Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

Structuring Conflict in Individual,Face-to-Face,and Computer-Mediated Group Decision Making: Carping Versus Objective Devil's Advocacy*

Researchers and practitioners have long been interested in the effects of cognitive conflict techniques on individual and group decision making. One widely used and studied technique, devil's advocacy (DA), has been found to enhance decision-making performance for both individuals and groups. Devil's advocacy begins with a recommended decision, followed by a critique of the decision that questions its assumptions. Researchers have not yet examined the effects of the objectivity of the devil's advocacy comments in computer-mediated environments. This paper reports the results of a laboratory experiment that focused on this question by comparing the effects of an objective, nonemotional DA to an emotional, “carping” DA within individuals and groups using either computer-mediated or face-to-face communication. In a manner consistent with prior research, both DA treatments were operationalized through the use of paper-based consulting reports. The results suggest that individuals and computer-mediated groups develop and consider more solution alternatives than face-to-face groups, and that subjects given the objective DA treatment produce higher quality decisions than those given the carping DA treatment. Face-to-face groups in the carping DA treatment considered the fewest alternative solutions in their decision-making process, reached the lowest solution quality, yet reached decision consensus in the fewest voting rounds. The practical implications of the results suggest that questioning statements made by a devil's advocate should be objective, regardless of group communication condition. Carping devil's advocacy appears to stifle group decision outcomes when groups are using face-to-face communication.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.     

Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection

This study addresses the part-machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array-based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non-hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p-median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture-model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry-size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array-based methods of ROC2 and DCA.     

Interface Style and Training Task Difficulty as Determinants of Effective Computer-Assisted Knowledge Transfer*

The transfer of expert knowledge to novices is one means of improving decision quality. Research needs to identify (1) the knowledge to be transferred to novices, and (2) the best method for transferring that knowledge. Studies that compare the judgment behavior of experienced and novice auditors address the first issue. The present study addresses the second issue in the context of using a computer-assisted training (CAT) program. CAT was selected for study because of evidence that it can both improve the effectiveness and reduce the costs of training. An experiment was conducted in which two factors were manipulated: (1) the design of the human-computer interface of the CAT program, and (2) the content of training tasks. The judgment of interest involved causal reasoning about the relationships between various internal control procedures and possible errors. The results indicate that alternative styles of interaction with a CAT program differ in terms of learning effectiveness. In addition, there was also evidence that training task content affected learning.     

Evaluating and Refining Expert Critiquing Systems: A Methodology*

Expert critiquing systems are a rapidly growing class of intelligent decision support systems that apply artificial intelligence techniques to criticize a user's proposed solution to a problem. Critic programs now exist in the medical, engineering, programming, knowledge acquisition, word processing, and other domains. Critic refinement is a nontrivial activity that, even when done well, consumes a sizable fraction of the complete effort to build and deploy a critiquing system. To ease that effort, it is important to adopt a rigorous approach that allows one to reproducibly measure the degree of success of the current critic version and to predict which refinements will improve the critic further. The current article presents one such approach with actual case studies that illustrate its usage and elaborate selected aspects of the refinement process.     

The Use of Stochastic Simulation in Knowledge-Based Systems

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems.