to catch up with the business technology shifts have ended up with disparate islands of diverse technologies.

Decreasing utility of the technology-push model Management and coordination of diverse technology architectures, data architectures, and system architectures poses obvious knowledge management challenges (Malhotra, 1996; Malhotra, 2001a; Malhotra, 2004b). Such challenges result from the need for integrating diverse technologies, computer programs, and data sources across internal business processes. These challenges are compounded manifold by the concurrent need for simultaneously adapting enterprise architectures to keep up with changes in the external business environment. Often such adaptation requires upgrades and changes in existing technologies or their replacement with newer technologies. Going business enterprises

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often have too much (unprocessed) data and (processed) information and too many technologies. However, for most high-risk and high-return strategic decisions, timely information is often unavailable as more and more of such information is external in nature (Drucker, 1994; Malhotra, 1993; Terreberry, 1968; Emery and Trist, 1965). Also, internal information may often be hopelessly out of date with respect to evolving strategic needs. Cycles of re-structuring and downsizing often leave little time or attention to ensure that the dominant business logic is kept in tune with changing competitive and strategic needs. As a result, most organizations of any size and scope are caught in a double whammy of sorts. They do not know what they know. In simple terms, they have incomplete knowledge of explicit and tacit data, information, and decision models available within the enterprise. Also, their very survival may sometimes hinge on obsolescing what they know (see for instance, Yuva, 2002; Malhotra, 2004b; Malhotra, 2002c). In other words, often they may not know if the available data, information, and decision models are indeed up to speed with the radical discontinuous changes in the business environment (Arthur, 1996; Malhotra, 2000a; Nadler and Shaw, 1995). In this model, incomplete and often outdated data, information, and decision models drive the realization of the strategic execution, but with diminishing effectiveness. The model may include reactive and corrective feedback loops. The logic for processing specific information and respective responses are all pre-programmed, pre-configured, and pre-determined. The mechanistic information-processing orientation of the model generally does not encourage diverse interpretations of information or possibility of multiple responses to same information. As depicted in Figure 1, this model of KM is often driven by technological systems that are out-of-alignment with strategic execution and may be characterized as the technology-push model. This model has served the needs of business performance given more manageable volumes of information and lesser variety of systems within relatively certain business environment. However, with recent unprecedented growth in volumes of data and information, the continuously evolving variety of technology architectures, and the radically changing business environment, this model has outlasted its utility. The limitations of the technology-push model are evident in the following depiction of IT architectures as described in Information Week by LeClaire and Cooper (2000): The infrastructure issue is affecting all businesses . . . E-business is forcing companies to rearchitect all or part of their IT infrastructures – and to do it quickly. For better or worse, the classic timeline of total business-process reengineering – where consultants are brought in, models are drawn up, and plans are implemented gradually over months or years – just isn’t fast enough to give companies the e-commerce-ready IT infrastructures they need . . . Many companies can’t afford to go back to the drawing board and completely rearchitect critical

Figure 1 How ICT systems drive and constrain strategic execution

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TECHNOLOGY PUSH MODEL OF KM

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‘‘ The gap between IT and business performance has grown with the shifting focus of business technology strategists and executives. ’’

systems such as order fulfillment and product databases from the bottom up because they greatly depend on existing infrastructure. More often, business-process reengineering is done reactively. Beyond its disruptive effect on business operations, most IT managers and executives don’t feel there’s enough time to take a holistic approach to the problem, so they attack tactical issues one-by-one. Many companies tackle a specific problem with a definitive solution rather than completely overhaul the workflow that spans from a customer query to online catalogs to order processing.

Strategic execution: the real driver of business performance The gap between IT and business performance has grown with the shifting focus of business technology strategists and executives. Over the past two decades, their emphasis has shifted from IT (Porter and Millar, 1985; Hammer 1990) to information (Evans and Wurster, 2002; Rayport and Sviokla, 1995; Hopper, 1990; Huber, 1993; Malhotra, 1995) to knowledge (Holsapple and Singh, 2001; Holsapple, 2002; Koenig and Srikantaiah, 2000a; Malhotra, 2004b; Malhotra, 2000b; Malhotra, 1998c) as the lever of competitive advantage. At the time of the writing, technology sales forecasts are gloomy because of the distrust of business executives who were previously oversold on the capabilities of technologies to address real business threats and opportunities. This follows on the heels of the on-and-off love-hate relationship of the old economy enterprises and media analysts with the new economy business models over the past decade. We first saw unwarranted wholesale adulation and subsequently wholesale decimation of technology stocks. All the while, many industry executives and most analysts have incorrectly presumed or pitched technology as the primary enabler of business performance (Collins, 2001; Schrage, 2002)[3]. The findings from the research (Collins, 2001) on best performing companies over the last three decades are summarized in Table I. These findings are presented in terms of the inputs-processing-outcomes framework used for contrasting the technology-push model with the strategy-pull model of KM implementation[4]. Subsequent discussion will further explain the relative advantages of the latter in terms of strategic execution and business performance. Given latest advances in web services, the strategic framework of KM discussed here presents a viable alternative for delivering business performance as well as enterprise agility and adaptability (Strassmann, 2003).

Will the real knowledge management please stand up? The technology evangelists, criticized by Stewart (2000), have endowed the KM technologies with intrinsic and infallible capability of getting the right information to the right person at the right time. Similar critiques (cf. Malhotra, 2000a; Hildebrand, 1999) have further unraveled and explained the ’’myths’’ associated such proclamations made by the technology evangelists. Specifically, it has been underscored that in wicked business environments (Churchman, 1971; Malhotra, 1997) characterized by radical discontinuous change (Malhotra, 2000a; Malhotra, 2002b), the deterministic and reductionist logic (Odom and Starns, 2003) of the evangelists does not hold. Incidentally, most high potential business opportunities and threats are often embedded within such environments (Arthur, 1996; Malhotra, 2000c; Malhotra, 2000d). Such environments are characterized by fundamental and ongoing changes in technologies as well as the strategic composition of market forces. Increasing failures rates of KM technologies often result from their rapid obsolescence given changing business needs and technology architectures. Popular re-labeling by vendors of many information technologies as KM technologies has not helped the situation. Skeptics of

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Table I Strategic execution as driver of technology deployment and utilization lessons from companies that achieved high business performance Lessons learned from some of the most successful business enterprises that distinguished themselves by making the leap from ‘‘good to great’’ (Collins, 2001) Lessons about outcomes: strategic execution, the primary enabler (1) How a company reacts to technological change is a good indicator of its inner drive for greatness versus mediocrity. Great companies respond with thoughtfulness and creativity, driven by a compulsion to turn unrealized potential into results; mediocre companies react and lurch about, motivated by fear of being left behind (2) Any decision about technology needs to fit directly with three key non-technological questions: What are you deeply passionate about? What can you be the best in the world at? What drives your economic engine? If a technology does not fit squarely within the execution of these three core business issues, the good-to-great companies ignore all hype and fear and just go about their business with a remarkable degree of equanimity (3) The good-to-great companies understood that doing what you are good at will only make you good; focusing solely on what you can potentially do better than any other organization is the only path to greatness Lessons about processing: how strategic execution drives technology utilization (1) Thoughtless reliance on technology is a liability, not an asset. When used right – when linked to a simple, clear, and coherent concept rooted in deep understanding – technology is an essential driver in accelerating forward momentum. But when used wrongly – when grasped as an easy solution, without deep understanding of how it links to a clear and coherent concept – technology simply accelerates your own self-created demise (2) No evidence was found that good-to-great companies had more or better information than the comparison companies. In fact both sets of companies had identical access to good information. The key, then, lies not in better information, but in turning information into information that cannot be ignored (3) 80 percent of the good-to-great executives did not even mention technology as one of the top five factors in their transition from good-to-great. Certainly not because they ignored technology: they were technologically sophisticated and vastly superior to their comparisons (4) A number of the good-to-great companies received extensive media coverage and awards for their pioneering use of technology. Yet the executives hardly talked about technology. It is as if the media articles and the executives were discussing two totally different sets of companies! Lessons about technology inputs: how strategic execution drives technology deployment (1) Technology-induced change is nothing new. The real question is not What is the role of technology? Rather, the real question is How do good-to-great organizations think differently about technology? (2) It was never technology per se, but the pioneering application of carefully selected technologies. Every good-to-great company became a pioneer in the application of technology, but the technologies themselves varied greatly (3) When used right, technology becomes an accelerator of momentum, not a creator of it. The good-to-great companies never began their transitions with pioneering technology, for the simple reason that you cannot make good use of technology until you know which technologies are relevant (4) You could have taken the exact same leading-edge technologies pioneered at the good-to-great companies and handed them to their direct comparisons for free, and the comparisons still would have failed to produce anywhere near the same results

technology have observed that real knowledge is created and applied in the processes of socialization, externalization, combination, and internalization (Nonaka and Takeuchi, 1995) and outside the realm of KM technologies. Practitioners’ inability to harness relevant knowledge despite KM technologies and offices of the CKOs caused the backlash and KM was temporarily branded as a fad. Scholarly research on latest information systems and technologies, or lack thereof, has further contributed to the confusion between data management, information management, and knowledge management.

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Recent reviews of theory and research on information systems and KM (Alavi and Leidner, 2001; Schultze and Leidner, 2002) seem to confirm Stewart’s (2000) observation about the key flaw of knowledge management: Knowledge management activities are all over the map . . . But no one claims the big question: why?

Hence, it is critical that a robust distinction between technology management and knowledge management should be based on theoretical arguments that have been tested empirically in the ‘‘real world messes’’ (Ackoff, 1979) and the ‘‘world of re-everything’’ (Arthur, 1996). We are observing diminishing credibility of information technologists (Anthes and Hoffman, 2003; Hoffman, 2003; Carr, 2003). A key reason for this is an urgent need for understanding how technologies, people, and processes together influence business performance (Murphy, 2003). Explicit focus on strategic execution as the driver of technology configurations in the strategy-pull KM framework reconciles many of the above problems. The evolving paradigm of technology architectures to on demand plug-and-play inter-enterprise business process networks (Levitt, 2001) is expected to facilitate future realization of KM value networks. Growing popularity of the web services architecture (based upon XML, UDDI, SOAP, WSDL) is expected to support the realization of real-time deployment of business performance driven systems based upon the proposed model (Kirkpatrick, 2003; Zetie, 2003; Murphy, 2003). The technology-push model is attributable for the inputs – and processing – driven KM implementations with emphasis on pushing data, information, and decisions. In contrast, the strategy-pull model recognizes that getting pre-programmed information to pre-determined persons at the pre-specified time may not by itself ensure business performance. Even if pre-programmed information does not become out-dated, the recipient’s attention and engagement with that information is at least equally important. Equally important is the reflective capability of the recipient to determine if novel interpretation of the information is necessary or if consideration of novel responses is in order given external changes in the business environment. The technology-push model relies upon single-loop automated and unquestioned automatic and pre-programmed response to received stimulus. In contrast, the strategy-pull model has built in double-loop process that can enable a true sense-and-respond paradigm of KM[5]. The focus of the technology-push model is on mechanistic information processing while the strategy-pull model facilitates organic sense making (Malhotra, 2001b). The distinctive models of knowledge management have been embedded in KM implementations of most organizations since KM became fashionable. For instance, the contrast between the models can be illustrated be comparing the fundamental paradigm of KM guiding the two organizations, a US global communications company and a US global pharmaceutical firm. The telecommunications company adopted the mechanistic information- and processing-driven paradigm of KM (Stewart and Kaufman, 1995): What’s important is to find useful knowledge, bottle it, and pass it around.

In contrast, given their emphasis on insights, innovation, and creativity, the pharmaceutical company adopted the organic sense-making model of KM (Dragoon, 1995, p. 52): There’s a great big river of data out there. Rather than building dams to try and bottle it all up into discrete little entities, we just give people canoes and compasses.

The former model enforces top-down compliance and control through delivery of institutionalized information and decision models. In contrast, the latter model encourages discovery and exploration for questioning given assumptions and surfacing new insights (Nonaka and Takeuchi, 1995).

Real time strategic execution: the real enabler of the RTE The issues of technology deployment, technology utilization, and business performance need to be addressed together to ensure that technology can deliver upon the promise of business performance. Interestingly, most implementations of KM systems motivated by the technology-push model have inadvertently treated business performance as a residual: what remains after issues of technology deployment and utilization are addressed[6]. This

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perhaps explains the current malaise of IT executives and IT management in not being able to connect with business performance needs (Hoffman, 2003). A sense-and-respond KM system that can respond in real time would need to consider the holistic and collective effect of: B

real-time deployment in terms of tech and human infrastructure (inputs);

B

real-time utilization in terms of what is done about or with information (processing); and

B

real-time performance in terms of how it delivers business performance (outcomes).

Deployment of intranets, extranets, or, groupware cannot of itself deliver business performance. These technologies would need to be adopted and appropriated by the human users, integrated within their respective work-contexts, and effectively utilized while being driven by the performance outcomes of the enterprise. To deliver real-time response, business performance would need to drive the information needs and technology deployment needs. This is in congruence with the knowledge management logic of the top performing companies discussed earlier. These enterprises may not have created the buzz about the latest technologies. However, it is unquestionable that these best performing organizations harnessed organizational and inter-organizational knowledge embedded in business processes most effectively to deliver top-of-the-line results. The old model of technology deployment spanning months or often years often resulted in increasing misalignment with changing business needs. Interestingly, the proposed model turns the technology-push model on its head. The strategy-pull model illustrated in Figure 2 treats business performance not as the residual but as the prime driver of information utilization as well as IT-deployment. The contrast between the inputs-processing-output paradigms of KM implementations is further explained in the following section to bridge the existing gaps in KM research and practice.

Gaps in KM implementation research and practice The ‘‘knowledge application gap’’ that is characteristic of the inputs- and processing-driven technology-push model have also been the subject of criticism in scholarly research on KM (Alavi and Leidner, 2001; Zack, 2001). However, these gaps seem to persist across most of theoretical research and industry practices related to information systems and knowledge management as shown in Table II. As discussed in Malhotra (2000a), such gaps have persisted over the past decade despite advances in understanding of KM and sophistication of technology architectures.

Figure 2 Strategic execution – the primary enabler of the RTE business model

Environment

()

STRATEGY

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Table II Driving KM with business performance from inputs- and processing-driven KM to outcomes-driven KM Additional theoretical and applied definitions of KM are discussed in Malhotra (2000a) Technology-push models of KM (Depicted in Figure 1) Inputs-driven paradigm of KM ‘‘Knowledge management systems (KMS) refer to a class of information systems applied to managing organizational knowledge. That is, they are IT-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application’’ (Alavi and Leidner, 2001) ‘‘Knowledge management is the generation, representation, storage, transfer, transformation, application, embedding, and protecting of organizational knowledge’’ (Schultze and Leidner, 2002) ‘‘For the most part, knowledge management efforts have focused on developing new applications of information technology to support the capture, storage, retrieval, and distribution of explicit knowledge’’ (Grover and Davenport, 2001) ‘‘Knowledge has the highest value, the most human contribution, the greatest relevance to decisions and actions, and the greatest dependence on a specific situation or context. It is also the most difficult of content types to manage, because it originates and is applied in the minds of human beings’’ (Grover and Davenport, 2001) ‘‘Knowledge management uses complex networks of information technology to leverage human capital. The integration of user-friendly electronic formats facilitates inter-employee and customer communication; a central requirement for successful KM programs’’ (eMarketer, 2001) ‘‘In companies that sell relatively standardized products that fill common needs, knowledge is carefully codified and stored in databases, where it can be accessed and used – over and over again – by anyone in the organization’’ (Hansen and Nohria, 1999) Processing-driven paradigm of KM ‘‘KM entails helping people share and put knowledge into action by creating access, context, infrastructure, and simultaneously reducing learning cycles’’ (Massey et al., 2001) ‘‘Knowledge management is a function of the generation and dissemination of information, developing a shared understanding of the information, filtering shared understandings into degrees of potential value, and storing valuable knowledge within the confines of an accessible organizational mechanism’’ (CFP for Decision Sciences special issue on Knowledge Management, 2002) ‘‘In companies that provide highly customized solutions to unique problems, knowledge is shared mainly through person-to-person contacts; the chief purpose of computers is to help people communicate’’ (Hansen and Nohria, 1999) Strategy-pull model of KM (Depicted in Figure 2) Outcomes-driven paradigm of KM ‘‘Knowledge Management refers to the critical issues of organizational adaptation, survival and competence against discontinuous environmental change. Essentially it embodies organizational processes that seek synergistic combination of data and information-processing capacity of information technologies, and the creative and innovative capacity of human beings’’ (Malhotra, 1998b)

The sample of ‘‘definitions’’ of KM listed in Table II is not exhaustive but illustrative. However, it gets the point across about the missing link between KM and business performance in research and practice literatures. Despite lack of agreement on what is KM, most such interpretations share common emphasis on the inputs- and processing-driven technology-push model. Review of most such ‘‘definitions’’ also leaves one begging for a response to Stewart’s pointed question to technologists’ evangelism about KM: ‘‘why?’’ In contrast, the strategy-pull model with its outcomes-driven paradigm seems to offer a more meaningful and pragmatic foundation for KM. At least as far as real world outcomes are concerned, this paradigm measures up to the expectations about KM policy and its implementation in worldwide organizations[7]. Better understanding of the gaps that we are trying to reconcile is possible by appreciating

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‘‘ Increasing failures rates of KM technologies often result from their rapid obsolescence given changing business needs and technology architectures. ’’

the contrast between the three paradigms of KM implementation that have characterized the technology-push and strategy-pull models of KM depicted in Figures 1 and 2. This contrast is explained in terms of their primary and differential focus on the inputs, processing, and outcomes. The inputs-driven paradigm considers information technology and KM as synonymous. The inputs-driven paradigm with its primary focuses on technologies such as digital repositories, databases, intranets, and, groupware systems has been the mainstay of many KM implementation projects. Specific choices of technologies drive the KM equation with primary emphasis on getting the right information technologies in place. However, the availability of such technologies does not ensure that they positively influence business performance. For instance, installing a collaborative community platform may neither result in collaboration nor community (Barth, 2000; Charles, 2002; Verton, 2002). The practitioners influenced by this paradigm need to review the ‘‘lessons about technology inputs’’ listed earlier in Table I. The processing-driven paradigm of KM has its focus on best practices, training and learning programs, cultural change, collaboration, and virtual organizations. This paradigm considers KM primarily as means of processing information for various business activities. Most proponents of RTE belong to this paradigm given their credo of getting the right information to the right person at the right time. Specific focus is on the activities associated with information processing such as process redesign, workflow optimization, or automation of manual processes. Emphasis on processes ensures that relevant technologies are adopted and possibly utilized in service of the processes. However, technology is often depicted as an easy solution to achieve some type of information processing with tenuous if any link to strategic execution needed for business performance. Implementation failures and cost-and-time overruns that characterize many large-scale technology projects are directly attributable to this paradigm (Anthes and Hoffman, 2003; Strassmann, 2003). Often the missing link between technologies and business performance is attributable to choice of technologies intended to fix broken processes, business models, or organizational cultures. The practitioners influenced by this paradigm need to review the ‘‘lessons about processing’’ listed earlier in Table I. The outcomes-driven paradigm of KM has its primary focus on business performance. Key emphasis is on strategic execution for driving selection and adaptation of processes and activities, and carefully selected technologies. For instance, if collaborative community activities do not contribute to the key customer value propositions or business value propositions of the enterprise, such activities are replaced with others that are more directly relevant to business performance (Malhotra, 2002a). If these activities are indeed relevant to business performance, then appropriate business models, processes, and culture are grown (Brooks, 1987) as a precursor to acceleration of their performance with the aid of KM technologies. Accordingly, emphasis on business performance outcomes as the key driver ensures that relevant processes and activities, as well as, related technologies are adopted, modified, rejected, replaced, or enhanced in service of business performance. The practitioners interested in this paradigm need to review the ‘‘lessons about outcomes’’ listed earlier in Table I. The contrast between the outcomes-driven strategy-pull model and the input- and processing- driven technology-push model is even evident in the latest incarnation of KM

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under the moniker of RTE. Given the confusion between KM and KM technologies that resulted in the backlash against technology vendors, it is germane to point out a similar future for the proponents of RTE. There is an imperative need for making a clear distinction between the business performance capabilities afforded by the RTE business model and the technologies that are labeled as RTE technologies. As discussed earlier, success in strategic execution of a business process or business model may be accelerated with carefully chosen technologies. However, in absence of good business processes and business model, even the most sophisticated technologies cannot ensure corporate survival.

Coming of the real time enterprise: the new knowledge management The RTE enterprise is based upon the premise of getting the right information to the right people at the right time (Gartner, Inc., 2002) in ‘‘real time’’, i.e. without latency or delay (cf., Lindorff, 2002; Lindquist, 2003; Margulius, 2002; Meyer, 2002; Siegele, 2002; Stewart, 2000). Enabling the RTE should lead to faster and better decisions, and enhanced agility and adaptability. RTE represents the future of knowledge enabled business processes: wherein digitized organizations interact with increasing and relentless speed and any specific ‘‘event’’ results in a real-time ‘‘response’’. For instance, businesses such as Gillette and Wal-Mart are trying to minimize the delay between a customer order, its shipment and the restocking of inventory with the help of radio-frequency detection (RFID) tags, also known as smart tags (Cuneo, 2003). The proponents of RTE technologies suggest that these technologies would help companies to learn to adapt, evolve, and survive within increasingly uncertain business environments. Their rationale still seems to be based on the technology-push model of KM and may perhaps benefit from recognizing the strategy-pull model as a complement. One such perspective of RTE (Khosla and Pal, 2002) that yet does not address Stewart’s (2000) big question: ‘‘why?’’ and may benefit from focus proposed above is listed below: Real time enterprises are organizations that enable automation of processes spanning different systems, media, and enterprise boundaries. Real time enterprises provide real time information to employees, customers, suppliers, and partners and implement processes to ensure that all information is current and consistent across all systems, minimizing batch and manual processes related to information. To achieve this, systems for a real time enterprise must be ‘‘adaptable’’ to change and accept ‘‘change as the process’’.

The RTE will be able to operate at speeds with split-second reaction times that may far exceed human speeds of gathering and processing of information, analysis, and response (Meyer, 2002). At least, that is what the proponents of ‘‘RTE technologies’’ such as Khosla and Pal (2002) claim. Examples of increase of business process velocity that are often attributed to information technology include the following examples (Gartner, Inc., 2002): B

trading analytics: from 30 minutes to five seconds;

B

airline operations: from 20 minutes to 30 seconds;

B

call center inquires: from eight hours to ten seconds;

B

tracking finances: from one day to five minutes;

B

supply chain updates: from one day to 15 minutes;

B

phone activation: from three days to one hour;

B

document transfer: from three days to 45 seconds;

B

trade settlement: from five days to one day; and

B

build-to-order PCs: from six weeks to one day.

RTE enterprises would harness everything from radio frequency sensors and smart dust to global positioning satellites and worker-monitoring software to monitor and control all processes and activities. There are obvious benefits of the automated event-driven architectures (Sliwa, 2003) for repetitive, structured, and routine decisions (Malhotra, 2004b). Well-tested business processes may be suitable candidates for acceleration with

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automation of manual activities and workflows (Malhotra, 2000d). However, the more critical problem can be understood in terms of the contrast between the technology-push model and the strategy-pull model. The programmed logic of the RTE may yield diminishing returns if environmental change outpaces the assumptions and logic embedded in its computerized networks. Split-second decisions based upon pre-determined ‘‘rules’’ are efficient as they follow the single-loop logic and are well suited to repetitive, structured, and routine decisions. However when such decisions are made regardless of the obsolescing business process or business model, the price is paid in terms of effectiveness (Drucker, 1994; Yuva, 2002). High-risk or high-return situations require reflection, and re-thinking as meaning of information could change and previously non-existent responses become feasible. This is all particularly applicable in contexts within which creativity and innovation facilitate emergence of new meaning, insights, and actions. Such complex meaning making and sense making capabilities for anticipating the unforeseen are yet unavailable in existing technologies (cf., Wolpert, 2001)[8].

RTE business models: function should drive the choice of form Successful RTE enterprises focus primarily on the function of the business model that guides the choice of the infrastructure form for accelerating strategic execution. Unsuccessful RTE enterprises, in contrast, often meet their fate because of the misplaced belief that form could somehow compensate for the inadequacy of the function. Successful RTE business models may be apparent in virtual companies such as e-Bay that owe most of their functioning to social capital embedded in their users, buyers, and sellers. Successful RTE business models may also be apparent in companies with brick-and-mortar stores such as Wal-Mart. Regardless of the variations in form, most such companies share a similar but distinctive focus on their higher purpose, which guides their strategy and its execution. This observation perhaps explains how some companies achieved most sustained business performance with lesser investments in related technologies. Often their success was attributable to a differentiated business model based on strong ties with customers and suppliers rather than most recent investments in CRM and SCM systems. Strategic execution of the business models was accelerated with the help of technologies. However, successful companies had superior business models and a consistent track record of strategic execution as a precursor. Smart and selective investments in technologies afforded them the ability to do more with less by accelerating their business capabilities. Also, strong ties with suppliers and customers enabled them to spread the risk of investing, deploying, and utilizing the technologies with their partners and customers[9].

Enabling the RTE: ends should drive the choice of means The misplaced emphasis of technology-push models arose from their primary focus on the means rather than the ends as explained in this section. Most such KM implementations often happened to be caught in the convoluted complexities of technology deployment and processing without making a real difference in business performance. Given the state of technology and the long time spans necessary for getting business systems in place, an obvious question is relevant about the superior business performers: how did the top performing companies manage to produce stellar business results despite having to choose same or similar technologies as their competitors? It may be argued that the top performers always kept their key focus on business performance. They adopted new technologies and adapted old technologies without compromising on that primary focus. Their technologies were used for pushing data, information, and decision models just like their competitors. However, unlike the competitors they vanquished, their choices of business processes and technologies were still driven by their primary focus on strategic execution. They may not have planned to be laggards in adopting new technologies or in spending less on such tech investments. Rather their slow but steady progress in selecting, eliminating, modifying, adapting, and integrating old and new technologies in service of their business models and business processes seemed to pay off. As they accelerated their already superior business models and business processes with new technologies, they realized greater returns in business performance. It may also be argued that many of their competitors imitated their choices of specific technologies often based upon ‘‘best practice’’ studies and

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‘‘benchmarks’’ (Malhotra, 2002d). Mistakenly treated as easy and assured solutions for fixing broken business processes and business models, new technologies further escalated the ‘‘knowledge application gap’’. Some of these comparison companies saw a spate of fickle and frequent technology and tech personnel changes, but their business problems persisted eventually leading to corporate failures or bankruptcies. In contrast, top performing companies have grown their business models around carefully thought out customer value propositions and business value propositions in spite of their adoption, or lack thereof, of latest technologies. Knowledge becomes the accelerator of business performance when identified with execution of business strategy rather than with the choices of tools and technologies that keep changing with time. In the eyes of the wise, knowledge and action are one (Beer, 1994).

Why do some RTE businesses succeed (where others fail)? The following cases were selected after reviewing the industry case studies of companies that were often described as benchmarks in terms of their RTE business models. Specific companies were chosen based on their visibility in the business technology press and popular media. The reviews of industry cases studies were guided by our interest in understanding the link between investments in advanced technologies and resulting business performance. Wal-Mart: RTE business model where technology matters less Some IT analysts have attributed Wal-Mart’s success to its investment in RTE technologies. However, Wal-Mart has emerged as a company that has set the benchmark of doing more with less. Wal-Mart did not build its competitive advantage by investing heavily or by investing in latest technologies (Schrage, 2002). A McKinsey Global Institute reports: The technology that went into what Wal-Mart did was not brand new and not especially at the technological frontiers, but when it was combined with the firm’s managerial and organizational innovations, the impact was huge.

More recently, Collins (2003) has predicted that Wal-Mart may become the first company to achieve trillion-dollar valuation within next ten years following the performance-driven model delineated in Table I and discussed earlier. In contrast to its competitors, Wal-Mart systematically and rigorously deployed its technologies with clear focus on its core value proposition of lowest prices for mass consumers. With that singular focus, it went about setting up its supply chains and inventory management systems to accelerate business performance. Long before anyone had heard about the RTE technologies, Wal-Mart was perfecting its logistic prowess based on the hub-and-spoke model of truck routes and warehouses underlying its inventory management systems. It was much later in the process when for its $4 billion investment in its supply chain systems its suppliers invested ten times that amount to accelerate its RTE business model underlying its supply chain network (Schrage, 2002). The business model created the strong linkages with suppliers, which not only heavily subsidized the costs of technology investments but also pre-committed the partners to the success of the shared systems. Simultaneously, given its retail channels, distribution network, and proximity to customers through market scanner data, it has preempted its suppliers from directly competing against it. Dell: RTE business model that does more with less Dell has developed and perfected its business model by developing strong ties with its customer base over the past 17 years. It perfected its business model over several years before accelerating its business performance with the aid of carefully selected technologies. It has cultivated outstanding relationships with its virtual supply chain partners including outsourcing providers (such as Solectron) and technology vendors (such as HP, Sony, and EMC). Dell also leverages its customer reach and range and market penetration for deriving commercial benefits from technologies developed by its technology partners. It has been developing and extending the real time logic over the past several years first for selling and servicing desktop computers, and later to aggregation and distribution of value-added products and services servers, storage, networking, printers, switches, and handheld

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computers. According to a survey of 7,500 companies conducted by Alinean (2002), Dell is a thrifty IT spender. Dell is equally frugal in its R&D spending (1.5 percent of revenues), according to a recent Business Week report, despite its continuing forays into new products and services. Through its alliances with partners such as EMC, Dell is able to leverage their research on product innovation while itself concentrating on perfecting the linkages with customers as well as suppliers. Dell’s early innovations in passionate pursuit for being the low cost ‘‘build on demand’’ leader for consumer computing products has yielded it the advantage of real time business performance. More recently, it has been able to accelerate the performance of its business model with the aid of carefully chosen technologies. GE: RTE automation for operational efficiencies GE views the real time movement as an extension of GE’s renowned emphasis on Six Sigma quality drive. The business model defined for maintaining quality standards has been extended to control costs by minimizing response time to problems affecting products purchased by its customers. GE’s CIO Gary Reiner tracks once every 15 minutes what he considers to be the few most critical variables including sales, daily order rates, inventory levels, and savings from automation across the company’s 13 worldwide businesses. He acknowledges that it is neither feasible nor desirable to track all kinds of information in real time even with the aid of digital dashboards. Most operational information is tracked on daily or weekly basis while other kinds of information is tracked on an exception-reporting basis. The company claims operational savings of 35-60 percent in costs involved in customer response, customer service, and sales. Most of these savings are attributable more to management control rather than to technologies that are used to enforce pre-negotiated contracts on its buyers who deal with its various suppliers. Operational automation that is executed in terms of command and control logic seeking compliance has not been without its adverse ramifications. GE has encountered labor management disputes resulting from the workers who are not accustomed to minute-by-minute electronic surveillance. Cisco: real time enterprise technology troubles Cisco has been lauded for its RTE technologies since three years ago when its market cap was 850 percent of its recent market capitalization during this year. The company prided itself about the RTE technologies that offered apparently seamless integration of real time data within and across its supply chain and customer ordering systems. The company had legendary faith in its technologies for predictive modeling and decision-making (Carter, 2001). In a Harvard Business Review article, the company’s CFO (of that time) claimed that: We can literally close our books within hours . . . the decision makers who need to achieve sales targets, manage expenses and make daily tactical operating decisions now have real-time access to detailed operating data.

Unfortunately, real-time access to data could not be of much help when, buoyed by its unparalleled growth over several quarters[10], Cisco made some fundamentally incorrect assumptions about the future. Cisco ignored a key lesson of KM that is often ignored by many others: the past may not be an accurate predictor of the future. While other networking companies with less sophisticated technologies had cut back on production schedules months earlier seeing impending downturn in demand, Cisco stuck to the forecasts of their ‘‘virtual close’’ system that they considered invincible. As Cisco (or, rather, its technology-driven forecasting systems) had never been proven wrong before, their business partners saw little merit in trying to question their proven wisdom. As a result of misplaced faith in the power of the forecasting systems, Cisco ended up writing off $2.2 billion in inventories and sacking 8,500 employees. Industry experts and analysts suggest that Cisco’s write-off resulted from its blindsided over-reliance on its much vaunted ‘‘virtual close’’ systems. Cisco’s case demonstrates that even the best technology offers no protection against bad management decisions, especially when the assumptions embedded in the dominant logic are taken for granted. Some Cisco executives do maintain that in absence of the RTE ‘‘virtual close’’, the outcome could have been worse. Cisco retains its optimism in perfecting its RTE systems hoping they would eventually provide high certitude in the face of increasingly uncertain business environment.

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Enron: destroyed in real time Given the dominant and intensive role of real-time information, many of the technologies associated with real-time response were initially adopted by financial services firms on the Wall Street. Given Enron Online’s primary business of exchanging and trading financial data, the real-time response model seemed like a match made in heaven. Enron planned to leverage its online exchange for facilitating direct real time instantaneous transactions in the online trading of energy market commodities. In its communique´ submitted to the Federal Trade Commission, Enron had emphasized that: Efficiency gains made possible by dynamic pricing and trading are especially well suited to Enron’s on-line business because electronic trading can match the speed with which commodity pricing changes. Transactions that used to take up to three minutes to complete over the phone now take just a second or two, including complex processes such as credit checks.

The company deployed Tibco’s vaunted ‘‘RTE platform’’, sought out new technology wherever possible, and planned to spend hundreds of millions of dollars on technology infrastructure. The management control and oversight vagaries of Enron’s management as well its insider- and self-dealings with fictitious entities are well documented in the records of the US Senate hearings as well as the public records of print and broadcast media. Post-hoc analysis of Enron’s RTE technologies confirms prior observations about the technology-push model (Berinato, 2002): If these [accounting irregularities] hadn’t come up, the IT inefficiency might well have come up to bite Enron . . . Enron IT was as cutting edge as it was Byzantine. There were plenty of great tools, but there was precious little planning . . . The core systems supporting the main revenue-generating activities were very disjointed . . . There were major disconnects from deal capture to risk management to logistics to accounting. They all worked from different data sources . . . They had teams and teams of people who had to comb through the data and massage it so that it made sense . . . There was a lot of magic, transforming apples into oranges and oranges into apples. Preparing annual reports was a joke . . . The breakneck deployment of state-of-the-art technology was done with little regard for a management plan.

When the cover about the collusion between Enron insiders and its auditing firm blew open, the RTE system triggered the freefall of Enron as it was also covering the risk exposure related to its instantaneous transactions. Unfounded and overly optimistic belief in technology as the means for generating profits despite an inadequate business model led to Enron’s downfall resulting in one of the largest corporate bankruptcies in US history[11].

Conclusion This article opened with the observation that although KM activities are ‘‘all over the map’’ in terms of technology implementations, however, no one has asked the ‘‘big question’’: why? Despite diverse propositions about ‘‘getting the right information to the right person at the right time,’’ almost everyone neglects to ask what knowledge to manage and toward what end. A review of the industry case studies of companies characterized in the recent years as RTE business enterprises surfaced some interesting insights. Recent industry analyses that have demonstrated inverse correlations between IT investments and business performance provided some motivation for this analyses. Additional impetus was provided by the contrast between the hype about ‘‘RTE technologies’’ propagated by some IT analysts and our in-depth analysis of companies that achieved success as RTE benchmarks. To some extent the search for the ‘‘next big thing’’ and the ‘‘killer app’’ is to blame for its narrow focus on IT and innovation as ends rather than means for achieving sustainable business performance (Business Week, 2003). The big question ‘‘Why?’’ should drive tactical and operational aspects of technology and process related innovations in an organizational KM implementation. As contrasted with the inputs- and processing-focused technology-push model, explicit and specific performance outcomes oriented focus of the strategy-push model, further emphasized the focus on the ‘‘big question.’’ The contrast between the three archetypes of inputs-, processing-, and outputs-driven paradigms of KM explained in Table I and Table II further aided deconstruction of the existing conceptualizations and practices of KM. One such conceptualization of KM that has

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been applied in diverse worldwide governmental and corporate practices was then discussed to motivate subsequent discussion on the RTE business models. The contrast between information-processing capabilities of latest technologies and needed sense-making capabilities was then explained. Additionally, the mechanistic emphasis of technology-based linkages was contrasted with appreciation for organic and socio-psychological relationships needed for nurturing knowledge processes. Two propositions were offered based on prior discussion – one pertaining to the form and function of the RTE, and the second relevant to the contrast between ends and means of achieving performance outcomes. Based upon original analyses, review of prior research, and review of industry case studies we made specific managerial recommendations about realizing the real time performance of enterprise business models. Specifically, we recommended that: B

organizational function should drive the choice of organizational form; and

B

ends should drive the choice of means.

The above propositions were then illustrated with the aid of RTE industry case studies that have been used by IT analysts to hype the benefits of RTE technologies. Based upon our analyses, we counter-argued that the benefits attributed to the RTE technologies should indeed be attributable to the RTE business model. We further contended that in absence of an effective RTE business model, even the most expensive and sophisticated technology could not ensure corporate survival in the short- or long-term. The RTE case studies lent support to the primary role of strategic execution as the lever for sustained business performance. As discussed, the successful RTE enterprises achieved their success by staying a step ahead of competition and by offering value propositions that inspired customers’ imagination instead of playing the ‘‘me too’’ game in an already crowded market. These companies also selected and integrated ICT capabilities that fit directly with what they were deeply passionate about, what they believed they could be the best at, and what directly drove their steady economic growth. The successful RTE businesses did not adopt new technologies motivated by fear of getting behind. Rather, they thought differently about technology as an accelerator of business momentum and not its creator. Unlike the successful models of RTE enterprises, the failures were characterized by thoughtless reliance on technology often grasped as an easy solution, without coherent understanding of how it links to strategic execution for business performance.

Background readings and research KMNetwork: www.kmnetwork.com/ The above portal provides unrestricted access to several full-text articles and research papers by the author that have preceded this milestone in fathoming the ongoing evolution and progress in the field of knowledge management. There are several excellent reviews of various types of information and communication technologies (ICTs) that are used within the realms of KM applications. The focus of this article is on the strategic and overarching framework of real time enterprises and business performance within which specific ICTs are used. For more specifics on technologies that are relevant to the input and processing aspects of both KM models discussed herein, the reader is advised to peruse Tsui (2002); O’Leary (2002); Conway (2002); Gray and Tehrani (2002); Gray and Tehrani (2002); Susarla et al. (2002); Wei et al. (2002); and Jackson (2001).

Notes 1. Strassmann’s research has primarily focused on macro-economic analysis of industry IT investments data and has not empirically studied the behavioral and strategic disconnects related to IT and KM performance discussed in this paper. Author’s seminal research in this context – referenced in this article – specifically focuses on these disconnects between IT, information, actions, and performance at individual, organizational, and national levels. Therefore, author’s research on behavioral-strategic disconnects between IT- and KM-performance complements research by others that has focused primarily on macro-economic aspects. An interesting focus for

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future practice and research is in terms of reconciling existing gaps between economic, sociological, and behavioral aspects of IT- and KM-performance as recommended in Malhotra (2003). 2. Some may argue that the interest in digitizing knowledge of business enterprises pre-dates 1990s as prior AI and expert systems have focused on such processes. Our focus in this article is on the ‘‘real-time enterprise’’ logic in which inter-connected value-chains can respond in real-time to supply and demand changes almost in real time. As the commercialization of the web occurred much later than the invention of the first browser version of Mosaic, such real-time capabilities of networking across enterprises were not available and as affordable in the post-1995 era. However, there are fundamental problems characterizing the AI and expert systems based focus on KM systems that is discussed in greater depth in the contrast between ‘‘sense making’’ and ‘‘information processing’’ capabilities explained in the Expert Systems With Applications journal special issue on knowledge management (Malhotra, 2001b). 3. This argument is supported by examples of technology pioneers of yesteryears that have faded into oblivion. For instance, Visicalc, the company that pioneered the spreadsheet lost out to Lotus 1-2-3 which itself lot out to Ms-Excel. The first portable computers came from Osborne, a company that ceased to exist long before portables became adopted by the mainstream. 4. The technology-push model and the strategy-pull model of KM implementation are discussed as contrasting ‘‘archetypes’’ for business environments ranging from highly routine and predictable environments to radically changing and discontinuous environments. It is, however, recognized that most real world business environments as well as most real world business contexts would fall between the two polar contrasts. Hence most such RTE models would effectively combine the two models for balancing new knowledge creation and commercial exploitation of existing knowledge. Balancing the two processes is discussed in author’s interview with the Institute for supply management featured in the knowledge management cover story of inside supply management (Yuva, 2002). Additional discussion on balancing the apparently paradoxical processes is available in Malhotra (2000a, 2001a, 2002a). 5. For more details on single-loop and double-loop learning, the reader is advised to see seminal writings of Chris Argyris such as Argyris (1990) and Argyris (1994). 6. In some cases of technology implementation such as ERP, the issues of technology deployment and utilization could never get addressed, resulting in snowballing downslide of business performance (see for instance, Strassmann, 2003). 7. Such as the US Federal Government, United States Army, European Commission, US Agency for International Development, Government of UK, Government of South Africa, Parliament of Victoria (Australia), Government of New Zealand, Government of Argentina, SAP North America, Microsoft Europe, Verisign, Telecom Italia, Organization of Islamic Capitals and Cities (Saudi Arabia), and United Nations and its worldwide agencies. More details accessible at: www.brint.com/ casestudies.html 8. Additional discussion on how existing ‘‘information processing’’ focus of technology on semantics (meaning) has yet to address the ‘‘sense making’’ capacities of human beings within the context of the new paradigm of self-regulation and self-control is available in Malhotra (2001b, c, 2002b). 9. It is understandable that WS-I and related web service based experiments (such as RosettaNet) provide hope for technological feasibility of real-time information exchange. However, despite the exploitation of most sophisticated technical standards, information exchange within and between enterprises remain more of a sociological and cultural issue than a technical issue. Hence, despite availability of technical standards that may ensure perfect real time communication, sociological and cultural artifacts impose a major burden. Conversely, concerns that tend well to such sociological and cultural concerns, as discussed in this article, accelerate their RTE business models through adoption of facilitating technologies. More in-depth discussion on this theme is available in the author’s Intel Corporation’s e-strategy research paper (Malhotra, 2001a). 10. Growth consisted of 40 quarters of straight growth and three immediate quarters of extreme growth to the tune of 66 percent. 11. One news story had the following remarks about Enron’s business model: ‘‘In the aftermath of the collapse, there have been suggestions that a few directors had mishandled the partnerships to siphon off funds to their own accounts. However, it is clear that the more than 3,000 partnerships,

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more than 800 of which were in tax havens like the Cayman Islands, played a far more purposeful role in Enron’s business model.’’ Despite real time availability of information, the corporate crisis in this case pertains to sociological and cultural issues such as senior management’s corruption and auditors’ dishonesty that led to ‘‘real time’’ cover-ups despite access to best technology.

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Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company, Oxford University Press, New York, NY. Odom, C. and Starns, J. (2003), ‘‘KM technologies assessment’’, KM World, May, pp. 18-28. O’Leary, D. (2002), ‘‘Technologies of knowledge storage and assimilation’’, in Holsapple, C.W. (Ed.), Handbook on Knowledge Management 1: Knowledge Directions, Springer-Verlag, Heidelberg, pp. 29-46. Porter, M.E. and Millar, V.E. (1985), ‘‘How information technology gives you competitive advantage’’, Harvard Business Review, Vol. 63 No. 4, pp. 149-60. Rayport, J.F. and Sviokla, J.J. (1995), ‘‘Exploiting the virtual value chain’’, Harvard Business Review, Vol. 73 No. 6, pp. 75-99. Sawhney, M. (2003), ‘‘Reality check’’, CIO Magazine, March 1, available at: www.cio.com/ archive/030103/netgains.html Schrage, M. (2002), ‘‘Wal-Mart trumps Moore’s law’’, Technology Review, Vol. 105 No. 2, p. 21. Schultze, U. and Leidner, D. (2002), ‘‘Studying knowledge management in information systems resarch: discourses and theoretical assumptions’’, MIS Quarterly, Vol. 26 No. 3, pp. 213-42. Siegele, L. (2002), ‘‘The real-time economy: how about now?’’, CFO (The Economist), February 1, available at: www.cfo.com/printarticle/0,5317,6651%7C,00.html Sliwa, C. (2003), ‘‘Event-driven architecture poised for wide adoption’’, Computerworld, May 12, p. 8. Stewart, T.A. (2000), ‘‘How Cisco and Alcoa make real time work’’, Fortune, May 29. Stewart, T.A. and Kaufman, D.C. (1995), ‘‘Getting real about brainpower’’, Fortune, December 11. Strassmann, P. (1997), The Squandered Computer: Evaluating the Business Alignment of Information Technologies, Information Economics Press, New Canaan, CT. Strassmann, P. (2003), ‘‘Enterprise software’s end’’, Computerworld, May 12, p. 35. Susarla, A., Liu, D. and Whinston, A.B. (2002), ‘‘Peer-to-peer knowledge management’’, in Holsapple, C.W. (Ed.), Handbook on Knowledge Management 1: Knowledge Directions, Springer-Verlag, Heidelberg, pp. 129-40. Terreberry, S. (1968), ‘‘The evolution of organizational environments’’, Administrative Science Quarterly, Vol. 12, pp. 590-613. Thickins, G. (2003), ‘‘Utility computing: the next new IT model’’, Darwin Magazine, April, available at: www.darwinmag.com/read/040103/utility.html Tsui, E. (2002), ‘‘Tracking the role and evolution of commercial knowledge management software’’, in Holsapple, C.W. (Ed.), Handbook on Knowledge Management 1: Knowledge Directions, Springer-Verlag, Heidelberg, pp. 5-27. Verton, D. (2002), ‘‘Insiders slam navy intranet’’, Computerworld, May 27, pp. 1-16. Wei, C., Piramuthu, S. and Shaw, M.J. (2002), ‘‘Knowledge discovery and data mining’’, in Holsapple, C.W. (Ed.), Handbook on Knowledge Management 1: Knowledge Directions, Springer-Verlag, Heidelberg, pp. 157-92. Wolpert, D.H. (2001), ‘‘Computational capabilities of physical systems’’, Physical Review E, Vol. 65 No. 1, pp. 1-27, available at: www.santafe.edu/sfi/publications/Working-Papers/96-03-008.pdf Yuva, J. (2002), ‘‘Knowledge management – the supply chain nerve center’’, Inside Supply Management, Institute for Supply Management, July, pp. 34-43, available at: www.brint.org/ KnowledgeManagementTheSupplyChainNerveCenter.pdf Zack, M.H. (2001), ‘‘If managing knowledge is the solution, then what’s the problem?’’, in Malhotra, Y. (Ed.), Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey, PA. Zetie, C. (2003), ‘‘Machine-to-machine integration: the next big thing?’’, Information Week, April 14, available at: www.informationweek.com/story/showArticle.jhtml?articleID ¼ 8900042

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Balancing business process with business practice for organizational advantage Laurence Lock Lee

Abstract Purpose – To provide an argument and a practical approach for achieving a balance between business process optimization and the use of human-centred business practices. Design/methodology/approach – The concepts of business process and business practice are positioned in the academic literature with related concepts like tacit and explicit knowledge, routine work, codification and bounded rationality. Process and practice are compared and contrasted prior to the development of a model for their co-existence and interaction. Laurence Lock Lee is the Principal Knowledge Management Consultant with Computer Sciences Corporation, Australia. E-mail: [email protected]

Research limitations/implications – This research builds on the separate research streams supporting business process management and business practice development. The argument for their co-existence still requires further field research to support the organizational advantages claimed. Practical implications – A framework and approach are presented which can be applied directly as part of new field research or practical application. Originality/value – This paper makes two original contributions. First, it anchors the modern concepts of business process and business practice to foundation concepts from the academic literature. Second, it provides a practical framework and approach for balancing business process and business practice, that can be practically applied by the reader. Keywords Knowledge management, Process management Paper type Research paper

Introduction The purpose of this paper is to champion the cause of John Seely Brown and Paul Duguid in their pleas to not lose sight of the inherent value of business practices formed from the tacit understanding of knowledge workers. Seely Brown and Duguid’s (2000) short paper on ‘‘Balancing act: how to capture knowledge without killing it’’ introduces the challenge of balancing business processes with business practice. This paper aims to provide added weight to the argument by positioning it within the academic literature. A connection will be briefly built to foundational theories of ‘‘bounded rationality’’ (Simon, 1979) and ‘‘evolutionary theory of economic change’’ (Nelson and Winter, 1982) and the general tacit knowledge verses explicit knowledge discussion. Having established a foundational argument for a dual focus on both business process and business practice, the paper moves on to provide a practical framework for identifying and managing the balance between the two. The use of the framework is illustrated with case study examples.

Foundation concepts The concepts of codification, explicit knowledge, tacit knowledge, routine work, processes, and practices are not new, but still engender a degree of confusion through their different interpretations. It is crucial to distinguish ‘‘process’’ from ‘‘practice’’ if one is to attempt to operationalize these concepts. In this paper it is argued that ‘‘process’’ is strongly associated with concepts like ‘‘explicit knowledge’’, ‘‘routine’’ and ‘‘codification’’ while and ‘‘practice’’ has similarly strong associations with ‘‘tacit knowledge’’, ‘‘heuristics’’ and ‘‘non-codification’’. Figure 1 provides a positioning of the business practice/business process argument in the literature by tracking three key themes through representative publications.

DOI 10.1108/13673270510582947

VOL. 9 NO. 1 2005, pp. 29-41, Q Emerald Group Publishing Limited, ISSN 1367-3270

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Figure 1 Positioning within the literature

Figure 1 identifies three themes culminating in the practice/process dialogue. The three themes can be traced back to Simon’s theory of ‘‘bounded rationality’’ (Simon, 1979). This theory identifies the limitations within which managers can employ rational decision-making techniques. Rational decision-making implies an ability to make explicit the ‘‘process’’ of decision-making. Outside the bounds of rationality managers will rely on intuition and emotion to guide their decision-making (Simon, 1987). The first theme traces Simon’s work through to the field of artificial intelligence and knowledge based systems. Within this field the CYC project, in development since 1984, is aimed at developing a system for storing commonsense knowledge and stands out as the most ambitious attempt to codify knowledge (Lenat, 1995). Identifying standard means for codifying knowledge evolved during the late 1980s supported by the European ESPRIT collaborative research program (Hickman et al., 1989), though these efforts have stagnated in favor of addressing knowledge from a more holistic perspective, i.e. knowledge management (KM). The KM pioneers viewed knowledge from an organizational perspective, in many cases making the argument for sharing tacit knowledge through socialization techniques, e.g. communities of practice, rather than blindly attempting to codify tacit knowledge within large knowledge bases for sharing (Sveiby, 1997; Allee, 1997; Lesser and Prusak, 1999). The second theme could be called the business process theme. Nelson and Winter (1982), in their work on the evolutionary theory of economic change, refer to Simon’s work in speaking of routine as the distinctive package of economic capabilities and coordinating functions that a firm possesses and can deploy in a repeatable fashion. For Nelson and Winter this includes the heuristic problem solving patterns of say a firm’s R&D department. The linkage between Nelson and Winter’s ‘‘routine’’ and the business process reengineering (BPR) phenomenon is more implied than explicit, with BPR promoting a focus on processes or routines that are core to businesses, removing all others that are deemed to be non-value-adding (Hammer and Champy, 1993). This position evolved to a finer articulation of classes of business processes, e.g. identity or core processes, priority, mandatory or background processes (Keen, 1997). The terms ‘‘business process’’ and now ‘‘business process management’’ (BPM) have been loosely used to identify with just about every activity that a firm participates in. Zairi’s (1997) examination of the literature has found that

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BPM is far from pervasive and is no more than structural changes, the use of systems such as EN ISO 9000 and the management of individual projects. Key features identified with a process were its predictable and definable inputs; a linear, logical sequence; a clearly definable set of activities; and a predictable and desired outcome. The concept of process infers something that is definable, describable and repeatable. In the context of BPM, we must tighten the specification to the extent that the process must be describable in a standardized business process language and computationally executed to provide the expected outputs in a repeatable fashion. This tighter specification of process will similarly require a tightening of the associated terms of explicit knowledge, codification and routine. An important contribution of the recent BPM initiatives is the creation of standard languages to describe a business process in computer executable form, e.g. BPML, BPEL[1]. Languages like BPML provide a link between the typical process designer’s flow charts, process maps, and executable computer code (Smith and Fingar, 2002). The third theme encompasses the dialog around tacit, explicit and codified knowledge, which could arguably be seen as a pre-curser to the KM theme, but has been identified for individual treatment here. Cowan et al. (2000) put forward an economist’s skeptical argument that very little knowledge is inherently tacit and that its codification is simply an argument of a cost/benefit analysis. In proposing this argument, the authors engage in a discussion around articulation and codification, which converges on a view that that what can be articulated, can be codified for economic benefit. Johnson et al. counter Cowan et al. specifically on the impracticality of the proposition on a number of fronts. The example of the art of bicycling is used as an example of how attempts at both articulation and codification of the practice of bicycle riding would rarely be useful to the novice rider, even if it were economically viable (Johnson et al., 2002). Off course these economic arguments ignore the very real sociological issues present. Polanyi considered human knowledge from the premise that ‘‘we know more than we can tell’’ (Polanyi, 1967, p. 4) with the natural extension that ‘‘we tell more than we can write down’’ (Snowden, 2002). Snowden adds the further heuristics that ‘‘knowledge can only be volunteered; it cannot be conscripted’’ and ‘‘we only know what we know when we need to know it’’ for managing knowledge, in contrast to the pure economic argument. Looking back to the BPM context, we could extend the analogy further to ‘‘we can write down more than we can write in BPML’’. Put succinctly, ‘‘we know far more than we can effectively automate’’, the gap arguably being attributable to business practice. Clearly some license has been taken in defining a business practice as the gap between what a human might know and use and what knowledge can be effectively converted for execution within a BPM system. A conventional use of the term ‘‘business practice’’ might refer to a medical or legal practice that would encompass both the tacit understandings and experiences of the staff within the practice as well as the business processes that the firm conducts. The more limited usage of the term here is justified by the emphasis the term connotes around a distinctive expertise developed around extensive work experiences. In summary, the current argument for balancing business process with business practice can be traced back to Simon’s theory of bounded rationality. In this paper we are interested in ‘‘business processes’’ in the context of BPM and therefore its definition is restricted to processes that can effectively be translated into a pre-defined business process language. Business practice is conveniently defined as the complement, i.e. those business activities that fall outside the scope of a business process. The remainder of the paper is devoted to the more practical aspects of balancing business process with business practice for organizational advantage.

Business process management Since the emergence of business process re-engineering we have seen two other influential, IT focused, ‘‘waves’’. The first of these was the emergence of ‘‘off-the shelf’’ ERP systems such as SAP, Peoplesoft, Oracle. The initial driver for ERP adoption was the need to rapidly standardize and support those largely non-differentiating business processes such as finance, human resources, maintenance and the like. The second ‘‘wave’’ was e-business;

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with the essential driver being the desire to leverage low cost internet-based technologies to streamline the way business organizations did business with each other. Despite the romance and the promise of radical new business models and the aspirations of the ERP vendors to fulfill the promise, the reality is that the business benefits have mostly materialized as a consequence of less romantic, business process improvement initiatives. Lower costs, reduced cycle times, more satisfied customers have come from incremental improvements to the way organizations are conducting their businesses. However, what the e-business wave has achieved is a realization that an organization’s business processes do not stop at the front door. They need to extend out to their suppliers, customers and alliance partners. From the IT perspective this also means extending internal systems to be externally facing, with the consequent difficulties in mixing and matching with the plethora of different vendor products that trading partners might use. The IT industry’s response to this challenge has been the formation of the business process management initiative (BPMI) with a mission to ‘‘standardise the management of business processes that span multiple applications, corporate departments and business partners, behind the firewall and over the internet’’ (see www.BPMI.org). With over 125 industry members, BPMI is looking at a means to separate the management of business processes from the software that support and implement them. The core undertaking has been the development of a common business process modeling language (BPML), which can enable business processes to be described and managed independently from the software used to implement and support them. The analogy has been drawn to the SQL data base query language which today allows common queries to be described in the standard SQL language, but executed against any relational data base system. BPM is still in its infancy, as are the products that support it. There are eight identified basic functions that would comprise a business process management system (www.BPMI.org): (1) Process discovery: finding out how things are actually done. (2) Process design: modeling, simulating and redesigning a process. (3) Process deployment: distributing the process to all participants. (4) Process execution: ensuring the process is carried out by all. (5) Process maintenance: resolving exceptions, adaptations. (6) Process interaction: allowance for human interaction with the process. (7) Process optimization: process improvement. (8) Process analysis: measuring performance and devising improvement strategies.

Business practice A business practice is seen as a frequently repeated act, habit or custom performed to a recognized level of skill. It is often thought of as the uncodified ‘‘know-how’’ resulting from human experience, improvisation and innovation. One of the key benefits attributed to KM has been the ability to share best practices across large organizations. While there are clearly some great success stories to tell around how knowledge has been leveraged around the sharing of practices across large organizations, there are a far greater number of stories around how elusive the benefits can be from attempts to share best practices. Different contexts, the ‘‘not invented here’’ syndrome, ‘‘our business is different’’ have all been offered as barriers to achieving success. The reality is that unless you are operating under a franchised business model, then there will be real differences across the businesses. Seely Brown (2001) has gone as far as to claim that the proportion of business practices that can be formally codified in process form is really only the tip of the iceberg, and that the vast majority of ‘‘knowledge’’ encompassed in a successful practice is uncodified and held tacitly in the minds of the staff performing the task (Figure 2).

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Figure 2 The explicit/tacit process/practice divide

The challenge lies in judging whether a ‘‘practice’’ is truly transportable across the different business environments; and this ability appears to be largely held in the collective judgment of expert practitioners who have a view across the different business domains. Hence the important role that communities of practice or expert networks play in facilitating the effective sharing of best practices.

Business process versus business practice Business processes and business process re-engineering gained much prominence and some notoriety in the early 1990s as companies were challenged to break out of their traditional indoctrinated ways of doing business. A typical re-engineering process would start with a ‘‘mapping’’ of current business processes and then an intense assessment of which non-value adding processes could be ‘‘obliterated’’. With the increased challenges of globalization, and commoditization, re-engineering is now re-emerging in the form of BPM (Smith and Fingar, 2002). The drive for BPM is coming from organizations wanting to engage in inter-enterprise collaboration, instigating a demand for a common way to implement inter-enterprise business processes that is independent of the technology used to support them. Fundamental to the BPM concept is a standard business process modeling language (BPML), which is designed to enable companies to jointly develop business processes with their partners and collaborators, without the need to enforce a common technology platform, e.g. SAP, PeopleSoft, Oracle (see www.BPMI.org). Business practices are often not explicit, but couched tacitly in the minds of the employees that conduct them. Etienne Wenger (1999) in his research on ‘‘communities of practice’’, uses an example of health claim processing. One might believe that these processes could be easily codified; yet the uncodified tacit understanding of the different claims processors substantially separates good and bad performance. While the written procedures for health claim processing were designed for individuals, the reality was that claims processors had to form themselves into a tight social ‘‘community of practice’’ to effectively deal with the contradictions, gaps and ambiguities that inevitably exist in written procedures. Wenger’s work is strongly supported by John Seely Brown and Paul Duguid (2000) in their plea for not ignoring business practice in the rush to automate processes. Johnson (2002) argues for distinguishing tacit from explicit knowledge and devising strategies to manage them independently. Johnson aligns explicit knowledge with intellectual property and knowledge stocks and tacit knowledge with interactive or facilitated knowledge processes or knowledge work. For every re-engineering success story there appeared to be many more failed attempts. There are many opinions about why re-engineering efforts fail; from a lack of appreciation organizational culture, power and structures (Cao et al., 2001) to simply poor integration or implementation (Al-Mashari et al., 2001). A common experience in the rush to obliterate non-value adding processes, was the oversight of the subtle uncodified activities

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that skilled and experienced employees perform, i.e. the people factor was often overlooked (White, 1996). Clearly practice and process need to co-exist. In most business processes, as we start to analyze them more closely, we typically find more ‘‘art’’ or tacitly held practices than we had anticipated. However the economic push for lower costs, faster response, quicker product launches will inevitably mean a growing need to codify and automate more ‘‘practices’’ through a greater focus on activities like BPM. However, KM practices will be required to both achieve a common understanding of the intent behind codified processes, and to generate ideas and innovations required for continuous business process improvement.

Rules for co-existence To summarize, the differences between process and practice are characterized Table I. The important point in trying to achieve an appropriate balance between process and practice is to know what tacit knowledge are the best candidates for trying to make explicit and which areas to not even try. Significant guidance can be gained from the artificial intelligence/expert systems discipline in this regard. A key learning being that knowledge acquisition and representation can be particularly difficult and complex. Some attempts have been made to develop a standard method for knowledge acquisition and representation. Perhaps the best known of these is the knowledge acquisition documentation and structuring (KADS) methodology for developing knowledge based systems (Hickman et al., 1989), initially launched as a European Co-operative Research project (ESPRIT). KADS could be viewed as the knowledge equivalent of BPM. Eventually the complexity of the different models required in KADS to be effective, worked against its larger scale adoption, and little is seen of it now. More recently, Papavassiliou and Mentzas (2003) have explored a modeling approach to the integration of knowledge management into weakly structured business processes. Expert systems are arguably the most sophisticated means for capturing tacit knowledge and making it explicit. Yet the majority of successful expert systems that have been deployed over the past 20 years, have been in well defined and constrained areas like fault diagnosis, credit assessments, schedule checking, and process control, and have largely failed in areas requiring some creative thinking like business planning, schedule creation, and new product development. In summary, there are definitely limits to the extent to which one can practically make tacit knowledge explicit. These limits are both in terms of our ability to accurately represent the knowledge in explicit form and practical limits on the ‘‘knowledge engineering’’ time it would take to achieve such a representation, if indeed it were possible. Most business processes found within organizations are simply documented in ‘‘rules and procedures’’ manuals that are distributed with an expectation that they will be consistently understood and applied. For anything other than simple routine tasks this is a dangerous assumption. First, for complex processes the business process designer has the challenge of accurately representing his or her tacit understanding of the business process intent in explicit written form. Secondly, those expected to perform the process will internalize their Table I Process vs practice Process

Practice

The way tasks are organized Routine Orchestrated Assumes predictable environment

The way tasks are done Spontaneous Improvised Responds to a changing unpredictable environment Relies on tacit knowledge Network or web-like

Relies on explicit knowledge Linear Source: Seely et al. (2000)

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understanding of the written process, with significant scope for this understanding to be quite different than the intent of the designer. This is where KM practices can assist in developing a common understanding of the business process intent by connecting designers and performers of the business process. This socialization process will eventually evolve into a common business practice around the business process. Figure 3 shows two cycles of process/practice interaction. The inner cycle is the ‘‘shared understanding’’ cycle. It starts with the process designer documenting the business process (tacit to explicit knowledge conversion). For a complex process one could argue that the document might represent less than 30 percent of what the designer actually understands about the process. The process performer is then expected to internalize this knowledge from the document (explicit to tacit conversion) that likewise for a complex process, might be a 30 percent efficient process. Therefore the degree of common understanding between process designer and process performer could be less than 10 percent[2]. To improve the level of common understanding, socialization (tacit to tacit knowledge transfer) processes are required. The research literature strongly supports the value of networks in facilitating the sharing of tacit knowledge (Augier and Vendelo, 1999; Hansen, 1999; Powell, 1998; Lesser and Prusak, 1999). Common vehicles for these socialization processes within organizations are communities of practice, i.e. cross-organizational groups who form naturally around a common interest or cause (see Wenger, 1999). The outer cycle is the innovation cycle. The improvement cycle for business processes is triggered by a gap between current and desired performance. Ideas for improvements need to be solicited, tested and agreed on for implementation. Again the community of practice is an excellent vehicle for socializing improvement ideas and innovations.

Figure 3 Process/practice interaction

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The following example is used to illustrate how process and practice inter-relate: Mary works for a government welfare agency involved with aged care. Because of her long experience with assessing candidates for government assistance for their nursing home care, she is asked to write the new manual to assist field case-workers apply a consistent process in assessing candidates for care. In preparing the manual, Mary struggles with trying to articulate all the factors she would personally apply in her assessments. However, she perseveres and distributes her draft manual for initial use. In reviewing how the case-workers interpret her procedures in use, she is somewhat surprised at the variety of interpretations they have taken. She quickly appreciates that it is not possible to capture in writing her full intent. To help develop a common understanding of the procedures with her case-workers she decides to form an informal community of practice around the aged care assessment processes. The group meets monthly to discuss their experiences with the assessment processes and agree on a common best ‘‘practice’’ interpretation of the documented ‘‘rules’’. Over time, the community grows and distributes across all the department offices state-wide. Group meetings start to become a forum for discussing improvement ideas. Ideas discussed at a local office forum are shared with the core community, who decides whether or not to include the suggested improvements into the process manual. As new case workers come into the department they are provided with the assessment ‘‘process’’ manual to help guide their assessment work. More importantly they are also introduced to the aged care assessment community of practice, from which they will gain the important ‘‘practice’’ knowledge required to effectively perform their duties.

A framework for balancing process with practice The framework details how to manage the business practice/business process balance at the application level. When we look closely at defined business processes, we will see many instances where human intervention is required. At times this intervention may be replaced by an automated response. In other instances it may not be cost effective or even possible to do so. In any case, once we have human interaction taking place we need to respect the different roles that humans can take in the overall business process as defined. We can draw guidance from here the human factors discipline, which has invented ‘‘task analysis’’ techniques for designing effective interfaces between knowledge based human tasks and programmed process tasks (Diaper, 1989). While these techniques are perhaps too involved for the casual user, the principles for carefully managing the interface should be adhered to. As an example, let’s take a look at a typical order entry process (Figure 4).

Figure 4 Framework example

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The business process can be mapped as shown. Key decisions can also be identified from each of the processes. Depending on the industry you are in, the level of human intervention will change from virtually none, to intimately involved in the whole process. The order entry officer will either be simply performing clerical tasks that might easily be automated, or playing the role of a business manager, negotiating each order on an individual basis. For example, Amazon.com has demonstrated that the order entry process can nearly be totally automated for commodities like books. However, for companies who create a unique product with every job, like a construction company, the order entry process and staff are viewed as critical to the business. Major cost savings are available for those businesses that can reduce the level of human intervention. We therefore see firms trying to standardize their offerings with defined pricing models and delivery mechanisms. On the other hand, customers are now becoming more demanding and are looking for more personalized attention, for which they are often willing to play a premium. Ultimately we need to understand the nature of the decisions that the order entry officer needs to make. Schroeder and Benbasat (1975) provide the characterization from their experiments with decision-makers across environments of varying complexity (Figure 5). The experiments demonstrate that there exists a point where further information is of diminishing value in supporting a decision when the environment is complex. For example, using the order entry example, if your company was in the business of making aircraft and a request came in to build the first space shuttle to fly to Mars, the decision to accept the order or not will be largely judgmental. We therefore need a framework that takes into account the complexity of the decision at hand. In essence, this is determining where the ‘‘bounds of rationality’’ lie. Decision complexity can then be used to determine what the appropriate mix should be of business process response and business practice response (Figure 6). The process proposed is summarized in Figure 7. Using the above framework implies a requirement to explicitly identify and categorize decisions that need to be made. It also implies a more systematic and disciplined approach to decision-making, something that does not come naturally. The need for more systematic decision support processes are strongly supported by the findings of Kahneman et al. (1982) in their studies on bias in human decision making. Their studies clearly demonstrated how decisions based on ‘‘gut feel’’ can be unintentionally impacted by human bias, leading to clearly erroneous decisions. Additional investment in time will be required to characterize decisions as routine, informational or judgmental and supporting them appropriately. This will be justified through time saved in not debating decisions that should be routine, or avoiding poor outcomes from more complex decisions, as a result of not involving appropriately expert staff (Figure 8). Figure 5 Decision complexity

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Figure 6 Balancing process and practice

Figure 7 Balancing process and practice around decisions

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Figure 8 Case study

Summary We have seen major progress made in the formalization of work into manageable business processes. BPR has had some undoubted success but also a fair share of failures. We have learnt some hard lessons about the limits to which one can effectively codify, or make explicit, the tacit knowledge of expert practitioners. The KM discipline has provided us with a multitude of techniques for ‘‘managing’’ largely knowledge based, business practices. The challenge has been to determine how we can facilitate the delicate balance between business process and business practice to ensure that they are appropriately balanced for optimal performance. For business processes to be effectively deployed, they must be surrounded by a healthy dose of business practice. A two-cycle model of interaction between process and practice was described. An inner cycle showed how a common understanding between process designer and process performer could only be achieved through their socialization, typically through informal communities of practice. The outer cycle showed the key role communities of practice also played in progressing ideas for process innovations and improvements.

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Moreover, an analytic framework has been provided to assist in achieving an appropriate process/practice balance for maximum organizational advantage. This framework articulates decisions at the application level. It provides a method for determining decision complexity and then indicates the business process or business practice based techniques recommended for supporting these decisions.

Notes 1. The two main contenders are BPML (see www.BPMI.org) and BPEL4WS (see www.oasis-open.org). 2. These percentages are based on the author’s experiences in the field.

References Allee, V. (1997), The Knowledge Butterworth-Heinemann, Oxford.

Evolution:

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Schroeder, R. and Benbasat, I. (1975), ‘‘An experimental evaluation of the relationship of uncertainty in the environment to information used by decision makers’’, Decision Sciences, Vol. 6 No. 3, pp. 556-67. Seely Brown, J. (2001), ‘‘Sharing knowledge across the organisation: knowledge dynamics and emerging corporate landscape for the age’’, CSC CIO Forum, August. Seely Brown, J. and Duguid, P. (2000), ‘‘Balancing act: how to capture knowledge without killing it’’, Harvard Business Review, May-June, pp. 3-7. Simon, H.A. (1979), ‘‘Rational decision making in business organizations’’, The American Economic Review, Vol. 69 No. 4, pp. 493-513. Simon, H.A. (1987), ‘‘Making management decisions: the role of intuition and emotion’’, The Academy of Management Executive, Vol. 1, pp. 57-64. Smith, H. and Fingar, P. (2002), Business Process Management: The Third Wave, Meghan-Kiffer Press, Tampa, FL. Snowden, D. (2002), ‘‘Complex acts of knowing: paradox and descriptive self-awareness’’, Journal of Knowledge Management, Vol. 6 No. 2, pp. 100-11. Sveiby, K. (1997), The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, Berrett-Koehler, San Francisco, CA. Wenger, E. (1999), Communities of Practice, Cambridge University Press, Cambridge. White, J. (1996), ‘‘Re-engineering gurus take steps to remodel their stalling vehicles’’, Wall Street Journal, 26 November, p. 1. Zairi, M. (1997), ‘‘Business process management: a boundaryless approach to modern competiitveness’’, Business Process Management Journal, Vol. 3 No. 1, pp. 64-80.

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The inseparability of modern knowledge management and computer-based technology Clyde W. Holsapple

Abstract Purpose – This paper makes the case that modern knowledge management (KM) is inseparable from a consideration of technology. While recognizing that there are many non-technological facets to KM research and practice, it takes issue with the perspective proposed by some that knowledge management has little or nothing to do with technology. Similarly, the perspective that equates knowledge management with information management is challenged.

Clyde W. Holsapple is a Professor at the School of Management, Gatton College of Business and Economics, University of Kentucky, Lexington, KY, USA. E-mail: [email protected]

Design/methodology/approach – The research method involves an analysis of the contrasting perspectives to show that each has blind spots that obscure a clear vision of the relationship between computer-based technology and knowledge management. Building on the ideas of Newell, van Lohuizen, and others, the research advances an alternative perspective to overcome limitations in the other two. Findings – The KM perspective introduced here neither dismisses technology nor identifies with it. From this perspective, this paper develops the contention that modern KM has been tremendously enriched by advances in computer-based technology (CBT), discussing several specific examples. Moreover, this paper concludes that CBT needs to be grounded in a clear, deep consideration of knowledge management. Research limitations/implications – As this is a relatively new perspective, the full extent of its utility will unfold over time as it is adopted, used, and extended. KM researchers can adopt this perspective to guide the conception and design their research projects. Moreover, several implications for business computing systems researchers are outlined. Practical implications – The new perspective offers students and practitioners a middle-ground between two extremes for framing their understanding and observation of KM and CBT phenomena. Originality/value – Both research and practice are shaped by the conceptions that underlie them. The paper furnishes a fresh, inclusive conception of the relationship between KM and CBT. Keywords Computers, Decision support systems, Electronic commerce, Knowledge management Paper type Conceptual paper

s a field of study and practice, knowledge management is here to stay. Yet, it is still in a formative stage, marked by differences in terminologies, emphases, and boundaries. This paper focuses on one of those boundaries: the relationship between knowledge management (KM) and computer-based technology. It advocates a perspective of the boundary that neither excludes technology, nor identifies with it. This is an inclusive perspective based on a conception of knowledge that recognizes multiple knowledge types (descriptive, procedural, reasoning), multiple gradations of knowledge, and diverse processors of diverse knowledge representations.

A

This research was supported in part by the Kentucky Initiative for Knowledge Management, established in 1988 at the University of Kentucky.

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Views on the relationship between KM and computer-based technology are wide-ranging. Some say that there is little or no relationship. Some contend that any such relationship is largely incidental. In contrast, others tend to use the terms information and knowledge interchangeably, seeing information technologies and systems as being at the core of knowledge management. Still others take positions between these poles. This diversity of perspectives spans the KM literature, being expressed with varying degrees of overtness. Adopting some view on the KM-technology relationship is unavoidable for the KM practitioner, researcher, or student (even though it may be done implicitly). The adopted

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DOI 10.1108/13673270510582956

‘‘ Computer-based technology is essential to an understanding and application of modern knowledge management. ’’

view is significant because it shapes one’s ability to appreciate KM issues, opportunities, challenges, and possibilities. This paper considers the role of technology in knowledge management. In so doing, it stakes out a position that there is neither a barrier that differentiates information from knowledge, nor can the terms knowledge and information be used interchangeably. Building on this, it argues that computer-based technology (CBT) is essential to an understanding and application of modern knowledge management. Furthermore, it concludes that knowledge management forms the rationale and intellectual basis for studying computer-based technology and systems. That is, KM is the ground on which technological advances grow, giving such advances sustenance, relevance, and a raison d’etre. Exploration of how technology can complement and mesh with human knowledge handling is where CBT researchers have added and can continue to add value to the knowledge management movement. This paper considers several examples of CBT that have been integral to improved knowledge handing: electronic commerce systems, the Deep Blue system, decision support systems, and research support systems. It also identifies and discusses several areas where CBT research has a potential to make further contributions to the KM field.

Boundary perspectives As background for exploring the KM-CBT boundary, consider two contrasting perspectives: exclusion and identification. The exclusive perspective sees knowledge management as being a strictly human and social phenomenon. It sees the representation and usage of knowledge as being exclusively a human endeavor. In sharp contrast, the identification perspective views knowledge management as mainly a re-naming of computer-based technology’s various monikers and variants such as data processing (DP) systems, information systems (IS), information technology (IT), enterprise resource planning (ERP) systems, intranet systems, data warehousing, and so forth. The exclusive perspective In KM conference presentations, articles, and web sites, it is not uncommon to encounter the perspective that knowledge management has little or nothing to do with technology. In this perspective, knowledge management is about human relationships, interpretations, processes, resources, and culture. Certainly, this is the case. However, the exclusive perspective goes a step further to explicitly or even emphatically exclude CBT from the KM domain. If it is to be considered at all, CBT is nothing more than an enabler to facilitate the practice of KM. Curiously, by their very natures, ‘‘enabling’’ and ‘‘facilitating’’ are hardly incidental; they are at least important, if not crucial. So, from the exclusive perspective, there is a well-defined boundary between KM and computer-based technology, in the sense that KM has little or nothing to do with technology and CBT is only concerned with information or data, but never with knowledge. In its extreme form, this perspective not only sees an impenetrable barrier between CBT and knowledge management, but even denies the existence of KM as anything more than a fad, buzzword, or label for managing work practices (e.g. Wilson, 2002). However, the exclusive perspective’s mainstream is ably represented by Galliers and Newell (2003) who ‘‘eschew IT-enabled knowledge management, both in theory and in practice.’’ They argue that the information technology research community has little to contribute to the development of the KM movement.

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It is useful to examine the roots of the exclusive perspective on KM. It appears to stem from a conception of knowledge that precludes the relevance of technology. It does so by defining knowledge as uniquely in the domain of human or social processing; if a computer does something, then knowledge cannot be involved. In defining away any role for technology in KM, the exclusive perspective labels the storage, generation, application, and distribution activities of computers as ‘‘data’’ management or ‘‘information’’ management, while reserving the KM term for activities performed by humans (‘‘information’’ becomes ‘‘knowledge’’ when it is processed in a human mind). Such labeling ignores other important and long-recognized abilities of CBT: storing, generating, applying, and distributing procedures and logic, as well as state descriptions such as data/information (Bonczek et al., 1981). Suppose a person makes a forecast based on his/her interpretation and analysis of a current situation. This forecast is the person’s ‘‘justified true belief’’ (i.e. it is held to be true with some appreciable degree of confidence) – his/her knowledge of what may happen. In this forecasting exercise, knowledge is being used and produced. The knowledge that is used may be of various types (e.g. situation descriptions, experience-based procedures, reasoning logic). Historically, middle managers and staff assistants spent considerable effort in these sorts of tasks before the advent of computers and decision support systems. Now, what if a computer system does what the person did in years past (perhaps even being modeled on that person’s methods), or does some portion of what was heretofore called knowledge work? Should we say that knowledge is not being used or produced, preferring to call it information or data instead? Should we say that this activity is now out of the KM domain because technologies have been devised to perform it? Should we say that the system that produced the same (or even better) forecast is unable to regard it as being true with the same (or even more accurate) degree of confidence, and to even take action on that ‘‘justified true belief,’’ because knowledge use and products are exclusively in the human domain? Should we say that in an organization that does not use such CBT, knowledge management is being done, while simultaneously, in another organization that is using CBT for the same task, we say that a KM phenomenon is not occurring? If we answer affirmatively to all of these questions, then we are comfortable with the exclusive view; if we answer negatively to one or more, then an alternative perspective on the boundary between KM and CBT is in order. The identification perspective The identification perspective considers the terms ‘‘knowledge’’ and ‘‘information’’ to be more or less synonymous. Thus, knowledge management and information management are interchangeable in this viewpoint. Moreover, computer-based technologies traditionally referred to as information technologies or information management systems are called knowledge management technologies. Critics such as Wilson (2002) contend that: B

IT vendors reposition their offerings to latch onto the current fashionability of KM in their marketing efforts, without really changing those offerings in any fundamental way;

B

consultants rename their IT services (and business process re-engineering services) as KM services to capitalize on the buzz surrounding knowledge management; and

B

academicians from IT departments appropriate the KM term as a way to enhance the topicality of their research and to seize programmatic turf in the offering of KM education.

