Examining the Antecedents and Consequences of CIO Strategic Decision‐Making Authority: An Empirical Study*

Despite the strategic importance of information technology (IT) to contemporary firms, chief information officers (CIO) often still have varying degrees of strategic decision‐making authority. In this study, we apply the theory of managerial discretion to define CIO strategic decision‐making authority and argue that the CIO's level of strategic decision‐making authority directly influences IT's contribution to organization performance. We also draw on the power and politics perspective in the strategic decision‐making literature to identify the direct antecedents to the CIO's strategic decision‐making authority. A theoretical model is presented and empirically tested using survey data collected from a cross‐industry sample of 174 matched pairs of CIOs and top business executives through structural equation modeling. The results suggest that organizational climate, organizational support for IT, the CIO's structural power, the CIO's level of strategic effectiveness, and a strong partnership between the CIO and top management team directly influence the CIO's level of strategic decision‐making authority within the organization. The results also suggest that the CIO's strategic decision‐making authority in the organization directly influences the contribution of IT to firm performance and that effective CIOs have a greater influence on IT's contribution when provided with strategic decision‐making authority.     

New Product Development Decision‐Making Effectiveness: Comparing Individuals,Face‐To‐Face Teams,and Virtual Teams*

A total of 411 subjects participated in two decision‐making experiments in order to examine the effectiveness of new product development project continuation decisions. Using escalation of commitment theory, in Study 1, individual versus face‐to‐face team decision‐making effectiveness was compared. Study 2, an extension of Study 1, compared the new product development decision‐making effectiveness of individuals, face‐to‐face teams, and virtual teams. A virtual team is a geographically and temporally dispersed and electronically communicating work group. In Study 2, the virtual teams communicated asynchronously via groupware technology. Our findings suggest that teams make more effective decisions than individuals, and virtual teams make the most effective decisions.     

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.     

The Effectiveness of Decisional Guidance: An Empirical Evaluation

Decisional guidance is defined as how a decision support system (DSS) influences its users as they structure and execute the decision‐making process. It is assumed that decisional guidance has profound effects on decision making, but these effects are understudied and empirically unproven. This paper describes an empirical, laboratory‐experiment‐based evaluation of the effectiveness of deliberate decisional guidance and its four types. We developed and used a comprehensive model consisting of four evaluation criteria: decision quality, user satisfaction, user learning, and decision‐making efficiency. On these criteria, we compared decisional guidance versus no guidance, informative versus suggestive decisional guidance, and predefined versus dynamic decisional guidance. We found that deliberate decisional guidance was more effective on all four criteria; suggestive guidance was more effective in improving decision quality and user satisfaction, and informative guidance was more effective in user learning about the problem domain, whereas dynamic guidance was more effective than predefined guidance in improving decision quality and user learning; and both suggestive guidance and dynamic guidance reduced the decision time.     

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.     

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.     

An Empirical Study of Equivalence Judgments vs. Ratio Judgments in Decision Analysis*

Two commonly used elicitation modes on strength of preference, equivalence and ratio judgments, were compared in an experiment. The result from the experiment showed that ratio judgments were less effective than equivalence judgments. Based on an iterative design for eliciting multiattribute preference structures, equivalence judgments outperformed ratio judgments in estimating single‐attribute measurable value functions, while being nearly more effective than ratio judgments in assessing multiattribute preference structures. The implications of the results from the experiment are that multiattribute decision‐making techniques should take advantage of the decision maker's inclination of making effective equivalence trade‐off judgments, and that useful techniques should be devised to incorporate different commonly used techniques, such as multiattribute utility theory and the Analytic Hierarchy Process, to elicit and consolidate equivalence trade‐off judgments.     

A Public Transportation Decision‐Making Model within a Metropolitan Area

Understanding the decision‐making factors associated with public transportation is essential in strategic development of public transportation to improve acceptance and utilization of mass transit systems. This research analyzes factors affecting attitudes toward public transportation and the choice of transportation mode by investigating the public transportation decision‐making process of working professionals using a survey methodology. The objectives of this research are to model the transportation decision‐making process of public transportation users in a metropolitan area and to determine key factors that affect the public transportation choices made by potential public transportation users. This study contributes to the literature by developing and testing an integrated theoretical framework for modeling an individual's public transportation decision‐making process using four independent variables: Perceived Public Transportation Security, Knowledge, Price, and Convenience. We develop the proposed theoretical framework based upon the extant literature and tested it using partial least squares structural equation modeling (PLS‐SEM). Based on the Theory of Reasoned Action, the Theory of Planned Behavior, and utility theory, we develop the factors and refine associated items using confirmatory factor analysis.     

Should the Model for Risk‐Informed Regulation be Game Theory Rather than Decision Theory?

