Implementation of a group decision support system utilizing collective memory
Introduction
Many organizational decision-making processes involve groups of executives performing periodic and repetitive activities. At each session, new sets of data, information, and knowledge may be generated. The composition of the decision-making group, however, may change: group members may retire, resign, or be transferred. New members of the group need to learn how previous decisions were made. While organizational policies and procedures will undoubtedly be of some use in this context, the intelligence and design activities from Simon’s characterization of decision-making [32] are likely to represent the richer knowledge that is useful in subsequent sessions. Therefore, mechanisms to capture the experiential knowledge of these groups can be of significant value to the organization in general, and the group in particular. The development of a shared repository that stores the knowledge of group members, retains the rules, policies, and standard procedures, and acquires relevant data and knowledge from the external environment will clearly assist these groups. The shared repository, together with the appropriate means for managing its content, represents an implementation of collective memory.
Zarecka [39] described collective memory as a socially articulated and maintained history. Pennebacker and Banasik [27] highlighted the dynamics that contribute to the issues associated with building and maintaining collective memories. Collective memory at the enterprise level, viz. organizational memory has emerged as an area of considerable research interest. At the workgroup level, collective memory is generally characterized as group memory. It can include the knowledge and experiences of the groups in the context of decision-making activities. Other cases where such memory will be of utility are multi-stage decisions and decisions that cannot be satisfactorily completed in a single session, creating a need to carry over intermediate findings to subsequent sessions.
This paper outlines the development and implementation of the prototype of a group decision-making environment that utilizes the work of prior groups stored in the collective memory. In an effort to enhance utility, the prototype was tested in a semi-structured decision-making environment involving multiple attributes. Collective memory components were captured using a hypermedia framework, and the collective memory was leveraged through the use of Intranet technology interfaced to traditional group decision support systems (GDSS).
Section snippets
Collective memory, group memory, and group decision-making
Memory is the facility to retain, recall, and manipulate past or present information as well as expectations about the future. Although the primary focus of memory-related research is on individuals, Halbwachs et al. [9] proposed that there is memory at the collective level, a socially constructed notion. Members of different social groups and institutions (such as family and associations) draw on their current context to recreate the past. Organizational memory, defined as “the means by which
Group decisions utilizing collective memory
Our work elicited the use of collective memory in an iterative decision that closely resembles judgment tasks. Judgment tasks are cognitive-conflict ones and have outcome and solution scheme multiplicity with solution scheme/outcome uncertainty [20]. They are commonly found when some form of problem reduction (or structuring) is performed through the selection of an appropriate solution scheme and then is used to evaluate the outcome. Recruitment and personnel hiring are good examples—typically
A prototype system to support iterative group decision-making
Support for group decision-making necessitated the creation of an appropriate meeting environment. The need for individual group members to explore the solution set and reach consensus over multiple decision sessions prompted the development of an appropriate meeting environment. A modular architecture was employed, as depicted in Fig. 1.
Information relevant to the decision (e.g., attributes and their scores for individual programs) was stored in a separate module. Data was available to the
An experiment using the prototype
Our research sought to examine the effectiveness of collective memory on group decision support system usage. While a field research approach would undoubtedly provide a rich and representative source of data, difficulties in identifying a priori natural settings involving collective memory information systems use coupled with the paucity of subjects and potential lack of comparability among subjects rendered these approaches less appropriate. The majority of GDSS research has involved
Discussion and limitations
The laboratory experiment revealed that the use of collective memory does speed up the decision-making process. The users of collective memory, however, browsed fewer information pages and considered fewer attributes in their work. The participants of the laboratory experiment were undergraduate business students who were presumed to be motivated to participate in the experiment—they received a waiver for an assignment in a course and were excited at the prospect of using electronic
Conclusions
The results from the use of the prototype are encouraging. One of the objectives of using collective memory is to make group members aware of the group norms. Huang et al. [14] found that GSS groups engaged in preference tasks have attenuated normative influence; i.e., reduced level of desire to conform to the expectation of others. A reduced normative influence is likely to have negative effect on group decision outcomes. However, based on our findings, we expect that inclusion of collective
William (Dave) Haseman is Wisconsin distinguished professor at the University of Wisconsin, Milwaukee. Dr. Haseman has a PhD in management information systems from Purdue University. He also has an MBA from the University of Wisconsin, Milwaukee. His undergraduate degree in electrical engineering is from Purdue University. Prior to joining the UWM faculty, he served on the faculty at Carnegie-Mellon University.
Professor Haseman’s research interests are in the area of group decision-making,
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William (Dave) Haseman is Wisconsin distinguished professor at the University of Wisconsin, Milwaukee. Dr. Haseman has a PhD in management information systems from Purdue University. He also has an MBA from the University of Wisconsin, Milwaukee. His undergraduate degree in electrical engineering is from Purdue University. Prior to joining the UWM faculty, he served on the faculty at Carnegie-Mellon University.
Professor Haseman’s research interests are in the area of group decision-making, emerging technologies, eBusiness, Web services, and the use of the Internet for business applications. He has been the principal investigator of several major research grants from national funding agencies such as the National Science Foundation and the US Department of Commerce and has extensive consulting experience.
He currently serves as the Director of the Center for Technology Innovation, an applied research center that is actively involved in the Milwaukee area IT community. He has designed a number of custom professional education programs taught in Milwaukee area companies. He served as the conference chair for America’s Conference in Information Systems (AMCIS 1999).
He has published over 30 books, book chapters, and journal articles and has made over 50 conference presentations. His articles have appeared in Accounting Review, MIS-Q, Operations Research, Datamation, Management Datamatics, Journal of Computing and Operations Research, Journal of Socio-Economic Planning, The Computer Journal, Journal of Medical Systems, Information Systems, Information Processing and Management, Policy and Analysis and Information Systems, Database Management, International Journal of Human-Computer Studies, Information Resources Management Journal, Annual Review of Communications, and Decision Support Systems.
Derek L. Nazareth is associate professor of management information systems at the University of Wisconsin, Milwaukee. He holds a PhD in management from Case Western Reserve University. His current research interests include application development using Web services, software reuse, data warehousing, machine learning, and knowledge base verification. His papers appear in Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering, Journal of Management Information Systems, Decision Support Systems, Knowledge Acquisition, OMEGA, Information & Management among others. He is a member of AIS, ACM, and INFORMS, and was the Program Chair for AMCIS 1999.
Souren Paul is an assistant professor of management information systems at the College of Business and Administration at Southern Illinois University, Carbondale. He holds bachelor’s and master’s degrees in electronics and tele-communications engineering from Jadavpur University, India, and a PhD in management information systems from the University of Wisconsin, Milwaukee. He has published research articles in Decision Support Systems, Information & Management, Journal of Information Systems Education, and Proceedings of Hawaii International Conference on System Sciences. His current research interests are in the areas of cognition and knowledge sharing in collaborative technology supported group work, virtual teams, and organizational knowledge management systems.