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 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.     

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.     

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.     

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.     

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 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 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.     

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 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.     

The Effects of an Interactive Graphics-Based DSS to Support Problem Structuring

Graphical, interactive, problem-structuring tools were devised and incorporated into a DSS. These tools were based on concepts of structural modeling and motivated by propositions and findings in memory theory and imagery theory. A laboratory study was then conducted to investigate the relationship between decision-maker characteristics and use of the problem-structuring aid. The experiment was designed to determine whether use of the tool to assist in problem formulation led to a better understanding of problem structure. A moderately complex management simulation and its companion DSS were used in the experiment. Eighty-four subjects were divided into two groups: a control group that did not use the Graphical Interactive Structural Modeling Option (GISMO), and an experimental group that did. The results showed a statistically significant effect between cognitive ability and use of GISMO. Field independents (i.e., high cognitive ability) effectively combined GISMO use with image processing skills to understand the structure of the problem domain, but field dependents (i.e., low cognitive ability) were ineffective. There was very little difference in problem understanding in field dependents who used the tool and those who did not.     

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.     

Data Envelopment Analysis for Managerial Control and Diagnosis*

Data envelopment analysis (DEA) is a linear programming-based technique that converts multiple input and output measures into a single comprehensive measure of productive efficiency. This is accomplished via the construction of an empirically based production frontier and by the identification of peer groups. Each unit is evaluated by comparison against a composite unit that is constructed as a convex combination of other units in its peer group. DEA has now been applied in a variety of managerial contexts. In this paper we draw on theories of decision making, measurement and control, the mathematical properties of DEA, prior reported applications, and our own experience, to assess the potential of DEA as a general management tool. We first make the distinction between managerial diagnosis and control. We show how measurement requirements differ for these two managerial decision contexts, and argue that DEA has the potential to provide support in each context. Measurement and decision support criteria for each activity are then developed by reference to the literature on diagnosis and control. Based on its mathematical definition and properties, the relevant attributes of DEA are then derived. The technique is evaluated in each capacity by comparison to the appropriate set of criteria. This evaluation is supported with evidence from our experience with DEA in a large public-sector organization. We argue that the structural properties of DEA, critical managerial choices in its application, and situationally specific factors, interact to determine the strengths and limitations of DEA in each decision context. Implications for research and practice are 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.     

An Empirical Investigation into Factors Relating to the Adoption of Executive Information Systems: An Analysis of EIS for Collaboration and Decision Support*

This study focuses on the organizational adoption of Executive Information Systems (EIS). A distinction is made between two related, complementary EIS capabilities—EIS for collaboration support (EIS_c) and EIS for decision support (EIS_d). EIS_c is relatively standardized and replicable, while EIS_d has to be developed in situ given the specific characteristics of the user and task. The adoption process is conceptualized as an initial transition from a state of nonadoption to adoption (adoption status) and subsequent internal propagation of the technology (adoption level). Data collected from a national survey are used to test hypotheses between identified contextual variables and the adoption status and adoption level of EIS_c and EIS_d. Adopters and nonadopters of both EIS_c and EIS_d do not differ in their organization size, suggesting that the traditional paradigm of “EIS as a technology for large firms” is no longer true. Environmental uncertainty is found to promote the transition from a state of nonadoption to adoption of both EIS_c and EIS_d while continuing to catalyze the internal propagation of EIS_d. While no differences are observed in IS department size between adopters and nonadopters of EIS_c, our results suggest that larger IS departments provide the resource base to explore the less standardized of the two capabilities, EIS_d. IS support is also found to be critical for the subsequent internal propagation of EIS_d. Furthermore, the adoption level of both EIS_c and EIS_d are found to be promoted by top management support. Implications of these results are discussed for the organizational adoption of EIS.     

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.     

Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: Methodology and Applications*

A learning-by-example algorithm, the ordinal learning model (OLM), that automatically generates symbolic rule-bases from examples was applied to four real-world multiattribute ordinal problem domains. The model automatically generates consistent and irredundant symbolic classification rules that mimic, in many aspects, the behavior of human subjects who solved similar problems during empirical studies. The OLM's performance is compared with those of regression analysis and with C4, a well-known symbolic learning-by-example decision tree building algorithm. The OLM uses mainly comparison operations and does not attempt to optimize the rule-bases it generates. Yet, the results show that the OLM's predictions are very accurate and the resulting rule-bases are relatively compact. The time required for constructing the rule-bases via the OLM was very competitive as well.     

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.     

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.     

Feedback in DSS as a Source of Control: Experiments with the Timing of Feedback

Well-designed feedback can improve decision making but to date there has been no comprehensive study of feedback in decision support systems (DSS) that could guide developers in its design. This work examines the opportunities and means to enhance cognitive control in decision making by providing appropriate feedback with DSS. It concentrates on the timing of feedback, which has been shown to affect the use of feedback and also demonstrates the potential advantages of using information technology over and above manual decision environments. Two experiments test the effects of controlling the timing of cognitive feedback on the user's cognitive control. The first tests the effect of timing one source of cognitive feedback, and the second tests the effect of timing two sources of feedback. The findings suggest that the design of feedback should become a routine part of the development of DSS.