Risk analysts frequently view the regulation of risks as being largely a matter of decision theory. According to this view, risk analysis methods provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision‐theoretic approach (such as cost/benefit analysis) to determine whether the risks are acceptable, and whether additional regulation is warranted. However, this view ignores the fact that in many industries (particularly industries that are technologically sophisticated and employ specialized risk and safety experts), risk analyses may be done by regulated firms, not by the regulator. Moreover, those firms may have more knowledge about the levels of safety at their own facilities than the regulator does. This creates a situation in which the regulated firm has both the opportunity—and often also the motive—to provide inaccurate (in particular, favorably biased) risk information to the regulator, and hence the regulator has reason to doubt the accuracy of the risk information provided by regulated parties. Researchers have argued that decision theory is capable of dealing with many such strategic interactions as well as game theory can. This is especially true in two‐player, two‐stage games in which the follower has a unique best strategy in response to the leader's strategy, as appears to be the case in the situation analyzed in this article. However, even in such cases, we agree with Cox that game‐theoretic methods and concepts can still be useful. In particular, the tools of mechanism design, and especially the revelation principle, can simplify the analysis of such games because the revelation principle provides rigorous assurance that it is sufficient to analyze only games in which licensees truthfully report their risk levels, making the problem more manageable. Without that, it would generally be necessary to consider much more complicated forms of strategic behavior (including deception), to identify optimal regulatory strategies. Therefore, we believe that the types of regulatory interactions analyzed in this article are better modeled using game theory rather than decision theory. In particular, the goals of this article are to review the relevant literature in game theory and regulatory economics (to stimulate interest in this area among risk analysts), and to present illustrative results showing how the application of game theory can provide useful insights into the theory and practice of risk‐informed regulation.     

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.     

The Impact of Geographic Proximity on What to Buy,How to Buy,and Where to Buy: Evidence from High‐Tech Durable Goods Market*

Social contagion effects due to geographical proximity refer to the social effects wherein the behavior of an individual varies with the behavior of other individuals who are geographically close. Although the influence of such effects on consumer choices has been established in several contexts, much of the extant studies have focused on its effect on consumers’ decision of whether to buy a new product or adopt a new innovation. There has been no systematic examination of the influence of geographic proximity on other aspects of consumers’ product buying process such as what to buy (i.e., brand choice), how to buy (i.e., the channel), and where to buy (i.e., retailers). Such effects can matter significantly in high‐technology and durable goods markets and therefore, it is critical to understand the scope of these on consumers’ choice of retailers and channel as well. Drawing on literatures from word of mouth effects, ecommerce, and consumers’ perception of risk in their purchase process, we develop a set of hypotheses on the effect of geographic proximity on consumers’ choices of what to buy, how to buy, and where to buy. Leveraging a microlevel dataset of purchases of personal computers, we develop brand‐, retailer‐, and channel‐related measures of proximity effects at the individual consumer level and estimate a joint disaggregate model of the three choices that make up a product purchase process to test these hypotheses. Our results indicate a significant contagion effect on each of the three choices. Furthermore, we find evidence of a greater effect of geographic proximity on inexperienced consumers—those who are new to the product category. Our results thus help develop a holistic understanding of the influence of social contagion effects on consumers’ decision making.     

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.     

The Effects of Interruptions,Task Complexity,and Information Presentation on Computer‐Supported Decision‐Making Performance

Interruptions are a frequent occurrence in the work life of most decision makers. This paper investigated the influence of interruptions on different types of decision‐making tasks and the ability of information presentation formats, an aspect of information systems design, to alleviate them. Results from the experimental study indicate that interruptions facilitate performance on simple tasks, while inhibiting performance on more complex tasks. Interruptions also influenced the relationship between information presentation format and the type of task performed: spatial presentation formats were able to mitigate the effects of interruptions while symbolic formats were not. The paper presents a broad conceptualization of interruptions and interprets the ramifications of the experimental findings within this conceptualization to develop a program for future research.     

Data‐Driven Classification Using Boundary Observations

Classification is often a critical task for business managers in their decision‐making processes. It is generally more difficult for a classification scheme to produce accurate results when the input domains of the various output classes coincide, to some degree, with one another. In an attempt to address this issue, this article discusses a data‐driven algorithm that identifies the region of coincidence, or overlap, for two‐group classification problems by empirically determining the convex boundary for each group. The results are extendable to multigroup classification. The class membership of a new observation is then determined by its relative position with respect to each of these boundaries. Due to minimal data storage requirements, this boundary‐point classification technique can adapt to changing conditions far more easily than other approaches. Test results demonstrate that the new classification technique has similar performance to a back‐propagation neural network under static conditions and significantly outperforms a back‐propagation neural network under dynamic conditions.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

Supply Chains in Sub‐Saharan Africa: A Decision Support System for Small‐Scale Seed Entrepreneurs*

It is necessary to infuse a consistent supply of improved seed varieties into local sub‐Saharan African crop production to improve low crop yields. The best distribution channel for the improved seed varieties may be small‐scale commercial seed companies, but local entrepreneurs struggle to determine whether such businesses are viable. Using a multi‐echelon supply chain approach, a decision support system (DSS) was designed to help African seed entrepreneurs make informed decisions about small‐scale seed chain businesses. Specifically, entrepreneurs make decisions about where to locate seed enterprises, with which farmers to contract, and where to store seed. Optimization and simulation modeling are used to evaluate infrastructure variables such as distance, transportation cost, and storage loss and cost in three development level areas. Currently, the decision tool is used in Mozambique, Malawi, Kenya, and Tanzania. The model has supported the start‐up of at least 17 small seed companies that are now introducing improved seed varieties into villages and farms. The DSS applies decision science research in a humanitarian application and offers important managerial implications about supply chain infrastructure to nongovernmental organizations and humanitarian groups. Such applications are vital as groups such as USAID, the Gates Foundation, and the International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT) continue to move toward micro‐enterprise, value chain, and market‐oriented development programs.     

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.     

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.     

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.