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.     

Analysis and Approximation of a JIT Production Line: A Comment

A recent Decision Sciences article by Jordan [9] presented a Markov-chain model of a just-in-time (JIT) production line. This model was used to estimate average inventories and production rates to find the optimal number of kanbans. Results for expected production rate were found to be consistently lower than those obtained by Huang, Rees, and Taylor [8] in a previous Decision Sciences article. Jordan attributed this unexpected outcome to some procedural problems in Huang et al.'s simulation methodology. In this paper, Markov-numerical analysis is used to compare the performance of Jordan's and Huang et al.'s methods of production control. Simulation analysis is then used to determine the effects of finite withdrawal cycle times. Results show that, for equal numbers of kanbans, Huang et al.'s two-card method of production control provides substantially greater expected production rates than Jordan's method. These results suggest that the Jordan model should not be applied to the problem of setting kanban numbers on manual JIT lines. Finally, we comment on the efficiency of Jordan's iterative method to obtain performance measures of tandem queues.     

FINDING SYNERGY BETWEEN DECISION SUPPORT SYSTEMS AND EXPERT SYSTEMS RESEARCH

This article examines the potential for synergy between decision support systems (DSS) and expert systems research. Three predominant research traditions in DSS (applications, design, and technology) are examined and used as a basis to identify ways in which expert systems and DSS research interrelate. A range of emerging trends in DSS research that focus on these commonalities is discussed.     

Cautions on the Use of the SERVQUAL Measure to Assess the Quality of Information Systems Services

The SERVQUAL questionnaire (Parasuraman, Zeithaml, & Berry, 1988) is one of the preeminent instruments for measuring the quality of services as perceived by the customer. In a recent Decision Sciences article, Kettinger and Lee (1995) suggested the use of a modified SERVQUAL instrument to assess the quality of the services supplied by an information services provider. However, a number of problems with the SERVQUAL instrument are discussed in the literature. This article provides an illustrative example utilizing data collected from 138 executive and information systems professional customers of a multibillion dollar information services provider in order to examine the validity and reliability of Kettinger and Lee's (1995) modified SERVQUAL instrument. Results of analyses do not confirm the findings of Kettinger and Lee. Moreover, it appears that the use of difference scores in calculating SERVQUAL contributes to problems with the reliability, discriminant validity, convergent validity, and predictive validity of the measure. These findings suggest that caution should be exercised in the use of SERVQUAL scores and that further work is needed in the development of measures for assessing the quality of information services.     

USING ARTIFICIAL INTELLIGENCE TECHNIQUES TO ENHANCE THE CAPABILITIES OF MODEL MANAGEMENT SYSTEMS

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reveiwed, and three scenarios are presented to show how future MMSs could be constructed using these techniques.     

A NOTE ON INTEGRATING INVENTORY AND PRODUCT SALES-MIX MODELS

In a recent issue of Decision Sciences, Raiborn and Harris [2] discussed the integration of inventory and product sales-mix models. While they formulated a model which reflected some of the interactions between product sales-mix and inventory decisions, the purpose of this note is to point out deficiencies in their decision rule analysis for solving such integrated models.     

User Perceptions of Decision Support Effectiveness: Two Production Planning Experiments*

Decision support systems continue to be very popular in business, despite mixed research evidence as to their effectiveness. We hypothesize that what-if analysis, a prominent feature of most decision support systems, creates an “illusion of control” causing users to overestimate its effectiveness. Two experiments involving a production planning task are reported which examine decision makers' perceptions of the effectiveness of what-if analysis relative to the alternatives of unaided decision making, and quantitative decision rules. Experiment 1 found that almost all subjects believed what-if analysis was superior to unaided decision making, although using what-if analysis had no significant effect on performance. Experiment 2 found that decision makers were indifferent between what-if analysis and a quantitative decision rule which, if used, would have led to significant cost savings. Thus, what-if analysis did create an illusion of control: decision makers perceived performance differences where none existed, and did not detect large differences when they were present. In both experiments, decision makers exhibited difficulty realizing that their positive beliefs about what-if analysis were exaggerated. Such misjudgments could lead people to continue using what-if analysis even when it is not beneficial and to avoid potentially superior decision support technologies.     

An Investigation of Equity as a Determinant of User Information Satisfaction

This paper reviews equity theory and proposes that perceived inequity in the allocation of management information systems (MIS) resources is likely to influence overall user information satisfaction (UIS). Therefore, in addition to currently identified factors such as quality of information products (QIP), user's knowledge and involvement level (KIL), and EDP staff and services (ESS), equity is also likely to be a relevant factor in determining overall UIS. The paper reports on a cross-sectional survey to obtain data for testing the relevance of equity related issues in the MIS context. Responses from 226 users showed a strong correlation (.67, p > .001) between equity and overall UIS. Further, the addition of equity to the previously identified factors that influence overall UIS (i.e., QIP, KIL, and ESS) significantly increased the explained variance (from .48 to .56). The results support the relevance of equity related issues in determining user attitudes. It is recommended that MIS researchers and professionals take into account the likely influence of equity perceptions on user attitudes and behavior, and strive to develop and implement equitable resource allocation mechanisms. Implications of the findings for future research in the areas of MIS, UIS construct, systems implementation, and management of MIS are also discussed.     

