DECISION SUPPORT SYSTEMS FOR ILL-STRUCTURED PROBLEMS: AN EMPIRICAL STUDY

Decision support systems (DSSs) are more complex than most other traditional decision-aid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable. The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems.     

The Determinants of DSS Success: An Integrated Model*

The results of a field study investigating the determinants of decision support systems (DSS) success are presented. A multivariate model was developed and tested using multiple regression hierarchical analysis on responses from 118 DSS users. Several specific hypotheses are also proposed and tested. Data analyses indicate that DSS success, as measured by DSS satisfaction and perceived benefits, depends on several factors: previous user experience with DSS, user involvement, user training, top management support, information sources, the level of managerial activity, and task structure, difficulty and interdependence. Based on the research results, several suggestions are made to improve the likelihood of successful DSS implementation.     

An Evaluation of the Role and Performance of a Decision Support System in Business Education*

With the rapid growth in end-user computing, there has been an increasing demand on business schools to train managers in the use of computers for effective decision support. While computer-aided instruction (CAI) has been in vogue for over a decade, there are very few studies that evaluate the impact of computers on decision-making skills in a business education context. The study makes use of a controlled experiment to evaluate the impact of computers on the accuracy and quality of decision making in a business decision support context. It also addresses the relationships between decision performance and various student characteristics such as aptitude, attitude, domain experience, domain expertise, gender, and system experience. The study revealed that computer support positively influenced both dimensions of decision performance.     

An Application of Expectancy Theory to Explain a Manager's Intention to Use a Decision Support System

A continuing gap exists between the capabilities of sophisticated computer-based information systems and the extent to which these systems are used by individuals. Studies which have examined the relationship between system utilization and various user, system, implementation, and organizational variables have provided few consistent findings. A new approach to this topic is suggested by a recent study by Davis, Bagozzi, and Warshaw [11], which indicates that individuals' intentions to use a system determine subsequent use. A large body of psychology-based research also supports this relationship between behavioral intentions and subsequent behavior. This study employs expectancy theory, which has often been used to examine behavioral intentions, to explain managers' intentions to use a decision support system (DSS). The results imply that the variables of the expectancy force model are determinants of a manager's behavioral intentions to use a DSS, and the variables of the expectancy valence model are determinants of the attractiveness of using a DSS to a manager.     

Presenting Geographic Information: Effects of Data Aggregation,Dispersion, and Users' Spatial Orientation

Geographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills. We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map-based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.     

DDP—Managerial opportunities and consequences

Applications of DDP are examined via two case studies to determine how the technology was used by management to aid decision-making; the level of management involvement in the MIS (Management Information Systems) design process and the nature of the relationship between managers and data processing departments were also considered. The study showed that because the technology was largely under end user control, better tailored information systems resulted and system output was more directly used by middle and top managers. This only occurred, however, if certain moderators, such as computing and modelling skills, were present in functional departments. DDP also offers opportunities for better relationships with data processing departments but the consequences include the need for better management training and functional support groups for systems implementation. A conceptual model is presented as a theoretical framework to guide organisations when using DDP.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

The bureaucratic side of computers: Memory,evocation and management information

Management information systems fail to support management because they are not flexible enough to keep pace with the unstructured nature of decision making processes. Observations made in four welfare management information systems suggest that computerization improves evocation—the means by which decision makers pool data from the collective memory, only to the extent that choices and the relevancy of data are predetermined. Facing rigid procedures and irresponsiveness to irregular data needs, managers of all ranks duplicate the computerized memory with manual records and files so as to retain access to their memory under their terms.     

Personal computing and support services

This paper reports the results obtained from a field study of 173 US companies which have implemented personal computing in the past five years. The study establishes the relationship between (i) the support services provided by the MIS department and its control over the PC acquisition process and (ii) and degree of information system (IS) management concerns with personal computing problems such as user training and user experience and the existence of selected organizational programs and policies for personal computing. This study provides empirical evidence concerning the above relationships by formulating and testing three propositions.     

