Decision Making in an Automated Environment: The Effects of Anonymity and Proximity with a Group Decision Support System*

Recent advances in information systems technology have made it possible to support the work of interacting groups using networked personal computers. A laboratory experiment was conducted using a group decision support system to evaluate effects of anonymity and proximity on group process in automated group problem solving. Twenty groups of four persons each performed an idea-generating task using an interactive electronic brainstorming program. This experiment's main findings were: (1) Group members working anonymously and apart generated more comments. (2) Working in the same room increased satisfaction. (3) Highest levels of perceived system effectiveness were reported under anonymity.     

Assigning Students to Groups: A Multi-Criteria Decision Support System Approach*

MCADSS is a multi-criteria allocation decision support system for assisting in the task of allocating students to groups. Based on multiple criteria, MCADSS's goal is to maximize the diversity of members within groups, while minimizing the average differences between groups. The project may be viewed from several perspectives: as a multi-criteria decision-making problem, as a “reverse'’clustering problem, or as a personnel assignment problem. The system is currently being used to allocate M.B.A. students into sections and study teams at The European Institute of Business Administration (INSEAD). This paper describes the rationale for MCADSS, design criteria, system methodology, and application results. It also suggests how this approach could be used for further applications.     

The Effectiveness of Graphical Presentation for Information Extraction: A Cumulative Experimental Approach*

Three laboratory experiments were conducted to assess the relative strengths and weaknesses of bar, symbol, and line graphs for performing a variety of elementary information extraction tasks using two dependent variables, time and accuracy. The findings indicate that the degree of support provided by a particular graph format for a particular data extraction task depends on the matching of format and task in terms of their anchoring characteristics. Anchoring, in this context, refers to the phenomenon that specific and diverse parts of a graph are segmented by the reader to act as salient and relevant cues, or anchors, when different classes of information are to be extracted from the graph. A data extraction task has high x-value (y-value) anchoring if the x-axis (y-axis) component is represented in the question as either a given value or an unknown value. Conversely, a task has low x-value (y-value) anchoring if the x-axis (y-axis) component is not represented in the question as either a given value or as an unknown value. Data extraction accuracy was not significantly affected by presentation format. Bars provided the best time performance for data extraction tasks having high anchoring on both axes but were not appropriate for tasks having low anchoring on either the y axis or both the x and y axes. Line graphs tended to be worse in terms of time performance for tasks having high anchoring on both axes although they were as fast or better than other representations for tasks having low anchoring on both axes. Symbol plots appeared to possess anchoring characteristics associated with both bars and line graphs. Symbols (as with bars) tended to produce a time performance superior to that of line graphs for tasks having high anchoring on both axes; and (as with line graphs) symbols allowed faster results than bar graphs for tasks having low anchoring on either the y axis or both the x and y axes.     

An Empirical Investigation of the Effects of Decision Aids on Problem-Solving Processes*

Despite the growing number of investigations examining decision support systems (DSS), relatively few empirical studies have evaluated the effects of DSS on problem-solving processes. This study uses protocol analysis to investigate the impact of a specific decision aid on problem-solving processes in a semi-structured problem. Results indicate that decision aids influence the problem-solving processes of decision makers. The effect of a decision aid is found to be contingent on familiarity with the decision aid, task familiarity, and the interaction of these two factors. Suggestions for incorporating interaction effects and problem-solving processes into future examinations are proposed.     

The Interaction Effects Between General Thinking Skills and an Interactive Graphics-Based DSS to Support Problem Structuring*

An interactive graphics-based problem-structuring aid, GISMO, based on concepts from structural modeling and motivated by research in imagery theory and cognitive psychology was incorporated into a decision support system. A laboratory experiment was conducted to investigate the relationships between three individual general thinking skills (GTS) and the effectiveness of the problem-structuring tool. A theoretical model of the roles of GTS and GISMO in problem formulation and a methodology for testing the model are presented. The model posits that the effectiveness of visual problem-structuring aids in formulating or understanding complex problems is influenced by specific cognitive skills (i.e., visual-thinking, verbal, and logical-reasoning skills). The results indicate GISMO use is related to higher levels of problem-structure understanding, regardless of verbal and logical-reasoning skills. Also, the results indicate a strong interaction effect between GISMO use and visual-thinking skill. Subjects classified as high visual thinkers benefited more from the use of GISMO than the low visual-thinking subjects. The results of the experiment lend support for the findings of a previous study of GISMO reported in the literature, and the image theorist view that the ability to create and use visual, mental images is related to better problem-solving performance.     