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.     

A generic accounting model to support operations management decisions

Information systems are generally unable to generate information about the financial consequences of operations management decisions. This is because the procedures for determining the relevant accounting information for decision support are not formalised in ways that can be implemented in information systems. This paper describes a formalised procedure, which is based on the following theoretical propositions: (i) cost behaviour is described on the basis of a company's contracts for purchasing and selling resources, and (ii) hierarchical relationships between decisions are recognised, because some decisions have to be made earlier than others. Earlier decisions determine the feasible alternatives for later decisions (e.g. through constraints in available production capacity or components), and the plans that supported the earlier decisions serve as instructions for later decisions. The procedure can be implemented in information systems to provide accounting information in case later decisions deviate from these instruc tions (within the limits of the real-world constraints) because new information becomes available.     

MANAGING THE DECISION SCIENCE TECHNOLOGY: A CASE FOR ETHICAL ANALYSIS?*

After ten years of rapid, if sometimes chaotic growth, it is time to reflect upon where Decision Sciences is headed. At stake are the essential questions of (1) should the growth of the discipline be managed, and if so (2) how should it be managed and who should do the managing? I argue that the growth of the discipline should be managed rather than be dictated by the technological imperative that states that any envisioned application of quantitative methods should be implemented. Moreover, I argue that, since to a large degree the implementation decision is an ethical question, the individual decision scientist must ultimately be held accountable. Finally, I suggest that Glenn Stassen's social ethic paradigm may be a useful vehicle for resolving intradiscipline conflicts. These abstract ideas are concretized by examining the growth of the area of artificial intelligence.     

PREFERENCE MAPPING OF PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEMS DESIGN

In a recent issue of this journal, Watkins [13] presented an approach for discovery of decision-maker perceptions of the complexity (dimensionality) of information items that might be supplied by a decision support system. Through use of multidimensional scaling and cluster analysis, relatively homogeneous groups of decision makers, sharing common perceptions of various information items, were formed. This prior research was referred to as a first step in suggesting that information reports could be tailored to groups of decision makers classified on the basis of common perceptions of information. The current research extends the prior study by evaluating decision maker preferences for information in a variety of decision-making scenarios in relation to the previously identified perceptions of the information. Based on the results of the study, conclusions are made which suggest that the tailoring of information to groups of decision makers should be based on both perceptions and preferences for information. Even so, it is demonstrated that the decision tasks have an impact on the preferences for information which may affect the attempt to tailor information to groups of decision makers.     

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.     

Task-Structure Relationship of Information Systems Development Subunit: A Congruence Perspective*

Several studies have examined the influence of organizational context (e.g., organization size, environment, strategy) on information systems (IS) performance/success. But very few studies have examined IS structure, and those studies have focused on a particular element of IS structure (e.g., decentralization). Thus, the multidimensional structural configuration of IS as an organizational subunit has escaped research attention. Further, the nature of the task to be performed by the IS subunit as a contextual factor has been virtually ignored in the IS research literature. In this paper, we first develop a congruence model for the task-structure relationship in an information systems development (ISD) subunit setting. Then we test this model as a multivariate relationship followed by subordinate bivariate analyses. Analysis of data from 41 hospitals’ ISD subunits reveals that a congruence relationship between task context and the structure of an ISD subunit is present. Our findings also show that an exclusive association exists between uncertainty in the task environment and the decision-making structure. Likewise, the association between equivocality of task content and the control structure of the ISD subunit is also exclusive. Implications of these findings for decision makers and researchers are discussed.     

A Framework for Examining the Interface between Operations and Information Systems: Implications for Research in the New Millennium*

Even though much research has been published in operations and information systems, both functional areas find their roots in other disciplines. While operations management evolved from operations research in the 1960s, the field of information systems is of more recent vintage and traces its original roots to computer science. Both disciplines now naturally have come closer together as information and process-technology-based changes force manufacturing firms to become more efficient and customer focused. Market and technology-driven e-commerce initiatives that are likely to dominate business strategies in the future cannot be successfully achieved without a successful integration of operations and information systems. In this paper, we present a unifying framework that can be used to better understand the management of the functional interface between operations and information systems. We also categorize and highlight the contributions of the articles that appear in this special research focus issue. Finally, research directions that emerge from our understanding of this interface are outlined in an effort to stimulate further thinking and research that can advance our knowledge of this interface area.     

A NOTE ON AN APPLICATION OF THE ANALYTIC HIERARCHY PROCESS

This note is a response to an article by Zahedi [2] published in the Winter 1985 issue of Decision Sciences. It demonstrates that there is no need to calculate eigenvalues for consistent pairwise comparison matrices in order to calculate normalized values of attributes.     

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.     

Dogmatism and Systems Development Personnel Performance

This research explores the relationship between the personal variable, dogmatism, and the performance evaluations of systems analysts within a Fortune 500 company. The Rokeach Dogmatism Scale was used to measure the analysts’ level of dogmatism. The analysts’ performance measurement was the product of the company's annual evaluation process. The results of the research indicate that personnel with the highest dogmatism scores generally receive the extreme performance ratings. The results of this study provide further support to consider dogmatism as an important personal variable influencing systems analyst job performance.