Indeed, the exclusive perspective may, in part, be a reaction the identification view. In opposition to the identification perspective, it seeks to clearly separate the concepts of knowledge and information, and does so in a way that precludes CBT from consideration in the study and practice of KM. By focusing on computer-based technology as the core of KM, the identification view tends to be unbalanced, giving inadequate attention to the many human elements of KM. These elements include trust, ethics, incentives, human relations, leadership, culture, organizational infrastructure, social networks, social capital, creativity and innovation,

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‘‘ It is no coincidence that the 1990s dramatic rise in KM development, adoption, and prominence coincided with advances in CBT connectivity and enterprise support. ’’

strategy, best practices, human competencies, knowledge sharing proficiencies, learning, and so forth. Clearly, such human aspects of KM deserve careful consideration, as they are likely to determine how CBT is applied in an organization and whether that application will have positive impacts. By equating knowledge with information, the identification perspective tends to be overly narrow, giving inadequate attention to the panorama of facets inherent in knowledge management. These facets include diverse knowledge resources, myriad knowledge processing possibilities involving activities and flows, and numerous situational influences on the conduct of knowledge management (Holsapple and Joshi, 2004). Regarding knowledge as nothing more than information also ignores the rich array of dozens of knowledge attribute dimensions that characterize any specific instance of knowledge and which deserve careful consideration by those who engage in knowledge work (Holsapple, 2003).

The role of computer-based technology in knowledge work Neither the exclusive perspective, nor the inclusive perspective, gives a clear vision of the KM-CBT boundary. They do no offer insight into what is needed along this boundary. They do not offer hindsight about what has been accomplished at the KM-CBT frontier. They do not offer foresight into possibilities about how CBT can assist KM. To better understand the role of computer-based technology in knowledge work, consider a different perspective. In this third perspective, knowledge is neither equated with information, nor is a barrier built between them. It is an inclusive perspective that views the boundary between KM and CBT as highly permeable, and that sees the value in CBT as ultimately coming from its contribution to KM efforts. The inclusive perspective The inclusive perspective is based on a conception of knowledge advanced by Newell (1982): knowledge is that which is conveyed in usable representations. These representations include symbolic, visual, audio, mental, digital, behavioral, and other patterns in time and space. When a specific representation is found to be usable by some processor, then for that processor it is knowledge. There are, of course, degrees of usability related to the clarity, meaningfulness, relevance, and significance of a given representation in a particular situation faced by the processor. There are also many attribute dimensions for characterizing an instance of knowledge and its degree of usability for a particular processor may be a function of where it lies on these dimensions (Holsapple, 2003). Observe that this definition of knowledge does not depend on the nature of its processor. It does not exclude computer-based processors; nor does it define knowledge in terms of information (as either being synonymous with information or in contrast with information). For purposes of the discussion that follows, we adopt Newell’s neutral, but very rich and unifying, conception of knowledge – admitting the possibility that usable representations exist for both human processors and computer-based processors. What, then, is the relationship between Newell’s conception of knowledge and the notion of information? Machlup (1980) asserts that all information is knowledge, but all knowledge is not information. According to van Lohuizen (1986), information is one of several states in a progression of knowledge: data, information, structured information, evaluation, judgment, and decision. An example of this progression is shown in Table I. A processor can work on one state in order to achieve another knowledge state higher in the progression. As we proceed from lower to higher states in the knowledge progression, usability (e.g. relevance,

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Table I Progression of knowledge states A progression of knowledge states (van Lohuizen, 1986) Datum Information Structured information An evaluation A judgement A decision

A sample progression 240 240 is the level of cholesterol 240 is the current level of cholesterol for John Doe John Doe’s level of cholesterol is now too high John Doe’s health is presently in severe jeopardy John Doe gets a prescription for Lipitor

importance) with respect to a particular situation increases, quality may improve, and possibilities of overload diminish. Unlike the exclusive perspective that considers only states above information (or structured information) as being knowledge, van Lohuizen regards all states in the progression as being knowledge of varying degrees of refinement. Each of the knowledge states in Table I belongs to the class of descriptive knowledge. This is the type of knowledge that describes the nature of some world. It could be a future world, the past world, the current world, a hypothetical world, or an expected world. The knowledge states that van Lohuizen discusses are gradations of descriptive knowledge. But, there are other major types of knowledge not covered by his progression (and typically overlooked by the exclusive and identification perspectives). These are procedural knowledge and reasoning knowledge (Bonczek et al., 1981; Holsapple and Whinston, 1987, 1988). In contrast to representations used to convey the characteristics of some world, there is knowledge that is concerned with procedures, with how to do something. Philosophers tell us that this procedural knowledge is very different in nature and function than descriptive knowledge (Russell, 1948; Ryle, 1949; Scheffler, 1965). A representation that a given processor can use to accomplish a series of steps is procedural knowledge for that processor. This could be steps that do something to physical materials (e.g. a recipe, assembly instructions) or to other knowledge representations. As an example of the latter, procedural knowledge may be instrumental in progressing from one state of descriptive knowledge to another (e.g. how to distill information from data, or how to derive evaluations from information). A third important knowledge type is concerned with reasoning (Holsapple and Whinston, 1988). Reasoning knowledge specifies what conclusion is valid when certain circumstances exist. It is knowledge that is concerned with logic, correlation, synchronicity, analogy, and perhaps even causality. When a representation is used by a processor to infer why something is the way it is or what action is appropriate in a particular situation, that representation is conveying reasoning knowledge to the processor. A processor may use reasoning knowledge in progressing from one state of descriptive knowledge to another. For instance, rules may be available that indicate which procedure to employ for distilling information from data or what line of reasoning will lead yield a sound decision in a given situation. Just as van Lohuizen recognizes gradations of descriptive knowledge (including data and information), analogous gradations of procedural knowledge and reasoning knowledge are also recognizable. As Table II indicates, the gradations reflect different degrees of sense-making that a processor, faced with some situation, can perform for any of the three knowledge types: making sense of a representation’s syntax, its semantics, its interrelationships, its validity, its applicability, and its actionability. For instance, at a semantic level, a processor focuses on the meaning of a descriptive representation (i.e. information), an algorithmic representation, or a rule representation. At an interrelationship level, the focus is on dependencies and consistencies among related descriptions, algorithms, or rules.

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Table II Gradations of descriptive, procedural, and reasoning knowledge Sense-making focus on Syntax (clarity) Semantics (meaning) Interrelationships (dependencies, consistency) Validity (correctness, confidence) Applicability (importance, relevance) Choice (actionability)

Progression of descriptive knowledge

Progression of procedural knowledge

Progression of reasoning knowledge

Datum Information Structured information

Algorithm syntax Algorithm semantics Connections and patterns among algorithms Algorithm validity Algorithm applicability Algorithm choice

Rule syntax Rule semantics Relationships among rules and sets of rule families Rule and rule set validity Rule and rule set applicability Rule choice

Evaluation Judgement Decision

Compared to either the exclusive or identification perspectives, the inclusive perspective offers a very different foundation for understanding the relationship between computer-based technology and knowledge management. It neither dismisses CBT by defining knowledge to preclude technological representations and processing, nor minimizes KM by equating it with information management. It admits the possibility that knowledge from any of the cells in Table II may be represented in ways that are usable to either human and/or computer-based processors. The discussion that follows adopts the inclusive perspective. Technology for knowledge management A basic KM assumption is that an organization’s performance and competitive standing suffer if it fails to effectively capture/preserve/generate/apply knowledge and make it flow appropriately within and beyond the organization (Singh, 2000). In the modern world this is done by augmenting innate human knowledge handling capabilities with computer-based technology. Limiting ourselves to paper, pencil, typewriters, filing cabinets, shelving systems, face-to-face meetings, telephone conversation, postal services, and the like is hardly a recipe for success in the twenty-first century. It is no coincidence that the 1990s dramatic rise in KM development, adoption, and prominence coincided with (or perhaps followed in the wake of) advances in CBT connectivity and enterprise support (Holsapple and Singh, 2000). The theme for the 2001 International Conference on Information Systems was to explore how technology can change our lives and our organizations. One significant way that it has done so has been to transform how knowledge work is actually done. If we look at what computer-based technology is, it is fundamentally concerned with digital approaches to representing and processing knowledge of various types and in various gradations. For descriptive knowledge the gradations – raw data to structured information to problem solutions for decisions – mirror the evolution of CBT from data processing systems to management information systems to decision support systems. All of these systems have dramatically changed the way knowledge work is done in organizations, releasing tremendous human resources, enabling organizational growth (and necessitating organizational restructuring), and facilitating improved performance. They have been instrumental in the rise of the knowledge-based organization (Holsapple and Whinston, 1987; Paradice and Courtney, 1989; Bennet and Bennet, 2003). Computer-based technology has transformed the way in which individuals and organizations accomplish knowledge work by amplifying, complementing, leveraging, and (in some cases) improving on innate human knowledge handling capabilities. Although efforts at managing knowledge certainly preceded the computer, it has been computer-based technology that has ushered in the modern era of knowledge management. In the last few decades, and especially in the last decade, there has been as much progress in understanding knowledge management and advancing its practice as occurred in the many preceding centuries that dealt with traditional, conventional, non-technologically-supported knowledge management. Much of this progress has been either stimulated by or enabled by advances in computing technology.

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Computer-based technology is concerned with the representation and processing of various distinct types of knowledge. A variety of technologies have been devised to represent and process reasoning knowledge, procedural knowledge, and the gradations of descriptive knowledge (Holsapple and Whinston, 1988, 1996; Tiwana, 2000; Tsui, 2003). Four areas for deploying these technologies are briefly highlighted: e-commerce, the Deep Blue experience, decision support systems, and research support systems. From different angles, each illustrates the inseparability of modern knowledge management from computer-based technology. Interestingly, the boom in knowledge management coincides with the 1990s boom in organizational computing: networking, e-commerce (fueled by the web and the internet), collaborative commerce, and enterprise and trans-enterprise systems. E-business, which includes e-commerce and collaborative commerce, is concerned with approaches to achieving business goals in which technological means for representing and processing knowledge are used to implement, facilitate, and enable the execution of activities within value chains and across value chains, as well as the decision making that underlies those activities (Holsapple and Singh, 2000). As such, knowledge and its management form the lifeblood and linchpin of e-business. Take e-Bay, for example. This CBT is an electronic marketplace whose fundamental nature involves the use of digital representations that convey knowledge to market participants and to the system itself. It acquires knowledge about sellers’ offerings, buyers’ commitments, and participants’ market experiences. It assimilates knowledge, filtering, screening, and organizing. It selects assimilated knowledge as needed to satisfy participants’ requests and to apply in its own internal processing for such activities as coordinating participant interactions and enforcing rules of conduct. It distributes knowledge about states of the world (e.g. issues alerts, shows auction status, indicates participant reputation), about procedures (e.g. how to initiate an auction, how to ensure a safe trading experience), and about logic (e.g. trading tips, policies). All of this KM is the essence of what the e-Bay system does, with an actual trading transaction between participants being practically incidental. This is what would be called knowledge work if carried out by persons involved in running a physical auction, although without the virtual, global scope of the CBT market. As another case of CBT involvement in knowledge management, consider the chess series between Garry Kasparov and IBM’s Deep Blue. In analyzing this series, Huang (1998) observes that it was a contest between an individual human’s ability to process his own considerable tacit knowledge and a computer system’s ability to process collectively constructed explicit knowledge. Huang contends that the Deep Blue victory demonstrates the value of applying technology ‘‘to assist in collaborative efforts and knowledge sharing to achieve winning results.’’ The larger lesson learned is that organizations can use CBT to ‘‘capture and reproduce tacit knowledge of their workers, to be reused at different times, in different locations, through different media, to create solutions more efficiently. This allows more time for individuals to use their intuitive strengths, defining and solving problems more creatively.’’ Decision support systems have a similar result. By aiming to relax cognitive, temporal, economic, or competitive pressures on decision makers, these systems give decision makers greater opportunity to exercise and exploit their own idiosyncratic KM capabilities (Holsapple and Whinston, 1996). Decision-making has long been recognized as a knowledge-intensive task. Knowledge comprises its raw materials, work-in-process, byproducts, and finished good. It involves processing representations of descriptive, procedural, and/or reasoning knowledge in order to help produce knowledge about what to do (Bonczek et al., 1981; Holsapple, 1995). In the case of artificially intelligent decision support systems (e.g. expert systems), the emphasis is on representing and processing reasoning knowledge. In the case of solver-oriented decision support systems (e.g. online analytical processing systems), the emphasis is on the representation and processing of procedural knowledge.

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‘‘ Neither is there a barrier that differentiates information from knowledge, nor can the terms knowledge and information be used interchangeably. ’’

Computer-based technology can support decisional activity in many ways, taking over knowledge work otherwise performed by human processors (e.g. staff assistants, middle managers). Advantages of decision support technology lie in the directions of speed, scale, reliability, and cost improvements for the knowledge work involved in decision-making. This knowledge work includes problem recognition, pattern discovery and interpretation, knowledge selection (e.g. filtering, screening, navigating, scanning), knowledge derivation (e.g. deriving forecasts, plans, designs, or recommendations relevant to a decision situation), problem solving (e.g. quantitative and qualitative analysis, synthesis), knowledge assimilation, knowledge acquisition, knowledge reuse, and knowledge distribution. In the case of a multiparticipant decision maker, such as a group or organization, a decision support system additionally may perform such functions as routing messages among participants, managing the public versus private knowledge stores, or enforcing knowledge workflows during the decision process. Research support systems aid investigative rather than decisional tasks. Instead of aiming to produce knowledge about what to do, their users seek to assemble and synthesize knowledge about what is, what works, or what could be. For instance, genealogical research is a widespread activity concerned with arriving at a knowledge of ancestry and descendents across many generations. Without engaging in such research, many persons would be unable to state the surnames of their ancestors for more than a couple of generations, much less the details of their lives, accomplishments, and difficulties in the historical contexts in which they lived. Genealogical researchers have an interest in knowing about such heritage, perhaps to better appreciate personal (or regional) histories and relations to current circumstances. Research support systems for genealogy include those that are oriented toward assimilation of pedigree and personal history knowledge as it is found or deduced, and corresponding selection of any desired portion of that archive for subsequent review. This can involve complex, directional network structures with nodes representing hundreds or thousands of related persons, plus many historical dimensions for each node. The volume and intricacy such knowledge is typically well beyond what most researchers could reliably commit to memory and makes paper documentation burdensome when it comes to knowledge selection. Other genealogical research support systems are oriented toward acquiring knowledge. Some of these are web-based repositories with census images, historical directories, biographical sketches, and records relating to births, marriages, deaths, courts, real estate, and cemeteries. In the pre-web era, such access generally required physical searches through bound volumes and microfilm in geographically dispersed courthouses and libraries. Genealogical forums (organized by surname or geography, for instance) comprise another CBT tool for acquiring and sharing knowledge, one that really had no effective prior counterpart. Through this technology, researchers who have complementary knowledge are able to identify each other and share what they know, resulting in new leads to investigate that would never have arisen otherwise. This technology has been a tremendous boon to these knowledge building efforts. Similarly, many CBTs form the essential backbone of communities of interest and communities of practice. Such communities are multipaticipant research efforts that seek to

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jointly build knowledge (e.g. lessons learned) about some phenomenon. Without modern technology, community efforts remain largely local and small in scale. It follows that KM researchers and practitioners need to be attentive to and contribute to designing the features and functions of these systems. After all, their sole purpose is to enable and facilitate KM activity that would not otherwise exist on a substantial scale.

Implications The modern knowledge worker is immersed in a social environment populated by other knowledge workers having various specialized knowledge and/or special skills for processing that knowledge, and in a technological environment populated by interconnected computer-based processors having access to digital representations of certain knowledge coupled with skills for making use of those representations. Of relatively recent vintage, this technological component of the knowledge worker’s world requires organizations to substantially alter their approaches to organizing work, fostering interactions, representing knowledge, processing those representations (e.g. in the course of solving problems), and taking action. Failure to do so jeopardizes the organization’s survival in a rapidly changing, highly competitive world, regardless of how an organization handles the social aspects of KM. The proposition that modern knowledge management is inseparable from a consideration of technology is compelling. We really cannot fully appreciate KM practices and possibilities without paying attention to technology, to the users of that technology, and to the impacts of that technology. If we were to eliminate technology from consideration, then modern knowledge management is gutted. Traditional KM success stories such as Buckman Labs’ K’Netix and Ernst & Young’s Ernie would disappear. They are technologically based. Moreover, technologies such as computer-mediated communication, computer-supported cooperative work, databases, digital documents, search engines, web crawlers, solvers and spreadsheets for deriving knowledge, text mining, data mining, pattern recognition in general, and all of the rest are out of bounds (or, at most, perhaps on the fringes of the knowledge management world). Instead, we inhabit a world in which computer-based technology has tremendously enriched knowledge management, in which technological advances will continue to do so, and in which technology is becoming increasingly important in KM efforts that aim to keep an organization competitive. A recent study asked CEOs and CKOs about the degree to which computer-based technology for performing and supporting the nine knowledge chain activities has yielded a competitive advantage for their organizations (Singh, 2000). For each of the primary knowledge chain activities, between 40 percent and 65 percent of respondents said yes, the practice of using technology in this knowledge management activity makes a substantial contribution to our organization’s competitive advantage. For each of the secondary knowledge chain activities, between 25 percent and 40 percent of the respondents said yes, it makes a significant contribution. For the secondary activities, they were asked about what they envisioned by 2005. That range jumped to 46-63 percent who saw technology as a key for competitive advantage through the particular KM activities. If we adopt the position that computer-based technology is inseparable from modern knowledge management, what are the messages and opportunities for CBT researchers? First of all, and perhaps most important, there is the message that CBT researchers are KM researchers, whether they recognize it or not. This is particularly the case for researchers concentrating on developing and studying the role of technology in business, management, and organizations. We do not simply design, develop, and deploy technology for its own sake, but rather because it helps us deal with knowledge in its various gradations, and of various types, along the way to better organizational and individual performance. In this effort to build such systems, it is useful to appreciate the broader KM context in which we operate. It may well be that doing so will add greater value to the work of business computing researchers. With such an appreciation, it also becomes clear that KM is far from a mere renaming of ‘‘information systems’’ (IS); rather it is the enveloping domain in which IS is indispensable and in which IS finds its non-technological footing and basis.

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Second, there are opportunities for researchers to improve on existing technologies for knowledge handling. These include enabling or facilitating the knowledge flows among knowledge processors, be those processors human or computer-based. They include technologies for supporting and performing knowledge manipulation tasks, for acquiring knowledge from the outside, for selecting knowledge from the inside, for knowledge generation (derivation and discovery), for distributing knowledge, for assimilating knowledge, and so forth. Another area is technologies for assisting in the measurement, the control, the coordination, and maybe even the leadership of knowledge and knowledge processors, again be they human or computer-based. Yet another approach is to devise improvements in technologies for making the right knowledge (be it descriptive, procedural, reasoning, or some combination thereof) available to the right processors (be they human, computer-based, some mix of these) in the right format, at the right time, for the right cost. A third area where CBT research can add value is to address the question of better understanding the users and the usage of technologies in knowledge management. What works, and under what conditions? Why does a particular technology not work well for KM? Why is it not helpful? What technology advances and breakthroughs can be achieved by spotting opportunities suggested through as understanding of the broader KM realm? How do we cultivate good fits between technological infrastructure and organizational infrastructure in the context of knowledge-based organizations? Finally, to advance the KM field, researchers need to study outcomes of using technology for knowledge management. What are its competitive impacts? How exactly can a particular technology be used in order to achieve a competitive advantage for one of the knowledge chain activities, perhaps by contributing to productivity, or by helping in agility, or by fostering greater innovation, or by enhancing reputation?

Conclusion While modern knowledge management and computer-based technology are inseparable, there most certainly is more to KM than technology. There are people, organizations, knowledge-based tasks, and their fits with technology. However, the existence of some non-technological factors does not mean that we should ignore or dismiss technology from consideration in KM research or KM practice. To do so would not only overlook many accomplishments to date, but, more importantly, would discard an important part of KM’s potential for the future. CBT and IS researchers must avoid the tendency to simply rename what they have been doing to call it knowledge management. It is fine to recognize that their work can contribute to the advance of KM and to intentionally aim at making such contributions. But, such efforts should be grounded in solid comprehension of major concepts, issues, and experiences in this very interdisciplinary field. KM must be recognized as a reference discipline for CBT in general and IS research in particular.

References Bennet, A. and Bennet, D. (2003), ‘‘The rise of the knowledge organization’’, in Holsapple, C. (Ed.), Handbook on Knowledge Management, Vol. 1, Springer, Berlin. Bonczek, R., Holsapple, C. and Whinston, A. (1981), Foundations of Decision Support Systems, Academic Press, New York, NY. Galliers, R.D. and Newell, S. (2003), ‘‘Back to the future: from knowledge management to the management of information and data’’, Information Systems and E-Business Management, Vol. 1 No. 1, pp. 5-13. Holsapple, C. (1995), ‘‘Knowledge management in decision making and decision support’’, Knowledge and Policy, Vol. 8 No. 1, pp. 5-22. Holsapple, C. (2003), ‘‘Knowledge and its attributes’’, in Holsapple, C. (Ed.), Handbook on Knowledge Management, Springer, Berlin. Holsapple, C. and Joshi, K. (2004), ‘‘A formal knowledge management ontology’’, Journal of the American Society for Information Science and Technology, Vol. 55 No. 7, pp. 593-612.

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Holsapple, C. and Singh, M. (2000), ‘‘Toward a unified view of electronic commerce, electronic business, and collaborative commerce: a knowledge management approach’’, Knowledge and Process Management, Vol. 7 No. 3, pp. 151-64. Holsapple, C. and Whinston, A. (1987), ‘‘Knowledge-based organizations’’, The Information Society, Vol. 5 No. 2, pp. 77-90. Holsapple, C. and Whinston, A. (1988), The Information Jungle: A Quasi-Novel Approach to Managing Corporate Knowledge, Dow Jones, New York, NY. Holsapple, C. and Whinston, A. (1996), Decision Support Systems – A Knowledge-based Approach, West Publishing, St Paul, MN. Huang, K. (1998), ‘‘Capitalizing on intellectual assets’’, IBM Systems Journal, Vol. 37 No. 4, pp. 570-83. Machlup, F. (1980), Knowledge: Its Creation, Distribution, and Economic Significance, Vol. 1, Princeton University Press, Princeton, NJ. Newell, A. (1982), ‘‘The knowledge level’’, Artificial Intelligence, Vol. 18 No. 1, pp. 87-127. Paradice, D. and Courtney, J. (1989), ‘‘Organizational knowledge management’’, Information Resources Management Journal, Vol. 2 No. 3, pp. 1-13. Russell, B. (1948), Human Knowledge, Simon & Schuster, New York, NY. Ryle, G. (1949), The Concept of Mind, Hutchinson, London. Scheffler, I. (1965), Conditions of Knowledge, Scott Foresman, Chicago, IL. Singh, M. (2000), ‘‘Toward a knowledge management view of electronic business: introduction and investigation of the knowledge chain model for competitive advantage’’, unpublished PhD dissertation, University of Kentucky, Lexington, KY. Tiwana, A. (2000), The Knowledge Management Toolkit: Practical Techniques for Building a Knowledge Management System, Prentice-Hall, Upper Saddle River, NJ. Tsui, E. (2003), ‘‘Tracking the role and evolution of commercial knowledge management software’’, in Holsapple, C. (Ed.), Handbook on Knowledge Management, Vol. 2, Springer, Berlin. Van Lohuizen, C. (1986), ‘‘Knowledge management and policymaking’’, Knowledge: Creation, Diffusion, Utilization, Vol. 8 No. 1, pp. 12-38. Wilson, T.D. (2002), ‘‘The nonsense of ‘knowledge management’’’, Information Research, Vol. 8 No. 1.

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Understanding computer-mediated interorganizational collaboration: a model and framework Lei Chi and Clyde W. Holsapple

Abstract Purpose – To develop a process model of interorganizational systems (IOS) collaboration and systematic framework for understanding and classifying IOS technologies for interorganizational collaboration. Design/methodology/approach – This paper synthesizes relevant concepts and findings in the IOS, economics, and management literature. It also presents empirical examples to illustrate key issues, practices, and solutions involved in IOS collaboration. Lei Chi and Clyde W. Holsapple are in the Decision Science and Information Systems Area, School of Management, Gatton College of Business and Economics, Lexington, KY, USA ([email protected]) ([email protected]).

Findings – An integrative model of IOS collaboration is introduced and knowledge sharing, participative decision making, and conflict governance identified as three behavioral process elements underlying effective interorganizational collaboration. Extending Kumar and van Dissel’s IOS framework to directly recognize these elements, a more complete collaboration-oriented framework for characterizing key elements of interorganizational collaboration and classifying IOS technologies is developed. Research limitations/implications – This paper brings together diverse ideas into a systematic view of collaboration via interorganizational systems. It contributes to a deeper, fuller understanding of issues involved in achieving collaborative advantage with IOS technologies. The paper also identifies factors and relationships that researchers should consider in designing empirical studies, posing hypotheses about collaboration via IOS, and analyzing results. Practical implications – The model and framework can serve as a check-list of considerations that need to be dealt with by leaders of collaboration-oriented IOS initiatives. The IOS framework and technology classification may also suggest ways in which IT vendors might provide better technological solutions, services, and software for interorganizational collaboration. Originality/value – This new IOS collaboration model and framework provide more complete and useful guidance for researchers, educators, and practitioners. Keywords Knowledge management, Organizations Paper type Conceptual paper

1. Introduction Interorganizational systems (IOS) have captured increasing interest of researchers and practitioners since Kaufman’s (1966) visionary arguments about extra-corporate systems and computer time sharing. By providing the electronic infrastructure for sharing task performance between firms, these systems have opened avenues to collaborative knowledge work in several directions. They have fostered a new set of organizational design variables, such as shared repositories of knowledge, real-time integration of interrelated business processes, electronic communities that foster learning and allow multiple relationships to occur simultaneously, and virtual organizations that enable dynamic assembly of complimentary resources and skills among the collaborating firms (Strader et al., 1998). Early examples of successful IOS users provided strong evidence that aggressive pursuit of new possibilities for joint performance improvement through IOS can be an important source of sustainable competitiveness (Johnston and Vitale, 1988). IOS can reduce the cost of communication while expanding its reach (time and distance), increase the number and

DOI 10.1108/13673270510582965

VOL. 9 NO. 1 2005, pp. 53-75, Q Emerald Group Publishing Limited, ISSN 1367-3270

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quality of alternatives while decreasing the cost of transactions, enable tight integration between firms while reducing the cost of coordination (Malone et al., 1987). They can also facilitate knowledge sharing and trust building (Holland, 1995; Li and Williams, 1999; Gallivan and Depledge, 2003), speed up expertise exploitation and knowledge application (Migliarese and Paolucci, 1995; Christiaanse and Venkatraman, 2002), and enhance innovation and knowledge generation (Thomke and von Hippel, 2002). Thus, by increasing competitive bases in achieving efficiency, flexibility, innovation, quality, and speed, IOS comprise an important class of knowledge management technology that offers significant opportunities for improving economic performance and competitiveness of many companies. To more fully realize the potential of integrating this interorganizational knowledge management technology with business processes and competitive strategies, a systematic study is needed to help identify innovative inter-firm applications based on IOS and identify key factors in facilitating effective collaboration via IOS. Most existing studies on IOS are based on anecdotes, personal opinions, and experiences rather than on systematic research studies (Venkatraman and Zaheer, 1994). They are fragmented regarding the uses and impacts of IOS, and largely focus on the roles of IOS as competitive weapons for achieving power and efficiency. Furthermore, underlying many studies is the assumption that humans produce errors while automation produces reliability. These studies view IOS as technologies designed and implemented to automate the relationships between firms. They largely fail to acknowledge the part human ingenuity plays in the work practice and the importance of learning (Sachs, 1995). Therefore, these studies provide limited understanding of the relationship between IOS and the knowledge-intensive phenomenon of interorganizational collaboration. Many innovative opportunities of exploiting IOS potential for learning and mining the funds of knowledge across organizations for greater competitiveness are likely to be overlooked. As such, this paper introduces a model and framework that more fully address the following questions faced by leaders of knowledge management initiatives and by researchers of knowledge management phenomena: B

What are the key elements underlying effective interorganizational collaboration among IOS participants?

B

How can IOS be classified to facilitate an understanding of collaboration? What are characteristics and candidate implementation technologies for each type of IOS?

B

What are key issues that a knowledge manager needs to address in IOS-based collaboration? How can these issues be addressed to enhance the processes and outcomes of this collaboration?

As a step toward answering these questions, this paper synthesizes relevant concepts and findings in the IOS, economics, and management literature to develop a process model of IOS collaboration and systematic framework for understanding and classifying IOS technologies for interorganizational collaboration. The rest of this paper is organized as follows: section 2 defines IOS as a class of knowledge management technology for fostering interorganizational collaboration; section 3 introduces a model of IOS collaboration and identifies key elements underlying effective interorganizational collaboration processes; section 4 uses these elements to extend a framework by Kumar and van Dissel (1996) for classifying IOS, resulting in a more fully developed collaboration-oriented framework; and sections 5 briefly discusses contributions and implications of this research for researchers, practitioners, and educators.

2. Defining IOS In 1966, Kaufman implored general managers to think beyond their own organizational boundaries and to explore the possibilities of extra-corporate systems for linking buyers and sellers or companies performing similar functions. Kaufman convincingly argued that these extra-corporate systems could greatly increase the efficiency of business operations and

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‘‘ In the broadest sense, an IOS consists of computer and communications infrastructure for managing interdependencies between firms. ’’

enhance cooperation between firms through time sharing. In 1982, Barret and Konsynski (1982) used the term ‘‘interorganizational information sharing systems’’ to describe such systems. In 1985, Cash and Konsynski (1985) clearly defined the concept of ‘‘interorganizational systems’’ (IOS) as ‘‘automated information systems shared by two or more companies.’’ Some well-known examples of IOS are American Airlines’ SABRE reservation system, American Hospital Supply’s ASAP system, the CFAR system between Wal-Mart and Warner-Lambert, and Cisco’s eHub. In the broadest sense, an IOS consists of computer and communications infrastructure for managing interdependencies between firms. From a knowledge management perspective, this infrastructure enables and facilitates knowledge flows among organizations (and their participating representatives) such that the needed knowledge gets to the relevant participants on a timely basis in a suitable presentation(s) in an affordable way for accomplishing their collaborative work. An IOS may involve one or more technologies, ranging from an electronic funds transfer system for data transmission to a collaborative CAD/CAM tool to a groupware system for joint product design. In recent years, rapid advancements in computer and communications technologies have made feasible many new applications of IOS that are greatly increasing the potential of effective inter-firm collaboration. For instance, groupware encompasses previously considered independent technologies (e.g. messaging, conferencing, collaborative authoring, workflows and coordination, and group decision support) and has arisen to support dynamic business processes involving communication, coordination, and cooperative work (Freed, 1999). The internet integrates technologies of the world wide web (hypertext transportation protocol (HTTP)), telnet, file transfer protocol (FTP), network news (network news transfer protocol (NTTP)), internet relay chat (IRC), and e-mail (simple mail transport protocol (SMTP); internet message access protocol (IMAP)). It provides high flexibility for quick electronic access to external data and linkages to potential customers and partners around the world (Strader et al., 1998). An extranet combines the advantages of the internet (global access) with those of local area networks (security, easy management of resources, and client/server functionality). Based on internet technology and protocols, an extranet provides information in a way that is immediate, cost-effective, easy to use, rich in format, versatile, and secure over a private network (Strader et al., 1998). Peer-to-peer (P2P) communication, by allowing users to bypass central exchanges and exchange information directly with one other, provides a promising alternative to the conventional client/server model. Compared to the client/server model, P2P may significantly reduce the complexity and expense of networking (McAfee, 2000). In addition, P2P networks have no bounds, while membership in the client/server model is limited. Thus, P2P may provide solutions to the potential communication overflows that restrict the communication capabilities of most current network communities (Yoshida et al., 2003). Wireless communication uses wireless devices, sensors, positioning locators, and networks to allow real-time communication with anyone at any time, anywhere. Radio frequency identification (RFID), global positioning systems (GPS), voice e-mail, enhanced specialized mobile radio (ESMR), and MicroBurst wireless are some of the available wireless technologies that may have important implications for the collaborative area of supply chain management (Shankar and O’Driscoll, 2002).

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Extensible markup language (XML) has quickly arisen as a standard data representation format. Being fully Unicode compliant, XML will greatly enhance EDI’s ability with its extensibility, platform-independence, and support for a universal data access. Simple object access protocol (SOAP) uses XML technologies to define an extensible message framework that allows structured information to be exchanged over a variety of underlying protocols and programming models in a decentralized, distributed environment. Web services description language (WSDL) defines an XML-based grammar for describing network services as a set of endpoints that accept messages containing either document-oriented or procedure-oriented knowledge. WSDL is extensible to allow the description of endpoints and their messages regardless of what message formats or network protocols are being used to communicate. Universal description, discovery, and integration (UDDI) defines a SOAP-based web service for locating WSDL-formatted protocol descriptions of web services (MSDN Library – msdn.microsoft.com/library). XML, SOAP, WSDL, and UDDI will provide a foundation for companies to have real-time access to structured and semi-structured knowledge resources around the globe.

3. A model of IOS collaboration Through an examination of the IOS literature, we identify eight distinct and critical motives[1] underlying an organization’s use of IOS: necessity, asymmetry, reciprocity, efficiency, agility, innovation, stability, and legitimacy. We contend that the leader of an knowledge management initiative contemplating or implementing IOS technology needs to carefully consider which of these motives are applicable to his/her situation, how they relate to relational bonding and behavioral processes, and what are their impacts on collaborative advantage: B

The necessity motive: an organization adopts the use of an IOS in order to meet necessary legal, regulatory, or deregulatory requirements from higher authorities (e.g. government agencies, legislation, industry, or professional regulatory bodies) that otherwise might not have been used voluntarily (as in the case of US Department of Transportation regulation in 1987 exemplified by Christiaanse and Venkatraman, 2002, and the case of London Stock Exchange’s Big Bang in 1986 studied by Clemons and Weber, 1990).

B

The asymmetry motive: an organization is prompted to use an IOS for purposes of exerting power or control over other organizations (Kling, 1980; Webster, 1995; Iacovou et al., 1995; Reekers and Smithson, 1995; Hart and Saunders, 1997).

B

The reciprocity motive: an organization uses an IOS in order to pursue common or mutually beneficial goals or interests and to facilitate collaboration, trust building, and coordination (Holland, 1995; Ferrat et al., 1996; Kumar et al., 1998; Pouloudi, 1999).

B

The efficiency motive: an organization is motivated to use an IOS in an attempt to improve both its internal efficiency and its interorganizational efficiency (Malone et al., 1987; Johnston and Vitale, 1988; Konsynski and McFarlan, 1990; Clemons and Row, 1991).

B

The agility motive: an organization is prompted to use an IOS to increase agility and responsiveness to environmental changes (Rockart and Short, 1991; Zaheer and Zaheer, 1997).

B

The innovation motive: an organization is induced to use an IOS for purposes of innovation and value creation (Strader et al., 1998; Bowker and Star, 2001; May and Carter, 2001; Thomke and von Hippel, 2002).

B

The stability motive: an organization is prompted to use an IOS in order to reduce environmental uncertainty and to achieve stability, predictability, and dependability in its relations with others (Li and Williams, 1999).

B

The legitimacy motive: an organization is motivated to use an IOS to increase its legitimacy and reputation in order to appear in agreement with prevailing norms, beliefs, expectations of external constituents, or prevalence of a practice in the industry (Teo et al., 2003).

Although each of the eight motives may be a separate and sufficient cause for an organization’s IOS adoption, the decision to use IOS is commonly based on multiple motives.

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‘‘ Effective knowledge sharing can promote understanding, suppress opportunistic behaviors, and induce commitment and trust among partners, thus leading to greater collaboration. ’’

Furthermore, these eight motives are likely to interact with each other. Certain motives will become dominant under favorable conditions and be suppressed under unfavorable conditions. For example, the underlying process of IOS use prompted by the asymmetry motive can be characterized by inequality, knowledge asymmetry, manipulation, coercion, or conflict. Under transparent knowledge sharing, participative decision making and effective governance for conflict resolution, the asymmetry motive is likely to be suppressed, while the reciprocity motive tends to become dominant. Concurrently, the reciprocity motive may interact with certain other motives. For example, when cooperative use of an IOS is also expected to lead to the fulfillment of other organizational requirements and expectations (e.g. higher levels of efficiency or productivity, greater agility, greater innovation, greater stability, or greater legitimacy or reputation), cooperative behaviors and collaboration will be more likely. Based on the interaction among the eight motives, we introduce the model of IOS collaboration depicted in Figure 1. An organization may be prompted to use an IOS under certain motives (e.g. stability). When such behavioral processes as transparent knowledge sharing, shared decision making, and effective governance for conflict resolution are promoted among IOS participants, cooperative behaviors are likely to be induced and prevail. These cooperative behaviors tend to interact with an organization’s effort to develop stable and reliable relations. Power plays are likely to be suppressed in the hopes that equity, reciprocity, and harmony will facilitate stability. As a result, trust and commitment will increase among the partners. Increased trust and commitment in turn will facilitate the processes of knowledge sharing, participative decision making and conflict resolution, which further enhances trust and commitment of the participants and ultimately yields better joint performance. Performance outcomes for knowledge-intensive work can be gauged in several ways: productivity, agility, innovation, and reputation (Holsapple and Singh, 2001). Collectively, improvements on these four dimensions are avenues for collaborative advantage. Therefore, the model asserts that knowledge sharing, shared decision making, and conflict governance are three key elements underlying effective interorganizational collaboration. By fostering trust and suppressing power plays, they not only can buttress the motivations of

Figure 1 A model of IOS collaboration

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‘‘ Performance outcomes for knowledge-intensive work can be gauged in several ways: productivity, agility, innovation, and reputation. ’’

organizations to collaborate via IOS, but also make for sustainable collaborative advantage. We next use these three elements in developing a collaboration-oriented IOS framework.

4. IOS frameworks The forgoing model identifies several variables (and their relationships) that will need to be managed or addressed to improve the chance of success for an IOS-based knowledge management effort aimed at interorganizational collaboration. It is also important for the leader of such an effort to have a framework for appreciating the nature and possibilities of IOS options in accomplishing this knowledge work. 4.1 Limitations of Some IOS Frameworks Several frameworks for understanding interorganizational systems have been proposed. For example:

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B

Barret and Konsynski (1982) classify IOS into five levels based on an increasing degree of the participant’s responsibility, cost commitment and complexity of operating environment. At level 1, a firm only serves as a remote input/output node for one or more higher level nodes. Level 2 participants design, develop, maintain, and share a single system such as inventory query system. Level 3 participants develop and maintain a network linking itself and its direct business partners. Level 4 participants develop and share a network with diverse application systems that may be used by many different types of participants. At level 5, any number of lower-level participants may be integrated in real time over complex operating environments.

B

Johnston and Vitale (1988) propose a framework using three dimensions: business purpose, relationships with participants, and information function. Business purpose indicates why an IOS is needed; it could be either to leverage present business or to enter a new information-driven business. Relationships refer to those participants linked by an IOS; they could be customers, dealers, suppliers, or competitors. Information function is concerned with the functionality that an IOS is intended to perform; it may handle boundary transactions, retrieve and analyze shared information, or be designed to manipulate information as part of ‘‘back office’’ operations in the participants’ organizations.

B

Meier and Sprague (1991) classify IOS into three categories: ordering systems that connect a manufacturer with its suppliers or a retailer with its customers; electronic markets that substitute the traditional means of trading with the electronic means of trading; and online information dissemination systems.

B

Hong (2002) classifies IOS into four types based on the role linkage (vertical vs horizontal) and the system support level (operational vs strategic) of the IOS participant: resource pooling, operational cooperation, operational coordination, and complementary cooperation. A resource pooling IOS links participants to perform common value activities in order to permit risk/cost-sharing by pooling resources. A complementary cooperation IOS represents a form of cooperation between firms playing different roles in an industry value chain. An operational cooperation IOS brings together firms in a common value chain primarily to improve the quality of customer service or to share information of common interest. An operational coordination IOS is used to link different roles of participants serving an industry value chain to increase operational efficiency.

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Although these frameworks enhance an understanding of the uses and impacts of IOS, they do not focus on IOS collaboration: B

According to the American Heritage Dictionary (1997), collaboration is defined as working together, especially in a joint intellectual effort. Working together implies managing interdependencies among participants toward some common end. Joint intellectual effort recognizes that collaboration is a knowledge management episode comprised of knowledge flows among participants who process knowledge resources in various ways and under various influences in pursuit of the common end (Holsapple and Joshi, 2000). Together with the three elements identified in the collaboration model in Figure 1, we thus contend that a good understanding of IOS collaboration requires the examination of four elements: managing interdependencies among knowledge processors, knowledge sharing, participative decision making, and conflict governance. However, these elements are not explained sufficiently by any of the above IOS frameworks.

B

The above frameworks tend to focus on the roles of IOS as competitive weapons for achieving power and efficiency. For example, Johnston and Vitale’s framework (1988) advances the concept of competitive advantage to explain the emergence and impact of IOS. It regards IOS as instruments that, by locking in customers and dominating suppliers, increase an organization’s bargaining power over them. The framework suggests that, in the drive to optimize its self-interest, the objective of an organization is to minimize its dependence on other organizations while maximizing the dependence of other organizations on itself (Kumar and van Dissel, 1996). Thus, such frameworks appear to be inconsistent with the spirit of interorganizational collaboration.

B

Furthermore, underlying these studies is the assumption that humans produce errors while automation produces reliability. These studies view IOS as technologies designed and implemented to automate the relationships between firms. They fail to acknowledge the part human ingenuity plays in the work practice and the importance of learning (Sachs, 1995). The work is viewed as a process flow or the sequence of tasks in operations that can be structured or coded, whereas the tacit, less structured learning process whereby people discover and solve problems is omitted. In this regard, many innovative opportunities of performance improvement by exploiting IOS potentials for learning and utilizing knowledge resources distributed across organizations are likely to be overlooked.

Kumar and van Dissel (1996) propose a framework that classifies IOS based on Thompson’s (1967) typology of interorganizational interdependencies. As described in section 4.2.2 below, by highlighting IOS’s role in managing inter-firm dependencies and stressing trust building through reducing potential conflicts for sustained collaboration, Kumar and van Dissel’s framework addresses some of the limitations of the above frameworks and provides a good basis for our collaboration-oriented IOS framework introduced here. 4.2 A Collaboration-oriented IOS framework The collaboration-oriented IOS framework is summarized in Table I. It adopts Kumar and van Dissel’s (1996) notions of using IOS for managing pooled, sequential, and reciprocal interdependencies. It also incorporates the IOS collaboration model’s three behavioral processes: knowledge sharing, participative decision making, and conflict governance, and expands the classification of IOS technologies based on practices for enhancing collaboration. We now explain the framework in detail. 4.2.1 Assumptions Three assumptions underlie the framework. First, organizations are assumed to make conscious, intentional decisions as to whether to use and how to use IOS for specific reasons within the constraints of a variety of conditions that limit or influence their choices. Second, IOS collaboration is viewed from an organizational (top-management) perspective, even though an IOS may be used between subunits or individuals in the collaborating organizations. An organizational perspective is assumed throughout the paper. Third,

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Table I A collaboration-oriented IOS framework Type of interdependency

Pooled interdependency

Sequential interdependency

Reciprocal interdependency

Configuration Coordination mechanisms

Standards and rules

Standards, rules, schedules and plans Medium Intermediate

Standards, rules, schedules, plans and mutual adjustment Low Highest

Value/supply-chain IOS

Networked IOS

Structured Semi-structured

Structurability Amount of direct human interaction

High Minimum

Type of IOS

Pooled knowledge resources IOSa Structured

Nature of knowledge exchanged Key issues in knowledge sharing

Design of interorganizational interfaces Compatibility Knowledge quality Privacy and confidentiality

Design of interorganizational interfaces Compatibility Knowledge quality Privacy and confidentiality Knowledge asymmetry

Key issues in decision making

Reduce uncertainty Inability to assimilate quality knowledge Loss of resource control Technological governance: Open standards; Industry-specific standards; Proprietary or company-specific standards

Reduce uncertainty Inability to assimilate quality knowledge Loss of resource control Technological governance: Open standards; Industry-specific standards; Proprietary or company-specific standards

Structured Semi-structured Unstructured Design of interorganizational interfaces Compatibility Knowledge quality Privacy and confidentiality Knowledge asymmetry Knowledge-sharing routines Reduce equivocality Inability to assimilate quality knowledge Share understanding Technological governance: Open standards; Industry-specific standards; Proprietary or company-specific standards

Business governance: Classical contracts; Institutional norms Reputation

Business governance: Neoclassical contracts; Institutional norms Reputation; Interpersonal trust ‘‘Codification’’

Business governance: Relational contracts; Institutional norms Reputation; Interpersonal trust ‘‘Personalization’’

Scheduling techniques Customer relationship management Supply chain management EDI systems Collaborative planning, forecasting and replenishment systems Workflow systems

CAD/CAM Collaborative authoring Calendaring systems Computer conferencing Threaded discussion Group decision support Organizational decision support

Governance mechanisms for conflict resolution

Focus of implementation technologies Examples of implementation technologies and systems

‘‘Codification’’ E-mail Fax Instant messaging Voice mail Electronic bulletin board FAQs Call center EFTPoS Web site Wireless device Peer-to-peer communication Broadband communications Intranet/extranet Internet Wireless networks EDI XML SOAP/WSDL/UDDI Databases, data warehouses Documents, archives Web browser Expert Finder tool Meta/web-crawler Taxonomy/ontological tools OLAP/simulation/modeling Data/text mining Intelligent agents Case-based reasoning Neural networks/genetic algorithm Rule engines

Note: aKumar and van Dissel (1996) refer to this type of IOS as ‘‘pooled information resources IOS’’ Source: Adapted from Kumar and van Dissel (1996, p. 287). Italicized areas indicate extensions introduced here

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knowledge sharing in this paper is considered in its broadest sense, including flows involved in knowledge transfer, knowledge generation, and/or related knowledge application. 4.2.2 Types of interdependencies and IOS classes Thompson (1967, pp. 54-55) distinguishes three different ways in which companies can be interrelated: (1) Pooled interdependency. (2) Sequential interdependency. (3) Reciprocal interdependency. In pooled interdependency, companies share and use common resources; ‘‘each renders a discrete contribution to the whole and each is supported by the whole’’ (e.g. the use of a common data processing center by a number of firms). Sequential interdependency refers to the situation where companies are linked in a chain with direct directional and well-defined relations, where the outputs from one task processor become inputs to another (e.g. the customer-supplier relationship along a supply chain). Reciprocal interdependency describes a relationship where each company’s outputs become inputs for the others (e.g. a concurrent engineering team consisting of customers, suppliers, distribution centers, dealers, shippers, and forwarders) (Thompson, 1967; Kumar and van Dissel, 1996). Pooled interdependency involves minimal direct interaction among the units, and coordination by standardization is appropriate. Sequential interdependency involves an increasing degree of contingency because each position in the chain must be readjusted if an upstream position fails to fulfill its expectation, and coordination by plan is appropriate. Reciprocal interdependency involves the highest degree of interaction because actions of each position in the set must be adjusted to the actions of many interacting positions, and coordination by mutual adjustment is needed (Thompson, 1967; Kumar and van Dissel, 1996). In correspondence with pooled interdependency, sequential interdependency, and reciprocal interdependency, Kumar and van Dissel (1996) suggest a three-part typology for IOS: (1) Pooled information resources IOS. (2) Value/supply-chain IOS. (3) Networked IOS. They regard interorganizational systems as technologies designed and implemented to operationalize the interorganizational relationships. They assume that the structure of the relationship influences the degree to which the relationship can be programmed and embedded in the IOS. (1) Pooled information resources IOS involve interorganizational sharing of a technological system, such as common repositories (e.g. databases, digital archives), common communication networks (e.g. internet, extranet, broadband networks), common communication protocols and standards (e.g. EDI, XML), common application systems (e.g. data/text mining systems), and electronic markets which may include some combinations of common databases, common application procedures and software, and/or common communications infrastructure. Here, we suggest that extending the notion of pooled information resources IOS to pooled knowledge resources IOS allows for a better understanding of IOS collaboration. We thus use the term of pooled knowledge resources IOS in the extended collaboration-oriented IOS framework. For instance, the Amico Library (www.amico.org) is an internet-based archive with digital copies of more than 100,000 paintings, sculptures and photographs initiated and shared by 39 museums from goliaths like the Metropolitan Museum of Art to smaller institutions like the Newark Museum (Mirapaul, 2003). The National Virtual

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Observatory is another initiative to create a common internet-based cosmic database for nation-wide collaboration in astronomy. The project is building sophisticated data/text mining systems and intelligent searching tools, and is creating an Internet-based registry of astronomical resources (Schechter, 2003). Another example of pooled knowledge resources IOS is Cisco’s eHub. eHub is a private electronic marketplace where participants share an extranet infrastructure that uses XML standards, and a central repository that pools together supply chain information for planning and executing tasks (Grosvenor and Austin, 2001). (2) Value/supply-chain IOS support structured and semi-structured customer-supplier relationships, which are likely to be coded and implemented through automation, and institutionalize sequential interdependency between organizations along the value/supply chain. The Collaborative Forecasting and Replenishment (CFAR) project initiated in 1995 presents an example of value/supply-chain IOS between Wal-Mart store and Warner-Lambert (now part of Pfizer) for forecasting and replenishing pharmaceuticals and healthcare products. CFAR is an internet-based EDI system that allows both companies to jointly create sales forecasts that include information, such as expected alterations to store layouts and meteorological information (King, 1996). Wal-Mart is also testing a wireless supply chain system with its suppliers including Pepsi, Bounty and Gillette. Wal-Mart uses radio frequency identification (RFID) to track shipments of Pepsi soft drinks, Bounty paper towels, and Gillette razors at a Sam’s Club store in Tulsa, OK, from manufacturer to warehouse to store to checkout counter. The process is illustrated in Figure 2. Information from RFID tags on each item in a Wal-Mart store goes into Wal-Mart’s 101-terabyte sales transaction database. Thus, suppliers can get a real-time view of what is happening at the store shelf level (Shankar and O’Driscoll, 2002). (3) Networked IOS operationalize and implement reciprocal interdependencies between organizations. Networked IOS provide a shared virtual space where people collaborate for emerging relationships and learning (Nonaka and Konno, 1998). They focus on supporting informal exchange of semi-structured or unstructured knowledge, which sometimes cannot be described as a business process, such as posting a question on the electronic bulletin board, asking an expert for a solution, and directly contacting customer to elicit needs or problems.

Figure 2 Wal-Mart’s wireless supply chain system for tracking and replenishment

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ComputerLink gives an example of the networked IOS. ComputerLink is a community health information network built in Cleveland for Alzheimer’s caregivers. ComputerLink involves using the internet, an electronic bulletin board, a decision support system, as well as e-mail and electronic encyclopedia facilities to provide clinical and financial services, and deliver just-in-time knowledge among patients, physicians, hospitals, clinics, and home health agencies. The e-mail facility allows individual users to communicate anonymously with a nurse-moderator and other Alzheimer’s caregivers. The nurse-moderator serves as technical liaison by providing systems and health support to ComputerLink users while maintaining all encyclopedia functions related to Alzheimer and care giving. The decision support system guides users through a myriad of scenarios allowing self-determined choices based on personal values. The bulletin board enables users to communicate through an electronic support-group public forum (Payton and Brennan, 1999). The three types of IOS form a Guttman-type scale (Thompson, 1967). That is, value/supply-chain IOS may possess the characteristics of pooled knowledge resources IOS; and networked IOS are likely to possess characteristics of both value/supply-chain IOS and pooled knowledge resources IOS (Kumar and van Dissel, 1996). 4.2.3 Key issues in knowledge sharing Knowledge sharing is a key aspect of IOS collaboration, as discussed for the collaboration model shown in Figure 1. Effective knowledge sharing can promote understanding, suppress opportunistic behaviors, and induce commitment and trust among partners, thus leading to greater collaboration. Knowledge sharing is primarily determined by two factors: transparency, and receptivity (Hamel, 1991). For each of these factors, we discuss implications for the three IOS technology classes that deserve consideration by leaders of interorganizational knowledge management initiatives. 4.2.3.1 Knowledge sharing transparency. Transparency refers to the ‘‘openness’’ of an organization to its partners (Hamel, 1991). It can be influenced by the design of interorganizational interfaces[2] (Malone, 1985; Hamel, 1991). In addition, knowledge quality, privacy and confidentiality can also influence transparency. Pooled knowledge resources IOS. B

Design of interorganizational interfaces: in pooled knowledge resources IOS, the coordination structure in terms of the level of roles, obligations, rights, procedures, knowledge flows, as well as analysis and computational methods used, can be clearly specified and standardized (Kumar and van Dissel, 1996). The knowledge exchanged tends to be highly structured, such as product descriptions, customer characterizations, and transaction status. As such, interorganizational interfaces mostly can be designed as protocols, rules, and standards built in shared software, tools, and systems. The transparency of an organization regarding what knowledge to share with whom and how to share can be determined by the degree of ‘‘openness’’ inherent in the embedded protocols, rules, and standards.

B

Knowledge quality: in pooled knowledge resources IOS, one or more users of the ‘‘commons’’ may treat the common as a free dumping ground and contaminate the shared knowledge archives by dumping/depositing corrupt knowledge representations, or allowing non-standard or unedited transactions onto the network, or even worse, unintentionally or intentionally infesting the system with viruses (Kumar and van Dissel, 1996). These contaminations will degrade the knowledge quality of the ‘‘commons,’’ whose attributes such as validity and utility, are important for quality decision making (Holsapple, 1995) and transparent knowledge sharing. Contaminations can be controlled by designing and enforcing representations and access standards through technological governance mechanisms for security, virus-scan, and access control (Kumar and van Dissel, 1996). Additionally, defining and measuring key knowledge quality attributes, such as validity and utility, and aspects

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of each (Holsapple, 1995), and properly maintaining these quality-related measures as knowledge moves across systems and organizations is important (Madnick, 1995). B

Privacy and confidentiality: in pooled knowledge resources IOS, because the ‘‘commons’’ are a public resource among IOS users, some users may misuse the system by ‘‘poaching,’’ ‘‘stealing,’’ or ‘‘unauthorized snooping’’ (Kumar and van Dissel, 1996; Levitin and Redman, 1998). A user may collect and summarize contents from the entire archive; or monitor and analyze transactions over the common network to develop strategies for private use; or collect and infer confidential and private information about another firm’s customers through lookups in a shared archive or through monitoring online transactions and then luring selected customers away from the current suppliers. Such misuses of the ‘‘commons’’ can pose serious issues of privacy and confidentiality, thus impeding transparent knowledge sharing while increasing the potential for opportunistic behaviors and free riding among IOS users. Misuses of the ‘‘commons’’ can be controlled by imposing security mechanisms, such as software safeguards, access control, and transaction logs (Kumar and van Dissel, 1996). Additionally, fostering norms or spreading values among IOS users that encourage transparent knowledge sharing, may also provide effective governance against misusing the ‘‘commons’’.

Value/supply-chain IOS. In value/supply-chain IOS, roles and mutual expectations between adjacent parties in a value/supply chain can be structured. Structured interactions could range from tracking EDI-based orders, to looking up databases of adjacent partners in the chain for sales forecasting, to transferring CAD-based specifications from customers to suppliers (Kumar and van Dissel, 1996). The knowledge shared can range from structured data, such as ordering and customer data, sales data, and production and inventory data, to semi-structured representations, such as market research, category management, and cost-related descriptions (Simatupang and Sridharan, 2001). As such, interorganizational interfaces in value/supply-chain IOS also can be largely designed as protocols, rules, and standards embedded in the software, tools, and systems (e.g. automated workflow systems), determining the transparency of an organization in terms of what knowledge to share, how much to share, with whom to share, and how to share. Similar to a pooled knowledge resources IOS, knowledge quality and privacy and confidentiality in a value/supply-chain IOS also influence the transparency of an organization. In addition, knowledge asymmetry presents another issue influencing the ‘‘openness’’ of an organization in a value/supply-chain IOS. Knowledge asymmetry[3] refers to the situation where different players in a value/supply-chain IOS are likely to have different states of private knowledge about resources (e.g. capacity, inventory status, and funds), various data-related costs, chain operations (e.g. sales, production and delivery schedules), performance status, and market conditions. This knowledge asymmetry can lead to misunderstanding among chain members about their mutual efforts at collaboration. Because of their different roles, positions, and objectives in the chain, conflict and suboptimal decisions may result, such as unproductive allocation of resources (Lee and Whang, 2000; Simatupang and Sridharan, 2001). Asymmetric knowledge may also cause difficulties among chain members in dealing with market uncertainty. For example, when the downstream players poorly estimate or distort demand conditions, the upstream players may experience larger variance of customer demand, producing difficulties in managing genuine levels of production and inventory. This can also produce difficulties in designing products that might be desirable, especially for innovative products (Simatupang and Sridharan, 2001). Furthermore, asymmetric knowledge can inhibit transparent knowledge sharing and increase the potential for opportunism, either prior to the contract or after the contract (Molho, 1997). Adverse selection can occur before a contract is signed; it involves misrepresentation or concealment of true capability, resource, and demand conditions that need to be shared. Moral hazards can occur after a contract is signed; they involve

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providing misleading characterizations of performance status, lowering of service level efforts, and committing only a minimum level of resources. To reduce knowledge asymmetry, it is necessary to develop clear performance measures and also promote mutual interests and trust among IOS participants. Additionally, financial incentives, such as productive-behavior-based incentives, pay-for-performance, and equitable arrangements (Simatupang and Sridharan, 2001), may be employed to promote transparent knowledge sharing and discourage dysfunctional behaviors. The use of technology may also help increase control by facilitating performance measures and monitoring (Gallivan and Depledge, 2003). Networked IOS. With networked IOS, the form, direction, and content of the relationships among participants are much less structured than with the other two types of IOS (Kumar and van Dissel, 1996). Reciprocal relationships can be viewed as consisting of exchange processes and adaptation processes; exchange processes represent ‘‘the operational, day-to-day exchanges of an economic, technical, social, or informational nature occurring between firms;‘‘ adaptation involves the processes whereby firms adjust and maintain their relationships by modifying routines and mutual expectations (Kumar et al., 1998, pp. 215). A networked IOS thus involves an increasing degree of human interaction and requires mechanisms such as trust to identify, assess, and manage the dynamically occurring equivocality and risks in the situation. The nature of the knowledge exchanged can range from structured (such as product data), to semi-structured (such as reports about industry trends), to highly unstructured (such as expertise and know-how, problem-solving skills, and ideas about a new product design). As such, many parts of interorganizational interfaces in networked IOS, unlike those in the other two types of IOS, cannot be designed as built-in protocols, rules, and standards. Instead, human processors positioned at organizational boundaries tend to interface with each other, with the aid of IOS. Thus, the ‘‘openness’’ of those individuals can greatly influence the transparency of knowledge sharing, in addition to the embedded rules and protocols, knowledge quality, and privacy and confidentiality. This ‘‘openness’’ can be enhanced through nurturing knowledge sharing routines. Knowledge sharing routines can be viewed as regular patterns of interorganizational interactions that permit the transfer, application, or generation of specialized knowledge (Grant, 1996; Dyer and Singh, 1998). These routines are largely dependent on an alignment of incentives to encourage transparent knowledge sharing and discourage opportunistic behaviors and free riding (Dyer and Singh, 1998). Financial incentives or informal norms of reciprocity may be employed to promote mutual interests and highlight common goals (Lewis, 1990; Badaracco, 1991), thus motivating transparent knowledge sharing (Mowery et al., 1996; Dyer and Singh, 1998). Technological governance mechanisms for knowledge security and system security may also be employed (Venkatraman, 1991; Kumar and van Dissel, 1996) to discourage dysfunctional behaviors. 4.3.2.2 Knowledge sharing receptivity. Receptivity is also termed assimilative ability (O’Leary, 2003), or ‘‘partner-specific absorptive capacity’’ (Dyer and Singh, 1998). It refers to an organization’s ability to assimilate knowledge and skills from its partners. Receptivity involves ‘‘implementing a set of interorganizational processes that allow collaborating firms to systematically identify valuable know-how and then transfer it across organizational boundaries’’ (Dyer and Singh, 1998, p. 665). It is a function of the compatibility between partners and the absorptiveness of receptors/processors (Hamel, 1991). Compatibility. Across all three types of IOS, incompatibility and inconsistency can result from geographically and functionally dispersed business operations, as well as differences in work processes and underlying cultures of organizations. There may be different semantics for the same term, or different identifiers for key business entities, such as customer or product, or different schemes for aggregating key indicators, such as sales or expenses, or different ways of calculating key concepts, such as profit or return on investments (Goodhue et al., 1992; Madnick, 1995). In addition to knowledge incompatibility, there may also exist incompatibilities of technological infrastructure across organizational boundaries. These incompatibilities not

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only can thwart an organization’s ability to identify and transfer valuable knowledge to/from its IOS partners, but may also increase the potential for mistrust and conflict. To enhance compatibility, common technological standards and knowledge representations with standard definitions and codes need to be shared, and a common language for communicating about business procedures and events must be established across IOS users. Absorptiveness of receptors/processors. In pooled knowledge resources IOS, given a low level of direct human interaction, patterned interorganizational interactions are mostly between human processors and computer processors, or computer processors and computer processors. Thus, the absorptiveness of receptors/processors can be largely determined and enhanced by the use of knowledge technologies that focus on ‘‘codification’’ (Milton et al., 1999), or the ‘‘storing,’’ ‘‘massaging,’’ ‘‘structuring,’’ ‘‘integrating,’’ ‘‘filtering,’’ ‘‘navigating,’’ and retrieving of reusable knowledge assets from/to shared repositories (O’Leary, 2003). Examples of such technologies may include artificial intelligence tools, meta/web crawlers, and taxonomy/ontological tools (Tsui, 2003). Additionally, technologies that facilitate ‘‘discovery,’’ and ‘‘capture/monitor’’ (Milton et al., 1999) across organizational boundaries, such as data/text mining, could also provide effective means for increasing an organization’s receptivity (Upton and McAfee, 1996; Majchrzak et al., 2000; Tsui, 2003). In value/supply-chain IOS, contacts between chain members can be largely patterned into human-computer and computer-computer interactions that employ interface standards. Besides standards, plans and schedules are also used for interorganizational coordination, increasing the degree of direct human interaction. Thus, in addition to the use of technologies for enhancing ‘‘codification,’’ shared knowledge backgrounds and common skills of human processors are also important in increasing the absorptive ability of an organization. In contrast with the other two types of IOS, networked IOS involve a high degree of human-human interaction. A large proportion of the critical knowledge handled by a networked IOS can be tacit and unstructured. As such, the receptivity of an organization can be greatly influenced by the absorptive skills of individual human processors. Technologies are used to provide process support for enhancing human absorptive skills: B

by connecting and locating people (Tsui, 2003) and optimizing the frequency and intensity of socio-technical interactions (Dyer and Singh, 1998);

B

by facilitating the sharing of context (Nomura, 2002) and development of common knowledge bases;

B

by increasing the capability of capturing and locating tacit and unstructured knowledge (Majchrzak et al., 2000; May and Carter, 2001); and

B

by improving analytical and decision-making capabilities.

Thus, technologies that focus on ‘‘personalization’’ (Milton et al., 1999; Tsui, 2003) and support learning, such as collaborative construction tools (e.g. CAD/CAM, collaborative authoring), computer conferencing, and group decision support systems are important in enhancing an organization’s receptivity. 4.2.4 Key issues in decision making Decision making is another key behavioral process that influences the outcome of interorganizational collaboration (recall the collaboration model in Figure 1). Imbalance in decision-making authority may lead to perceived injustice and mistrust, and create an environment prone to opportunism and conflict, while shared decision making can facilitate perceived equality and trusting relationship, thus enhancing interorganizational collaboration (Sarkar et al., 1998). In pooled knowledge resources IOS, the parties sharing the resources do not need to directly interact with each other, and the decision-making process of each party is relatively

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independent from that of others. However, perceived inequality and mistrust in decision making could arise from one party’s possession and control of the shared resources. For example, in the airline industry, American and United attempted to bias screen displays of their computer-based reservation systems to discourage price comparisons (Bakos, 1991), or to restrict travel agents from booking tickets from the other airlines (Malone et al., 1987). In those situations where shared knowledge resources are controlled by one of the partners and the controlling party is also a competitor of the other parties, this dominant party may use its controlling position to intentionally damage other parties (Copeland and McKenney, 1988). As a result, distrust in the system controls and perceived loss of power in decision making are likely to arise (Kumar and van Dissel, 1996), increasing conflict potential and inhibiting cooperative behaviors. One way to address this issue is to place control of common resources in the hands of a neutral third-party (such as a trade association, government agency, or joint venture company) (Konsynski and McFarlan, 1990; Kumar and van Dissel, 1996) in order to increase the participants’ perceived justice and control in decision making, thus increasing trust and collaborative effectiveness. In value/supply-chain IOS, particularly in a proprietary network, the loss of resource control or an inability to access to quality knowledge may induce perceived inequality and loss of power in decision making, thereby impeding trust building and retarding collaboration success. For example, in the mid-1980s, Ford Motor established a proprietary EDI system, Fordnet. In pursuing its agendas for reducing market uncertainty, or simply for locking trading partners into its trading relationship, Ford imposed its information handling practices on all of its European trading partners, extending its own hardware systems into its suppliers’ premises, dictating product and inventory coding according to its own propriety system, and dictating the type and frequency of data to be exchanged (Webster, 1995). Consequently, many Fordnet users experienced decreased trust arising from a perceived loss of decision power in the trading relationship. Additionally, the transaction-specific investment in the Fordnet EDI also increased the risk of suboptimization perceived by the Fordnet users, further impeding the collaboration between Ford and its partners. As suggested by the case of Fordnet, the use of more open standards and migration from a proprietary network to a more open network may provide a viable solution for promoting participative decision making and increasing perceived justice and reciprocity. Furthermore, promoting shared understanding and mutual interests among participants would also enhance perceived equality and decision power, facilitating the growth of trust and collaboration. In networked IOS, decision making is quite different from that in the other two types of IOS. It involves highly interrelated processes and intense interactions among participants. Many studies have indicated that use of interactive technologies can greatly enhance the shared processes in decision making (Bowker and Star, 2001). Some have found that technologically mediated communication creates less role differentiation among the participants than does face-to-face communication (Kiesler and Sproull, 1996). Others have found that for groups communicating via e-mail, there tends to be uninhibited communication, more communication among participants of different status, and more equal participation (Kiesler et al., 1984; Rice and Rogers, 1984; Siegel et al., 1986). However, in networked IOS, the increased degree of human interaction and mutual adaptation may also increase performance equivocality and human misunderstanding. Thus, reducing such misunderstanding becomes important in facilitating the decision-making process in networked IOS. One approach to reduce misunderstanding is to foster trust. Another approach involves central repositories that provide a common knowledge base for sharing visions and contexts among the participants, such as discussion forums, frequently asked questions (FAQs) facilities, and electronic libraries with problem definitions, successful experiences and best practices (Majchrzak et al., 2000; May and Carter, 2001).

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4.2.5 Governance mechanisms for conflict resolution Besides transparent knowledge sharing and participative decision-making, the model portrayed in Figure 1 identifies conflict governance as a third key element underlying effective interorganizational collaboration. In an IOS network, conflict could arise from opportunistic behaviors, impeding the success of interorganizational collaboration. Opportunistic behaviors may occur when managing shared technology-based resources, such as shared archives. Or, they may take place in the transactional activities that are handled by the IOS. Thus, both technological governance and business governance are needed for preventing opportunism and resolving conflict so as to foster trust and enhance collaborative advantage. Technological governance: this includes various technical protocols, standards, system security controls, and knowledge security controls. Technological governance can be decomposed into three subtypes: (1) Open standards, such as XML. (2) Industry-specific standards, such as the SWIFT standard used in the international banking industry (Keen, 1991) and universal product code (UPC) in the grocery industry (Cash and Konsynski, 1985). (3) Proprietary or company-specific standards, such as the manufacturing automation protocol (MAP) used by General Motor (Keen, 1991). Open standards and industry-specific standards are likely to be used in pooled knowledge resources IOS because of a large number of participants involved. Proprietary or company-specific standards may be used in value/supply-chain IOS and networked IOS (Li and Williams, 1999). Recent trends indicate that value/supply-chain IOS and networked IOS are moving toward the use of more open standards: B

Business governance: business governance involves formal governance, such as legal contracts (Macneil, 1974, 1978; Ring and van de Ven, 1992), and informal governance, such as institutional norms (Zucker, 1986), reputation (Zucker, 1986; Resnick et al., 2000; Adler, 2001), and trust (Arrow, 1974; Ouchi, 1979; Bradach and Eccles, 1989; Williamson, 1993).

B

Formal governance: based on characteristics of the transactions to be conducted for the three IOS classes, three types of legal contracts can be applied. With pooled knowledge resources IOS, arms-length market transactions are likely to be involved, and thus a classical contract[4] would be appropriate. With value/supply-chain IOS, recurrent transactions are likely to occur between the chain members, and thus a neoclassical contract[5] would be appropriate. With networked IOS, relational activities take place, and thus a relational contract[6] is suitable.

B

Informal governance: with pooled knowledge resources IOS, given the minimum amount of interaction, institutional norms that define each other’s behaviors (Zucker, 1986) and reputation that is established through a network of trusted third parties (Zucker, 1986; Resnick et al., 2000; Adler, 2001) may serve as effective governance mechanisms supplementing the classical contract. In value/supply-chain IOS and networked IOS, with an increasing degree of human interaction, trust assumes a greater role as an effective mechanism for governing opportunistic behaviors and resolving conflict (Arrow, 1974; Ouchi, 1979; Bradach and Eccles, 1989; Williamson, 1993). Reputation and norms of reciprocity can also provide useful governance, as well as further facilitate the growth of trust.

4.2.6 IOS technologies Based on the characteristics and roles of each type of IOS as well as the practices involved in knowledge sharing, decision making, and conflict governance, we next classify a variety of candidate IOS technologies and application systems: B

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They are used to provide shared knowledge resources to reduce uncertainty, achieve economies of scale and scope by sharing costs and risks among participants (Konsynski and McFarlan, 1990). Implementation technologies require a focus on ‘‘codification’’ (i.e. ‘‘capturing existing knowledge and placing this in repositories in a structured manner’’) (Milton et al., 1999, p. 619; Tsui, 2003). Thus, technologies for communications (e.g. communications networks, standards and protocols) and for content management (e.g. shared repositories, knowledge acquisition and retrieval) can serve as good application candidates. Table II shows some examples. B

Value/supply-chain IOS involve relatively structured and linear relations between adjacent chain members, whose interaction interfaces can be largely standardized. They are used primarily for purposes of reducing uncertainty, streamlining flows of knowledge, services, and products, and increasing efficiency. Implementation technologies also focus on ‘‘codification.’’ It is worth noting that interdependencies between firms are different from the ways in which tasks/activities are interrelated. For example, sequential dependency between firms along a supply chain may involve many different tasks/activities relationships, such as ‘‘sharing,’’ ‘‘flow,’’ ‘‘fit,’’ concurrent tasks, task-subtask (Malone and Crowston, 1999, p. 429; Holsapple and Whinston, 2001, p. 585). ‘‘Sharing’’ relationships occur when multiple activities use the same resource. ‘‘Flow’’ relationships arise when one activity produces a resource that is used by another activity, involving sequencing, transfer, and usability. ‘‘Fit’’ relationships occur when multiple activities collectively produce one resource. Concurrent tasks arise when multiple activities occur simultaneously. Task-subtask relationship arises when one activity involves multiple subactivities.

Table II Pooled knowledge resources IOS: implementation technologies and applications Interfirm communication

Messaging services

Channel management

Communications network

Communication standards and protocols

Content management

Shared repositories Knowledge acquisition and retrieval

Knowledge discovery and generation

E-mail Fax Instant messaging Voice mail Publishing services Open posting (e.g. electronic bulletin board) Controlled posting (e.g. FAQs) Call center Electronic funds transfer at point-of-sales (EFTPoS) Web site Wireless device Peer-to-peer communication Broadband communications Intranet Extranet Internet Wireless networks Electronic data interchange (EDI) Extensible mark-up language (XML) Simple object access protocol (SOAP) Web services description language (WSDL) Universal description, discovery, and integration (UDDI) Databases, data warehouses Digital documents, archives Knowledge navigation (e.g. web browser) Knowledge search (e.g. expert finder tool, meta/web-crawler, taxonomy/ontological tools) Analytics (e.g. OLAP, simulation, modeling) Mining (e.g. data mining, text mining) Artificial intelligence (e.g. intelligent agents, case-based reasoning, neural networks, genetic algorithm, rule engines)

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Therefore, the coordination technologies that focus on supporting structured and semi-structured tasks/activities along the value/supply chain may serve as good candidate technologies for value/supply-chain IOS. These technologies may include scheduling resources and tasks across companies (Malone and Crowston, 1999; Holsapple and Whinston, 2001, p. 585), managing customer-supplier relationships (Holsapple and Whinston, 2001, p. 585), and interorganizational workflow automation (van der Aalst, 2000). Scheduling techniques involve managing the ‘‘sharing’’ relationships based on the mechanisms, such as ‘‘first come/first serve,’’ priority order, budget, managerial decision, and competitive bidding, and also the ‘‘flow’’ relationships, such as CPM and PERT for project management. Managing customer-supplier relationships focuses on the ‘‘flow’’ relationships between activities along a value chain. Technologies may involve customer relationship management, supply chain management, EDI systems, collaborative planning, forecasting and replenishment systems. Workflow automation is used for structured business processes across firms with a predefined set of tasks and routing constructs. Workflow automation involves managing concurrent tasks, task-subtask relationships, and multi-participant tasks. Table III lists some examples of candidate technologies and applications. B

Networked IOS have a focus on people and their work styles, especially how they create ideas and what knowledge resources they use. Networked IOS are particularly instrumental in three aspects: agile problem solving by delivering just-in-time knowledge among individuals across organizations, expertise co-development by supporting deeper and more tacit knowledge sharing among professionals, and innovation by optimizing interactions with customers and utilizing their knowledge (Nomura, 2002). Each of these aspects highlights human ingenuity and involves a tacit and less structured learning process. Thus, implementation technologies focus on ‘‘personalization’’ (i.e. ‘‘locating and connecting people’’) (Milton et al., 1999; Tsui, 2003, p. 6). Groupware, threaded discussions, computer conferencing, and collaborative construction tools (e.g. design, authoring) may serve as good candidates. Table IV lists some examples.

5. Conclusion IOS are assuming an increasing role in facilitating and enabling interorganizational collaboration. Yet, the existing literature on IOS is fragmented and provides limited understanding of the relationship between IOS technologies and the knowledge-intensive phenomenon of interorganizational collaboration. In this paper, we introduce an integrative model of IOS collaboration and identify knowledge sharing, participative decision making, and conflict governance as three behavioral process elements underlying effective Table III Value/supply-chain IOS: implementation technologies and applications Interfirm coordination

Scheduling resources and tasks

Managing customer-supplier relationships

Workflow automation

e.g. ‘‘First come/first serve,’’ priority order, budget, managerial decision, competitive bidding, CPM, PERT for project management e.g. Customer relationship management, supply chain management, EDI systems, collaborative planning, forecasting and replenishment systems e.g. Concurrent tasks, task-subtask relationship, managing multi-participant tasks

Table IV Networked IOS: implementation technologies and applications Interfirm cooperative work

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Collaborative construction Collaborative timing/meeting management Threaded discussion Multi-participant decision support

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e.g. CAD/CAM, authoring e.g. Calendaring, computer conferencing e.g. Community of practice e.g. Group decision support, organizational decision support

interorganizational collaboration. Extending Kumar and van Dissel’s framework to directly recognize these elements, we develop a more complete collaboration-oriented IOS framework for characterizing key elements of interorganizational collaboration and classifying IOS technologies. For researchers, this paper contributes to a deeper, fuller understanding of issues involved in achieving collaborative advantage with IOS technologies. Both the IOS collaboration model and the collaboration-oriented IOS framework provide a basis for further exploration of the uses and impacts of IOS technologies in interorganizational collaboration. They identify factors and relationships that researchers should consider in designing empirical studies, posing hypotheses about collaboration via IOS, and analyzing results. For educators, this paper brings together diverse ideas into a systematic view of collaboration via interorganizational systems. It outlines IOS characteristics, classifies them, and highlights issues related to their deployment. As such, the model and framework can be used to identify and structure course content concerned with collaboration and IOS. For practitioners, this paper provides useful guidance for IOS users by highlighting key elements of collaboration, presenting empirical examples and addressing key issues, practices, and solutions involved in the IOS collaboration. The model and framework serve as a checklist of considerations that need to be dealt with by leaders of collaboration-oriented IOS initiatives. The IOS framework and technology classification may also suggest ways in which IT vendors might provide better technological solutions, services, and software for interorganizational collaboration. As more and more interorganizational system links are established between firms, the question of how to develop collaborative IT relationships and optimize the use of IOS grows in importance. The answer involves methods to promote such process behaviors as knowledge sharing and participative decision making among IOS users, while simultaneously aligning with effective governance mechanisms to facilitate these behaviors, inhibit opportunistic behaviors and power plays, and ultimately yield collaborative advantages in the directions of greater productivity, agility, innovation, and/or reputation.

