1. The Changing Structure of DSS Research: An Empirical Investigation through Author Cocitaion Mapping The 2004 IFIP International Conference on Decision Support Systems (DSS2004) Decision Support in an Uncertain World

2. Introduction DSSs are a relatively young field of study. As a field of study continues to grow and becomes a coherent field, study of the intellectual development of the field is important (Culnan (1986). Researchers can benefit by understanding this process and its outcomes because it  reveals the vitality and the evolution of thought in a discipline  gives a sense of its future  identifies the basic commitments that will serve as the foundations of the field It atrophies if it cuts itself off from curiosity, diversity, and reflection" For DSS to become a coherent and substantive field, a continuing line of research must be built on the foundation of previous work. Without it, there may be good individual fragments rather than a cumulative tradition (Keen, 1980).

3. This research assesses on-going changes in the intellectual development and structure of DSS research. Emphasis on contrasting the structural changes over the period of: - 1969 through 1990 and - 1991 through 1999

4. DATA A total of about 1600 citing articles in the DSS area 632 -- 1969 through 1990 (28.7 articles/year) 984 -- 1990 through 1999 (98.4 articles/year) A total of about 25,000 cited references taken from the citing articles

5. Research methodology Author cocitation analysis (ACA) "a set of data gathering,, analytical, and graphical display techniques that can be used to produce empirical maps of prominent authors in various areas of scholarship" The tools used in ACA factor analysis multidimensional scaling cluster analysis.

6. Research methodology The Assumptions in Author cocitation analysis (ACA) “cocitation is a measure of the perceived similarity, conceptual linkage, or cognitive relationship between two cocited items (documents or authors).” “Cocitation studies of specialties and fields yield valid representations of intellectual structure.” Steps in ACA 1. Selection of Authors 2. Retrieval/compilation of Cocitation Frequencies 3. Multivariate Analysis 4. Interpretation

7. When does the cocitation occur? When a citing paper cites any work of authors in reference lists Ref. of Paper #1 Ref. of Paper #2 Ref. of Paper #3 Ackoff AckoffAckoff BonczekAckoff Ackoff Bonczek ApplegateBlanning BlanningApplegate BlanningWhinston Blanning Whinston

8. Sample Cocitation Matrix Ackoff ApplegateBonczek BlanningWhinston Ackoff Applegate Bonczek Blanning Whinston

9. AckoffApplegate Bonczek Blanning Whinston Ackoff Applegate 1 Bonczek 1 0 Blanning 2 0 2 Whinston 2 1 1 2

10. Author Cocitation Analysis "a set of data gathering, analytical, and graphical display techniques that can be used to produce empirical maps of prominent authors in various areas of scholarship" (McCain 1990)

11. Can you see the trunk, branches, and the roots of the tree?

12. ACA: A Tool for Digging Up the Roots, Trunks, Branches

13. Results Factor analysis of the data (1990-1999) extracted 11 factors Six major areas of DSS research 1. group support systems 2. design 3. model management 4. implementation/user interfaces 5. Evaluation 6. multiple criteria DSS Five contributing disciplines 1. cognitive science 2. computer supported cooperative works 3. organizational science 4. social psychology 5. MCDM

14. Systems Science Organization Science Cognitive Science Implementation User Interface Foundations Model Management Artificial Intelligence MCDSS MCDM GDSS Artificial Intelligence Communication Organization Science Psychology

15. Theory, Applications, and Contributing Disciplines of DSS Interfaces (Dialogue) Model J Contributing disciplines J1.Artificial Intelligence J2.Cognitive Science J3.Communication Theory J4. MCDM J5.Organization Science J6.Systems Science J7.Psychology Decision maker(s) Interface DataModel Design Implementation Evaluation Marketing DSS POM DSS Financial DSS Decision maker(s) SDSS GDSS ODSS KBDSS MCDSS data Organizational goals Improving effectiveness of the DM’s problem-solving process A specific DSS C D E F G H I B Functional app.

16. What has happened/is happening in DSS research since 1991? the DSS area has undergone profound structural changes in the intellectual structure over the past 10 years (1990- 1999). -- steady, strengthening, emerging, dying, and slowly growing areas. The steady areas model management organizational science multiple criteria decision making Artificial Intelligence

17. What has happened in DSS research? The Strengthening Areas GSS The Emerging Areas Design Implementation Cognitive science The Dying Areas Foundation and Individual Differences (appeared to be no longer active)

18. Group DSS The result of this study clearly shows that GDSS has become a central part of DSS research area over the past five years. Some of the important recent developments (1) There have been continuing developments and enhancements of GDSS tools to support and augment the existing group DSS and electronic meeting systems such as the following: l -- An idea consolidator l -- An optimization-based group decision tool for combining subjective estimates and extracting the underlying knowledge of group members