An Empirical Investigation of Information Search Strategies with Implications for Decision Support System Design

Understanding how people search through and combine information before making decisions is an important concern in the study of decision making and in the design of decision support systems (DSS). The purpose of this study is to examine DSS search strategies in relation to the body of empirical research on information load. Prior research is examined in relation to basic empirically-testable hypotheses and compared to the results of this study as a way of validating the procedures used here. The transfer of information load empirical research to a DSS scenario is confirmed statistically. Then, results of an analysis of information search patterns under DSS conditions are described. Finally, implications for DSS design are discussed in terms of possible search support mechanisms for specific search strategies.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

A Prototype Expert Decision Support System for the Market Appraisal of the Single Family Residence

The assessment of the current market value of residential property is a potential source of anxiety for the typical homeowner. Common criticisms of the existing appraisal process include excessive subjectivity and inconsistency across properties and through time. This paper examines the nature of decision support required for valuation decisions and the appropriateness of applying expert system technologies to evaluate the recoverable value of the single-family residence. The expert system approach allows for the integration of the qualitative and quantitative aspects of appraisal, while introducing the beneficial dimensions of increased objectivity, comprehensiveness, and consistency. A prototype expert system is offered that enables the end user to conduct a market analysis of a subject property. Unlike most expert system applications that seek a diagnostic or classification response, this study explores a numerical intent for the system, in a decision-making environment that is traditionally viewed as highly judgmental. The system's early validation results show promise of proving effective as such an evaluation aid.     

Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage,Commercial, and Consumer Lending

The concepts of expert systems and decision support systems have received considerable attention recently. While systems have been proposed for various problem areas in business, difficulties still exist in the knowledge acquisition phase of development. This paper presents a recursive partitioning analysis (RPA) approach to knowledge acquisition. The RPA production system approach was applied to data sets representing the mortgage, commercial, and consumer lending problems. Comparison of the classification rates across these problems to the results of a generalized inductive inference production system (Quinlan's ID3 algorithm) and across the mortgage and commercial lending problems to traditional statistical modeling approaches indicated that the RPA approach provided superior results while using fewer variables.     

Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids*

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.     

Automated Discovery in Managerial Problem Formulation: Formation of Causal Hypotheses for Cognitive Mapping

Development of knowledge acquisition techniques known as automated discovery systems has occurred in deep and narrow domains of knowledge. Automated discovery is the generation of new knowledge by a computer system on its own, without the help of another knowledge source. This paper describes research and validation of an automated discovery system for a wide and shallow domain—business management. The system continues recent advances in expert systems research which have enhanced cognitive mapping, a problem formulation tool. The system perceives the behavior of distal variables in the environment through probabilistic cues-to-causality, and generates previously unknown hypotheses by aggregating the probabilities into a single criterion of causal relatedness. The system is validated against the source code of a simulated managerial environment, and causal relationships posited by decision makers experienced in the play of the gaming simulator.     

The Effects of Memory Structure on Using Rule-Based Expert Systems for Training: A Framework and an Empirical Test*

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure.     

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.     

Decision Processes for Developing Strategic Applications of Information Systems: A Contingency Approach*

Strategic applications of information systems (IS) are considered by IS researchers to be determined by contextual factors such as environmental uncertainty, and influenced by attributes of the processes preceding them, such as planning and top management support. For better management of the process leading to these applications, it is essential to understand the relationship between process attributes and contextual factors. Utilizing a contingency approach, this article takes a step toward such an understanding. Based on successful strategic IS applications from 81 large companies, it examines the relationship between the context of these applications and the decision-making process leading to them. The results indicate that the external environment is related to whether a rational or political decision-making process is used. The IS function seems to influence the use of the decision process implied by IS researchers, one in which the top management champions the strategic application, while the IS managers contribute by conducting in-depth analysis. However, the organization structure was not related to any decision process attribute. The implications of these findings for future research and practice are also discussed.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

An Examination of Two Screen/Report Design Variables in an Information Recall Context*

Screen/report design for output organization is still very much a common sense-driven activity. This paper identifies a preliminary set of factors beyond display format for examination as screen/report design variables. A laboratory experiment was conducted to assess the influence of one of these factors, spatial layout of information, along with display format in an information recall context. Individual imagery orientation (verbalizer/visualizer) was used as a moderating variable in the experimental design. The findings indicate that spatial layout of information significantly influenced performance when the recall task involved pattern detection. In fact, a central location of data in a document was the most conducive to pattern recall performance. Further, terminal items facilitated pattern recall better than early input items. With respect to display format, the results support previous findings that a tabular display enhances performance for point-value recall tasks while graphs exhibit an edge over tables for pattern types of recall tasks. Overall, the verbalizers demonstrated a complete supremacy in performance. Interaction between individual imagery orientation and display format indicates that, while the verbalizers and the mixed type were immune to display format variations, the graphical stimulus enhanced the pattern recall performance of the visualizers.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.