Notes 1. This is an application of Oliver’s (1990) contingency theory of interorganizational relationship formation. Oliver proposes six critical causes – necessity, asymmetry, reciprocity, efficiency, stability, and legitimacy – as generalizable predictors of interorganizational relationship formation across organizations, settings, and linkages. These are used to structure our examination of the IOS literature. Our examination yielded two additional motives: innovation and agility. 2. The notion of interorganizational interface comes from Malone’s (1985) concept of ‘‘organizational interface.’’ Malone (1985, p. 66) suggests that ‘‘the term ‘interface’ was originally used in computer science to mean a connection between programs or program modules;’’ later, the phrase ‘‘user interface’’ becomes common and is used to include the connection between a human user and a computer system; in the same spirit, this usage can be extended to include ‘‘organizational interface,’’ which can be defined as ‘‘the parts of a computer system that connect human users to each other and to the capabilities provided by computers.’’ Here, we further extend the concept of ‘‘organizational interface’’ to interorganizational interface to emphasize the parts of the computer systems that connect human users to each other and to the capabilities provided by computers shared by two or more organizations. 3. In the literature, a widely used term is ‘‘information asymmetry.’’ In this paper, we extend this concept to knowledge asymmetry to emphasize that a less structured or more tacit knowledge dimension (e.g. vision and understanding about certain markets and demands) are involved in the interplay between different value/supply chain members. 4. Classical contract involves one-time, short-term, arms-length market transactions between autonomous and independent parties. ‘‘The conditions associated with these transactions are ‘sharp in;’ that is, they are accompanied by a clear-cut, complete, and monetized agreement. They are also ‘sharp out,’ i.e. the seller’s debt of performance and the buyer’s debt of payment are

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unambiguous.’’ ‘‘The property, products, or services exchanged tend towards the non-specific, and can be transacted among many traders’’ (Ring and Van De Ven, 1992, p. 485). 5. Neoclassical contract involves relatively short-term, ‘‘repeated exchanges of assets that have moderate degrees of transaction specificity. The terms of these exchanges tend to be certain, but some contingencies may be left to future resolution.’’ ‘‘The parties see themselves as autonomous, legally equal, but contemplating a more embedded relationship’’ (Ring and Van De Ven, 1992, p. 487). 6. Relational contract involves ‘‘long-term investments that stem from groundwork laid by recurrent bargaining on the production and transfer of property rights among these legally equal and autonomous parties. The property, products, or services jointly developed and exchanged in these transactions entail highly specific investments, in ventures that cannot be fully specified or controlled by the parties in advance of their execution’’ (Ring and Van De Ven, 1992, p. 487).

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Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations Jay Liebowitz

Jay Liebowitz is in the Department of Information Technology, Graduate Division of Business and Management, Johns Hopkins University, Rockville, Maryland, USA ([email protected]).

Abstract Purpose – To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations. Design/methodology/approach – In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map. Findings – The analytic hierarchy process, when coupled with social network analysis, can be a useful technique for developing interval measures for knowledge-mapping purposes. Research limitations/implications – The analytic hierarchy process may become tedious and arduous for use in large social network maps. More research needs to be conducted in this area for scalability. Practical implications – As social network analysis is gaining more prominence in the knowledge management community, the analytic hierarchy process may be able to provide more valuable measures to determine the strengths of relationships between actors than simply using ordinal numbers. Originality/value – Coupling the analytic hierarchy process with social network analysis provides a novel approach for future knowledge-mapping activities. Keywords Social networks, Analytical hierarchy process, Auditing Paper type Research paper

1. Introduction Why are organizations so enamored with knowledge management? One important reason is that organizations are hoping that knowledge management processes will allow the creation of knowledge to take place for increasing innovation in the organization (Chauvel and Despres, 2002; Earl, 2001). Innovation may be in the form of improved organizational business processes, new products or services, or better ways for customer relationship management. For example, JD Edwards applied knowledge management to their customer relationship management by developing knowledge management for internal sales support first, taking the lessons learned and successes into a second stage, and extending ‘‘the knowledge garden’’ to its business partners and integrators (Harris et al., 2003). Knowledge management can be in the form of idea management systems that allow employee ideas and suggestions to be captured and shared online. These idea management systems, such as Imaginatik’s Idea Central (Pluskowski, 2002), allow the capture and sharing of ideas across the organization, and provides an efficient review process to evaluate the ideas. Increased socialization encouraged by this approach can lead to the impromptu formation of communities of practitioners who discover people with similar interests from the ideas and the ensuring interactions (Pluskowski, 2002). These interactions can hopefully lead to innovations in the organization.

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DOI 10.1108/13673270510582974

‘‘ A knowledge map portrays the sources, flows, constraints, and sinks (losses or stopping points) of knowledge within an organization. ’’

To enhance the knowledge flows between people to stimulate innovative thinking, organizations should first conduct a knowledge audit and develop a knowledge map of the sources, sinks, and flows of knowledge in the organization. In other words, whom do people go to in the organization for answers to questions or how are departments in the organization interacting within and between each other? To help develop the knowledge map, social network analysis (SNA) and associated visualization tools can be used to aid in the analysis of this information. One way to improve the current state-of-the-art in SNA is to develop new ways to produce interval/ratio measures of relations between the various individuals in the organization to determine the strength of their ties. This paper discusses a novel approach in applying the analytic hierarchy process (AHP) to generate the ratio scores for the valued graphs to be used in SNA in order to develop a knowledge map of the organization. This paper will first discuss knowledge mapping and then describe SNA. Then, the AHP will be explained and linked with SNA to produce valued graphs used for knowledge mapping.

2. Knowledge mapping A key part of knowledge management (KM) is performing a knowledge audit to determine knowledge flows within an organization. Specifically, the knowledge audit process involves (Liebowitz et al., 2000): (1) Identify what knowledge currently exists in the targeted area (typically select a core competency that is cross-departmental/functional): B

determine existing and potential sinks, sources, flows, and constraints in the targeted area, including environmental factors that could influence the targeted area;

B

identify and locate explicit and tacit knowledge in the targeted area; and

B

build a knowledge map of the taxonomy and flow of knowledge in the organization in the targeted area. The knowledge map relates topics, people, documents, ideas, and links to external resources, in respective densities, in ways that allow individuals to find the knowledge they need quickly.

(2) Identify what knowledge is missing in the targeted area: B

perform a gap analysis to determine what knowledge is missing to achieve the business goals; and

B

determine who needs the missing knowledge.

(3) Provide recommendations from the knowledge audit to management regarding the status quo and possible improvements to the knowledge management activities in the targeted area. An essential output of the knowledge audit process is the knowledge map for providing insight for improving business and organizational processes. A knowledge map portrays the sources, flows, constraints, and sinks (losses or stopping points) of knowledge within an organization. Well-developed knowledge maps help identify intellectual capital (Liebowitz, 2003), socialize new members, and enhance organizational learning (Wexler, 2001). Knowledge maps have been used for a variety of applications, even for developing a knowledge map of knowledge management software tools (Noll et al., 2002). An organization should map its knowledge to (Grey, 1999):

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‘‘ A key part of KM is performing a knowledge audit to determine knowledge flows within an organization. ’’

B

encourage re-use and prevent re-invention, saving search time and acquisition costs;

B

highlight islands of expertise and suggest ways to build bridges to increase knowledge sharing;

B

discover effective and emergent communities of practice where learning is happening;

B

provide a baseline for measuring progress with KM projects; and

B

reduce the burden on experts by helping staff find critical information/knowledge quickly.

Some of the key principles in knowledge mapping are establish boundaries and respect personal disclosures, recognize and locate knowledge in a wide variety of forms, and locate knowledge in processes, relationships, policies, people, documents, conversations, suppliers, competitors, and customers. The types of questions asked to develop a knowledge map include (Grey, 1999): B

What type of knowledge is needed to do your work?

B

Who provides it, where do you get it, how does it arrive?

B

What do you do, how do you add value, what are the critical issues?

B

What happens when you are finished?

B

How can the knowledge flow be improved, what is preventing you doing more, better, faster?

B

What would make your work easier?

B

Who do you go to when there is a problem?

Typically, information is collected for the knowledge map by using the following methods (Grey, 1999): B

conduct surveys, interviews, and focus groups;

B

observe the work in progress;

B

obtain network traffic logs, policy documents, org charts, process documentation;

B

explore the common and individual file structures;

B

concentrate on formal and informal gatherings, communications, and activities;

B

gather from internal/external sources; and

B

move across multiple levels (individual, team, department, organization).

To help develop a knowledge map, SNA can be used. The next section will address SNA techniques and tools.

3. Social network analysis techniques and tools According to Hanneman (2002), SNA is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities. SNA involves actors (seeing how actors are located or ‘‘embedded’’ in the overall network) and relations (seeing how the whole pattern of individual choices gives rise to more holistic patterns). SNA has been used in sociology, anthropology, information systems, organizational behavior, and many other disciplines. For example, Cross et al. (2001) used SNA to partly determine if employees of the acquired firm are socializing and seeking answers to questions from those of the acquiring firm. Related link analysis has also been used in such applications as developing socio-spatial knowledge networks for chronic

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‘‘ To help develop the knowledge map, SNA and associated visualization tools can be used to aid in the analysis. ’’

disease prevention (Cravey et al., 2001) and developing academic networks and expertise in British higher education (Eldridge and Wilson, 2003). The basic idea of SNA is that individual people are nested within networks of face-to-face relations with other persons. Families, neighborhoods, school districts, communities, and even societies are, to varying degrees, social entities in and of themselves (Hanneman, 2002). The social network analyst is interested in how the individual is embedded within a structure and how the structure emerges from the micro-relations between individual parts (Hanneman, 2002). This could then be applied at an organizational level to see how the ‘‘actors’’ (e.g. employees, departments, etc.) relate to each other via their interactions. Through SNA, a knowledge map could be generated to aid in the knowledge audit process. Within SNA, there are a variety of strategies used to collect measurements on the relations among the set of actors. One method is called ‘‘full network methods’’ whereby information about each actor’s ties with all other actors is collected. One major limitation of this technique is that it may be costly and difficult to collect full network data. A second group of methods is called the ‘‘snowball methods’’ whereby the analyst begins with a focal actor or set of actors. Then, each of these actors is asked to name some or all of their ties to other actors. Then, these ‘‘other actors’’ are asked for some or all of their ties, and the snowball effect continues until no new actors are identified (or until some stopping rule is determined). The limitations with this method are that those who are ‘‘isolated’’ may not be identified and it may be unclear as to where to start the snowball rolling. The third major strategy used in SNA is the use of egocentric networks. With this approach, one would begin with a selection of focal nodes (egos) and identify the nodes to which they are connected. Then, one would determine which of the nodes identified in the first stage are connected to one another. Egocentric methods focus on the individual, rather than on the network, as a whole (Hanneman, 2002). In order to measure the information collected about ties between actors, there are nominal, ordinal, and interval/ratio levels of measurement. The zero-one binary scale is an example of a nominal scale and would show if ties are absent (zero) or present (one). The multiple-category nominal measures of relations is similar to multiple choice versus the true-false binary representation. The ordinal measures of relations are similar to a Likert scale and can determine the ‘‘strength’’ of ties. However, the third class of measures of relations, namely the interval/ratio method, is the most advanced level of measurement that allows the actors to discriminate among relations (e.g. this tie is three times as strong as that tie) (Hanneman, 2002). Examples of social networks are shown in Figures 1-3. 3.1 Social network analysis tools There are a variety of SNA tools that are available for developing and visualizing social networks. The International Network of Social Network Analysis (www.sfu.ca/,insna/) is an excellent site that provides access to these various tools. Some of the leading SNA software is: B

Agna (www.geocities.com/imbenta/agna/index.htm);

B

Analytic Technologies (Ucinet, Krackplot, Netdraw, Anthropac etc.) (www.analytictech. com/);

B

Classroom Sociometrics software (www.classroomsociometrics.com/);

B

Fatcat (www.sfu.ca/ , richards/Pages/fatcat.htm);

B

InFlow (www.orgnet.com/index.html);

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Figure 1 Social network of 9/11 terrorist network (www.orgnet.com/hijackers.html)

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Figure 2 Social network analysis of individuals within an IT department (using InFlow software) (www.orgnet.com)

B

Java for Social Networks (www.public.asu.edu/ , corman/jsocnet/);

B

MultiNet (www.sfu.ca/ , richards/Multinet/Pages/multinet.htm);

B

Negopy (www.sfu.ca/ , richards/Pages/negopy4.html);

B

NetMiner (www.netminer.com/NetMiner/home_01.jsp);

B

Pajek (http://vlado.fmf.uni-lj.si/pub/networks/pajek/default.htm);

B

Siena (http://stat.gamma.rug.nl/snijders/siena.html);

B

SocioMetrica LinkAlyzer (www.md-logic.com/id142.htm);

B

STOCNET (http://stat.gamma.rug.nl/stocnet/); and

B

Visone (www.visone.de/).

InFlow, Krackplot, and NetMiner are three of the leading SNA tools. InFlow was developed by Valdis Krebs and has been used for a myriad of applications ranging from analyzing

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Figure 3 Social network analysis (www.orgnet.com) – org. mapping depicting departments

patterns of e-mail usage to discovering book buying behaviors at Amazon.com (Johnson, 2003). InFlow performs network analysis and network visualization in one integrated package (www.orgnet.com). Krackplot, originally developed by David Krackhardt and now distributed by Analytic Technologies, is a social network visualization program. NetMiner also is a network analysis and visualization tool that includes 15 analysis tools commonly used in SNA research.

4. The analytic hierarchy process An important part of SNA is being able to provide the measures of relations (i.e. strengths of the ties) between the actors. We previously indicated that the interval/ratio method is preferred for determining the measures of relations. One technique that has not been applied to SNA to calculate the interval/ratio measures is the AHP. AHP was developed by Thomas Saaty (1980) at the University of Pittsburgh to aid decision makers in the evaluation process. It quantifies subjective judgments used in decision-making, and has been applied in numerous applications throughout the world (Saaty, 1982). AHP uses pairwise comparisons in order to determine relative levels of importance or preference. Expert Choice is a software package that automates the use of AHP on a PC. Let us take an example using AHP for use in SNA. The first step is to construct a tree hierarchy to show the goal at the top, then criteria in the next level, and then alternatives at the lowest level. If we are trying to develop a knowledge map using SNA, then we would like to determine knowledge flows between individuals in the organizations. Thus, we might want to ask Jay (an employee in the organization) the question ‘‘who do you ask when you have a question involving . . . ’’ to be the goal at the top of the hierarchy, as shown in Figure 4. The criteria might be: office politics, institutional knowledge, general advice, and content knowledge. The alternatives might be Bill, Bob, Mary, and June (of course, you should

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Figure 4 AHP/expert choice application

include all the necessary individuals as pertinent to the application). Then, Jay can start to enter his verbal judgments to weight the criteria and then weight the alternatives versus each criterion, using AHP/Expert Choice, as follows: With respect to the goal, are ‘‘office politics’’ more important than ‘‘institutional knowledge’’, and if so, how much more important? A scale of relative importance developed by Saaty is applied to translate the verbal judgments into numeric values: (1)

Equal importance.

(3)

Moderate importance.

(5)

Strong importance.

(7)

Very strong importance.

(9)

Extreme importance.

The values of 2, 4, 6, and 8 are intermediate values. Jay would then enter all the pairwise comparisons for the criteria, which would result in weighted criteria that add up to one. In this example, Jay’s preferences on his criteria, with respect to the goal, are shown in Table I. Now, the overall weight that Jay is assigning to each criterion must be determined. It is calculated by taking each entry and dividing by the sum of the column where it appears (http://mat.gsia.cmu.edu/mstc/multiple/node4.html). For example, the (office politics, office politics) entry would end up as 1=ð1 þ 1=3 þ 1 þ 3Þ ¼ 0:188. The other entries are shown in Table II. The average weights on the criteria suggest that the most important criteria, in order, according to Jay are content knowledge (about 39 percent), general advice (about 29 percent), office politics (about 22 percent), and institutional knowledge (about 10 percent). Note that the percentages should add up to 100 percent. The next step is to weight the alternatives versus each criterion using pairwise comparisons. The same procedure is conducted. The relative scores for each of the alternatives versus the criteria are shown in Table III. Table I Jay’s preferences on his criteria

Office politics Institutional knowledge General advice Content knowledge

Office politics

Institutional knowledge

General advice

Content knowledge

1 1/3 1 3

3 1 3 3

1 1/3 1 1

1/3 1/3 1 1

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Table II The overall weight of Jay’s entries

Office politics Institutional knowledge General advice Content knowledge

Office politics

Institutional knowledge

General advice

Content knowledge

Average

0.188 0.062 0.188 0.563

0.333 0.100 0.333 0.333

0.300 0.100 0.300 0.300

0.124 0.124 0.375 0.375

0.223 0.096 0.287 0.394

Table III The relative scores for each of the alternatives versus the criteria

Office politics Institutional knowledge General advice Content knowledge

Bill

Bob

Mary

June

0.433 0.250 0.357 0.147

0.238 0.250 0.172 0.548

0.169 0.250 0.235 0.158

0.161 0.250 0.235 0.147

In terms of whom Jay should ask for getting answers to his questions about office politics, Bill would be the best person to provide advice (due to the highest weight (0.433) for Bill versus that for Bob, Mary, and June). Referring back to our overall weights, we can now get an overall value for each alternative. This is called the synthesis step. For example, the overall value for Bill is calculated as: ð0:223Þð0:433Þ þ ð0:096Þð0:250Þ þ ð0:287Þð0:357Þ þ ð0:394Þð0:147Þ ¼ 0:281: Again, these weights should total one. Based on the synthesis, Bob has the highest weight (0.342), then Bill (0.281), Mary (0.192) and lastly June (0.185), as shown in Figure 5. This suggests, for example, that Jay seeks out Bill’s help the most when it comes to getting answers to Jay’s questions on office politics and general advice, and Bob’s help the most

Figure 5 Synthesis using AHP/expert choice

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Figure 6 Valued graph based upon AHP results

when Jay has content knowledge-related questions. We could then use these values to be the ‘‘strengths of the ties’’ between Jay and the other individuals. Thus, a valued graph as shown in Figure 6 can be developed using these ratio values. These could then be integrated within the SNA to help develop the knowledge map. Integrating AHP with SNA can be a novel approach. However, there is a possible drawback. If there are many individuals in the organization, it may become laborious and tedious to apply the pairwise comparisons as there will be a tremendous number of comparisons to be made. But, if the focus of study is on interactions within a specific department or between departments in the organization, then this approach may be quite feasible.

5. Summary This paper introduces the integrated use of the AHP with SNA to produce interval/ratio measures for use in an organization’s knowledge map. This approach has wider implications in the knowledge management field than just knowledge mapping. For example, Weber and Aha (2002) indicate that about 70 percent of the lessons learned systems are ineffective. A major reason given is the lack of an active or ‘‘push’’ feature for analysis and dissemination of the lessons. In order to determine what lessons would be appropriate to ‘‘push’’ to users, a user profile could be developed. Here, AHP could be applied in building the profile to determine the relative preferences on which topics of interest are relatively more important than others to the user in order to receive lessons learned in these areas. SNA could then be used to develop the valued graphs used in a knowledge map for depicting, for example, the different types of lessons received. Developing AHP weightings on nodes should enhance the typical SNA algorithms for things like centrality, betweenness, and others. We could also use the weightings to do a more fine-grained filtering of the maps (e.g. remove weak links). Additionally, SNA maps may be used to annotate an AHP decision hierarchy. For example, when traversing the decision hierarchy, we might be able to understand how a particular weighting was achieved by referencing across to the people making the judgments to see how they might be related in a social network sense (e.g. tightly clustered or quite disparate). This paper hopefully encourages others to apply AHP or other methods to determine the ratio measures of relations used in SNA for knowledge mapping. In this manner, knowledge maps could become more meaningful in depicting the strength of relationships for organizational business process improvement.

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References Chauvel, D. and Despres, C. (2002), ‘‘A review of survey research in knowledge management: 1997-2001’’, Journal of Knowledge Management, Vol. 6 No. 3, pp. 207-23. Cravey, A., Washburn, S., Gesler, W., Arcury, T. and Skelly, A. (2001), ‘‘Developing socio-spatial knowledge networks: a qualitative methodology for chronic disease prevention’’, Social Science and Medicine Journal, Vol. 52 No. 12, pp. 1763-75. Cross, R., Borgatti, S. and Parker, A. (2001), ‘‘Beyond answers: dimensions of the advice network’’, Social Networks Journal, Vol. 23 No. 3, pp. 215-35. Earl, M. (2001), ‘‘Knowledge management strategies: toward a taxonomy’’, Journal of Management Information Systems, Vol. 18 No. 1, pp. 215-33. Eldridge, D. and Wilson, E. (2003), ‘‘Nurturing knowledge: the UK higher education links scheme’’, Public Administration and Development Journal, Vol. 23 No. 2, pp. 125-209. Grey, D. (1999), ‘‘Knowledge mapping:a practical overview’’, available at: www.it-consultancy.com/ extern/sws/knowmap.html (accessed March). Hanneman, R. (2002), ‘‘Introduction to social network methods’’, available at: www.faculty.ucr.edu/ ,hanneman/ Harris, K., Kolsky, E. and Lundy, J. (2003), ‘‘The case for knowledge management in CRM’’, Gartner Research Note, Stamford, CT, April 21. Johnson, S. (2003), ‘‘Who loves ya, baby?’’, Discover, Vol. 24 No. 4. Liebowitz, J. (2003), Addressing the Human Capital Crisis in the Federal Government: A Knowledge Management Perspective, Butterworth-Heinemann/Elsevier, Oxford. Liebowitz, J., Montano, B., McCaw, D., Buchwalter, J., Browning, C., Newman, B. and Rebeck, K. (2000), ‘‘The knowledge audit’’, Journal of Knowledge and Process Management, Vol. 7 No. 1. Noll, M., Frohlich, D. and Schiebel, E. (2002), ‘‘Knowledge maps of knowledge management tools: information visualization with BibTechMon’’, in Karagiannis, D. and Reimer, U. (Eds), Practical Applications of Knowledge Management 2002 Conference Proceedings, Springer-Verlag, New York, NY. Pluskowski, B. (2002), ‘‘Dynamic knowledge systems’’, White Paper, Imaginatik Research, Boston, MA, June, available at: www.imaginatik.com Saaty, T. (1980), The Analytic Hierarchy Process, McGraw Hill, New York, NY. Saaty, T. (1982), Decision Makers for Leaders, Wadsworth Publishing, Belmont, CA. Weber, R. and Aha, D. (2002), ‘‘Intelligent delivery of military lessons learned’’, Decision Support Systems Journal, Vol. 34, pp. 287-304. Wexler, M. (2001), ‘‘The who, what, and why of knowledge mapping’’, Journal of Knowledge Management, Vol. 5 No. 3, pp. 249-64.

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A knowledge-based system to support procurement decision H.C.W. Lau, A. Ning, K.F. Pun, K.S. Chin and W.H. Ip

Abstract Purpose – To propose an infrastructure of a knowledge-based system to capture and maintain the procurement information and purchasers’ knowledge, regarding how to choose partners in the supply chain network, with the adopting of the neural networks that mimic the operation of human brain to generate solutions systematically. Design/methodology/approach – The proposed system encompasses hybrid artificial intelligence (AI) technologies, Online analytical processing (OLAP) applications and neural networks. Findings – Be able to capture the procurement data and vendors’ information that are generated in the workflows to ensure tthat he knowledge and structured information are captured without additional time and effort. Recognizes the void of research in the infrastructure of the hybrid AI technologies for knowledge discovery. Research limitations/implications – Neural network does not have the sensibility characteristic of the purchasing staff, it is not able to identify the environment changes, which need to re-adjust the output to fit the environment. H.C.W. Lau is an Associate Professor, A. Ning is a Project Associate and W.H. Ip is an Associate Professor, all in the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong. K.F. Pun is a Senior Lecturer in the Department of Mechanical Engineering, The University of the West Indies, Trinidad and Tobago, West Indies. K.S. Chin is an Associate Professor in the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong.

DOI 10.1108/13673270510582983

Practical implications – The proposed system obtains useful information related to the trend of sales demand in terms of customer preference and expected requirement using the OLAP module and then based on this information, the neural network provides recommendation related to the supported suppliers that are capable of fulfilling the requirements. Originality/value – This paper proposes a knowledge-based system that offers expandability and flexibility to allow users to add more related factors for analysis to enhance the quality of decision making. Keywords Deductive databases, Decision support systems Paper type Research paper

Introduction To compete in the ever-changing global market, it is crucial for the manufacturing firms to be able to exploit and develop its competitive advantages and achieves low cost productions by seeking reliable venders who provide the best value supplies to the firms. Most of the manufacturing firms rely heavily on the purchasers’ expertise and personal network to make decision on the selection of vendors. Indeed, it is critical for manufacturers to maintain and exploit the purchasers’ knowledge and expertise for the long-term benefit of the corporation (Davenport, 1997). Knowledge management is a set of business processes that capture and deliver the collective experience. Corporations are investing seriously in the development of knowledge management systems, especially the professional services firms, e.g. consultant firms, accounting firms, etc. Ofek and Sarvary (2001) identified the reasons why knowledge managements are emerging the professional services firms. Growth and globalization are part of the objective as both created the opportunity to utilize the dispersed experience of the expertise in the firms. Another key reason of deployment of knowledge management system is due to the recent advances in the information technologies that enable the firms to build systems that integrate and collaborate the experts’ experiences that ensure the companies provide better services to the customers. Therefore, knowledge management becomes a crucial tool for the corporations to survive in the volatile marketplace and to achieve competitive edge (Tiwana, 2000). There is no doubt that knowledge is one of the organization’s most valuable resources, indeed, there are companies that treat knowledge

VOL. 9 NO. 1 2005, pp. 87-100, Q Emerald Group Publishing Limited, ISSN 1367-3270

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‘‘ The learning process of a human is through repetitive learning cycles which is similar to the learning process of a neural network. ’’

as an asset, just as real as any other assets that appear on the companies balance sheet. For example, Skandia performs an internal audit of the company’s intellectual properties and issues annual reports to stockholders to prove the investors the value of Skandia’s knowledge capital (Davenport and Prusak, 2000). In order to manage knowledge efficiently in an organization, the companies have to define where the knowledge is kept, for instance, the memos, reports, presentations, press articles, etc., then put these unstructured forms of data into knowledge repositories. In fact, these knowledge repositories can be categorized into three types: (1) External knowledge, such as the journal articles and market research information on the competitors. (2) Structured internal knowledge, such as the bills of materials, product specifications, and production procedures. (3) Informal internal knowledge, such as know-how, in the minds of people in the organization (Davenport and Prusak, 2000). In order to build a system with a knowledge repository that captures and embeds the value added knowledge and able to enhance the ability of decision making in the corporation, it is necessary to build the knowledge based system that has the capabilities in categorizing and sorting out tremendous amount of data while generating information to support decision making with intelligence features. For example, for the internal knowledge repository, Hewlett-Packard’s electronic sales partner has added value to their repository through careful categorization and filtering. Therefore, the proposed system encompasses hybrid artificial intelligence (AI) technologies, on-line analytical processing (OLAP) applications and neural networks. The system repository captures the procurement data and vendors’ information that are generated in the workflows to ensure the knowledge and structured information are captured without additional time and effort. The proposed system obtains useful information related to the trend of sales demand in terms of customer preference and expected requirement using the OLAP module and then based on this information, the neural network provides a recommendation related to the supported suppliers that are capable to fulfill the requirements. The OLAP module is responsible to categorize, access, view, and analyze the data in the repository and generate inputs to feed the neural networks. For the neural network module in this research, the feed-forward back-propagation network is selected to build a system, which enhances decision making in choosing the most appropriate vendors. However, it should be noted that other neural network’s configurations, such as feed backward back propagation, could also be used. When the trained network is available, it is possible that recommended action can be obtained with the purpose to rectify some hidden problems, should that occur at a later stage. Therefore, in the training process of the neural network, the nodes of the input layer of the neural network represent the data from the OLAP and those of the output layer represent the predictions and extrapolations.

Related studies Today’s industrial environment is rapidly changing due to global competition as well as fast advances of information technology. The major activity of manufacturing firms is no longer confined to product productions but lies in the systematic managing of knowledge in order to create products that meet customer’s needs.

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‘‘ The major activity of manufacturing firms is no longer confined to product productions but lies in the systematic managing of knowledge in order to create products that meet customers’ needs. ’’

It had been proven that the best way to capture and share knowledge is to embed knowledge management into the job of the workers, and to ensure that knowledge management is no longer a separate task that requires additional time and effort to input what they have learned and to learn from others. Partners Healthcare, a Boston-based umbrella organization that included Brigham and Women’s Massachusetts General, and several other hospitals and physician’s groups, had adopted an information technology system that is built with this concept. The system was built by targeting on the essential work process (the physician order entry) and formally reported problems (drug allergies and lab reports). The system was linked to a massive repository that contains constantly updated clinical knowledge and patient’s records so that the physicians can handle the medical facts when providing a consultation for the patients. A controlled study illustrated that the system reducing the medication errors by 55 percent, improvements on prescribing the proper drugs, and prescribing cheaper but more effective drugs for patients. These improvements not only saved lives but saved money as well (Davenport and Prusak, 2000). Turban (1988), Mockler (1989) and Ba et al. (2001) had examined the development of a knowledge-based and decision-support system from a management point-of-view. They had studied the incentive issues in the information system areas of knowledge management and supply chain coordination. They outlined the requirements for designing incentive-aligned information systems, such as embody the right incentives so that users would not have an incentive to cheat the system nor be better off by distorting the information. On the other hand, Ofek and Sarvary (2001) studied how knowledge management affects competition among the professional service firms in terms of reducing operating costs and creating added value to customers by significantly increasing the product quality. They analyzed the competitive dynamics and market structure that emerge as the result of the firms competing with knowledge management systems and their study showed that knowledge management leads to quality improvement, and the higher ability to leverage the customer base was able to actually lower profits and lead to industry shakeout. These research studies had studied knowledge management from a management prospective and how a knowledge-based system affected business operations. On the other hand, some researchers proposed models and system architectures for knowledge-based systems. A number of researchers had proposed and developed knowledge-based systems with the agent technology. Koch and Westphall (2001) presented the work on an application of distributed artificial intelligence for computer network management by implementing the software platform using an intelligence autonomous agent. Alternatively, Montaldo et al. (2003) proposed an agent-based architecture that was applied to workflow management system to manage new functionalities, such as customer relationship management in electronic commerce. They proposed architecture use agent technology to distribute the intricacies of managing manufacturing workflow information according to the complexity of the enterprises. Besides agent technology, a number of studies had been found to use the OLAP system for decision support and knowledge discovery purposes. Tan et al. (2003) assessed the opportunities and challenges of the combination of data warehousing and web technology in terms of efficiency improvements, security, key success factors, and benefits. They found that a combination of data warehouses, analytical applications, and internet technologies were able to eliminate the reporting and analysis processes, which truly exposed the power of the web. Moreover, Thomas and Datta (2001) had proposed a concept model of data cube and algebra to support OLAP operations in a decision support system. The algebra

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provided a means to concisely express complex OLAP queries for knowledge discovery and enhanced the overall ability of the knowledge-based system to implement OLAP applications with the standard relational database technology. Furthermore, Weigend et al. (1999) addressed the recent trend of increasing electronic access to documents through text categorization that becomes the center of knowledge management. In their study, they had brought upon the problems of using a divide-and-conquer strategy that mimicked the hierarchical structure of knowledge that was ignored in flat inference models and provided the internal structure of the categories in order to improve text categorization performance. Moreover, Davenport and Prusak (2000) positioned the knowledge technologies: knowledge component, case-based reasoning, constraint-based, expert systems, web, notes, and neural nets, into dimensions that distinguish different types of knowledge technologies in terms of time to find a solution and level of user knowledge required. Among the knowledge technologies, web, notes, and neural networks required a high level of user knowledge and a lot of time to find a solution. Furthermore, Kumar and Olmeda (1999) and Fowler (2000) had studied knowledge discovery and management with the employment of artificial intelligence (AI) technologies, such as case based reasoning, knowledge based system, neural networks, etc. Both studies found that by embedding hybrid AI technologies they are able to outperform all the individual methods in terms of performance in knowledge discovery. However, there was a void of research in the infrastructure of the hybrid AI technologies for knowledge discovery. Therefore, to fill the gap in the research, we proposed the infrastructure of a knowledge-based system that captures and maintains information and expert knowledge, and proposes a solution to users.

System infrastructure It is indeed difficult to capture and embed informal knowledge that resides within minds of people into a system. McDonnell Douglas, which is now part of Boeing, tried to develop an expert system that contains the expert knowledge to determine the aircraft are positioned properly for landing. They gathered the human knowledge of the ground crews by interview and over-the-shoulder observation. The system took two years and a tremendous amount of resources to capture the human expertise that demonstrated how difficult it is to capture and embed tacit knowledge in a system (Davenport and Prusak, 2000). However, it would be less time consuming and less resources are needed to develop a system that captures and embeds structured knowledge. For the proposed system, the transaction data and vendor’s data are selected to be the input because they are structured information which are easier to be captured through transaction documents and embed the data into the system. The primary objective of the system is that it should generate outputs that should be as good as or even better than the decision made by an expert would have made, provided that the same set of input data is given. The learning process of a human is through repetitive learning cycles which is similar to the learning process of a neural network. Therefore, a neural network is selected to be part of the hybrid system to take advantage of its capability to operate with incomplete data to generalize, abstract, and reveal insight (Wasserman, 1989; Sharda, 1994; Kasabov, 1999). A neural network is a statistically oriented tool that excels at using data to classify cases into categories (Davenport and Prusak, 2000). Neural networks ‘‘learn’’ patterns from data directly by examining the data repeatedly, searching for relationships, automatically building models, and correcting the model’s own mistakes over and over again (Dhar and Stein, 1997). In other words, neural networks build models from data by trial and error. Data analysis capabilities become crucial as a tremendous amount of information is pouring into the company through different mediums, while the company itself is creating loads of information as well. Data mining tools become popular as they identify and characterize interrelationships among multivariable dimensions without requiring human efforts to formulate specific questions. It is concerned with discovering newfound and vital information, which enables the executives to find out the undiscovered facts from the valuable data. Nowadays, there are many off-the-shelf data mining applications. For example, Microsoft SQL server, which has incorporated with OLAP technology, provides services in accessing, viewing, and

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‘‘ Neural networks have been found to be very good at modeling complex and poorly understood problems. ’’

analyzing large volumes of data with high flexibility and performance (Thomsen, 1999; Peterson, 2000). According to the definition of OLAP Council, OLAP is a: Category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user (Inmon, 1992).

The OLAP tool is used to assist decision makers in creating appropriate knowledge and models by browsing the related data groups, and defining the model-based relations between the data groups (Lau et al., 2001). It creates multidimensional views of data in relational databases. It has the capabilities to manipulate and analyze large volumes of data from multiple perspectives (Laudon and Laudon, 2000). The key of adopting OLAP is that it enables executives to gain insight to a wide variety of possible views of information with quick and interactive access (Forman, 1997). Most importantly, OLAP has the ability to provide managers with information that they need to make effective decisions related to an organization’s strategic directions. It generates a multidimensional view of aggregate data to provide quick access to strategic information for further analysis by its special drill-and-slice functionality. It has ability to provide just-in-time information for effective decision-making. Using OLAP, executives are able to access information buried in the database and to analyze data across any dimension and at any level of aggregation (Lau et al., 2001). Nevertheless, OLAP technology lacks the ability in predicting and forecasting forthcoming events or alerting the possible unnoticed mishaps (Lau et al., 2002). Therefore, an intelligent element is needed to enhance the performance of the decision support system. A neural network is therefore suggested to complement the OLAP technology in the proposed system infrastructure, taking advantage of neural network learning and intelligence capability. A neural network, also known as connectionist set, is a set of simple but highly interconnected processing elements that are capable of learning information that is presented to network; it is a system arranged in patterns similar to biological neural nets and is modeled like the human brain (Dhar and Stein, 1997). However, the neural network’s limitation is that its input and output variables are linked together based on a ‘‘black-box’’ mechanism and therefore cannot be easily explained. This drawback is now dealt with the approach of OLAP that adopts traditional data management technology. Like other simulation models, the neural network substitutes the real system in predicting and controlling system responses for the purpose of dynamic control. It is a technology that has been used for prediction, clustering, classification, and alerting of abnormal patterns (Haykin, 1994). The proposed system contains a knowledge-based system, which encompasses an OLAP module and a neural network module. The knowledge-based system extracts data from system repository which stores the transaction data and vendors’ information and enshrine the data in OLAP to take advantage of its provision of multi-dimensional views on the scattered data and generation of aggregated data to gather and provide the vendors’ information for further assessment (see Figure 1). As mentioned above, OLAP is lack of intelligence function; a neural network is built to complement the deficiency of OLAP. A neural network is responsible for assessing the candidate vendors based on their performance history, and to benchmark against the product desire relevant criteria with the best-of-class performance measure. A neural network can be effectively applied to a vast array of problems and data, many of which have been thought to be too complex or lack sophisticated theoretical models. Furthermore, neural networks do not learn by adding representatives to their knowledge base, instead, they learn by modifying their overall structure in order to adapt to the contingencies of

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Figure 1 System infrastructure

g

y

m

Purchasers

N Network

Final Decision

the world they inhabit (Sauter, 1997; Luger and Stubblefield, 1998). Indeed, neural networks have been found to be very good at modeling complex and poor understood problems for which sufficient data are collected (Dhar and Stein, 1997). Most importantly, neural networks are especially suitable when used as a forecasting and further analysis tool because it is capable to find a solution based on categorizing a multi-dimensional input vector and thereby selecting an appropriate output vector (Fowler, 2000). A benchmarking process that is part of the neural network module is used to select the most appropriate vendors, providing the relevant facts and data that are needed as input for the projected performance. Specific measures should be aligned with strategic objectives to ensure that the factors for benchmarking are consistent with the corporate goals. The benchmarking process predicts, based on the industry past data, how well or how poor the vendors will perform over the forthcoming period of time. Since the system predicts the performance of the vendors with the machine-learning feature based on historical data, neural network lends itself to be the most appropriate method with its power for generating a forecast.

System implementation The OLAP module provides analytical capabilities and the neural network model benchmarks the vendors to suggest the most suitable vendor. It is assumed that the companies using the system already have records of the best-of-class vendor performance as a benchmarking reference in its relevant industry and detailed information on the vendors. The procedures and operations of the system are shown in Figure 2. Before the implementation of OLAP, the calculated member is constructed by the measures. The first step of the process is to identify the criteria in terms of quality, cost, and delivery schedule based on the new product requirements. The purpose of the OLAP module is to collect data related to the procurements and vendors, and generate aggregated data to feed the neural network. The OLAP module is capable of providing insights to the data by putting the data into multi-dimensional views. The OLAP module evaluates the vendor based on six assessment factors:

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Figure 2 Proposed system processes

r

t

(1) Product type. (2) Product methodology. (3) Materials. (4) Production cost. (5) Delivery time. (6) Defect rate. Each of the factors would have a weight (W) that signifies the importance of the relevant factor. The OLAP aggregated the data to generate the seven assessment factors and assesses each by applying the weight on each factor to generate scores (S): Total weighted score ¼

n X

Si W i :

i¼1

The OLAP module maintains the information about each vendor with vectors containing the following factors: product code, product type, production methodology, materials, production cost, defect rate, delivery time, vendor name, vendor phone, customer service, production methodology score, quality score, production cost score, delivery time score, and customer service score.

