Knowledge that Transforms

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsEric K. ClemonsEric K. Clemons is Professor of Operations and Information Management at The Wharton School of the University of Pennsylvania. His research and teaching interests include strategic information systems, information economics, the changes that information technology enables in the competitive balance between new entrants and established, previously dominant industry participants, and the impact of information technology on procurement and outsourcing. Industries offocus include international securities markets and financial services firms, consumer packaged goods retailing, telecommunications, and travel. He specializes in assessing the competitive implications of information technology, and in managing the risk of large-scale implementation efforts. Dr. Clemons is also Project Director for the Reginald H. Jones Center’s Sponsored Research Project on Information: Industry Structure and Competitive Strategy, area coordinator for Information: Strategy, Systems, and Economics, and senior fellow in the Wharton Financial Institutions Center.Dr. Clemons’s education includes an S.B. in physics from MIT and an M.S. and Ph.D. in operations research from Cornell University. He has twenty-four years experience on the faculties of Wharton, Cornell, and Harvard, and consulting experience in the private and public sectors both domestically and abroad. In his consulting practice, Dr. Clemons focuses on helping clients anticipate the fundamental impacts information technology will have on the structure of their industries and on the future strategies available to their firms. In addition to his more traditional consulting practice, he has added participation as a facilitator of strategic change, often employing scenario analysis.Dr. Clemons is currently a member of the editorial board of the Journal of Management Information Systems and the International Journal of Electronic Commerce. He has served on the Congressional Office of Technology Assessment study of securities markets and on the Quality of Markets Advisory Board of the London Stock Exchange.Bruce W. WeberBruce W. Weber is Associate Professor of Statistics and Computer Information Systems, and Director of the Stuart Subotnick Financial Services Center at the Zicklin School of Business, Baruch College, City University of New York. From 1992 to May 1999, Dr. Weber was a member of the faculty of the Stern School of Business, New York University, and a visiting professor in management science at the Graduate School of Business, Columbia University. He has an A.B. in applied mathematics from Harvard University, and an M.A. and Ph.D. in decision sciences from the Wharton School of the University of Pennsylvania. His research examines the economic impact of next-generation financial technologies and, in particular, securities market trading mechanisms. His work has been cited in the Financial Times, the Wall Street Journal, and the New York Times, and he has been an invited speaker at CFTC and SEC hearings on the regulation of electronic markets. He is the codeveloper with Robert Schwartz of the HeadTrader simulation on the Nasdaq web site: http://www.nasd.com/HeadTrader/ATG-main.htm

Well-managed organizational memories have been emphasized in the recent management literature as important sources for business success. Organizational memory infonnation systems (OMIS) have been conceptualized as a framework for information technologies to support these organizational memories. OMIS development may include several projects and may take many years. This paper classifies and analyzes OMIS development scenarios to provide guidance and support for OMIS development. This includes the definition of the roles of participants, a work schedule, and a definition of deliverables and deadlines. The mostly evolutionary nature of OMIS development requires that the results of these steps be monitored and adjusted when needed. Furthermore, a contingency framework and eight rules have been hypothesized, partially based on ex-post analysis of three cases (Veronica Broadcasting, Andersen Consulting, and Nationale-Nederlanden General Insurances). The paper provides concepts and a framework that may be an aid to research on refining the rules for analysis and design of OMIS.

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsRobert O. BriggsRobert O. Briggs is Research Coordinator at the Center for the Management ofInformation at the University of Arizona and Director of Product Management at Ventana Corporation. As a researcher, he has published more than fifty scholarly works on the theoretical foundations for using collaborative technology to enhance group productivity, group creativity, and group satisfaction. His work on organizational transition to collaborative technology led to new insights about how to conceive of and deploy group support systems in order to create self-sustaining and growing communities of users. He is ranked by Decision Line as one of the most productive information systems researchers worldwide. As Director of Product Management at Ventana Corporation, Dr. Briggs applies research findings from the laboratory and the field to the development of new collaborative technologies, processes, and working environments, and oversees the future evolution of collaborative software products.Jay F. NunamakerJay F. Nunamaker, Jr. is Regents and Soldwedel Professor of MIS, Computer Science and Communication and Director of the Center for the Management of Information at the University of Arizona, Tucson. In 1996, Dr. Nunamaker received the DPMAEDSIG Distinguished IS Educator Award. The GroupSystems software resulting rom his research received the Editor’s Choice Award from PC Magazine in 1994. At the GroupWare 1993 conference, he received the GroupWare Achievement Award along with recognition of GroupSystems as best of show in the GDSS category. In 1992 he received the Arthur Anderson Consulting Professor of the Year Award. Dr. Nunamaker received his Ph.D. in systems engineering and operations research from Case Institute of Technology, an M.S. and B.S. in engineering from the University of Pittsburgh, and a B.S. from Carnegie Mellon University.Ralph SpragueRalph H. Sprague, Jr. is Professor of Decision Science in the College of Business Administration at the University of Hawaii. He has over thirty years of exerience in teaching, research, and consulting in the use of computers and information technologies in organizations. His specialties are decision support systems, strategic systems planning, management of information systems, and electronic document management. He has served as chairman and co-chair of the Hawaii International Conference on System Sciences for the past twenty years.

:In a securities market, the initiator of a large transaction can expect the realized price of his or her trade to be inferior to the current market price immediately prior to his appearance in the market. This “transaction implementation cost” phenomenon is a major concern of institutional money managers, both in portfolio selection and in trade implementation strategy. A considerable amount of current research in finance theory deals with modeling and prediction of these costs for equities trading, and commercial products and services recently have become available for probabilistically estimating real-time transaction implementation costs versus transaction size on a stock-specific basis.Earlier papers described the concept of satisfaction-or preference-based trading, with optimization of trade matching on the basis of mutual preference. A market structure based on this design began trading listed equities on the Pacific Exchange on January 29, 1999 under the trade name OptiMark™. The Nasdaq market plans to begin trading using the OptiMark system later in 1999, followed by the Osaka Securities Exchange and the Toronto Stock Exchange in 2000. A prima-facie benefit of this approach is the ability to specify trading strategies that explicitly account for transaction implementation cost estimates as a function of the trade size.In this paper, we present the underlying theoretical framework that unites the concepts of preference-based trading and probabilistic transaction cost estimation. In particular, we develop an analytical generalization of the current market structure constructs of market orders and limit orders. We describe a feasible optimization problem whose solution yields optimal preference profiles, given current market conditions (as reflected by the probability distribution of transaction implementation cost) and a trader-specified coefficient of urgency. This enables the seamless integration of the functions of portfolio selection (the purview of modem portfolio theory) and transaction implementation. We illustrate the application of this theory to a prototype trading workstation.

The paper reports on conceptual development in the areas of database mining and knowledge discovery in databases (KDD). Our efforts have also led to a prototype implementation, called MOTC, for exploring hypothesis space in large and complex data sets. Our KDD conceptual development rests on two main principles. First, we use the crosstab representation for working with qualitative data. This is by now standard in on-line analytical processing (OLAP) applications, and we reaffirm it with additional reasons. Second, and innovatively, we use prediction analysis as a measure of goodness for hypotheses. Prediction analysis is an established statistical technique for analysis of associations among qualitative variables. It generalizes and subsumes a large number of other such measures of association, depending on specific assumptions the user is willing to make. As such, it provides a very useful framework for exploring hypothesis space in a KDD context. The paper illustrates these points with an extensive discussion of MOTC.

:The conceptual design of most computer-based information systems reflects a dualism of technology. During the development phase, part of the work-domain-related knowledge is formalized and encoded in the software, making it difficult for users to reflect on and use this knowledge. This design/use dualism contributes to the deterioration of the interpretive flexibility of information systems. We propose an information systems architecture called Dual Information Systems (DIS) that helps bridge the design/use dualism by providing organizations with a set of services that enable and reinforce both effective, institutionalized working and the questioning and (re)construction of computer-supported work routines. DIS have a four-layered conceptual structure: (1) people draw on the business layer to work and learn; (2) people use the breakdown layer to handle unexpected breakdowns; (3) self-organizing project teams use the project layer to create innovative work and IS (re)designs; and (4) the knowledge-sharing server stores these redesigns and makes them organizationally available to facilitate working and learning as well as subsequent redesign efforts. In this paper, we first outline the theoretical background, conceptual design, and generic services of DIS. Next, we elaborate on the work-process benchmarking service of DIS, which supports project teams in analyzing and redesigning computer-supported work through lateral sharing of knowledge of work processes between business units. Finally, we demonstrate the benchmarking service with the help of the ReDIS prototype.

:The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. This article presents research in which we sought to validate these properties of SOM, called the Proximity and Size Hypotheses, through a user evaluation study. Building upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. We also demonstrated a positive relationship between the size of an SOM region and the number of documents contained in the region. We believe this research has established the Kohonen SOM algorithm as an intuitively appealing and promising neural-network-based textual classification technique for addressing part of the longstanding “information overload” problem.

:A latent variable approach to the evaluation of CASE tools is used to assess user needs and applications. Responses are consistent with the taxonomy of upper and lower CASE tools. Results indicate the importance of analysis and prototyping features. Some existing tools are rated significantly higher than others in terms ofthese features. The study also reveals a link between organizational size and the demand for upper and lower CASE tool features. Smaller organizations use CASE tools in the design stage and rely on teamwork and collaboration facilities. Larger firms focus on lower CASE facilities such as prototyping to build completed systems.