An exploratory cognitive DSS for strategic decision making
Introduction
Decision support systems (DSS) were envisioned to be “executive mind-support systems” that seek to establish a symbiosis of human mind and computer by allowing for a high degree of human–computer interaction [29], [30]. Up to now, this grand vision has yet to become a reality. Most of today's computer-based decision support has focused on support for the behavioral aspects of decision making and extensions of the analytical capabilities of decision makers. For example, executive information systems (EIS) provide executives with information on critical business success factors in a timely and user-friendly fashion. Various decision support systems provide their users quantitative modeling tools and easy data access. The cognitive aspect of decision support, however, has received relatively little research, although it has long been recognized as an important consideration of decision support systems design.
Cognitive orientation or mental models play a very important role in a decision maker's understanding of business environments and ill-structured problems. Donaldson and Lorsch [3] conducted an exploratory study on the goal formulation and strategic decision-making processes of top executives in 12 Fortune 500 companies. They found that corporate executives are constrained not only by objective financial goals and constituency demands, but also by an elusive set of psychological constructs of their own beliefs. They observed that “these interrelated beliefs act as a filter through which management perceives the reality facing its firm. Thus, psychological constructs serve two essential and significant functions. One is to simplify: to translate a world that can be overwhelmingly complex and ambiguous into comprehensible and familiar terms. The other is to provide continuity and stability when change threatens to undermine the lessons of experience” [p. 79]. Similarly, Porac and Thomas [18] suggest that a mental model—a set of deeply held assumptions and beliefs—provides a useful conceptual tool for decision makers to simplify complex business environments and to impose order on volatile competitive conditions to reduce uncertainty.
On the other hand, outmoded assumptions and beliefs are detrimental to the survival of today's firms in the turbulent business environment. Senge [24] observed that many good strategies in business fail to get implemented and systemic insights never find their way into operating policies because they conflict with executives' deeply held assumptions of how the world works. These assumptions limit decision makers to familiar ways of thinking and acting [p. 174]. Gilad [6] cited many business downfalls as a result of obsolete mental models and business blind spots held by the executives in these firms.
Research progress in the cognitive aspect of decision support has been slow because of our limited understanding of executive cognitive processes and the fact that computer technology does not lend itself easily to cognitive support. However, new generation of computer technology and our increased understanding of managerial cognition in recent years have created a renewed research interest in this area. Traditional decision support systems and executive information systems have been successful in helping executives build mental models about their business reality by providing layered information. But they provide little direct aid in detecting and unlearning outmoded mental models. An ideal decision support system should be a part of a human–computer collaborative learning environment where the computer plays a more active role in facilitating the decision maker's creative thinking and providing tools to surface tacit assumptions and beliefs, and aid his or her forward thinking.
This paper reports on a research project that looks into the cognitive process of strategic decision making to identify some cognitive simplification processes that decision makers employ in dealing with complex decision-making situations. Then, a set of IS functions is designed to aid the decision maker's cognitive processes. The paper is organized as follows. In the next two sections, a review on the cognitive aspect of executive work and cognitive simplification processes is presented. Then, a Web-based system architecture for multi-participant cognitive decision support systems (CDSS) is proposed. The design, implementation, and evaluation of an exploratory prototype system are discussed. Finally, conclusions and future research are presented.
Section snippets
Managerial cognition and IS support
Mintzberg [15] identified not only 10 roles of top executives but also recognized the importance of mental models in executive work. Mental models are commonly referred to as deeply held assumptions and beliefs that enable individuals to make inferences and predictions. They can be represented in many forms: tokens, spatial relations between entities, temporal or causal relations among events. Mintzberg contends that executives use the information they collect to develop a series of mental
Cognitive simplification process
Research in cognitive psychology, behavioral decision theory, and strategic decision making has identified several cognitive simplification processes or heuristics which decision makers use when they deal with complex, ambiguous, and uncertain decision situations [14], [20], [23]. These processes include availability, adjustment and anchoring, prior hypothesis, and reasoning by analogy. Although they are useful in some circumstances, they are also the causes for several types of judgment errors
A cognitive approach to decision support systems design
A conceptual model is proposed based on the discussions in Section 3. The model shown in Table 1 consists of three supporting modes: retrospective, introspective, and prospective. DSS functions are proposed for each mode. Case Memory provides the user with tools to manage business cases, his or her experience, the opinions of others, speculations, and even rumors. Personal experience, speculations, and rumors are termed “soft” information, which is often used in strategic decision making [27].
Exploratory assessment
The validation of most decision support systems has been a challenging task [11], [16]. The difficulty in this research lies in the assessment of the cognitive impact of using the prototype system. There are many factors that may affect the decision maker's thinking. It is extremely difficult to separate and control these factors. A laboratory study may provide a controlled environment, but the lack of a real-world setting will make the study results less useful. Nevertheless, an exploratory
Comparison of CDSS with typical DSS/ESS
The main purpose of this research was to investigate a new way of decision support. The proposed system is meant to enhance current DSS/ESS. Table 3 shows the comparison between current DSS/ESS systems and the CDSS. The system capabilities in the table are based on Turban [26] and Young [29].
The technical features of a DSS/ESS can be divided into three groups: information retrieval (features 3 through 6), quantitative modeling (feature 7 through feature 9), and qualitative modeling (features 10
Applicability issues
The prototype presented in this paper serves as an experimental vehicle to explore cognitive decision support. Consequently, it does not address all the issues of its applicability to practice. More research and development are needed to prove the system's practical usefulness. However, we believe that as computer hardware and software technology continues to advance and more and more managers become computer savvy, the chance of its successful applications in real world will be improved.
The
Conclusion
The prototype system reported in this research adopts a design focus that is different from that of the current decision support systems. Rather than focusing on the executive's information need on “critical success factors” and the need for specific decision support, this research emphasizes the need to support the executives' thinking process. The three-mode prototype system was tested in a two-phase case study involving six small business executives. The findings provide some initial
Jim Q. Chen is an associate professor of Business Computer Information Systems at St. Cloud State University. His current research interests include Web application development methodologies, E-commerce, and executive decision support systems. His recent publications appeared in Journal of Internet Commerce, Information Systems Management, Marketing Management Journal, Logistics Information Management, Journal of Computer Information Systems, Review of Accounting Information Systems, Total
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Jim Q. Chen is an associate professor of Business Computer Information Systems at St. Cloud State University. His current research interests include Web application development methodologies, E-commerce, and executive decision support systems. His recent publications appeared in Journal of Internet Commerce, Information Systems Management, Marketing Management Journal, Logistics Information Management, Journal of Computer Information Systems, Review of Accounting Information Systems, Total Quality Management, among other journals.
Sang M. Lee is currently the University Eminent Scholar and Management Department Chair at the University of Nebraska-Lincoln. His research interests include IT-supported knowledge management infrastructure, competitive strategies in the digital economy, global business, and multiple objective decision support. He has published more than 50 books and 180 journal articles, mostly in MIS, POM, global business, and management science. He is a Fellow of the Academy of Management, Decision Sciences Institute, and Pan-Pacific Business Association. He served as President of DSI and is currently President of the Pan-Pacific Business Association.