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To identify the competency of the potential vendors, the total weight score of each of the vendors is compared with an ideal score assigned that depends on the product type. For example, the ideal score for product type code C95-G81M is 0.85. Any vendor with total weighted score higher than 0.85 would be considered as suitable candidate and further analysis would be carried out to identify the best vendor among the selected group who meet the threshold. However, if no vendor meets the threshold, the system would advise the user to seek new vendors or lower the threshold qualification level. With the OLAP module being regarded as a front-end part, the neural network module is employed as a back-end part of the proposed system. The neural network module is made to evaluate the selected vendors, which are identified in the OLAP module, who are the best among the group to undertake the specific purchase order for the parts of a product. With the data results from the OLAP module, this module analyzes the vendors’ relevant characteristics of which are necessary to meet the standard of the product (see Figure 3). The input layer of the neural network includes three categories: (1) Quality: the quality standard of the vendor is measured by the defect rate and scrap rate of the materials supplied. (2) Cost: the cost includes the material cost and delivery cost. (3) Delivery schedule: the delivery schedule describes the records of on-time delivery. All of which are abstracted from past company performance records, are studied and utilized. Each category is assigned with a score ranging from 1 (poor or low) to 7 (excellent or high). For the neural networks consists of 15 input nodes (five sets of the last five records including quality, cost and delivery schedule) and five output nodes (various suggested action to be taken) are used, as shown in Figure 4. The historical records each associated vendor is submitted to the input layer of the neural network. After processing, the neural network will give an output node that is the score that includes the performance value on reliability of quality, consistence of delivery time, and competitiveness of cost. To identify the most qualified vendor, the output score is then compared with the best-in-class performance standard to benchmark the vendor’s performance, and judged against the expected Figure 3 Neural network mapping

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Figure 4 Mapping of input and output nodes of neural network module of the proposed system

Latest Track

Assessment Report

N

r

p

m

standard score of the specific product to see the fitness of the vendor’s performance with the product desire quality and standards. Figure 5 showed an example of the performance record of a vendor with the vendor’s quality, cost, and delivery schedule. From the example, one can see the trend of delivery is improving with an upturn tendency of quality. However, the cost is fluctuating dramatically, which may suggest that the vendor is putting afford in high quality and delivery on time, but the vendor may still have difficulty in controlling the cost. After the vendor is selected and the purchase order is issued, performance reviews of the selected vendor are carried out continuously with the latest data captured by the system repository to update the records. This would be able to ensure the system is acquiring more knowledge and experience through the learning process over time.

Figure 5 Performance of vendor historical records

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It is important to note that the performance of the neural network depends heavily on the training sets of data. In other words, enough training for the neural network model is essential, especially trained with the actual purchasing orders in which decisions have been made rather than with the data that is not realistic. Furthermore, it is desirable to have better distribution of training sets that cover as many situations as possible.

Case example To validate the proposed system, a prototype has been developed and tested in GPP Limited, which produces plastic toys and premium products in Hong Kong and exports their products directly to customers in the US, Europe, and Japan. The company manufactures toys for a number of worldwide cartoon companies; it produces products based on the customer needs. The company has a procurement department that is responsible to purchase raw materials and parts from all over the world. However, as the company expands rapidly and works with numerous numbers of vendors, the purchasing staffs is now facing the problems in identifying the appropriate vendors for specific product parts and raw materials. The procedures that GPP Ltd used to select the vendors were based on the experience of the purchasing staff. However, problems have occurred due to the purchasing staff turnover, and the selection process often leads to unexpected outcome such as late delivery, high defect rate, etc. In addition, the vendor selecting process is inconsistent as the decisions are made with subjective views of the purchasing staff. The hybrid system encompassing OLAP and neural network was built using Visual Basic as the main development program with Qnet (qnetv2k.com) for the implementation of neural network module. GPP Ltd provides sets of historical records of the performance of vendors who have supplied materials and parts to GPP Ltd, as well as best-in-class performance measures of the industry. The new product that is used to test the system is a cartoon character plastic watch – Super FaFa Watch. GPP Ltd decided to assemble the watch in-house and order all the parts from vendors to reduce production cost. The product specifications are shown in Table I. To identify the potential vendors after identifying the assessment factors, the next step is to assign the weights to the relevant factors. The factors include the production methodology score, quality score, production cost score, delivery time score, and customer service score, and the weights are 0.3, 0.2, 0.4, 0.1, and 0.1 respectively. A total weighted score is calculated for all the vendors and the vendors who have total weighted score of more than 0.75 are chosen and considered to be potential vendors who are suitable to supply the parts and materials based on the given product specifications. Four vendors have a total weighted score of more than 0.75 and they are PP Ltd, SWS Ltd, WY Ltd, and KCC Ltd. Training for the neural network is necessary to ensure the neural network produces reliable outputs. GPP Ltd trained the network with 120 sets of historical data based on previous actual results. The vendors selected from the OLAP module would be compared with their performance against the best-in-class performance and the product (Super FaFa Watch) desire quality and standard. The factors that are fed into the neural network include quality, cost, and delivery schedule. Table I Product specifications

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Assessment factors

Specifications

Product name Product type Production methodology Material Production cost Defect rate Delivery time

Super FaFa Watch Plastic watch Assembly Plastic bracelet, watch dial, plastic stamp ,$4,000 ,5 percent 30 days

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Figure 6 shows the latest five records of the selected vendors. The quality of supply of PP Ltd is dropping, so as its delivery schedule was not always on time. However, PP Ltd is able to offer better discount to GPP Ltd over time. Indeed, it is difficult to tell the projected performance of PP Ltd as the trends are fluctuating without a consistent path. There is an upward trend on all the characteristics of SWS Ltd. In other words, SWS Ltd is able to supply quality parts and delivery on time, but it also charges a higher price over time. In contrast, WY Ltd offers more discounts to GPP Ltd and it is able to deliver on time, but the quality of its supplied products is having a downturn. It would be difficult to predict the future performance of WY Ltd since there are ups and downs in different areas based on the historical data. Lastly, KCC Ltd is able to provide quality supplies and offer more discounts in the recent transactions. However, it is in generaly unable to deliver on time. After such information has been input, the neural network module gives an assessment report back to the user, thus supporting the user to take action if deemed necessary. In Table II, ‘‘0’’ output from the neural node indicates a negative suggestion to the associated statement and ‘‘1’’ is the positive suggestion whereas ‘‘0.5’’ indicates that there is not enough data to justify a firm suggestion.

Figure 6 Selected vendors performance

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Table II Output from the neural node Company/output from NN module Potentially competent Suggested to be replaced Service quality is compromised to meet the quoted price Further assessment of company performance is required Delivery time seems to be inconsistent due to certain company problems

PP Ltd

SWS Ltd

WY Ltd

KCC Ltd

0.5 0

0.5 0

1 0

1 0

1

0

0

1

1

0

1

0

1

0

1

0.5

Based on the analysis by the neural network, the results of benchmarking with the best-in-class performance and the product desire quality and standard, KCC Ltd is suggested by the system. To evaluate the performance of the proposed system, GPP Ltd has used the proposed system to select vendors for a number of products and has compared the results with the decision made by the purchasing staff. The result is shown in Table III and indicates that the adoption of the proposed system is able to choose the right vendors, however, there is room for further improvement of the proposed system and the system is expected to produce more accurate output as the neural network is trained with more data.

Evaluation From the case example, one can find that the system has embedded the knowledge of the purchasing staff and is able to make decisions for selecting suitable vendors consistently based on the historical performance of the vendors and the requirements of the product. Although the proposed system is able to provide impressive results, the system still has some limitations. The maintenance and amendment of the system requires a thorough understanding of the system and relationships among the parameters. In order to increase the system accuracy, the management of the feedback system plays a vital part in the system maintenance. The information engineer is responsible to learn the needs of the enterprise, and to be able to define requirements in order to learn how to design an enterprise-wide information system. Since the neural network is built with sets of historical data, it is difficult to guarantee the network will provide satisfactory results, especially when the network is used in different situations where the input feed into the network is not from the same domain. Moreover, the neural network does not have the sensibility characteristic like the purchasing staff; it is not able to identify the environment changes which need to re-adjust the output to fit the environment, which leaves room for further improvement to the system. To make sure that the predicted variable could be adjusted in response to change in the performance of vendors, updated data should be fed into the repository and passed to the neural network for training regularly.

Table III Vendors selection results Assessment Delay in final product delivery Defect rate Customer complaint

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By human (%)

By proposed system (%)

Management expectation (%)

23 15 12

15 8 8

5 5 5

Conclusion Economic organizations always devote their full efforts to obtain the best available information in order to make information decisions. The proposed system that embedded human knowledge with the OLAP system and the neural network has been described in the paper. It demonstrates the benefits of using combinations of technologies to form an integrated system, which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. The unique feature of this knowledge-based system has been tested with impressive results while comparing with the decisions made by the purchasing staffs. Further research and development of the system can be done to expand the system domain and to provide flexibility to the system that allows users to add more related factors for analysis in order to enhance the quality of decision making in choosing the most appropriate vendors.

References Ba, S., Stallaert, J. and Whinston, A.B. (2001), ‘‘Research commentary: introducing a third dimension in information systems design – the case for incentive alignment’’, Information Systems Research, Vol. 12 No. 3, pp. 226-39. Davenport, T. (1997), ‘‘Secrets of successful knowledge management’’, available at: http://webcom. com/quantera/secrets.html Davenport, T.H. and Prusak, L. (2000), Working Knowledge, Harvard Business School Press, Boston, MA. Dhar, V. and Stein, R. (1997), Seven Methods for Transforming Corporate Data into Business Intelligence, Prentice-Hall, Upper Saddle River, NJ. Forman, S. (1997), ‘‘OLAP Council’’, White paper, available at: www.olapcouncil.org Fowler, A. (2000), ‘‘The role of AI-based technology in support of the knowledge management value activity cycle’’, Journal of Strategic Information Systems, Vol. 9 No. 2/3, pp. 107-28. Haykin, S. (1994), Neural Networks. A Comprehensive Foundation, Macmillan, New York, NY. Inmon, W.H. (1992), ‘‘Data warehouse – a perspective of data over time’’, Database Management, February, pp. 370-90. Kasabov, N.K. (1999), Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, NetLibrary Inc., Boulder, CO. Koch, F.L. and Westphall, C.B. (2001), ‘‘Decentralized network management using distributed artificial intelligence’’, Journal of Networks and Systems Management, Vol. 9 No. 4, pp. 375-88. Kumar, A. and Olmeda, I. (1999), ‘‘A study of composite or hybrid classifiers for knowledge discovery’’, Journal of Computing, Vol. 11 No. 3, pp. 267-77. Lau, H.C.W., Ip, R.W.L. and Chan, F.T.S. (2002), ‘‘An intelligent information infrastructure to support knowledge discovery’’, Expert System with Applications, Vol. 22, pp. 1-10. Lau, H.C.W., Bing, J., Lee, W.B. and Lau, K.H. (2001), ‘‘Development of an intelligent data-mining system for dispersed manufacturing network’’, Expert Systems, Vol. 18 No. 4. Laudon, C.K. and Laudon, J.P. (2000), Management Information Systems: Organization and Technology in the Networked Enterprise, Prentice-Hall, Englewood Cliffs, NJ. Luger, G.F. and Stubblefield, W.A. (1998), Artificial Intelligence Structures and Strategies for Complex Problem Solving, 3rd ed., Addison-Wesley Longman, Glen View, IL. Mockler, R.J. (1989), Knowledge-based Systems for Management Decisions, Prentice-Hall, Englewood Cliffs, NJ. Montaldo, E., Sacile, R. and Boccalatte, A. (2003), ‘‘Enhancing workflow management in the manufacturing information system of a small-medium enterprise: an agent-based approach’’, Information Systems Frontiers, Vol. 5 No. 2, pp. 195-205. Ofek, E. and Sarvary, M. (2001), ‘‘Leveraging the customer base: creating competitive advantage through knowledge management’’, Management Science, Vol. 47 No. 11, pp. 1441-56.

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Peterson, T. (2000), Microsoft OLAP Unleashed, 2nd ed., Sams Publishing, Indianapolis, IN. Sauter, V.L. (1997), Decision Support Systems: An Applied Managerial Approach, John Wiley, New York, NY. Sharda, R. (1994), ‘‘Neural networks for the MS/OR analyst: an application biography’’, Interfaces, Vol. 24 No. 2, pp. 116-30. Tan, X., Yen, D.C. and Fang, X. (2003), ‘‘Web warehousing: web technology meets data warehousing’’, Tehcnology in Society, Vol. 25, pp. 131-48. Thomas, H. and Datta, A. (2001), ‘‘A conceptual model and algebra for online analytical processing in decision support databases’’, Information Systems Research, Vol. 12 No. 1, pp. 83-102. Thomsen, E. (1999), Microsoft OLAP Solutions, Wiley, New York, NY. Tiwana, A. (2000), The Knowledge Management Toolkit, Prentice-Hall, Englewood Cliffs, NJ. Turban, E. (1988), Decision Support and Expert Systems: Managerial Perspective, Macmillan Publishing Company, New York, NY. Wasserman, P.D. (1989), Neural Computing Theory and Practice, Van Nostrand Reinhold, New York, NY. Weigend, A.S., Wiener, E.D. and Pedersen, J.O. (1999), ‘‘Exploiting hierarchy in text categorization’’, Information Retrieval, Vol. 1 No. 3, pp. 193-216.

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The ‘‘global’’ and the ‘‘local’’ in knowledge management Joseph G. Davis, Eswaran Subrahmanian and Arthur W. Westerberg

Joseph G. Davis is at the School of Information Technologies, and Language and Knowledge Management Research Laboratory, The University of Sydney, Sydney, NSW, Australia ([email protected]). Eswaran Subrahmanian is Principal Research Scientist at the Institute for Complex Engineered Systems (ICES), Carnegie Mellon University, Pittsburgh, PA, USA ([email protected]). Arthur W. Westerberg is University Professor and Swearingan Professor of Chemical Engineering at Carnegie Mellon University, Pittsburgh, PA, USA ([email protected]).

Abstract Purpose – This paper aims to unravel the complexities associated with knowledge sharing in large global organizations through a field study carried out in a large, multinational company (Du Pont), focusing on the critical issues, concrete practices, bottle-necks, and constraints in knowledge sharing. The tension between ‘‘local’’ production of much of the knowledge and its globalizing is specifically addressed. Design/methodology/approach – Qualitative analysis based on a detailed case study of the knowledge-sharing practices in two business units, two functional areas (R&D and engineering project management) in four countries. Findings – Focus on certain types of organizational knowledge to the exclusion of others can be counter-productive. Knowledge management (KM) systems need to be integrative and flexible enough to facilitate the dynamic interplay between different forms of knowledge across the space and time. Research limitations/implications – The results of a case study are somewhat limited in terms of their generalizablity. Practical implications – The insights from the study offer useful guidelines for designing systems and processes for sharing and managing knowledge in large, diversified organizations. Originality/value – Most field-based investigations into knowledge management tend to focus on specific KM projects. This is one of the few comprehensive studies that analyzed knowledge-sharing practices and constraints at both local and global level in large organizations. It elucidates the key facilitators and inhibitors of knowledge sharing in such organizations. Keywords Knowledge management, Knowledge organizations, Multinational companies Paper type Case study

Introduction

This research was funded by a grant from the Carnegie Bosch Institute, Pittsburgh, PA under the ‘‘impact of global information revolution on international management’’ program.

DOI 10.1108/13673270510582992

The notion of contemporary organizations as knowledge producing, sharing, and disseminating entities is gaining rapid currency among researchers in a variety of fields. While the critical role played by the stock and application of knowledge in economic development at the macro-economic level is relatively well understood (Machlup, 1980; Nelson and Winter 1982; Eliasson et al., 1990), its centrality in the management of individual firms is more of a recent concern. This interest is perhaps a response to the challenges posed by an increasingly complex business environment characterized by intensified competition, greater globalization, and compressed product life cycles and the consequent information overload for senior management. Concurrently, advances in information and communications technologies (ICT) in the form computer-supported cooperative work (CSCW) systems, groupware, internet, intranet, and the world-wide web (WWW) offer capabilities for developing effective solutions to the KSM problem. The issues referred to above have been faced in a more acute form by large, multinational corporations (MNCs) for which the forces of global integration, local differentiation, and

VOL. 9 NO. 1 2005, pp. 101-112, Emerald Group Publishing Limited, ISSN 1367-3270

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‘‘ Information overload is a constant refrain, especially among the R&D personnel we interviewed. ’’

worldwide innovation have become more compelling. Several scholars have argued that such firms have had to devise means to enhance their global flexibility and learning levels in order to stay competitive (Bartlett and Ghoshal, 1989; Doz and Prahalad, 1991). This is increasingly achieved through the adoption of new organizational capabilities for pooling world-wide knowledge and to transfer and adapt innovative product and process technologies and project management know-how to international markets. We investigate empirically the organizational and technological mechanisms employed by MNCs to promote knowledge sharing and to develop and manage their intellectual resources. The implicit assumptions that underpin the category of ‘‘knowledge’’ in this context as reflected in the relevant literature and contemporary organizational practices will be explored and the local-global dialectic in the creation and sharing of this knowledge will be investigated. The KSM problematic in large, global organizations is exacerbated by the local-global dialectic arising from the tension between ‘‘local’’ production of much of the knowledge and its globalization and recreation in new contexts. The local production occurs as a result of distributed R&D operations, joint ventures with strategic partners, and through collaborative work with advanced and demanding customers. From an anthropological perspective, most local knowledge is constituted as complex and coherent wholes in the form of intricate webs of meaning (Geertz, 1983). Van Krogh et al. have addressed some of the challenges in globalizing and recontextualizing such local knowledge through a process of triggering, packaging/dispatching, and re-creation of the knowledge in new contexts (Van Krogh et al., 2000, pp. 207-239). This study was carried out as a field-based investigation into the sources and the conceptualization of organizational knowledge and KSM practices in R&D and project management in a large MNC – Du Pont, a diversified, manufacturing company headquartered in the US. The company has extensive R&D and project management operations in a number of countries. The data gathering was carried out through in-depth, semi-structured interviews with key R&D and engineering project management (EPM) executives in four different countries in two separate business units and the corporate headquarters.

Overview of the relevant literature Organizational knowledge There is emerging consensus that perhaps the most important source of sustainable competitive advantage in an increasingly turbulent global business environment is knowledge. The organizational capability to create, recognize, disseminate widely, and embody knowledge in new products and technologies is critical when faced with shifting markets, rapid product obsolescence, hyper-competition, and financial upheavals (Nonaka, 1991). From an epistemological standpoint, most of what is characterized as organizational knowledge falls within the purview of ‘‘weak knowledge’’ (Goldman, 1999). This is sharply contrasted with ‘‘strong knowledge’’ which is the traditional focus of philosophical inquiry. Strong knowledge imposes very stringent criteria for the use of the term ‘‘knowledge’’ which includes true belief backed up by valid justification or warrant for the belief, and the systematic exclusion of alternative possibilities. Such knowledge is rarely attainable in organizational domains. Besides, this form of skeptical rationalism bears little resemblance to the action-centered processes of generating, sharing, and utilizing knowledge in organizations. It is unlikely that the ‘‘strong knowledge’’ epistemology is particularly helpful[1] in view of the inter-subjective and social nature of much of organizational

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‘‘ Diversity of media and the lack of integration present challenges for effective knowledge processing. ’’

knowledge. The latter tends to privilege consensus, immediacy, and perceived use value over strong-verifiability, super-objectivity, and transcendence. As well, weak knowledge tends to be ‘‘sticky’’ and local and rendering it more global is one of the significant challenges for knowledge management. Traditional microeconomic theory depicts (technical) knowledge as a quasi-public good. It is characterized by high levels of indivisibility and non-excludability. Its generation is the result of scientific research and general methodological procedures. Its transfer is largely unproblematic and is viewed as a spontaneous aspect of the economic system. The ability to appropriate the knowledge by the innovator is low even though patenting and intellectual property rights can reduce the scope for societal benefits from the knowledge (Arrow, 1969, 1994; Antonelli, 1999). This perspective has been challenged by a number of researchers. The distinction between technological information and technological knowledge is sharply drawn with the latter conceptualized as incorporating a set of capabilities and competences needed to utilize the knowledge which in turn can be leveraged to generate new knowledge. Such knowledge is generated by a process characterized by cumulativeness and path dependence (Jorde and Teece, 1990; David, 1993). Knowledge, according to this view, is highly localized and embedded in the previous background and experience of individual firms. It is the result of a learning process and involves highly specific and relatively ‘‘tacit’’ knowledge processing (Antonelli, 1999). The central role of knowledge in the firm and the organizational processes and mechanisms for its integration and sharing across national borders is the primary basis of Kogut and Zander’s theory of the multinational corporation. They have also highlighted the need for the mechanisms to be sensitive to the degree of tacitness or codifiability of the knowledge (Kogut and Zander, 1992a, b). A range of definitions and perspectives on knowledge has been presented in the organizational literature. Kerssens-Van Drongelen et al. (1996) defines knowledge primarily in the context of R&D as ‘‘. . . information internalized by means of research, study, or experience, that has value for the organization’’ (Kerssens-Van Drongelen et al., 1996, p. 214). Similar conceptualizations of knowledge as the result of processing and refining of information have been implicitly or explicitly employed by a number of authors. This view is an extension of the information-processing paradigm popularized by March and Simon (1959), Simon (1977), and Galbraith (1974). More recently Simon (1996) has argued that the challenge for today’s managers is to filter and extract relevant knowledge from the vast amounts of potential and actual information available from internal and external sources. Effective systems need to be designed to intelligently filter information. In a similar vein, Davenport et al. (1998) describe knowledge as information combined with experience, context, interpretation, and reflection and knowledge production as comprising value addition to information (Davenport et al., 1998). Information is the flow of messages or meanings that might add to, restructure, or change knowledge according to Machlup (1980). Coyne (1997) refers to these and related representations of knowledge as the system theoretical view according to which the essential knowledge is contained in the information content and the subjective, inter-subjective, and spatial aspects are largely ignored. An alternative view has been championed by Dretske (1981), Nonaka (1991 1994), among others. This perspective acknowledges the importance of subjective factors such as beliefs and their links to actions as well as the relatively tacit dimension of knowledge. Knowledge is associated with beliefs produced and sustained by information (Dretske, 1981). Information

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represents a flow of messages but knowledge is created and organized from it, anchored by the commitments and beliefs of the concerned individuals. There is also a connection between such knowledge and the subject’s ability to plan and act. The more implicit and tacit dimension of knowledge has also been highlighted. For Polanyi (1967), explicit or codified knowledge is what is transmittable through formal and systematic languages. Tacit knowledge is more personal and subjective, making it difficult to be formalized and communicated. It tends to be deeply rooted in action, commitment, and involvement in a specific context. According to Nonaka, individuals are able to recreate their own systems of knowledge to accommodate ambiguity, noise, and randomness generated in the organization in its interaction with the external environment (Nonaka, 1994). Such knowledge resides in situated communities of interpreters rather than in texts or messages and these make sense only in particular interpretive contexts (Reddy, 1979). These communities emerge not through absorption of abstract knowledge but when members become insiders and acquire the community’s shared vision and speak the same language (Brown and Duguid (1991). The notion of contextualization of knowledge and evolving communities of practice have particular resonance for MNCs given the geographic distances and cultural differences across units around the world. Following Coyne (1997), we refer to this perspective as the pragmatic view. Table I presents a somewhat stylized set of distinctions between the systems-theoretic and pragmatic perspectives on organizational knowledge. It is worth noting that the dichotomy between system theoretical and pragmatic perspectives and their respective epistemological and ontological assumptions pervade most of the writings on organizational knowledge. As we would expect, the fundamental differences between the system theoretical and pragmatic views in characterizing organizational knowledge are reflected in the divergent approaches and perspectives on knowledge creation, sharing and management in organizations. In general, the former tends to focus on structural and systemic approaches while the latter emphasizes human-centered processes such as socialization, self-organizing teams, extended social interactions, personnel rotation etc. Besides the diverse modes of knowledge creation and transformation, the globalized firms are faced with the challenge of mobilizing and integrating fragmented forms of knowledge spread all over the world (Cohendet et al., 1999). As well, developments in information and communications technologies (ICT) are increasingly making it easier to separate, transport, and trade knowledge (Antonelli, 1996).

Research methods and techniques The fundamental research questions we addressed were the following: B

What are the different mechanisms by which large, multi-national corporations (MNCs) pool, exchange, and share knowledge?

B

What procedures are employed to contextualize, validate, verify, and authenticate the knowledge generated? What is the nature of the local-global problematic?

B

What is the role of information technology in supporting KSM?

Table I Perspectives on organizational knowledge

Source Form Transfer Organization Philosophical perspective

j

System theoretic view

Pragmatic view

Documents, databases, systems, prototypes, processes and procedures, manuals etc. Codified or codifiable, explicit Exchange of documents, electronic means, formal training Relatively mechanistic Cartesian, separation of mind and body

People, communities

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Tacit, implicit, intuitive Socialization, apprenticeship, osmotic, situated learning Organic More holistic, unity of mind and body

‘‘ The ongoing changes in computer hardware, operating systems platforms, data formats, and software versions tend to limit the archival value of electronic documents.‘‘

The field study was carried out at Du Pont, a large, diversified, global manufacturing company with its headquarters in the US. Du Pont has world-wide operations and a strong commitment to understanding and sharing knowledge across the various business functions and units. The industries and markets that Du Pont serves range from traditional and specialized nylons, polymers, polyesters, and fibers to agriculture, nutrition, and pharmaceuticals organized under eight business segments and 20 business units. Through most of its history, Du Pont has seen itself as a science-based company with the mission of bringing the benefits of science to the marketplace in ways that benefit the shareholders and society. Scientific and technological knowledge in chemical and material sciences and biological sciences is the basis for the company’s business portfolio. In order to keep the investigation focused and within manageable proportions, we restricted our data collection activities to knowledge in of two functional areas (R&D and engineering project management (EPM)), two business units (Engineering Polymers and Microcircuit Materials, a part of the iTECH business unit previously named Photopolymers and Electronic Materials), in four countries (USA, UK, Switzerland, and Japan). R&D and EPM are both highly knowledge-intensive domains that are at the core of Du Pont’s global operations. While R&D activities at Du Pont are performed at the corporate level as well as distributed at multiple sites in each of the business units, EPM is largely centralized at the corporate level. Both Microcircuit Materials (MCM) of iTECH and Engineering Polymers (EP) have manufacturing, marketing, and R&D operations spread out across the Americas, Europe, and Asia. The data gathering and analysis methods most appropriate to the questions we have raised are primarily qualitative and interpretive. Direct observation is clearly the best approach to investigating these problems where the process dynamics needs to be captured and the potential for multiple, conflicting interpretations can be expected. However, direct observation of the focal phenomenon can be extremely difficult and can impose an inordinate burden on the participating organizations. Accordingly, we settled for in-depth interviews with a cross section of professionals. By asking a common and structured set of questions based on semi-structured questionnaires, we attempted to unravel the complex and sometimes implicit processes of knowledge sharing, their relative efficacy in different contexts, and the bottlenecks to effective sharing. It also enabled us to understand and interpret knowledge sharing in context, which is very important from our perspective. We also collected copies of documents that provided us with a more comprehensive portrait of KSM activities. This makes up, to some extent, for the limitations arising from not being able to investigate knowledge management issues longitudinally. The nature of this kind of interpretive data gathering required that we analyze, interpret, and reanalyze the data as it was collected. All except eight of the 44 respondents who participated in the study were interviewed at their normal places of work. This was important because it gave them an opportunity to access and refer to documents, to demonstrate the system(s) they use, and to point to additional sources. Each interview lasted between 75 and 90 minutes and the responses were recorded by two of the researchers. A profile of the participants in the study is presented in the Appendix.

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Case study: findings and discussion Explicit forms of knowledge Du Pont has a long tradition of relatively successful deployment of procedures and systems for codification and document management. It pioneered the development of systems for the storage and retrieval of scientific and technical information. The migration of many of the document and knowledge bases to the web is progressing steadily. At the corporate level, the C3PO initiative involving the company-wide rollout of Lotus Notes and related intranet software is meant to provide the architecture for global collaboration. As well, the corporate information services (CIS) unit is responsible for managing Du Pont’s proprietary knowledge, competitive intelligence function, and the libraries. The general emphasis on codification of knowledge is intended to ensure greater standardization and company-wide dispersion of such knowledge. We present below some of the significant issues related to their use and perceived effectiveness. Information overload is a constant refrain, especially among the R&D personnel we interviewed. It appears that the amount of information that is ‘‘pushed’’ at people through e-mail, document attachments and databases in addition to the physical means is much higher than what can be meaningfully processed by most in the time available. The filtration and search capabilities of Lotus Notes document databases and the intranet are far too primitive at this stage in relation to the need. One of the respondents compared these to dumpsters in which one had to forage hard to find something useful, if any. The time spent scanning large volumes of marginally relevant information also came in the way of the critical reflection needed to summarize and present in succinct form, their own work for the benefit of others. This issue points to the limits of codification in the absence of means for filtration and contextualization of the vast amount of local information. Diversity of media and the lack of integration present challenges for effective knowledge processing. Much of the information needed by the respondents is already in electronic form but it is fragmented across a variety of incompatible computer systems and databases. In addition, each knowledge professional typically inherits one or two filing cabinets containing hard copies of correspondence, memos, reports etc. when they begin a new assignment. The differences in the indexing schemes and search mechanisms for the different document bases impede routine activities such as finding useful fragments of information and synthesizing them in the context of particular tasks. There are critical issues with respect to archival practices that bear on knowledge sharing and management. The traditional, paper-based systems appear to be withering while a robust and company-wide electronic regime is yet to emerge. The ongoing changes in computer hardware, operating systems platforms, data formats, and software versions tend to limit the archival value of electronic documents. The old adage of ‘‘if only a company knew everything it knows’’ could be paraphrased as ‘‘if only a company ‘knew’ everything that is buried in inaccessible computer systems distributed in time and space’’. Repositories are available at different levels in the Du Pont units we studied. There are individual repositories and databases that hold much of the local knowledge that is generated. There are also repositories at the level of small groups of people who work together on the same project or with one or a group of customers. The groups need not always be co-located or working synchronously. Finally there are business units or corporate-level information bases that are maintained. It is inevitable that there will be some duplication and inconsistency across the three levels of repositories. More importantly, the limited general access and the terminological and linguistic differences across these levels hinder the potential for knowledge sharing. As we shall see later, critical human interlocutors emerge at the interfaces between the levels to ameliorate the problem.

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Several commentators have emphasized the importance of naturally occurring local learning and innovation that takes place at all levels of an organization (e.g. Brown, 1991). A related issue is one of facilitating the wider sharing of relevant aspects of such local knowledge through appropriate organizational processes and systems. We found in the course our study that in many situations, such knowledge is embedded in organically evolving and bootlegged systems quite independent of the formal mechanisms available for systematizing and sharing. The reasons for the emergence of such local mechanisms are varied and include: B

the need for specialized structures and capabilities to represent the knowledge;

B

lack of timely support and assistance for the use of standardized tools and mechanisms; and

B

idiosyncratic attachment to particular computer tools and legacy systems by some of the employees.

In any case, the need to track, evaluate, support, and selectively integrate such local and bottom-up systems cannot be overstated. These are useful initiatives arising from the need to formalize and disseminate localized knowledge with potentially wider implications that arise as a by-product of routine operations. We were able to document a number of these at both MCM and EP though the preponderance of such initiatives at MCM is worth noting. The central engineering group at Du Pont has traditionally been the pioneers in the use of electronic collaboration technology. This is not particularly surprising given the global distribution of project work and the need for rapid, multi-directional knowledge flow among Du Pont engineers, vendors, external contractors, and joint venture partners. As one of the respondents put it, it is a trade-off between getting to work remotely with the collaboration tools or being away from family for long durations. Also, a great deal of effort has been expended in the past aimed at codifying and standardizing a large part of the process knowledge generated over the past 50 years of project work. We observed a preponderance of explicit knowledge-based mechanisms being successfully deployed and used in EPM. More importantly, systematized knowledge in the areas of systems for plant safety (design and monitoring) and environmental pollution prevention have become saleable commodities in the form of consulting packages. There is growing demand for such packaged know-how especially in the Asia-Pacific region. The ability of the engineering design group to transcend the local-global divide may be attributable to the maturity of the knowledge base and the relative homogeneity in engineering education. There are differences between the Du Pont units in the modes of structuring and organizing knowledge. For instance, MCM operates in a highly dynamic market place in which rapid learning from the customers and adapting to their changing needs are normative. This has led to the organization of MCM repositories to be primarily based on products, customer segments, and application areas and only secondarily on materials. This is despite the fact that the training and specialization of the scientists and technical support personnel is by materials. In contrast, the business strategy of EP is driven by material push and the knowledge organization at EP is primarily along the material dimension (such as Hytrel, Minlon, Delrin etc.) and within each by major customer groups and other equipment manufacturers (OEMs). The need for multiple and divergent classification schemes and indexing systems is a routine aspect of knowledge processing. This has important implications for the design, implementation, use, and maintenance of company-wide KSM solutions with their implicit assumptions regarding the relative homogeneity of knowledge representation schemes. The resources needed to carry out the difficult and unrewarding task of combining tacit and often fragmented forms of explicit knowledge into systematized and reusable knowledge and to make it publicly available is considerable. While central engineering has been successful at this for some time, the relationship between the availability of organizational slack and the group’s ability to standardize and deliver such knowledge is worth exploring. One of the issues arising from the massive outsourcing and downsizing at

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central engineering relates to the reduction in slack and its effect on codification work. There are also concerns about continual erosion of the knowledge base with the department having to rehire ex-employees and to bring in employees from contracting firms to stem the erosion.

Relatively tacit knowledge It is certainly the case that knowledge workers consult a variety of sources of explicit knowledge and fall back on their learning from formal study and training. However, in the course of actually doing their jobs, most of the learning arises from engaging with real problems, gathering a range of relevant information from diverse sources, and discussing key issues with colleagues and other professionals. Most of the R&D respondents in particular have a strong network of people spread out all over the world that they can turn to for consultation and guidance. A surprisingly large number of such contacts are from outside their own units in other business units to departments in Du Pont or from external entities such as vendors, customers, contractors, universities, and joint venture partners. Some of the respondents can count upwards of 300 people in their network and the intensity of interaction varies over time depending on the tasks on hand. These are typically bottom-up formations and successful R&D scientists spend considerable time and energy developing and nurturing such networks. The diffusion of tacit knowledge in particular through these social networks is quick. The track record of the informants in terms of both the reliability of the acquired knowledge and trust largely determines the length of such associations. Informal protocols of reciprocity also feature in the assessment of track record. Du Pont has long recognized and promoted mechanisms for such networking. In-company technology conferences and periodic seminars involving people from diverse businesses and technology areas provide facilitative forums for making initial contacts and extending one’s networks. Other processes include apprenticeship training for new scientists under well-established R&D personnel as well as the rotation of the latter through newer research sites. The Utsonomiya (Japan) research facility in MCM was established and developed primarily through people rotation and active apprentice-type training programs. In addition, short visits by scientists either to the experimental station or to other research sites within the business units are seen as useful mechanisms for the sharing of less tangible aspects of knowledge. The reliance on these mechanisms based on ‘‘high bandwidth’’ channels was more pronounced in R&D as compared to EPM and within R&D, greater in MCM in comparison with EP. This is consistent with the general observation that higher the role of tacit knowledge, greater the reliance on human- and organization-centric mechanisms. There is some concern in the R&D groups that the increasing dependence on electronic communication and coordination and the reduced opportunities for face-to-face contact are causing a slow erosion in this mode of knowledge sharing. One of the requirements for any social network to remain active and to be effective in knowledge sharing is the periodic opportunity to ‘‘catch-up’’ through face-to-face meetings, albeit infrequently. Such meetings enable the establishment and/or reinforcement of a common ground needed to cultivate and develop the trust and reciprocity implicit in such relationships. When the operations are globally dispersed as they are in MCM or EP, some of the faster and less expensive mechanisms such as telephone, e-mail, net meeting and even videoconferencing are constrained by linguistic, cultural, and time-zone related barriers. Sustaining such networks involves expensive travel and movement. The curtailment of resources for this purpose in recent years and the increasingly rigorous quarterly financial reporting systems are putting pressure on the quality and vitality of such networks. This is particularly hard on the younger scientists who are in the process of developing their personal networks and have to rely on remote, electronic mechanisms to a very large extent. Human agents were sought for a variety of reasons. Probably the strongest one that came up repeatedly in our interviews was the perception that it resulted in significant efficiencies and time savings. An illustration from an engineering polymers R&D center at Meyrin,

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Switzerland is illustrative. One of the laboratories there provides analytical support and carries out a wide range of routine and specialized tests on properties of new materials, identification of additives etc. When a scientist approaches the supervisor who has managed this laboratory for the past 15 years, he can give good and immediate answers to questions such as whether the same test has been carried out previously and if not whether the results of a closely matched previous test can be extrapolated for a particular problem that a scientist is trying to solve. The information needed to extract the knowledge is available in the form of a database of previous tests carried out and the results but this may not have the format and flexibility requirements that the scientist needs. Searching through this database is also time-consuming. Besides, the effort and difficulty involved in externalizing the tacit dimension of subtle matches with previous tests is non-trivial. In performing even routine research tasks such as interpreting the results of certain complex analytical tests, sitting down with experts who can help with the interpretation saves much time and reduces some of the guesswork. Arising from a combination of historical factors, Du Pont R&D is faced with a bimodal distribution age distribution of its personnel. The age cohort in the 50-plus range is very large, in part due to the expansionary spiral of the 1950s through early 1970s. The 40-50 age cohort is small but there is a small increase in numbers in the 25-40 age group. This introduces vulnerabilities in the form of potential erosion of tacit knowledge base in the absence of tacit-to-tacit transfer and tacit-to-explicit conversion of knowledge. Such programs are resource-intensive and can only succeed in a climate of trust and security. Also, they need to be guided by sound judgment on the future value of different bodies of tacit knowledge in relation to the company’s strategic direction and the emerging trends in the relevant product-market environments. The phased implementation of the integrated electronic coordination architecture and the C3PO project has generated incentives and pressures to make a transition to new ways of collaborating and knowledge sharing. For some aspects of knowledge work this is only a matter of adapting to a new set of computer tools and they complement other modes. However, the extent to which this regime can or needs to supplant the existing, organic and informal mechanisms of tacit knowledge sharing is unclear at this stage. Many of the respondents do make a clear distinction between electronic collaboration and the sharing of knowledge. There is a clear understanding in Du Pont of the limits of the electronic communication, collaboration, and coordination systems from a knowledge processing perspective. These systems have laid the foundation for communication, information sharing, and some amount of collaborative work across distances. It has also provided the means for more efficient and wider yet selective dissemination of documents and codified knowledge. However, the recognition that people and their networks have to be effectively and seamlessly interwoven with the computer networks and systems has triggered the search for new approaches. Even the most ardent proponents of electronic communication and coordination concede that knowledge management can only be roughly 30 percent based on the systems being implemented and the rest has to be based on people. Accordingly, a campaign to facilitate the evolution of communities of practice through the use of communication and collaboration systems has been launched. These communities are largely organic formations comprising members with specialized skills or know-how. They may rely on technology to identify potential members and establish communities by sharing useful information, offering guidance and critique, recounting related experiences etc. There are currently over a dozen such communities (such as winding experts, people with extrusion skills) in existence at Du Pont and they function like virtual guilds. A project to inventory and assess the effectiveness of such on-line communities is currently in progress. Innovation-oriented knowledge processing involving new product and process development necessitates the integration of vast amounts of explicit knowledge with certain unique and deeply personal insights acquired through direct practical experiences.

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The validity and veracity of aspects of the new knowledge being synthesized need to be tested and established through ongoing experimentation and consultations with domain experts. Much of the explicit knowledge available needs to be interpreted in the new context. We have documented a number of cases in which both routine and radical innovations were produced by combining different types of knowledge and making leaps that cut across traditional boundaries drawn by existing technologies, business units, and even disciplines. Once the feasibility of the initial idea is established, detailed experiments and trials, and tests can follow. One of the main aspects of the integrated knowledge processing described in the foregoing is the pivotal contributions by key knowledge practitioners who typically combine extensive amount of tacit knowledge with other knowledge in order to bridge the local and the global. We were able to identify four such knowledge practitioner roles that typically operate at the interfaces between disciplines, technologies, business units, functions, and businesses and customers. Each of these plays an important yet differing role in ameliorating the local-global divides. A brief description of each of the roles is presented below: B

High level synthesizer: these are typically senior technology or R&D managers with a wide range of experiences across several business units and in several operational and functional areas. They are adept at environmental scanning and closely monitor the trajectories of various technologies. They operate at the interfaces between different technology areas and business units. They develop and maintain a vast network of formal and informal knowledge sources. They look for opportunities arising from various combinations of different types of knowledge.

B

Librarian: librarians operate at the interfaces between materials, processes and product markets. By virtue of their ability to painstakingly gather, assimilate, index, and store copious amounts of information pertaining to material properties, costs, customer requirements, and changes to the requirements over time, they are able to provide timely assistance and service to other knowledge workers. They deal primarily with explicit knowledge. It takes nearly 10-15 years’ experience to grow into this knowledge practitioner role.

B

Knowledge engineer: knowledge engineers operate at the classic interface between R&D, marketing, and customers. They work closely with the customers to adapt existing products and to develop new products and applications. Some of the work might appear rather routine but the tacit dimension of knowledge is in their assessment of customers’ current and future needs. This knowledge has to be integrated with detailed and explicit knowledge concerning the company’s products, processes, and technologies.

B

Knowledge operators: knowledge operators are typically technical or customer support personnel who are usually front-line employees located close to the operational realities of the business and the market place. They accumulate and transmit tacit knowledge in the form of embodied skills (Nonaka and Takeuchi, 1995). They tend to work at the interfaces between R&D and manufacturing or R&D and customer operations. They work very closely with knowledge engineers.

Conclusion This paper contributes to the growing literature on the organizational processes and mechanisms for knowledge sharing and management especially in large, global companies. By drawing on and synthesizing related bodies of writing, we have attempted to further the debate on what constitutes organizational knowledge. The diverse forms in which such knowledge manifests leads to a review of some of mechanisms and systems for knowledge sharing and management in contemporary organizations and the issues that needs to be addressed to effectively ‘‘globalize’’ at least part of the locally generated knowledge. A detailed field study exploring the major knowledge sharing issues, practices, constraints, and mechanisms was carried out in selected departments and business units of a large, multinational company with

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knowledge-intensive operations. The field study lends qualified support to the proposition that exclusive focus on certain types of knowledge can be counter-productive. Knowledge sharing and management systems and processes in large global companies need to be integrative and flexible enough to facilitate the dynamic interplay between different forms of knowledge across the space and time.

Note 1. The role and influence of strong knowledge in the activities of the R&D department cannot be ruled out.

References Antonelli, C. (1996), ‘‘Localized knowledge production processes and information networks’’, Journal of Evolutionary Economics, Vol. 6, pp. 281-96. Antonelli, C. (1999), ‘‘The evolution of the industrial organization of the production of knowledge’’, Cambridge Journal of Economics, Vol. 23, pp. 243-60. Arrow, K. (1969), ‘‘Classificatory notes on the production and transmission of technical knowledge’’, American Economic Review, Vol. 59, pp. 29-35. Arrow, K. (1994), ‘‘Methodological individualism and social knowledge’’, American Economic Review, Vol. 84, pp. 1-9. Bartlett, A.B. and Ghoshal, S. (1989), Managing across Borders: The Transnational Solution, Harvard Business School Press, Boston, MA. Brown, J.S. (1991), ‘‘Research that reinvents the corporation’’, Harvard Business Review, Vol. 69 No. 1, pp. 102-11. Brown, J.S. and Duguid, P. (1991), ‘‘Organizational learning and communities of practice: toward a unified view of working, learning, and organization’’, Organization Science, Vol. 2 No. 1, pp. 40-57. Cohendet, P., Kern, F., Mehmanpazir, B. and Munier, F. (1999), ‘‘Knowledge coordination, competence creation, and integrated networks in globalized firms’’, Cambridge Journal of Economics, Vol. 23, pp. 223-41. Coyne, R. (1997), ‘‘Language, space, and information’’, in Droege, P. (Ed.), Intelligent Environments, Elsevier, Amsterdam, pp. 495-516. Davenport, T.H., de Long, D.H. and Beers, M.C. (1998), ‘‘Successful knowledge management projects’’, Sloan Management Review, Vol. 39 No. 2, pp. 43-57. David, P.A. (1993), ‘‘Knowledge property and the system dynamics of technological change’’, Proceedings of the World Bank Annual Conference on Development Economics, The World Bank, Washington, DC. Doz, Y. and Prahalad, C.K. (1991), ‘‘Managing MNCs: a search for a new paradigm’’, Strategic Management Journal, Vol. 12 No. 5, pp. 145-64. Dretske, F. (1981), Knowledge and the Flow of Information, MIT Press, Cambridge, MA. Eliasson, G., Foster, S., Lindberg, T., Pousette, T. and Taymaz, E. (1990), The Knowledge-Based Information Economy, The Industrial Institute for Economic and Social Research, Stockholm. Galbraith, J.R. (1974), ‘‘Organization design: an information-processing view’’, Interfaces, Vol. 4 No. 3, pp. 30-6. Geertz, C. (1983), Local Knowledge: Further Essays in Interpretive Anthropology, Basic Books Inc., New York, NY. Goldman, A.I. (1999), Knowledge in a Social World, Oxford University Press, Oxford. Jorde, T.M. and Teece, D.J. (1990), ‘‘Innovation and cooperation: implications for anti-trust’’, Journal of Economic Perspectives, Vol. 4, pp. 75-96. Kerssens-Van Drongelen, I.C., de Weerde-Nederhof, P.C. and Fissher, O.A.M. (1996), ‘‘Describing the issues of knowledge management in R&D: toward a communications and analysis tool’’, R&D Management, Vol. 26 No. 3, pp. 213-30.

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Kogut, B. and Zander, U. (1992a), ‘‘Knowledge of the firm, combinative capabilities, and the replication of technology’’, Organizational Science, Vol. 3 No. 3, pp. 383-97. Kogut, B. and Zander, U. (1992b), ‘‘Knowledge of the firm, technology transfer, and the theory of the multinational corporation’’, working paper, Institute of International Business, Stockholm School of Economics, Stockholm, December. Machlup, F. (1980), Knowledge: Its Creation, Distribution, and Economic Significance, Princeton University Press, Princeton, NJ. March, J.G. and Simon, H.A. (1959), Organizations, John Wiley, New York, NY. Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA. Nonaka, I. (1991), ‘‘The knowledge-creating company’’, Harvard Business Review, Vol. 69 No. 6, pp. 96-104. Nonaka, I. (1994), ‘‘Dynamic theory of organizational knowledge creation’’, Organization Science, Vol. 5 No. 1, pp. 14-35. Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company, Oxford University Press, New York, NY. Polanyi, M. (1967), The Tacit Dimension, Doubleday Anchor, Garden City, NY. Reddy, M. (1979), ‘‘The conduit metaphor: a case of frame conflict in our language about language’’, in Ortony, A. (Ed.), Metaphor and Thought, Cambridge University Press, Cambridge, pp. 284-324. Simon, H.A. (1977), The New Science of Management Decision, Rev. ed., Prentice-Hall, Englewood Cliffs, NJ. Simon, H.A. (1996), ‘‘Knowledge and the time to attend to it’’, paper presented at the Carnegie Bosch Institute International Conference on High Performance Global Companies, Boca Raton, FL, April 21, available at: http://cbi.gsia.cmu.edu/work/96-2.htm Van Krogh, G., Ichijo, K. and Nonaka, I. (2000), Enabling Knowledge Creation, Oxford University Press, Oxford.

Appendix Table AI Participant profile Central R&D Total no. interviewed No. of PhDs Percent of PhDs Average no. of years at Du Pont Range of years at Du Pont

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Central engineering

5 5 100

Exp. station

5 0 0

19.2 11-26

23.6 14-34

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Micro circuit materials Bristol Utsonomiya RTP (UK) (Japan)

3 3 100

9 5 56

7 4 57

29.0 11-33

21.0 9-34

11.3 1-18

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5 0 0 11.6 9-14

Engineering polymers Meyrin Parkersburg Exp. (Swiss) (WVA) station

Total

8 6 75

1 1 100

1 1 100

44 25 57

13 2-26

32 32

21 21

17.6 1-34

Knowledge management systems: finding a way with technology John S. Edwards, Duncan Shaw and Paul M. Collier

Abstract Purpose – To consider the role of technology in knowledge management in organizations, both actual and desired. Design/methodology/approach – Facilitated, computer-supported group workshops were conducted with 78 people from ten different organizations. The objective of each workshop was to review the current state of knowledge management in that organization and develop an action plan for the future. Findings – Only three organizations had adopted a strongly technology-based ‘‘solution’’ to knowledge management problems, and these followed three substantially different routes. There was a clear emphasis on the use of general information technology tools to support knowledge management activities, rather than the use of tools specific to knowledge management. Research limitations/implications – Further research is needed to help organizations make best use of generally available software such as intranets and e-mail for knowledge management. Many issues, especially human, relate to the implementation of any technology. Participation was restricted to organizations that wished to produce an action plan for knowledge management. The findings may therefore represent only ‘‘average’’ organizations, not the very best practice. John S. Edwards, Duncan Shaw and Paul M. Collier are all at the Aston Business School, Aston University, Birmingham, UK.

Practical implications – Each organization must resolve four tensions: between the quantity and quality of information/knowledge, between centralized and decentralized organization, between head office and organizational knowledge, and between ‘‘push’’ and ‘‘pull’’ processes. Originality/value – Although it is the group rather than an individual that determines what counts as knowledge, hardly any previous studies of knowledge management have collected data in a group context. Keywords Knowledge management, Communication technologies, Organizations Paper type Research paper

Introduction One of the fundamental questions in knowledge management is that of the appropriate role of information technology in knowledge management in organizations. There are various possible positions on this. Is an organization’s knowledge management system just an information technology one? Is information technology a part, but only a part, of a knowledge management system? Or is information technology really not a key issue in managing an organization’s knowledge, compared with others such as people or process issues?

This research was funded by CIMA, the Chartered Institute of Management Accountants in the UK. The authors are also grateful for the comments of the anonymous referees.

In this paper we try to shed light on these questions, using some of the results of a study into what a variety of organizations in the UK currently do by way of knowledge management, and what they believe they should be doing. We begin by reviewing some of the literature on information technology, knowledge management and knowledge management systems. We then explain the background to our study and briefly describe the methodology we used. We then concentrate on the three organizations in the study that have pursued what we identified as ‘‘technology-based’’ solutions. Discussion of the general issues raised by these three cases (and others) leads to our conclusions and thoughts about the future of knowledge management systems.

DOI 10.1108/13673270510583009

VOL. 9 NO. 1 2005, pp. 113-125, Emerald Group Publishing Limited, ISSN 1367-3270

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Information technology for knowledge management This paper concentrates on technological aspects of knowledge management (KM), although this is not to imply that this is the most important area. Davenport and Prusak (1998) describe KM as involving organizational, human and technical issues, with the advice that the technical should be treated as least important of the three. Dieng et al. (1999) add financial, economic and legal issues to this list. Our brief literature review here will similarly center on technology, and on knowledge management systems, again without wishing to imply that this is therefore the most important aspect of KM. Many authors have written about the use of various types of software in knowledge management, including Junnarkar and Brown (1997), Offsey (1997), Liebowitz (1998), Borghoff and Pareschi (1998), Dieng et al. (1999), Alavi and Leidner (1999), Hendriks and Vriens (1999), Earl (2001) and Alavi and Leidner (2001). Since the early days of knowledge management there has been a particular stream of thinking that stresses the use of knowledge-based systems software in knowledge management. Strapko (1990) was discussing this point even before the term knowledge management came into common use, while Liebowitz has been one of its main proponents, arguing that expert systems have a crucial role in institutional memory, because of their ability to capture business rules. Becerra-Fernandez (2000) gives a different kind of example, a people-finder system. It is clear that expert or knowledge-based systems software, and artificial intelligence (AI) software more generally, does have a role to play in supporting knowledge management, but in addition, so does more conventional software. Table I shows the most common forms of both AI-based and conventional software that have been suggested by various authors as offering support for knowledge management. It is noticeable that different authors address this discussion in terms varying from the very general (such as knowledge based systems and databases) to the very specific (such as genetic algorithms and workflow). Table I shows the terms as authors have used them. Surveys of the use of knowledge management systems include those by Alavi and Leidner (1999) and Zyngier (2001), and a less formal one by Edwards et al. (2003b). Our intention is not to go into detail about the various types of supporting software here, discussing their advantages and disadvantages, since our focus in this paper is on which of these systems organizations currently use, and would like to use.

Table I Different types of support for knowledge management

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AI-based

Conventional

Case-based reasoning Data mining Expert systems Genetic algorithms Intelligent agents Knowledge-based systems Multi-agent systems Neural networks ‘‘Push’’ technology

Bulletin boards Computer-supported co-operative work Databases Data warehousing Decision support systems Discussion forums Document management Electronic publishing E-mail Executive information systems Groupware Information retrieval Intranets Multimedia/hypermedia Natural language processing People finder/‘‘Yellow Pages’’ Search engines Workflow management

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Study background and methodology The organizations We conducted ten workshops, one in each of ten different organizations. Two of the organizations agreed to participate as a result of direct contact made by the researchers. Eight organizations agreed to participate following a mailing to MBA alumni of the university. These contacts became the sponsors of the research and arranged for the participants from their organizations. We sought organizations with a genuine interest in, and concern for knowledge management, and we also wished to ensure that a variety of different sizes and types of organization was included. Between five and ten participants – all from the same organization – attended each workshop. In total there were 78 participants who came from a variety of functional areas. Each workshop included an accountant, a requirement of our funding from the Chartered Institute of Management Accountants. With that exception the participants in each workshop were those selected by each organization. The criteria suggested by the researchers were that the participants should include ‘‘a sufficient spread of people with awareness of, and responsibility for, knowledge management’’ and also ‘‘one person responsible for securing the commitment of resources towards achieving whatever outcomes and actions are decided upon’’. In the event, most participants were middle or senior managers, with a sprinkling of junior managers and operational-level staff. In all but two of the workshops, one participant was at director-level or equivalent. By having a director present the groups seemed more confident in the strategy they were generating as they were getting immediate informal feedback on how the board might react, and so were able to appreciate whether or not they would realistically be allowed to implement any proposed actions. Of the ten organizations, six were for-profit, three were not-for-profit or non-profit-distributing and one was public sector. One of the not-for-profit organizations also received significant government funding. Three of the six for-profit organizations were listed PLCs, two of which were divisions of FTSE 100 companies. Two organizations were privately owned and one was a subsidiary of an overseas PLC. In terms of ‘‘business’’ sector, one was in retailing, two in manufacturing, one in design/distribution, three in services, one in consumer protection, one in social housing and the public sector organization was a police force. The participating organizations are summarized in Table II. The identity of the organizations has been disguised for reasons of confidentiality.

Table II Participating organizations Organization

Brief description of organization

Restaurants

Retail/service business with about 12 major brand names, division of FTSE100 PLC Public sector/police force with 3,700 staff and £140 million budget Design/installation of high technology equipment, subsidiary of overseas-listed company Manufacturing high technology, £100 million turnover and 800 employees, privately owned International technical/engineering consultancy, division of FTSE100 PLC Business-to-business services, 12,000 employees, turnover £200 million, listed PLC Non-profit distributing membership-owned research and development, 550 employees Non-profit registered social landlord, 500 employees managing 5,500 homes Manufacturing industrial products, privately owned Not-for-profit membership owned non-statutory consumer protection body

Police DesignInst HighTechManuf Consult B2BService R&D Housing ManufIndProd ConsumProt

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As part of our analysis (Edwards et al., 2003a), we classified the organizations’ preferred knowledge management ‘‘solutions’’ into three types: (1) Technology. (2) People. (3) Process-based. Five organizations’ approaches were classified as people based (HighTechManuf, consult, B2BService, R&D, and housing). Two were process based (ManufIndProd and ConsumProt). We add further detail here on the three organizations preferring an approach to knowledge management that placed a particular emphasis on technology: (1) ‘‘Restaurants’’ was the restaurants division of a listed PLC operating under about a dozen major brand names throughout the UK. Restaurant turnover was £1 billion in the last financial year. Most participants were from the planning and insight department. Because of the selection of participants, the workshop emphasized ‘‘head office’’ knowledge rather than the knowledge in the operating units. (2) ‘‘Police’’ was an English police force with 2,400 police officers, 1,300 support staff and a budget of £144 million. Prior to the workshop, ‘‘Police’’ had increased the police levy (the portion of the council tax that pays for police services) by 33 percent and wanted to develop a communications strategy, ‘‘a shared commitment to a shared plan’’. (3) ‘‘DesignInst’’ was the design and installation division of a high technology equipment supplier, a subsidiary of an overseas listed company. They were implementing a new enterprise accounting system and wanted to ‘‘make sense of the information we have’’. The workshop approach The methodology used to run the workshops is one that has evolved during more than 15 years of research, initially called strategic options development and analysis (SODA) (Eden and Ackermann, 1989) and more recently being renamed journey making to take account of advances in the method (Eden and Ackermann, 1998). Journey making, a mnemonic for JOint Understanding, Reflection, NEgotiation of strategY, supports groups in surfacing, exploring, synthesizing and critically reflecting for personal and collective learning (Shaw et al., 2003). During a journey making workshop computer technology is used extensively to help the participants to surface, explore and synthesize their views. Each participant has access to a laptop computer that is networked. Instead of shouting out views to the facilitator, or writing them onto ‘‘post-it’’q notes, participants type their views into the computer which is running a brainstorming-type software, Group Explorer. The views are normally four to ten words in length to make them descriptive, rather than cryptic, to the other participants. Once participants have finished typing their views into the computers, all the views are shown on a large projection screen using Decision Explorer software. They will have been clustered by content by the facilitator, to assist the group members cognitively to manage the mass of information on the screen (up to 100 different views) (Grise and Gallupe, 1999). Then participants have the opportunity to read other participants’ views, expand on them, or critique them (Shaw, 2003). Following this activity group discussion ensues on the views, clusters and causal relationships. Normally a large number of views are considered and a tremendous amount of complexity arises. The different perspectives of (up to) 15 people are each considered systematically using a transparent, structured and logical process. This ensures that: B

the group will make real progress rather than going round in circles;

B

there is equalization of air-time between participants, reducing the dominance of any individuals; and

B

each option can be fully considered before being dismissed or integrated into the action plan.

Computer brainstorming has the advantage that people:

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B

can share ideas/views simultaneously, rather than all fighting for ‘‘air-time’’ (Pinsonneault et al., 1999);

B

have anonymity when they share their views, to encourage controversial views to be shared (Cooper et al., 1998);

B

can accurately record their views, rather than a facilitator misunderstand (Eden and Ackermann, 1998); and

B

the group can edit and move views, en masse, rapidly.

The output from this process takes the form of a group causal map, or strategic map, an example of which is shown in Figure 1 (this example, from B2BService, has been chosen to show that there was more to the workshop discussions than IT). This map can be analyzed to identify a range of actions that might be implemented to improve the situation. Group consideration and negotiation supports the identification of the right actions to implement. Through this entire process the participants are building a map, negotiating agreement, and giving commitment to the group to support action being taking to address the situation (Eden and Ackermann, 1998). A consistent four-stage process. In this research we adopted a consistent approach to all the workshops. This involved having the same facilitator, and either one or two observers. We also used identical technology and software and a standard four-stage agenda, which consisted of: (1) Stage 1: what knowledge informs your business? (2) Stage 2: what processes are currently used to harness this knowledge? (3) Stage 3: what processes should be used to harness this knowledge? (4) Stage 4: how do we (or should we) evaluate how good we are at harnessing this knowledge? (‘‘We’’ refers to the participants’ organization.) Figure 1 An example of a map produced in one of the workshop sessions

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Although this agenda was consistent, it was used flexibly rather than restrictively. We recognized that there was no one best solution to knowledge management, and so allowed each organization to determine the specific content of the workshops within the broad field of knowledge management and the research questions, to suit their own interest. After the first session (which was necessary to get the group thinking together about knowledge management) the participants decided whether our agenda was appropriate for them and ‘‘whether (they) would regret not having discussed something else’’. Sometimes the group followed our agenda throughout, but more often they added sessions and refocused others to be more relevant to the expertise in the group and the urgency of particular issues. For example, one group added a session which asked ‘‘how can we get reluctant people to pull information off the intranet?’’ The debate which surrounded the validation/amendment of our agenda provided insight into the pressing knowledge management issues which faced the organizations.

Extent of technology use in KM The workshop discussion covered many aspects of knowledge management in the participating organizations. In this paper we concentrate on knowledge management systems and the role of information technology, but discussion of other issues may be found in Edwards et al. (2003a). Of the ten organizations in the study, information technology was a significant element of the discussion in all but one of them. The one exception was ‘‘ManufIndProd’’, where although two types of information technology (e-mail and knowledge-based systems) were mentioned during the identification of processes that were relevant to current KM, neither was pursued in the subsequent detailed discussions. We now summarize the discussion relating to information technology firstly in the three organizations in which it was the major focus of discussion, and then in the six organizations where it was a significant but minor element. This includes the various types of IT that participants mentioned as being relevant to supporting KM, and a little indication of the context of the discussion. Direct quotations from workshop participants are shown in italics.

Restaurants A feature of the workshop held for ‘‘Restaurants’’ was its focus on ‘‘head office’’ knowledge (such as sales, marketing and financial aspects) rather than on ‘‘operational’’ knowledge (such as how to cook and serve meals in the restaurants, which dishes were most popular, and so on). Restaurants claimed to make extensive use of technology in supporting knowledge management, including internet searching, an intranet, MIS, accounting and payroll systems (the latter ‘‘for details about staff’’), shared databases, an ‘‘electronic library’’ and an externally held data warehouse. They also talked about ‘‘cubes’’ of information, by which they meant OLAP-style analyses, although these were not yet available because of the unreliability and inconsistency of the data. Their desire for the future was thus for a single source of knowledge that required standard site technology . . . ‘‘the long term fix is dependent on a technological solution’’. Interestingly, plans were already in hand for such a technological solution to be implemented, but until the workshop took place the participants did not seem to have appreciated the significance of this system as regards to KM. There was definitely an ‘‘aha’’ moment during the workshop as this became apparent, and the new system became more and more central to the action plan for KM that they were trying to devise. The best way to describe Restaurants is that they were led to a technological approach to KM – perhaps due to a focus on understanding the customer. Despite the extensive use of technology in Restaurants, participants had a broad view of KM but realized during the workshop how important technology was to it.

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Police ‘‘Police’’ as an organization was new to formal KM, and the managerial initiative that led to their participation took a broad view of what KM meant. However, most of the suggestions and discussion in the workshop were strongly technology-based. Police forces in general make extensive use of information technology and indeed other types of technology (everything from helicopters to DNA profiling), and this force is no exception. Current uses of IT for KM included e-mail, the intranet, and the Police National Computer, although more than any other organization the descriptions often concentrated on the hardware (e.g. notebooks or personal organizers) rather than on what was done with it. Police also make extensive use of video, although discussion of this technology is beyond the scope of this paper. A great deal of the discussion in the workshop focused on how much and how well the official IT systems were used, as illustrated by the following quotes: How many people are logged on [is not a good measure] . . . of how many people used it. A whole room will use data if one person is logged on. A PC [police constable] has to access information at the beginning of their shift before going on patrol to be able to do their job. We need to make some things only available via the intranet, for example [forms for] expense payments, annual leave, overtime.

An issue not raised in any other workshop was that of the use of unofficial IT systems. ‘‘Privately owned organizers and laptops that people shouldn’t have are a barrier to communication.’’ The overall focus of the Police workshop was on communications. For the future, the intranet was ranked as the most effective tool for corporate communications and second (after intelligence led policing) for operational communications: If you only put information in one place, that is where people will go to use it.

A corporate web site was also identified as a good way to communicate with external stakeholders. The best description for this case is that police were opportunist with information technology, or indeed that they cannot keep away from technology – to improve processes and provision to ‘‘customers’’/stakeholders. DesignInst ‘‘DesignInst’s’’ attitude to KM was definitely technology-driven, although there is scope for a little debate as to whether they were driven to technology, because of the support needed for their business processes, or driven by technology, with their interest in KM arising from the introduction of a new information system. It was this system, and the uses that could be made of it, that formed the focus for the workshop. Discussion in this workshop concentrated almost entirely on the future, rather than the present. They identified their new ‘‘enterprise accounting system’’ as a key resource for all aspects of the business, including KM, and also the need to make fragmented knowledge more coherent. Examples of this included a discussion of online access to stock availability. One participant said: The management system will not do anything for us in relation to ‘how do we find new customers’’.

The same person also said that: Not enough time spent on outward looking areas, we are too inward looking.

Another commented that:

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The nature of what is produced is that the system doesn’t provide product development information.

A frequent theme was the need to define a list of MIS requirements and reduce duplication of information held. They called this a ‘‘flight deck’’ for the business. Interestingly, what was never explicitly discussed was whether the new system addresses the clusters that participants identified during the workshop as the key elements of knowledge their organization needed. There was a real conflict in the workshop between the ‘‘systems’’ side of the business and the ‘‘product’’ side, hence the difference between the two themes of reducing duplication and satisfying customers. HighTechManuf Current uses of information technology for KM were identified as including the internet (for searching), an intranet, e-mail, bulletin boards and shared files. They also identified that the organization had ‘‘islands’’ (meaning they were unconnected) of databases. In considering the processes that ‘‘HighTechManuf’’ should use for KM, the participants discussed a cluster of ideas related to IT, but the focus was on internal communications (the label given to the cluster) rather than the technology itself. By internal communications they meant what needed to be communicated, and to whom. This was typical of their overall ‘‘people’’ focus. Much of the discussion centered on sharing and storing operational manufacturing knowledge, for which solutions such as a printed sheet of instructions (laminated to resist grease etc.) were preferred to an information technology solution. Consult Current uses of IT for KM in ‘‘Consult’’ included e-mail, internet, intranet and shared databases. In discussion they characterized databases as a ‘‘compendium of knowledge’’. A specific concern was the over-use of e-mail, which resulted in a suggestion for ‘‘message boards’’ rather than one to one e-mails. They also wished to see more and better integrated databases. Again, their overall focus was on people, especially in relation to business aspects, rather than technology. B2BService Current IT relevant to KM was identified as including e-mail, sundry databases, and accounting software. The focus of their discussions on KM was very much more concerned about market than processes ‘‘if we are driven by internal process issues we will fail in the marketplace’’. Thus they considered ‘‘how to achieve KM’’ – ‘‘KM through . . . ’’ under a variety of headings, including a large cluster of ideas labeled ‘‘KM through IT’’, as shown in the map in Figure 1. Items here included an intranet (and access to it from offsite), an extranet, a data warehouse, better MIS, video conferencing, better use of the web and protocols for the use of e-mail. Although technology was clearly an important element, people were seen as an even more important aspect, as witness the clusters on human resources and (social) networking. R&D This organization was a very advanced information technology user. Systems identified as relevant to KM included databases (internal and external), e-mail, bulletin boards, the internet, and an intranet. However, they felt that the introduction of this technology had not always been effective. ‘‘Informal mechanisms have been replaced by formal e-mail and the information doesn’t get shared as well’’. They also had a history of confidentiality (because of the nature of their work) which meant that many databases were not accessible, one participant called this ‘‘anal’’! Looking to the future, they stressed the need for scanning of documents to help form a centralized document repository, enabling intranet access and search, and with abstracting features. However, they felt that technology was not the main issue in KM in their organization. Rather, it was crucial to concentrate on the people aspects, especially the fact that the nature of the business had undergone a significant change.

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Housing Current uses of information technology for KM discussed in this workshop included e-mail, an intranet, and shared files. ‘‘Housing’’ was also the only organization to mention the current use of its own web site for communicating knowledge to external partners. For the future, participants talked about an ‘‘electronic library’’ and encyclopedia, plus an extranet, and a portal for news reports etc. They identified a critical need for ‘‘summarizing, abstracting, disseminating’’ rather than just placing files somewhere where people might (or might not) choose to access them. However, this was a relatively minor part of the discussion compared to their interest in partnerships and (social/organizational) networking, and in the issues raised by staff being split between their two main offices. ManufIndProd ‘‘ManufIndProd’s’’ discussion concentrated on processes because of the recent management buyout and a possible future change of location of their only site. ConsumProt ‘‘ConsumProt’’ also had a process focus to their discussions on KM. In discussing current processes, they identified e-mail and a cluster of more than a dozen items relating to different databases. Interestingly, participants chose to include MS Word and MS Project files in this cluster. Despite this extensive discussion about retaining knowledge, or perhaps information, in databases, participants felt that it probably was not important to develop technology further. This was mainly because of the difficulty of achieving any payback on such an investment over ‘‘ConsumProt’s’’ limited future life (its functions as a voluntary regulatory body are due to be taken over by a statutory body in approximately two years). In the circumstances, they thought it more important to transfer knowledge to people’s heads to enable them to get replacement jobs, and also to be able to transfer knowledge to the replacement organization. The latter need in particular accounted for much of the process focus. Table III summarizes the various IT-based knowledge management systems mentioned by participants in the ten workshops.

Discussion Although based only on a small and relatively informal survey, the paper by Edwards et al. (2003b) gives a flavor of the expectations of academics and practitioners about different types of knowledge management system. Those most often cited were intranets, groupware, search and retrieval tools, and data mining software. The type of information technology support most favored for specific uses in their survey was groupware, but interestingly no use of groupware for KM was mentioned in this sample of cases at all. Perhaps this may have been because our sample did not include any management consultancies; they were amongst the pioneers of groupware, and are probably the most advanced users of it. In fact, the only interest expressed in groupware was that participants in at least two of the workshops enquired about buying their own copies of the software that was used to run the workshops. In the cases we studied, there was a clear emphasis on the use of general information technology tools (such as e-mail, shared databases and intranets) to support KM activities, rather than the use of tools specific to KM. This is consistent with the findings of Zhou and Fink (2003) for Australian organizations. The best example of a specific KM tool that we found was that Restaurants strongly advocated the use of a data analysis system based on OLAP or business intelligence principles (their reference to ‘‘cubes of data’’). Restaurants already used an external data warehouse. B2BService also wanted to see a data warehouse, while R&D and Housing were interested in repositories, although the discussion on this topic in the Housing workshop seemed to have a ‘‘pie in the sky’’ element to it. With the more general tools, the issue seems to be how to use them effectively in supporting KM. None of the organizations with intranets seemed to be confident that they were using them well, or even that they knew how to use them well. E-mail was used in all of the ten organizations (although one did not see a significant connection with KM), but in almost

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Restaurants Police DesignInst HighTechManuf Consult B2BService R&D Housing ManufIndProd ConsumProt

Organization X X X X X X X X X X

E-mail

X X O X O

X X

Internet/ web X X O X X O X X

Intranet/ portal

O

O

O

Extranet X X O X X X O X X X

Shared databases/files

O

O O

O

X

Repository

O

X

Data warehouse

Table III Use of IT in KM – current systems (shown as X) and suggested future (shown as O)

X

X O

Bulletin/ message boards

X

KBS

X

O

X

Accounting system

X

O

Their own web site

O

X

MIS

O

OLAP

X

Payroll system

every case there was dissatisfaction with its use, especially the tendency to copy everyone in on everything. All of the organizations saw shared databases of some kind as important for KM, but there was often uncertainty as to how best to achieve this. It is generally accepted that there is no ‘‘one size fits all’’ solution to the use of technology to support KM in organizations. Three of our ten organizations emphasized technology; at the other end of the scale, two scarcely mentioned it. What each organization has to do in terms of supporting its KM activities is to strike an appropriate balance between various tensions apparent in the organization. This balance will differ, not only between different organizations, but also perhaps for the same organization at different times. We have identified four related tensions influencing decisions about IT and KM. The first is the tension between the quantity and the quality of the information and knowledge being managed (not helped by the confusion between information and knowledge displayed by many participants). Examples of this were Restaurants’ inclusion of the payroll system as a source of support for KM, and a strong emphasis on shared databases in several workshops without much specific discussion of their content. A relevant question is: ‘‘has technology simply increased the volume of unfocused data without helping to convert it into usable knowledge?’’ This reinforces findings elsewhere in the literature, as Alavi and Leidner (2001) put it: ‘‘Hoards of information are of little value’’. Related to this is the second tension, between centralized and decentralized organizations. Restaurants, as we have seen, wanted a centralized ‘‘solution’’: a single source of knowledge based on standard site technology. However, this may have been influenced by the fact that all of the workshop participants were from head office. Police were very aware of this tension, especially the use of what one might call ‘‘independent’’ KM technology (ranging from the unofficial to the dubiously legal). DesignInst expressed this tension as being between an inward and an outward focus. A question arising here is: ‘‘does the decentralized organization conflict with centralized knowledge ‘systems’ – does the KM strategy imply a more centralized organization?’’ This raises significant issues about the roles of the formal and informal organization in knowledge management. The importance of the informal organization, especially social networks, has long been recognized in management literature generally; see for example Krackhardt and Hanson (1993). These ideas have been taken note of in the knowledge management literature, although as Holtham and Courtney (1998) point out, informal mechanisms may preclude wide dissemination of knowledge. However, we believe that the relevance of informal information systems to knowledge management, such as those in Police, has not been previously recognized. A third related tension is between ‘‘head office’’ and operational knowledge. Restaurants scarcely considered operational knowledge at all in their workshop. For Police this tension is a well-known problem, but unfortunately without a well-known solution. Anecdotal evidence in Police is that operational knowledge is shared reasonably effectively, but greater efforts to systematize this may have the opposite of the desired effect. This had already happened in a pilot project in another organization, R&D. A question arising here is ‘‘who decides which knowledge needs to be managed?’’ This does not appear to have been addressed so far, at least in the literature relating to knowledge management systems. The fourth tension is between ‘‘pushing’’ information and knowledge out to people and leaving them to ‘‘pull’’ it when needed. There was general agreement that universal ‘‘push’’ systems did not work. This is consistent with the literature (e.g. Damodaran and Olphert, 2000). Holtshouse (1998) explains the need to balance push and pull approaches. However, Police in particular recognized that some people were much more likely to choose to pull knowledge for themselves than others were. A question here is thus: ‘‘how does one involve what one police participant called ‘recalcitrant non-communicators’?’’ The need to involve those who might least wish to be involved has been raised in other information systems contexts, for example expert systems (see Edwards et al., 2000).

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Finally, we see the general problems of alignment – making sure that the solution fits the organization’s business processes. This is shown by the very different technology-based solutions favored by DesignInst, Restaurants and Police.

Conclusions Different solutions are appropriate and organizations need to find the solution that is right for their context. There is a range of approaches that can be taken in considering technology to assist KM – even just three technology-focused cases, as reported here, give three very different approaches. Within this we have identified four tensions that each organization must resolve: (1) Between the quantity and quality of information/knowledge. (2) Between centralized and decentralized organization. (3) Between head office and organizational knowledge. (4) Between ‘‘push’’ and ‘‘pull’’ processes. Finding the way to make best use of generally available software such as intranets and e-mail for KM is perhaps the biggest single challenge. Whatever technological route is adopted, there will also be many issues, especially human ones, relating to the implementation of that solution. There is insufficient room to address these here. Limitations The most apparent limitation of the study is that participation was restricted to organizations that expressed an interest in knowledge management, and presumably wished to receive some assistance from the researchers. This would therefore exclude both those who had no interest in KM, and, more importantly, those who felt that they did not need any assistance with KM. Our findings may therefore represent only ‘‘average’’ organizations, not the very best practice. Although our study was limited to UK organizations, we believe that the findings will still be representative of organizations in other industrialized countries, because of the variety of organizations covered. This will be true unless there are countries significantly ahead of, or behind the UK in KM adoption.

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Connected brains Question and answer systems for knowledge sharing: concepts, implementation and return on investment Paul Iske and Willem Boersma

Abstract Purpose – In this paper the aim is to describe the role that question-driven knowledge exchange systems can play in the transfer of knowledge between people and to describe the conditions to be fulfilled for successful implementation. Design/methodology/approach – The conclusions in this paper are based on interpretation of results of case studies. These are combined with literature research. Paul Iske is Chief Knowledge Officer at ABN AMRO Bank, Oostzanerdijk, The Netherlands. Willem Boersma is CEO at Integral Knowledge Utilization BV, Badhuisweg, The Netherlands.

Findings – The major conclusion of the work is that question and answer (Q&A) systems are more promising than traditional Yellow Pages systems. However, some challenges remain the same, especially those related to motivating people to ask (the right) questions. Research limitations/implications – The authors believe that further study would be helpful to better understand the causal relationships between the success of a Q&A-driven knowledge system and the context where they are applied. More case studies and a fundamental study of the types of knowledge and organizations that could benefit from this approach would help people to make better decisions when considering the implementation of a Q&A system. Practical implications – The aim of this work is to help people make better decisions when they consider the implementation of a system that connects people with a knowledge question to people with the relevant knowledge. It helps them to understand whether such a system can add value at all and, if so, how to increase the probability of success. Originality/value – As far as is known, there has not been a study so far, explicitly focusing on this type of system and the comparison of the application of Q&A systems to ‘‘traditional’’ Yellow Pages. The application of scenario-thinking to this field is also new. Keywords Knowledge management, Culture (sociology), Return on investment Paper type General review

1. Introduction and background Today’s professionals are confronted with the ‘‘information-based, knowledge-driven, service-intensive economy’’ (Bartlett and Ghoshal, 2002): A learning organization is an organization skilled at creating, acquiring, interpreting, transferring, and retaining knowledge, and at purposefully modifying its behaviour to reflect new knowledge and insights (Garvin, 2000).

If one asks someone about the most valuable asset of the organization, the answer is very often: the people, the employees, the staff, etc. However, it seems that this crucial asset is not always being used in the most effective way. We have done some research (Iske, 2004) and asked a very simple question: ‘‘what percentage of your talent, ideas and experiences do you use in your job (this is not exact science, just select the percentage that first came to your mind)?’’ The average of the answers (number of respondents approximately 1,000) is just below 60 percent, with typically a local maximum around 20 percent and the absolute

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DOI 10.1108/13673270510583018

maximum around 70 percent (see Figure 1). Though it is clear that it is not possible to use the full 100 percent of someone’s intellectual capabilities (though there were some people in the age category 50 þ who indicated they were using 100 percent of their brain capacity!), it is clear that a higher return on investment in human capital is feasible! Another conclusion from this research is that there is not much difference between people neither with different education levels, nor between men and women or between various ages. Without doubt, the evolution of IT-technology has sparked new interest and new developments in the area of knowledge management. Internet and intranet technology have enabled communication from one-to-one, many-to-many, many-to-one and one-to-many. Especially (business) communities nowadays have a wealth of tools at their disposal to effectively communicate and share information. A web-portal, for instance, can play a crucial role in developing the intranet for what it is supposed to facilitate or make possible: personalized information, knowledge retrieval, and virtual collaboration. We all know that knowledge is much more than information: it includes the experience, skills, ideas and attitudes of people in a context where value can be created. This has as a consequence that knowledge management is about connecting people to people and people to content. Especially the relationships between people, based on the knowledge they want to share and develop, can benefit from a combined technical and more human-focused, anthropological approach. The other important factor is ‘‘context’’. Value can only be created in a certain context and therefore it is of utmost importance to understand the various contexts in the business environment and business processes. Relevant knowledge can be identified only in this way and information overload can be avoided. In a previous article we have explained that without context, there can be no value of knowledge (Iske and Boekhoff, 2001). To structure our discussion, we will use the framework of the so-called knowledge value chain, which is given in Figure 2. The knowledge value chain covers the following fundamental knowledge processes: B

knowledge analysis (starts with the mission, vision and strategy (MVS) of the organization: what do we have to know, what do we know?);

B

knowledge development;

B

knowledge capture;

B

knowledge transfer;

B

knowledge application; and

B

knowledge evaluation.