THE IMPACT OF ALTERNATE DECISION TECHNIQUES ON USER BEHAVIOR

The quantitative disciplines have focused their efforts primarily upon more complete and efficient methods for analyzing decision problems. Almost totally neglected in these efforts is the interplay between a decision technique and its ultimate user. This interface is of crucial importance to the continued success of the decision sciences in organizations. Data are presented to support the contention that decision techniques and their resultant impact on job content interact with psychological variables to cause variation in user satisfaction and performance. These results are similar to the effects hypothesized to occur at blue collar levels in organizations.     

Client/Server System Success: Exploring the Human Side

Client/server systems (CSS) are emerging as the new paradigm for systems development, replacing or integrating legacy applications with systems that provide higher quality user interfaces on the client component and substantial user-transparent data processing capability on one or more servers. Due to the dramatically different CSS development approach and the CSS emphasis on user friendliness, research findings based on non-CSS should be retested in the CSS context. Using a relatively well-established theoretical foundation, this study focuses on the human aspects of CSS implementation. Specifically, four major factors important to success with this emerging technology are studied: the degree of end-user involvement in the CSS development process, end-user characteristics, developer skills, and management support for the CSS project. The measures of CSS success are end-user satisfaction with the CSS, system usage, and its impact on end-users' jobs. The results indicate that the four independent variables explain a significant percentage of the variance in the dependent variables. Based on the results, recommendations are made for the management of CSS implementation and for further research on this important emerging technology.     

ORGANIZATIONAL CONTEXT,USER INVOLVEMENT,AND THE USEFULNESS OF INFORMATION SYSTEMS*

This study investigates organizational factors related to user involvement in information system development and perceived system usefulness. Using a sample of 118 usermanagers in 34 companies, the results show that user involvement in design and implementation is related positively to users' perceptions of system usefulness. However, organizational factors were not found to be related significantly to user involvement, as had been originally hypothesized. Rather, organizational factors either had a direct relationship with perceived usefulness or moderated the relationship between involvement and usefulness. Two factors reflecting an MIS department's maturity (size and age) were found to reduce users' perceptions of system usefulness. Also, users at higher levels and those making more structured decisions found their systems more useful, although the user's level was found to moderate perceptions of system usefulness during the design stage. Three other organizational factors (level of MIS manager, MIS department scope, and decentralization of authority) also were found to have moderating effects.     

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.     

A Knowledge Based Decision Support System for Setup Reduction

The issue of setup reduction is important for firms seeking to incorporate advanced procedures and concepts such as flexible manufacturing systems (FMS) and just-in-time (JIT) manufacturing to improve manufacturing productivity. Investing adequate amounts in setup reduction is a complex decision affected by many factors including the existing level of automation, worker characteristics, product features, and the manufacturing environment. The consequences of improper investment (i.e., over- and underinvestment) includes possible disruption of manufacturing operations as well as wasted resources. Much of the knowledge involved in the investment decision is heuristic and experience-based, and takes on greater significance when the form of the setup reduction function is not known a priori. This paper describes the development of a knowledge-based decision support system (KBDSS) that combines both heuristic and procedural knowledge to provide support for the setup reduction investment decision. The system uses codified manufacturing expertise to improve both the accuracy and effectiveness of setup-reduction decision making. Use of the KBDSS is illustrated by two examples based on disparate manufacturing contexts.     