19. Group DSS l -- A group software for modeling and analyzing business process re-engineering; l -- An interactive videodisk-based GDSS for directing the pattern, timing, and contents in group decision making; l -- A prototype GDSS for multicultural and multilingual communication to translate among several foreign languages such as English, German, Korean and Spanish;

20. Group DSS (2) A wide range of GDSS/electronic meeting systems/decision conferencing system applications has been reported to support/facilitate the following areas : l -- Strategic management meetings l -- Quality improvement process l -- Knowledge acquisition for multiple experts l -- Distributed decision making involving fairly large numbers of participants (tens to hundreds);

21. Group DSS l -- Developing a cognitive map of users of object-oriented techniques for understanding individual and group perceptions [48]; l -- Developing national economic policy [49]; l -- Expediting the requirements specification in the system development process l -- Facilitating the United States Army’s group decision making in geographically distributed environments ; and

22. Group DSS (3) GDSS is being integrated with other technologies such as ES and case-based reasoning, etc. A prototype system that embedded ES into GDSS is developed to make a GDSS a more user-friendly and powerful tool The distributed artificial intelligence approach for designing and developing group problem solving systems is being investigated to coordinate organizational activities in a distributed environment

23. GDSS Empirical Research Model (U. of AZ) Group Task Context EMS Process Outcome

24. Model management Some of notable approaches include the following: (1) Development of Graph-based modeling : Jones presented a prototype system of graph- based modeling, NETWORKS, which allows the user to represent a wide variety of decision problems in a graphical form such as bar chart, decision tree, decision network, etc.. Further, the users manipulate the models (e.g.., deleting/adding subtrees for decision trees) using a graph-grammar by applying a set of operations (or productions).

25. Model management (2) Object-oriented approach : Using the object-oriented framework, Muhanna [37] designed and implemented a prototype model management system to build, store, retrieve composite models and to maintain the integrity of model bases through providing the functional capability of model sharing, integration, and reusability. e.g., a DSS formulates and integrates optimization and simulation modeling and heuristic reasoning for non-expert users through an object-oriented domain-specific knowledge base.

26. Model management (3) Modeling by analogy (Analogical modeling) is suggested as potentially fruitful avenues for increasing the productivity of model formulation. This approach is using a process by which model X is constructed based on a known model for problem X and the similarity between problems X and Y [31]. (4) In addition to a knowledge base which stores facts and rules, case- based reasoning systems maintain a case base which is a repository of all previous cases solved. To find a solution for a new problem, the system identifies the most similar case from its case base to be applied.

27. Model management (4) Active modeling systems are expert systems embedded modeling systems which provide intelligent support to the modelers. e.g. a knowledge-based linear programming (LP) model construction system

28. Model management (5) Integrating model management and inductive machine learning in an adaptive decision support system. by incorporating machine learning capabilities for model management. the system adapts itself to the environment through continuously updating and refining the knowledge-base. (6) Model integration using metagraph: the process of model integration can be significantly expedited by utilizing certain connectivity properties in metagraph.

29. User Interfaces Intelligent agents (a.k.a. intelligent interfaces, adaptive interfaces) research is an emerging interdisciplinary research area involving researchers from such fields as expert systems, decision support systems, cognitive science, psychology, databases, etc. The primary purpose of agent research is to "develop software systems which engage and help all types of end users" in order to reduce work and information overload, teach, learn, and perform tasks for the user.”

30. A NEW GENERATION OF ACTIVE/INTELLIGENT DSS WHAT A GREAT IDEA! A SYSTEM THAT BRINGS TOGETHER THE ANALYTICAL STYLE OF DSS AND THE JUDGMENTAL STYLE OF ES/AI

31. CONCLUSION AND IMPLICATIONS FOR FUTURE DSS RESEARCH This study identified a dynamic dimension of DSS research areas to account for the ongoing changes in its "disciplinary matrix" -- the four emerging areas (Implementation, Design, and Cognitive science); continuously growing areas (GDSS, Model management, MCDM, and Organization science); and dying areas (Individual difference and Foundations).

32. Focus of DSS research appears to be shifting from the study of DSS components (data, model, individual differences of decision makers) during the periods of 1970 through 1990 to the design, implementation, and user-interface management (which have not been shown to be substantive DSS research subspecialties in the previous research), to provide useful guiding principles for practitioners in the integrated processes of design, implementation, and evaluation of decision support systems.

33. CONCLUSION AND IMPLICATIONS FOR FUTURE DSS RESEARCH World Wide Web-based DSS is another emerging topic in the DSS area. The World Wide Web is increasingly being used as the client/server platform of many business organizations due to its network and platform-independence and very low software/ installation/maintenance costs. The web-based solutions are low cost vehicles for easily accessing, analyzing, and distributing timely business information from corporate databases through OLAP. The Internet and corporate intranets opened a wide possibility of building global DSS/interorganizational DSS to deal with problems of global natures. As we enter the age of the global village where geographical and temporal boundaries are shrinking rapidly, global DSS/Interorganizational DSS support systems are emerging as the new frontiers in management information systems area.