Figure 1 What percentage of your intellectual capacity do you use? (n ¼ 930)

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Figure 2 The knowledge value chain

A

The first part of the knowledge value chain is more strategic in nature whereas the second part has more operational aspects. Various instruments support the processes in the value chain, which is illustrated in Figure 2 (Weggeman, 1998). Knowledge is someone’s ability to make decisions necessary to execute a specific task. This ability can therefore be seen as the interaction between insights (from the past), information (the present) and imagination (the future): (1) Information (synonyms: explicit, encyclopedian or codified knowledge). This is knowledge, which, by the owner of this knowledge, can be written up or this is knowledge that is acquired from knowledge that was already put into symbols (language, drawings, schemes etc.). (2) Insights (synonyms: implicit or tacit knowledge) consists of: B

the collection of personal experiences as a basis for feelings, associations, fantasies and intuition (definition of experience: knowledge acquired by observation and practice);

B

the repertoire of skills: manual skills, analytical skills, communicative skills etc. (description of skill: dexterity, competence); and

B

attitude: the position and behavior that comes from basic assumptions and values, which is characteristic for someone in a specific situation.

(3) Imagination: B

the ability to visualise possible futures; and

B

someone’s ability to generate ideas

Therefore, we could use the following expression for knowledge: K ¼ I £ I £ I, or K ¼ I (Iske, 2004). It should be clear that knowledge emerges in the interaction between the three factors: information, insights and imagination. So, it is also an interaction between external (information) and internal (insights, imagination) factors. To emphasize that managing knowledge is essential for achieving business and personal goals, rather than a goal in itself, we propose to speak about knowledge-conscious management: it is about managing the business, processes, customer relations, ambitions,

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etc. in a way that good use is made of new and existing knowledge! Thus, it is clear that we can talk about knowledge (-conscious) management on various levels, see Figure 3. At each level (individual, team, organization, and network), different rules apply and different processes and instruments need to be identified and implemented. As we will see in this article, however, Q&A systems can add value at each level. This requires that such a system facilitates the sharing, development and stimulation of the factors that make up knowledge, i.e. insights, information, imagination.

2. What knowledge is relevant? Knowledge mapping In almost all KM projects the creation of a so-called knowledge map is one of the key activities. This map has to be developed by analyzing the knowledge that supports the people in the organization so that the business processes and projects run efficient and effective. These processes include the strategic business decision processes, ensuring alignment with business strategy. The knowledge map is a set of knowledge domains for each of which the following questions are asked: B

Is knowledge in the specific area of strategic importance for the business?

B

If yes, who has/wants to know what?

B

Where is the knowledge and how do we make it available?

A knowledge map illustrates or ‘‘maps’’ how knowledge flows throughout an organization. After the knowledge map has been constructed, prioritizing based on the strategy will be the first step towards actual development and implementation of a knowledge management process. For instance, one could implement a governance model in which subject matter experts (knowledge owners) will be made responsible for development, capturing and maintenance of the knowledge/information. The governance model should avoid that the knowledge database becomes a ‘‘databasement’’: an environment where people dump information without checking the relevance and without maintenance. In Figure 4 a generic form of a knowledge map is presented. The crucial question in all cases is: are we talking about stock or flow? This is due to the ‘‘dualistic’’ behavior of knowledge: we tend to capture as much as we can, but most often, knowledge is being created by people, transferred from one person to another person and it is directly connected to the context. Without context no knowledge! Here lies the principal justification for the development of knowledge-information systems: systems that point to people and environments where knowledge may be found and/or created! Figure 3 Various levels of knowledge management

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Figure 4 A generic knowledge map

3. Knowledge management systems In the past 20 years, information technology has provided companies and individuals with new possibilities to manage and share knowledge. The choice for a specific IT-implementation is situation dependent and several types of systems can be identified. The development of a knowledge repository is an example of a tool that supports the ‘‘stock’’ approach. In this approach knowledge/information is being captured and made accessible for re-use. However, it is almost impossible, time-consuming and/or unattractive to capture and codify all knowledge. A different type of tool is represented by yellow page applications. These are curriculum vitae-oriented and highlight which person has what knowledge within the organization. These tools are also not easy to maintain. In fact, people have to constantly review their data in order to keep the system up to date. A critical success factor for the implementation of such tools is strong management support. Often these applications fail due to the fact that management does not motivate their employees to use the tool. The answer to the question ‘‘what is in it for me?’’ has to be provided by management. The tool needs to be marketed, a reward system is necessary and participation of the employees has to be won. A third group of applications is e-mail-based tools. These seem to work very well in practice, at least in dedicated communities, where people share the same interests. One major feature of these applications is that they are reactive tools. Whereas the two first types fail to show people who exactly they are helping and with what, e-mail-based tools, by asking and answering a question, allow experts to know that they helped someone. They also provide feedback on what information was needed to be helpful. Moreover, they are more natural to use, reactive, and relatively easy to embed in the organization. These applications come close to the normal way of working: asking your colleagues for help if a problem arises.

4. From ‘‘traditional’’ yellow pages to question and answer-driven applications Combined people-based and content-based KM environment In an on-line community environment, people create valuable content by aggregating knowledge. Furthermore, capturing, recording, analyzing and interpreting both the behavior

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‘‘ A knowledge map illustrates or ‘maps’ how knowledge flows throughout an organization. ’’

and the knowledge exchanged create value. In Figure 5 these processes (Hagel and Armstrong, 1997), which are also used to describe the dynamics in on-line market places, are described. In recent years a large number of yellow page systems have been implemented. The authors started doing so in the early days of the web, as is illustrated in a screenshot of the starting page of a system they implemented for Shell Research in 1995/1996 (Figure 6). The goal was to present the organization, the various teams and departments and the people working in these teams. Users could browse or search through the system and everyone was responsible for updating their own pages. Based on this model several other implementations followed, among which a system aimed at creating a network of companies in The Netherlands in the area of bio-energy. This system started in 1997 and was active for more than five years. However, many of these yellow pages implementations suffer from decreasing use and poor maintenance within a period of one to two years. Reasons for this are: B

the systems are not integrated in the (primary) business processes. It is not enough to state that it is important that people know how to find experts;

B

there is no connection between the context of entering information and the context of using information. The question ‘‘who am I helping with my information’’ is not answered;

B

the information providers need to be pro-active in keeping the system up-to-date. However, there is no direct reward for this effort;

Figure 5 Communities and value creation in the knowledge value chain

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Figure 6 Screen shot of the starting page of Shell research yellow pages in 1995(!)

B

a lack of communication about the system, poor understanding of the potential value of the system by end-users as well as management, and no management support for the system; and

B

an unattractive user-interface or, in the worst case, a poor technological implementation.

From these observations, it becomes clear that it requires a very thought-through strategy and a lot of stamina to develop a sustainable yellow-pages solution. An example of a corporate yellow pages system that has taken a number of years to develop in a tool that is more or less institutionalized within the organization is shortly described in the Philips case study below (Case 1). Recently, as an alternative for yellow-page systems, question and answer systems have become increasingly popular. Question and answer systems are systems where users interact with each other by asking questions and providing answers. The questions and answers are stored in a categorized way and are easily retrievable for further use. This means that after a period of intensive use a very valuable knowledge database will have been built and that when people ask questions a properly designed Q&A system will first search through the already available answers before sending the question to subject experts. A similarity with yellow pages is that persons do need to register themselves as expert on a subject in order to receive questions. An important difference, however, is that they only need to fill in a very limited profile and that their profile builds itself over time by means of the questions they have answered. Moreover, when they answer a question they can be sure that there is a real need for this answer by a real person. While the Q&A game is the basis of a Q&A system, it might be extended at will by adding e.g. best practices, frequently asked questions, and valuable resources. Extensive personal and

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Case 1: yellow pages in Philips (Iske, 2002) Philips has developed a Yellow pages application that enables people to locate colleagues with the right experience. It is built on the principle that you can find any information and any experience with the help of your colleagues. It allows for sharing pockets of knowledge (contributions) in the form of best practices and links. For those who are not able to find information directly, there is a possibility to pose open questions. Other members can answer open questions. Members are able to subscribe to all open questions from various categories. On average each question receives three answers. Surveys show that people experience good added value. The application is highly personalized (personal homepage) and information is built according to peoples needs. All contributions are linked to a two level subject taxonomy, which resembles the main working areas and subsequent categories, within Philips. Members have the possibility to forward, rate and comment any contribution in the system. The advanced search option enables members to select any combination of the subject taxonomy as well as geographical and business structure options (‘‘I am looking for a purchaser in Taiwan with SAP experience’’). The system is in use since 1999 and has over 26,000 members and has 5,000 hits per week. Membership is voluntary. It is part of the intranet infrastructure within Philips. Some observations and lessons learned: † people use the system more for finding people than for asking questions; † language might be a barrier: some people are reluctant to publish a question in poor English; † because of wide exposure, there is no abuse of the system; † presence of executive management legitimates participation; † seamless integration in intranet infrastructure stimulates usage; † people should always get answers – for this Philips started with gatekeepers who followed up on unanswered questions; and † maintenance is key – there is one full time employee working on the administration/maintenance.

system administration modules for e.g. limiting the number of questions per expert or adding business rules for questions on a specific topic, reporting and e-mail integration are additional features that are available in these type of systems. So how and why can these Q&A systems do a better job than the yellow-page systems? The answer is fourfold: (1) They are easier to integrate in the (business) processes. Only when a specific problem arises in the normal working practice, an answer needs to be obtained. Answers need not to be provided beforehand and out of context. (2) There is a direct relation between effort and result: by simply answering questions, users provide their knowledge, which is not only used immediately, but also stored for possible future use. Moreover, since the questions are originating directly from the context where the knowledge will be used and since the answer receives direct feedback from the knowledge seeker, the context is specified for future use as well. (3) They can learn from implementation mistakes made in yellow page projects regarding communication and management support. (4) They can benefit from an improved, interactive user-interface and more mature technology. An easier direct integration with e-mail and instant messaging applications also helps. People will receive, for instance, notifications of questions and answers via e-mails with hyperlinks to the web-application.

5. Implementation challenges for knowledge management systems As mentioned before, there are several general issues related to knowledge management. Most notably, each knowledge management program involves a change management effort. A successful change management project can be divided into seven steps: making

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contact, creating awareness, building understanding, testing to get acceptance, which should lead to institutionalization and finally to internalization. The change curve is illustrated in Figure 7. From many case studies it has become clear that without appropriate effort spent in each step, the following level is difficult to achieve in a sustainable way. Many (large) knowledge management projects focus on institutionalizing, which usually means organization-wide implementation of new tools and/or processes. However, for a sustainable, value-adding change, this approach is usually insufficient. Careful building of awareness, strategic testing and ensuring acceptance are key success factors in any change project. From the study of successful and failed knowledge management and change projects a list of success factors can be extracted. Each of these enablers/barriers can be related to at least one of the three key areas: culture, process/organization, and technical infrastructure: B

Culture. There must be a culture that does not discourage knowledge sharing.

B

Leadership. The leaders have to be leading and give commitment to knowledge management to make it a success.

B

Reward system. To make a success of knowledge management, the incentives have to be in line with the behavior wanted.

B

Information and communication technology. Information and communication technology enables the quick finding and using of information and also enables communication with and searching for people.

B

Shared language. To make it possible to share knowledge, speaking the same language and using the same meaning to a word is very important.

B

Information need clear. To know which information must be stored and be available, the need for information must be explicit. Having this knowledge available makes it workable.

B

Performance measurement. To make knowledge management a success, performance measurement must be done to get a clear perspective on whether it’s positively effective or not and to motivate people.

B

Resource availability. There must be resources available to execute all the work needed to make knowledge management a success.

B

Clear processes. It must be clear what kind of processes are part of knowledge management and how these processes are defined, this helps to make explicit what must be done to use knowledge better.

Figure 7 The change curve

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B

Direction. It must be clear in what direction the company is heading, so the employees can act accordingly.

6. Specific issues for Q&A-driven knowledge applications It turns out that motivating people to ask questions in a Q&A system presents a bigger challenge than making people answer them. In most cases people are willing to answer questions as long as they are relevant and the answering process is simple and not time-consuming. However, it takes a lot of communication, integration in the (business) processes, targeted incentives, demonstrated results and patience before it becomes the second nature of people to ask questions via a Q&A system. It has been demonstrated that rewarding both the knowledge seeker and provider (e.g. the ‘‘Q&A of the month’’) stimulates others to use the system because it acknowledges both the active behavior of the seeker and the willingness and expertise of the expert. In general, Q&A systems tend to suffer from either a very limited functionality or a poor user experience. When the functionality is limited, the system provides hardly any added value over using e-mail, while when the user experience is too complex, especially the not very computer-savvy users tend to stop using the system after the first try. Therefore, a compromise has to be found between functionality and user interface, where a lot of attention needs to be paid to small details. Only when these are well taken care of, the system has a chance to succeed. Nothing is more scary than asking a question to an empty, not yet very active, system. Therefore an initial fill of the system needs to be created by a small group of active, committed professionals, who provide an initial load of frequently asked questions, create a sense of activity and make sure questions from new users are promptly answered. An active moderator/administrator is also needed here. A brief survey was performed with several companies and experts in The Netherlands on what they considered implementation issues for Q&A systems. The main concern of the interviewed individuals is that answering the questions might cost time for the experts. They are worried that the experts do not want to answer lots of questions, because they have other work to do. What these companies and experts do not see is that the questions are being asked anyway, and if the right expert is found in less time it will save time somewhere else. Of course, the experts within a company have to be explained what is in it for them and they need to be made aware of the benefits for the company as a whole when using this system. Another concern is the need for face-to-face contact. For particular questions personal contact is required and the interviewed companies think that such a system will decrease the personal contact. However, in the case of a Q&A system the one who asks the question will know who answered it and if he thinks that he needs to have a meeting with that expert to further elaborate on this subject it can be arranged. The fact is, however, that not for each question or problem you need to have face-to-face contact, so with the introduction of the Q&A system a decrease in this personal contact will be one of the consequences (stop the ‘‘meeting-culture’’). In the case below we describe an implementation of a Q&A system within a network of organizations in the Dutch healthcare sector. Here it is extremely important that the user interface is simple, because of the strong differences in IT-literacy in the network. Furthermore, since the system has been implemented on the internet, measures had to be taken so that not everyone could ask medical questions to the experts, who often are medical specialists. There are a number of examples where people met each other after they made contact through the system. Here too, the ongoing challenge is to stimulate people to ask questions, more than experts to answer them (Case 2).

7. Building the business case For various reasons one might want to make an assessment of the impact the Q&A system has achieved. One reason could be that a business case has been produced that needs to

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Case 1: good healthcare innovation practice (Iske et al., 2003) A unique initiative started in The Netherlands in the area of local healthcare innovation projects during the second half of 2001. All over the country healthcare innovation projects are carried out, in various locations including hospitals, GP practices, pharmacies etc. In most cases the Ministry of Health, other semi-governmental institutions or healthcare insurance companies, finance these projects. Most often these projects are very but the knowledge and expertise associated with such a project can hardly ever be made available to others in similar circumstances. By the end of 2001 this problem had been recognized by many parties who are committed to the area of healthcare innovation. These parties collaborated for over a year and this has resulted in a guideline for healthcare innovation initiatives: good healthcare innovation practice (GHIP). This guideline will secure that all stakeholders and all process steps (generation, selection, realization, evaluation and dissemination) of a healthcare innovation project will be addressed, resulting in a separate GHIP dossier which will make knowledge easily exchangeable and interpretable. Meanwhile the Dutch Healthcare Insurance Council has formalized this guideline. The GHIP Knowledge and Coordination Centre (GHIP KCC) will play a key role in the implementation of the guideline as a tool to benefit others in similar circumstances. The GHIP KCC offers a ‘‘click and brick’’ infrastructure for knowledge sharing (see www.ghip.nl). How does it work in daily life? Initially, a database with project dossiers is being created. However, from the very beginning it has become clear that much more knowledge about the projects and about the innovation process itself will remain in the heads of the people involved. Therefore, the GHIP expert panel has been created (see Figure 8): via this Q&A-driven knowledge system people can find knowledge and ask questions about previous innovation projects and about the GHIP process itself. The system has been embedded in the process in the following ways: (1) Generation step – people must demonstrate that they have used the system to support their project proposal. In particular, it must be clear that there will not be any reinvention of the wheel. (2) Evaluation step – evaluators of a project will be selected from the GHIP expert panel only. (3) Dissemination step – in order to fulfill the requirements for knowledge dissemination, for each project at least one person involved has to sign up as expert in the GHIP expert panel, so that information and knowledge that is not in the GHIP dossier still will be accessible. This integration of a Q&A system in the (primary) process is a good example of knowledge-conscious management and this holistic approach clearly demonstrates the added value that can be delivered.

be validated at a certain moment in time (the so-called ‘‘ROI’’ – return on investment calculation). Furthermore, insight in the results could also be used to motivate users of the system, both people with questions and the experts. There are various sources of value creation by a Q&A system (see Figure 9) as is being explained in various frameworks (Sveiby, 1997; Edvinsson and Malone, 1997; Kaplan and Norton, 1996; Lev, 2002):

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B

Financial: the use of a Q&A system results in direct cost savings or increase in revenues.

B

Innovation: the system is an innovative way to support communication and knowledge exchange and can play a major role in stimulating innovation within the company by enabling discussion on solutions to all kind of problems.

B

Processes: the system can be used to improve current business processes, in particular those for communication and information exchange.

B

Customer: the system can be used to give customers improved and direct access to the knowledge in the organization. Furthermore, by being able to respond efficiently and effectively to customer needs, questions and problems, a contribution will be made to improving the customer experience. Hence customer capital is being created.

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Figure 8 Screen shot of the home page of the GHIP expert panel (2003)

Figure 9 Five areas of value creation

B

Human: by participating as an expert, the contributors to the system will have more exposure and will have more recognition that will lead to improved employee satisfaction. Furthermore, when employees have a convenient, ‘‘human’’-type of communication tool at their disposal they will enjoy the working environment more. If this contributes directly or indirectly to the retention rate of employees the contribution to the ‘‘human capital’’ could already justify the investments.

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A lot of measuring methods are based on the traditional (financial) performance measurements, such as increasing profits and reducing the costs. However, for a knowledge management project these are hard to quantify. Advantages like employee satisfaction, customer satisfaction and increased speed to market are hard to measure as well. Therefore, management needs concrete examples in which they can see direct benefits of this approach in their own situation. Here one has to realize that knowledge is context-dependant. In convincing the management and employees why knowledge and managing knowledge is important and can help in building a successful business, it is necessary to create a certain context in which it is clear what the value of the knowledge and of knowledge management can be to that organization. Direct quantitative measures Measuring direct business impact is the most powerful way to demonstrate the added value of knowledge (management). To directly assess the added value requires an intimate relation between the knowledge processes and the primary business processes. As an example, one could consider marketing and sales. An example in this case could be the development of a best practice proposal (including commercial texts, product descriptions, a pricing model etc.), which will lead to a reduced time to produce proposal (which can be measured quantitatively) and increased hit-rate (idem). Direct financial results could be achieved by exchanging knowledge in the area of suppliers: increase of buying power and reuse of knowledge from consultants are examples of results that lead to directly measurable financial benefits. Direct qualitative measures With these measures one tries to describe the (potential) benefits of an (KM) intervention, without being able to quantify the total added value. The benefits follow from stories from the business, which might be quantifiable. However, it is usually difficult to predict up-front what the situations will be in which the benefits are realized. This is the area of scenario-thinking that will be discussed in the next section. Indirect quantitative measures These measures are used to obtain insight in the maturity, quality and effectiveness of the knowledge management tools, processes and culture. In-direct quantitative measures include user statistics of databases, intranets, number of questions being asked in expert systems, number of documents in the knowledge repository, number of people who have attended a course, and number of workshops on a certain subject, etc. For an example reporting of a Q&A system, see Figure 10. Indirect qualitative measures These measures give insight in the way the knowledge management efforts are being perceived. By collecting feedback one can measure user satisfaction. It gives insight in the reputation of the KM infrastructure. The feedback can be gathered through questionnaires, testimonials in corporate magazines, during department meetings, in appraisal discussions or in conversations with clients or via client-feedback.

8. An analytical approach for assessing the value of knowledge (management) For discussion purposes, we propose a formula (Iske and Boekhoff, 2001) (see Equation (1)) that captures the essential features of the knowledge value chain. At the end, it enables us to categorize and prioritize properties of organizations, knowledge management activities and even the value of it all: X {pðKðVÞ; GÞrðKðVÞ; GÞaðKðVÞ; GÞV ðK; GÞ}: V P ðKðVÞÞ ¼ ð1Þ G

Equation (1) reads: the total potential value V P of the knowledge K that is stored in environment V equals the sum over all contexts G of the probability p that this knowledge is related to the context G multiplied by the connectivity r that indicates how easy it is to

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Figure 10 Indirect, quantitative reporting in a Q&A system

transport the knowledge from the environment V to the context G, multiplied by the activation coefficient a that indicates how easy it is to activate the knowledge (to use it) in the context G, multiplied by the added value V that is achieved within the context G. We can describe the elements in the first formula that need to be discussed in more detail as: B

Knowledge relevance indicator p – the level to which knowledge is considered relevant for the business (processes) and the level to which business issues lead to new knowledge. The relevance indicator is influenced by a number of factors, e.g. creativity (can you think of other applications for this knowledge?) and process/system thinking (do we understand where and how this knowledge can be used in certain process). This factor plays a key role in the calculation of the value of intellectual properties: a patent is only worthwhile is one knows in what product development process the knowledge will be usable.

B

Knowledge connectivity factor r – the level to which it is possible to transport knowledge from an environment (source) to the context (work situation, business process). Factors that influence this parameter include: geographic distance, language barriers, cultural barriers, ICT tools.

B

Knowledge activation factor a – the level to which it is possible to activate knowledge (to use it) in a specific context. Sometimes knowledge is not completely ‘‘finished’’ yet and an

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‘‘ Without context no knowledge! ’’

extra effort has to be undertaken to make it useable. This is one of the key challenges in knowledge transfer between academia and the industry. Added value V – the level to which knowledge has added value (is useful) in a specific context. Here we could think of things like time saving because of the availability of templates, best practices, but also increased revenues because of specific client knowledge.

B

The total potential value of all knowledge in the organization could now be expressed as: X V P ðKÞ ¼ V P ðKðVÞÞ: ð2Þ V

Here we will take the over all sum of all the possible environments V in the company. Note that it is clear from the first equation that if one of the factors is zero, there is no value added, irrespective of the values of the other parameters. Quantitative insights into the environmental parameters that determine the value of the factors in the equation will help to optimize the return on investment of knowledge-related projects. In general, one should focus on the smallest parameter (the weakest link) to achieve optimal improvement. This equation helps us directly to understand the value of a Q&A system: B

Knowledge relevance indicator p – when a question is being asked, the context becomes immediately clear.

B

Knowledge connectivity factor p – the system facilitates the transfer of knowledge from the source (expert) to the context (question-asker).

B

Knowledge activation factor a – when somebody asks a question and receives an answer he/she is quite likely to use it.

B

Added value V – the added value should be clear in the context in which the knowledge is being used. In fact, it can only be defined in the application environment. In some Q&A systems people can rate the quality of the answer and can also indicate what the value ha been to them, e.g. expressed in money/time saved or (extra) revenues generated.

So, the Q&A system is helping to create value because all factors in the knowledge-value equation (1) are being addressed!

9. Scenario approach As was concluded before, the value of knowledge only has a meaning within the context in which it is being used. We will now apply this by using the scenario (or case-study) approach (Denning, 2001). Convincing people with the help of recognizable, possible cases (scenarios) is a methodology familiar to human reasoning and thinking (Bergmann, 1998). This approach is based on the similarity of problems and missed opportunities. Problems are being solved by using experiences from others (Bergmann, 1998). The idea is to have people recognize themselves in a story or case that is being presented and then show them how to deal with the problem. Case studies and storytelling will allow them to see the organization from another perspective and then take decisions and change behavior in accordance with these new perceptions, insights and identities (Denning, 2001). The scenarios that are being used are mainly about general problems that may occur within a certain context. This context is important since it gives meaning and depth to the information (Reamy, 2002). The main idea with scenarios is to create a context that everyone will recognize, and in this way show the possible value of (management of) knowledge, in this case the implementation of a Q&A system.

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The subjects of the scenarios can be found through desk research, but also through interviews with process and topic experts. A scenario is a description of a diagnostic situation and is divided into at least three parts, namely the description of the symptoms, the description of the failures and their cause and finally the description of the repair strategy (Bergmann, 1998). The scenario approach can be used for management, but also for employees. If the management recognized themselves in the problems and the missed opportunities that were mentioned in the cases, so should the employees. Thus before implementing the system, the management has to discuss with the employees the necessity for the system by using exactly the same scenarios. As mentioned above, a scenario approach can be used to highlight and quantify the benefits of a Q&A system. In a recent study (Iske et al., 2002) some scenarios were tested and an Excel model was created to estimate the quantitative benefits. They include: B

knowledge networking;

B

missed business opportunities;

B

customer satisfaction;

B

inefficient research;

B

redundant work;

B

knowledge leakage; and

B

direct customer support.

Tables I and II describe two scenarios in more detail. These are knowledge leakage (due to turn-over of staff) (Table I) and direct customer support (Table II).

10. Cultural impact Just as different societies have their own culture, so do organizations. Naturally the individuals within the organization will have different personalities, but they do have something in common, which is called the corporate culture (Morgan, 1997). This corporate culture will have a strong influence on knowledge management and Q&A systems. Corporate culture can break or make any system, including a Q&A system. According to the Hofstede (1997) culture it has five dimensions, namely power distance, uncertainty avoidance, individualism versus collectivism, masculinity versus femininity and time-orientation (Child and Faulkner, 1998). The most important dimension in this respect is the collective versus the individual one. If the individual perspective rules, the main idea will be that knowledge is power. There will be a lot of competition between the employees, they constantly will want to distinguish themselves from others and they will only handle out of self-interest and satisfaction. They do not see each other as being a member of a collective item, the organization. In such an organization it will take much more time to implement a system based on knowledge sharing, because the employees simply will not see the benefits. On the other hand, if the collective perspective rules the employees will already be used to share knowledge and the whole atmosphere will be more friendly and cooperative. In such organizations it will not be a problem to convince the employees of the benefits of a knowledge sharing system. However, there is a change that they might be worried about. This is the decrease in personal, face-to-face contacts, as described in the previous section. In most of these organizations there will be an informal working-environment and people will value personal contacts. Therefore management has to pay attention to the impact of such a system on these contacts. Then there are also organizational cultures that are very resistant to change. This happens mostly in the older organizations with employees that are working for that organization for most of their lives. They are used to set ways of working and they will not change easily.

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Table I The knowledge leakage scenario Knowledge leakage Description

This case occurs when a firm loses an expert who leaves and takes with him his accumulated knowledge acquired through his work and previous education and experience. All his undocumented knowledge is therefore lost and the firm must live with the consequences Impact on The organization that suffers this situation may face grave consequences. The organization loss of the knowledge may slow down some projects or activity but could also have much more severe consequences. The lost knowledge may cause the firm to have to forfeit some business opportunities or even lose part of their competitive advantage Q&A system With a Q&A system and some time, the knowledge of every expert will be impact documented through the questions asked to this expert. The most important subjects will be covered intensively while the more obscure topics may be covered more unevenly Q&A system Q&A system allows the firm to document and therefore retain the knowledge of all benefits its experts Q&A system allows the firm’s employees to be more productive as they do not have to answer questions more than once Q&A system allows the firm to build a knowledge base that can only grow and will not suffer setbacks with every staff departure Key quantitative Cost associated with hiring a new expert to replace a lost one. Cost associated measures with training employees to recapture lost knowledge. Value of lost business opportunities due to loss of expertise. Quantity of questions documented Sample In an organization, people older than 55 are offered a good package when they quantitative voluntarily leave the organization. One person developed a computer program impacts for flow modeling. He was hired back as a consultant because nobody knew how to use and change the program When a specific problem occurs in a high profile project, the remark ‘‘he/she would have known the answer’’ is avoided since the problem/answer can be found in the system Typical questions Any question related to an expertise or knowledge possessed by a single person asked or a limited number of persons Who knows about the dos and don’ts in implementing just-in-time manufacturing? What are the pitfalls with international branding? What approach works best for implementing SAP in an entertainment business such as a circus? Key assumptions The questions are documented and the system becomes part of the way of doing things Typical firms Consulting firms, services firms, manufacturing firms, organizations with which benefit from temporary staff (including consultants). Any firm where individuals are singled this scenario out with specific valuable knowledge

Management has to invest a lot of time in training these employees and in convincing them of the benefits of the change. A positive aspect of a Q&A system will be that the less social employees will get the opportunity to answer questions. Previously nobody would ask them a question, because they did not know the person or did not know that the person had the expertise. When the questions are being asked via the computer new unknown experts can be discovered. Finally, it is necessary to give a warning about rewarding in relation to corporate culture. When an expert answers a question, in some systems the answer will be graded and this will lead to a ranking of experts, which can be of help when the company wants to reward the experts. Whether this ranking and rewarding system will turn out to have positive effects, in the sense that the employees will answer the question as good as possible, or negative effects, in terms of competitive and jealous behavior, also depends on the culture. If the culture is a more informal and friendly one, the competition will also be friendly and they will be happy for the one that is the best expert in that year, but if the

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Table II The customer support scenario Direct customer support Description

Impact on organization

Q&A system impact

Q&A system benefits

Key quantitative measures Sample quantitative impacts Typical questions asked Key assumptions Typical firms which benefit in this scenario

The case is about providing a Q&A system to customers and allowing them to use it like internal users of the organization. Many firms today have web sites with a frequently asked question section and also make it possible for clients to e-mail the firm for more information. Providing them with a Q&A system would push the support one step further and would definitely strengthen the relationship with the client. There are three possible levels of integration with the customers identified at this time. The first one would have customers linked only to internal users and only they would provide information to customers. The second level would provide customers with all questions and answers in the system including those questions submitted by other clients and possibly competitors. Finally, the last level would have no restriction and would allow two customers to interact together The first impact will be the increased satisfaction of the employees as redundant questions should be limited. Also the customers should have access to more information faster. The possibility of learning from other clients may spark ideas for new business opportunities with the organization linking them. The cost associated with supporting clients should also be reduced. Finally all the benefits of improving customer satisfaction should kick in The Q&A system will force the organization to be more transparent if clients have access to its knowledge management system. Some questions may have to be avoided to avoid problems. For example, pricing strategies or sale strategies should probably not be discussed in a Q&A system if clients can have access to the info Q&A system will reduce the customer support costs Q&A system will increase customer satisfaction (and generate all the benefits associated with it) Q&A system will provide valuable knowledge about the customers, their desires and need Q&A system will increase the transparency of the firm Cost of managing client relationship (customer support staff per customer) All the measures of a customer satisfaction model The support center reduced its annual cost by $250,000 The customer retention rate increase by 10 percent generating additional revenues of $100,000 What Asian country is the best to set up a sales office from a banking perspective? Can I use this device when I switch to Windows XP? Customers will use the application and not just pick up the phone Customer-oriented firms, service firms, firms with few large clients, software firms, firms selling knowledge-intensive products or services

culture is a more individual-based one, it might happen that the experts thwart each other.

11. Conclusions The development and implementation of a Q&A system is a complex project, where attention should be paid to all change management aspects. Of course, there is the IT part of the project: Tools have to be selected and/or developed that work seamlessly together and that have to be integrated in the overall IT architecture and infrastructure of the organization. Quite often, this also requires enhancement of the current infrastructure and the development of a support organization dealing with infrastructure issues, including connectivity and authorization. Then, as argued above, the right processes and organizational structure need to be in place. These range from knowledge mapping to a consistent approach for facilitating and supporting communities: all of the knowledge management processes need to be clear and accepted in order to be able to have them reflected in the Q&A system architecture.

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‘‘ The value of knowledge only has a meaning within the context in which it is being used. ’’

Finally, and probably most important, attention needs to be paid to the behavioral aspects and the culture in the organization. Typically, by introducing a new business tool such as a Q&A system, sometimes changes in the work processes are anticipated or required. The more one can integrate the on-line environment into the natural way of working, the higher the probability that the project will be successful. It is therefore mandatory that a thorough study is made of the implementation environment and the anticipated users. From experience we know that ‘‘they come by foot and leave by horse!’’ Based on these arguments it is clear that the development and implementation of a (knowledge) Q&A system is a business project rather than an IT project. The project management needs to find the right balance between the hard (IT) issues and the soft ones. Communication and change management skills are indispensable to be able to develop and implement a knowledge Q&A system that will be a value-adding tool for the users in the organization. Q&A systems can offer huge opportunities for creating value by developing, sharing and applying knowledge. Q&A systems can offer an environment that supports the various steps in the knowledge value chain, integrated in the organizational processes and environments. In these respects they offer a major step forward from the more traditional yellow pages systems. Although the identification, selection and implementation of the right tool and the development of the appropriate architecture and infrastructure are a technical exercise, the real challenge is found in the development of the right processes, organization and culture. We recommend strongly that each knowledge Q&A system project is focusing on the personal and collective objectives of the users. A real understanding of the business, the processes and, especially the members of the organization, will guide the developers in understanding where and how knowledge can add value and what contribution the knowledge Q&A system environment can make. The paradigm should no longer be: ‘‘if we build it, they will use it’’, but instead: ‘‘if they use it, it will build itself!’’

References Bartlett, C.A. and Ghoshal, S. (2002), ‘‘Building competitive advantage through people’’, MIT Sloan Management Review, Vol. 43 No. 2, pp. 34-41. Bergmann, R. (1998), ‘‘Introduction to case-based reasoning’’, available at: www.cbr-web.org Child, J. and Faulkner, D. (1998), Strategies of Co-operation, Oxford University Press, New York, NY, p. 233. Denning, S. (2001), The Springboard: How Storytelling Ignites Action in Knowledge-Era Organizations, Butterworth-Heinemann, Woburn, MA, p. 223. Edvinsson, L. and Malone, M.S. (1997), Intellectual Capital: Realizing your Company’s True Value by Finding its Hidden Brainpower, HarperBusiness, New York, NY. Garvin, D.A. (2000), Learning in Action – A Guide to Putting the Learning Organization to Work, Harvard Business School Press, Boston, MA. Hagel, J. III and Armstrong, A.G. (1997), Net Gain, Expanding Markets through Virtual Community, Harvard Business School Press, Boston, MA, p. 49. Hofstede, G. (1997), Allemaal Andersdenkenden, Contact, Amsterdam. Iske, P. (2002), ‘‘Building a corporate KM community’’, KM Magazine, Vol. 6 No. 4.

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Iske, P. (2004), ‘‘Are you challenging your brains?’’, survey of under 1,000 people in The Netherlands, available at: www.knocom.com Iske, P. and Boekhoff, T. (2001), ‘‘The value of knowledge doesn’t exist’’, KM Magazine, Vol. 5 No. 2. Iske, P., Kalter, E. and Naber, L. (2003), ‘‘A healthy outlook for KM’’, KM Magazine, Vol. 7 No. 3. Iske, P. et al. (2002), Measuring the Impact of Q&A Systems, Exchange Project, Erasmus University, Rotterdam. Kaplan, R.S. and Norton, D.P. (1996), ‘‘Using the balanced scorecard as a strategic management system’’, Harvard Business Review, Vol. 74 No. 1, pp. 75-85. Lev, B. (2002), Intangibles: Management, Measurement and Reporting, Brookings Institution, Washington, DC. Morgan, G. (1997), Images of Organization, Sage Publications, Newbury Park, CA, p. 129. Reamy, T. (2002), ‘‘Imparting knowledge through storytelling’’, KM World, Vol. 11 No. 6, pp. 8-11. Sveiby, K.E. (1997), The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, Berrett-Koehler, San Francisco, CA. Weggeman, M.C.D.P. (1998), Kennismanagement, Inrichting en Besturing Van Kennisintensieve Organisatie, Scriptum, Schiedam.

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Note from the publisher Emerald structured abstracts have arrived! After months of preparation by journal editors, authors and Emerald publishing staff, structured abstracts are ready for publication in all Emerald journals. The abstracts appear in journals from the first issues of all 2005 volumes and a glance at any article title page in this issue of the Journal of Knowledge Management will illustrate the format and style of the new-style abstracts. The format differs slightly in the electronic version of articles on Emerald’s website but this is only a cosmetic variation and takes account of the different medium and way in which people use abstract information. The idea for the structured abstracts came about at the start of 2004 and a small team has worked on the design and introduction of structured abstracts throughout the year. Thanks to the hard work of everyone involved in producing this journal, Emerald is now able to showcase the abstracts for the first time. We believe they provide real benefits to our readers and researchers and that they answer some of the key questions journal users have about a paper without them having to scan or read the entire article. Some of these questions might include: B

‘‘What research has been conducted on this topic?’’

B

‘‘How was the research approached – what methods were used?’’

B

‘‘What were the main findings?’’

B

‘‘Are there any literature reviews on this topic and are they selective or inclusive?’’

B

‘‘So what? The authors have shown this but what does this mean for my work/organization?’’

B

‘‘I want to conduct research in this area but what questions still need to be answered?’’

B

‘‘Has this work got any relevance and value for me?’’

B

‘‘What did the writer set out to show?’’

Structured abstracts provide the answers to these kinds of questions without the researcher having to go any further into the article itself. Authors can be more confident that their paper will be noticed and read by others with a real interest in the topic or research. As far as possible, we have alerted our authors and editorial team members to this change via Literati Club Newslines and communications with journal editors. Authors who have been asked to rewrite their abstracts in the new format have readily obliged. The response from all parties has been very encouraging: Structured abstracts are increasing in popularity among the social and behavioral sciences. There’s overwhelming evidence that readers (and indexers) glean more from structured abstracts (Jonathan Eldredge, MLS, PhD, AHIP, Associate Professor, School of Medicine, Academic & Clinical Services Coordinator and Author, Health Sciences Library and Informatics Center, Health Sciences Center, The University of New Mexico, USA).

For more on structured abstracts and their value for researchers and writers, read the short paper by Liz Bayley and Jonathan Eldredge at: http://research.mlanet.org/ structured_abstract.html Everyone has difficulties in the digital environment in weighing up the value of any piece of information and structured abstracts go some way towards a remedy to the problem of information overload. Emerald is the very first publisher in the management field to introduce structured abstracts and whilst we are mindful that this means change for authors and researchers, we feel our pioneering work in this area gives our journals a strong competitive

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advantage. We are pleased and proud to be the first in the field to implement this extremely good idea. Unfortunately, we are unable to go back through more than 40,000 papers already in Emerald’s database to change already-published abstracts into structured ones. On a more positive note, however, nearly 5,000 new papers will be deposited into the database this coming year and all will be accompanied by a structured abstract. Emerald would be pleased to hear what you think about this initiative. E-mail your views to Sue de Verteuil, Head, Editorial Developments at: E-mail: [email protected]

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