Cognitive Fit: A Theory-Based Analysis of the Graphs Versus Tables Literature*

A considerable amount of research has been conducted over a long period of time into the effects of graphical and tabular representations on decision-making performance. To date, however, the literature appears to have arrived at few conclusions with regard to the performance of the two representations. This paper addresses these issues by presenting a theory, based on information processing theory, to explain under what circumstances one representation outperforms the other. The fundamental aspects of the theory are: (1) although graphical and tabular representations may contain the same information, they present that information in fundamentally different ways; graphical representations emphasize spatial information, while tables emphasize symbolic information; (2) tasks can be divided into two types, spatial and symbolic, based on the type of information that facilitates their solution; (3) performance on a task will be enhanced when there is a cognitive fit (match) between the information emphasized in the representation type and that required by the task type; that is, when graphs support spatial tasks and when tables support symbolic tasks; (4) the processes or strategies problem solvers use are the crucial elements of cognitive fit since they provide the link between representation and task; the processes identified here are perceptual and analytical; (5) so long as there is a complete fit of representation, processes, and task type, each representation will lead to both quicker and more accurate problem solving. The theory is validated by its success in explaining the results of published studies that examine the performance of graphical and tabular representations in decision making.     

Graphical Presentations of Data Envelopment Analyses: Management Implications from Parallel Axes Representations*

The conversion of multivariate data analysis into information that managers can readily use in their decision making has always been a challenging task. In this paper we provide a parallel axis representation of multivariate data and demonstrate how this representation may be used as a tool for implementing the results of data envelopment analysis. We use an analysis of the efficiency of electricity generating plants to illustrate the technique.     

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.     

COMPUTER GRAPHICS AS DECISION AIDS: DIRECTIONS FOR RESEARCH*

Computer-generated graphics are becoming increasingly available to decision makers. Despite claims on the part of vendors that the use of graphics will improve decision speed and quality over traditional methods of data display, the available evidence is far from supportive. Initial studies show graphics to be no more effective in communicating information than tables. Correct interpretation of graphical displays appears to require training, which most users lack. Furthermore, there is evidence that those features that make a graph visually attractive—such as color, design complexity, and realism—may actually detract from accurate comprehension. This paper summarizes the literature dealing with the human use of graphics, develops several propositions based on persistent trends in the literature, and suggests directions for future research.     

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.     

META-DESIGN CONSIDERATIONS IN DEVELOPING MODEL MANAGEMENT SYSTEMS*

This paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge-representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem-solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems. In order to discuss issues pertinent to automatic modeling, a graph-based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.     

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.     

TWO DESIGN PRINCIPLES FOR KNOWLEDGE-BASED SYSTEMS*

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem-solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role.     

A Learning Curve Analysis of Expert System Use*

In this article, we study how an expert system affects novice problem solving in a financial risk analysis domain. We demonstrate that novice performance is improved after exposure to an expert system. Further, we show that novice performance continues to improve when the system is withdrawn. By comparing learning curves for people with exposure to those without, we can assess how much the system has benefitted its users. We demonstrate a quantitative methodology to measure the increment of learning due to the use of an information technology. We also explore the issue of how expertise is transferred from the system to the user.     

CONTINUITY IN MIS/DSS LABORATORY RESEARCH: THE CASE FOR A COMMON GAMING SIMULATOR*

To date, case analyses and field studies have predominated in MIS/DSS research. Laboratory studies seem not to have been given the attention they deserve. This trend is partially due to: (1) the high costs associated with developing decision simulators and related software to support laboratory research and (2) difficult problems with external validity because of unrealistically simple simulated decision environments and DSS software. Since MIS/DSS laboratory research has been most successful when studies have occurred in an interrelated stream, the development of common, portable, and flexible simulators seems desirable. Common simulators would reduce the time and cost of developing experiments, provide for research continuity, comparability, and replication, and facilitate researcher-to-researcher communication. Sufficiently complex simulators and DSS software would reduce problems with external validity by providing more realistic laboratory models. This paper presents a software package that offers considerable potential as a common tool for DSS research.     

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.     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

Modelbase Construction with Object-Oriented Constructs*

This paper proposes modelbase construction mechanisms in a decision support system (DSS) for representing and managing models of diverse management science/operations research modeling paradigms, using object-oriented database management systems (ODBMS) constructs. It focuses on the construction of a modelbase that maintains logical independence among the DSS components including modelbase, database, and solvers, but relieves the mismatching characteristics by facilitating intelligent and stabilized integration of them. As a conceptual framework to build such a modelbase, this research uses generic model concepts, and adopts structured modeling language (SML) as a paradigm-neutral model representation sublanguage. In the modelbase, three model abstraction layers including model type, model structure, and model instance are devised to facilitate the capture of multiple modeling paradigms and specific application models in different instantiation levels. The constructs and methods discussed are flexible enough to be applied to a wide variety of decision-making and problem-solving paradigms. A prototype system is developed under a commercial ODBMS called OBJECTSTORE with the C++ programming language, and diverse model manipulation commands are illustrated by an object-oriented structured query language (OSQL).     

An Examination of IT Planning in a Large,Diversified Public Organization*

The conduct of information technology (IT) planning processes has been of dominant managerial concern in public and private organizations. Yet, current IT planning research offers little guidance on the types of planning actions and behaviors that are appropriate to organizational contexts. We focus on the conduct of the IT planning process by describing an exploratory research project. The project empirically examined a number of issues associated with managing IT planning within a single large public enterprise, particularly identifying actions and behaviors related to producing quality IT plans. The results of the study yield some useful set of guidelines; most interesting is the importance of building IT infrastructure based on IT sophistication and innovativeness for large agencies to improve plan quality. Other recommendations include having a focused, balanced technology and business planning orientation that incorporates a strategic outlook, support of top management, attention to business planning, and early consensus among planning members.     

Fading Frames: Data Presentation and Framing Effects*

This article describes three experiments that investigate the impact of data presentation on framing, which is the differential evaluation of objectively equivalent information given different frames of reference. The experiments were designed to vary the amount of information and the format of presentation (tables versus graphs) provided to the decision maker to gain an understanding of the mechanism underlying framing. The inferred information model proposed by Johnson and Levin [11] is utilized to explain the results of the three experiments in terms of the redistribution of weights attached by the decision makers to the attributes of the prospective outcomes. The results of the three experiments indicate that framing effects are observed only when the presentation of data facilitates the shifting of weights among the attributes of the available alternatives. The results further indicate that framing is eliminated as more information is displayed. However, presentation of additional information does not eliminate framing when a graphical format is utilized. Finally, no framing effects are observed when uneven amounts of information are used to describe two alternatives in the same decision task.     

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 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.