The Effects of Group Attitudes Toward Alternative GDSS Designs on the Decision-making Performance of Computer-Supported Groups*

Empirical investigations of the effects of group decision support systems (GDSS) accord relatively little attention to the role of attitudes developed by groups toward the GDSS. This study draws upon the theory of social influence to examine the role played by group attitudes in mediating the impact of GDSS designs on group decision-making performance. We found that, in addition to capabilities of GDSS designs, group attitudes toward GDSS were influential in affecting group decision-making performance. The managerial implications of the study are: (1) effective GDSS designs for supporting groups in equivocal decision-making contexts should include structures for communication and consensus support and (2) attempts should be made to enhance user attitudes toward the GDSS through design features of the GDSS, training, and facilitation of positive social influences within the group. The study also provides encouraging evidence about the value of social theories, such as the social influence model and the adaptive structuration theory, in investigating GDSS effects. Further, this study illustrates the value of the partial least squares (PLS) analysis method for testing holistic structural models of GDSS effects.     

Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach

This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN model's ability to perform well on exemplars (observations) that were not used during training (out‐of‐sample); improved generalizability enhances NN's acceptability as a valid decision‐support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in‐sample or out‐of‐sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real‐life data to demonstrate the use of MGA.     

The Emergence of Artificial Intelligence as a Reference Discipline for Decision Support Systems Research

It is timely and appropriate to examine both philosophical and pragmatical issues associated with formalizing the adoption of artificial intelligence as a reference discipline for decision support systems research. This paper reflects on where we were when the first special issue of Decision Sciences on expert systems and decision support systems was published, addresses the dynamics of what has taken place subsequent to the publication of that first special issue, sets forth a proposition to stimulate ongoing dialog with respect to synergies between the decision support system research agenda and the research agenda of the artificial intelligence discipline, and demonstrates how the papers appearing in this follow-up special issue of Decision Sciences are representative of an emerging, challenging, and exciting new decision support systems era.     

A Model of the Antecedents of Perceived Ease of Use: Development and Test*

The Technology Acceptance Model (TAM) has been widely used to predict user acceptance and use based on perceived ease of use and usefulness. However, in order to design effective training interventions to improve user acceptance, it is necessary to better understand the antecedents and determinants of key acceptance constructs. In this research, we focus on understanding the determinants of perceived ease of use. Data from three experiments spanning 108 subjects and six different systems supported our hypothesis that an individual's perception of a particular system's ease of use is anchored to her or his general computer self-efficacy at all times, and objective usability has an impact on ease of use perceptions about a specific system only after direct experience with the system. In addition to being an important research issue in user acceptance research, understanding antecedents of perceived ease of use is also important from a practical standpoint since several systems in which millions of dollars are invested are rejected because of poor user interfaces. Moreover, the actual underlying problem might be low computer self-efficacy of the target user group. In such cases, training interventions aimed at improving computer self-efficacy of users may be more effective than improved interface design for increasing user acceptance.     

THE STRUCTURE OF EXECUTIVE INFORMATION SYSTEMS: AN EXPLORATORY ANALYSIS

This paper reports on a study which attempted to identify the structure of executive information systems and evaluate their relationship to decision making. The study centered on answering the question: “Where and how do senior executives get their decision-making information?” The data, provided by five senior executives, were gathered by a variety of means which included personal interviews, questionnaires, and logs of the executives' incoming-information transactions for a two-week period. Our findings support beliefs that senior executives receive much information from the environment, that informal systems play a role equal to that of formal systems, and that computers do not provide much information directly to an executive. The study also found that internal information is important and that preferred sources and media vary with different decision roles. The authors suggest that the scope of MIS and DSS be broadened to match those information networks managers actually rely on in daily practice.     

AN EXPERT SYSTEM APPLICATION FOR DECISION SUPPORT IN LAW ENFORCEMENT

Expert systems offer promise for decision-making support in stressful circumstances such as those that occur when law enforcement officials respond to hostage-taking incidents. These are life-or-death situations in which the costs of decision error are enormous. This paper reports on an expert system being developed and tested to aid police decision makers in hostage-taking incidents. Knowledge is represented by rules embedded in an incident-based decision-tree network. Four different decision makers who have similar but distinct information needs are supported and their efforts are coordinated by the system, which must function in real time as the crisis unfolds. Clear, concise, highly focused dialogue is required at the user interface since frequently only seconds are available in which to make a decision and implement it.