Decision Processes for Developing Strategic Applications of Information Systems: A Contingency Approach*

Strategic applications of information systems (IS) are considered by IS researchers to be determined by contextual factors such as environmental uncertainty, and influenced by attributes of the processes preceding them, such as planning and top management support. For better management of the process leading to these applications, it is essential to understand the relationship between process attributes and contextual factors. Utilizing a contingency approach, this article takes a step toward such an understanding. Based on successful strategic IS applications from 81 large companies, it examines the relationship between the context of these applications and the decision-making process leading to them. The results indicate that the external environment is related to whether a rational or political decision-making process is used. The IS function seems to influence the use of the decision process implied by IS researchers, one in which the top management champions the strategic application, while the IS managers contribute by conducting in-depth analysis. However, the organization structure was not related to any decision process attribute. The implications of these findings for future research and practice are also discussed.     

Toward Computerizing a Causal Modeling Approach to Strategic Problem Framing*

This paper proposes an object-oriented approach to the development of interactive software for the purpose of managerial problem solving. A prototype is being developed using CSM causal mapping to represent each manager's perceptions of the relationships between key variables of a firm's strategic situation. This paper suggests the design of GDSS that would enable a group of managers to discuss, learn from each other, and possibly develop consensus about decisions or their causes. Issues involving future development are discussed.     

Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

Decisions to Outsource Information Systems Functions: Testing a Strategy-Theoretic Discrepancy Model

In recent years, the decision to outsource information systems (IS) functions has become a viable strategic alternative in managing the increasingly complex IS functions. In this study, the IS outsourcing phenomenon is conceptualized as a strategic decision in the organization. Drawing on resource-based theories, resource dependence theories, and other theories of strategic management, a discrepancy model of this decision is developed. Relationships between a number of strategy-theoretic factors and the IS outsourcing decision are hypothesized. These factors include IS resource performance discrepancies manifested in the form of gaps in information quality, IS support quality, IS cost effectiveness and financial performance, as well as the strategic orientation of the firm. Results of the study indicate that, while cost consideration and the firm's financial performance are not associated with the IS outsourcing decision, difficulties in providing good information outputs and IS support services are associated with the decision. Overall, the findings suggest that the current trend toward outsourcing represents a continuing evolution of the IS function as it attempts to fulfill its traditional mission of providing high quality information resource to the firm. When the performance of the delivered resource begins to slip in the current environment of rising expectation and technological complexity, outsourcing may become a strategic response of necessity. The paper concludes with a discussion of the implications of the results for practicing managers and suggestions for future research.     

Planning Forward by Looking Backward: Retrospective Thinking in Strategic Decision Making*

Retrospective thinking occurs whenever one remembers something from the past, but one can also think retrospectively about hypothetical future events, by imagining that the event has already transpired and then working backward in the mind from the future toward the present. Designers, artists, planners, and other creative individuals utilize retrospective thinking when they mentally envision the object they wish to create and then thinkabout how it might be constructed. Some theorists and some previous research have suggested that forward and backward thinking are different cognitive processes, but there has been little investigation on the nature of these differences. This article examines the literature and describes a laboratory study designed to investigate retrospective thinking as a technique for increased idea generation. Subjects (n=64) were professional planners and managers with an average business experience of over 20 years. In a planning task, significantly more ideas were generated in the retrospective thinking mode than in the prospective mode, but the ideas were not found to be of greater quality. Evidence emerged that a subset of subjects were better able to make use of the retrospective technique, suggesting that selection along with training may be appropriate for the technique's mosteffective use.     

Real Estate Taxation and Commercial Mortgage Underwriting

Recent disclosures about problem commercial real estate loans have exposed the underwriting process to intense scrutiny. This study focuses on mortgage loan underwriters of life insurance companies. After a review of the tax changes that affected commercial real estate from 1969 through 1988, the study tests how loan underwriters reacted to changes in tax benefits. To overcome the interdependent effects of interest rates and capitalization rates, a variation of the Black-Scholes pricing model is used to test the impact of changes in tax benefits. The results indicate that the underwriters do not fully incorporate the value of tax benefits in the underwriting decision. During the period of the largest tax benefits, 1982 to 1986, underwriters became more conservative and increased their equity requirements.     

EFFECTS OF CONFLICTING ANALYSES ON MANAGERIAL DECISION MAKING: A LABORATORY EXPERIMENT

Researchers in a variety of disciplines have recommended the use of multiple conflicting data analyses to improve managerial decision making through the challenging of assumptions. This study deals with the effects of single data analyses and conflicting analyses on managers' solutions to a case analysis task. Results showed that managers who received conflicting analyses produced solutions with higher expected profits than those who received single analyses. Implications for the use of analytic decision aids are noted.     

Formulation Tactics and the Success of Organizational Decision Making*

One hundred and seventy-seven cases profiling organizational decisions were analyzed to determine how managers carry out formulation as they initiate a decision-making process. Analysis revealed that formulation was carried out by applying problem, idea, target, and reframing tactics. Decision adoption rates, decision value, and time to carry out the decision-making process were used to determine the success of these tactics, controlling for situational effects of importance, urgency, resources, and management level. Reframing was found to be the most successful tactic under all conditions, but was the least frequently used by decision makers. Problem and idea tactics were the least successful and success did not improve with additional resources, but these tactics were used more often than the other tactics. Problem tactics were even less successful when applied to urgent and important decisions. Target tactics were surprisingly effective for crises and important decisions. Decision makers seem prone to use ineffective formulation tactics and give little consideration to the opportunities or constraints imposed by the situation as a tactic is selected.     

DEVIL'S ADVOCACY AND DIALECTICAL INQUIRY EFFECTS ON PREDICTION PERFORMANCE: TASK INVOLVEMENT AS A MEDIATING VARIABLE

Critics of previous laboratory experiments comparing devil's advocacy (DA) to dialectical inquiry (DI) have suggested that these experiments produced misleading results because (1) they used subjects who had low levels of task involvement and (2) the DI treatment used was confusing to subjects and required further explanation to be useful. The present study examines the effects of four inquiry methods—expert (E), DA, DI, and DI with explanatory statement (DI+)—on subjects' performance at a financial prediction task. Results show that DA, DI, and DI + were superior to E when the state of the world differed significantly from assumptions underlying the expert's plan. For subjects with high task involvement, DI and DI + were more effective than E and DA. The results support some of the criticisms of previous laboratory research and suggest that future research on these decision aids should include task involvement as a factor.     

The Relationship between Procedural Rationality and Political Behavior in Strategic Decision Making

Despite the central place of rationality and political behavior in the decision-making literature, we know little about the relationship between these two dimensions. Can decisions be made using both rational and political methods, or must managers use one approach or the other? These questions were addressed in a study of 61 strategic decisions in 24 companies using a multiple-informant, structured interview protocol. Results indicate that procedural rationality and political behavior are independent dimensions of the strategic decision-making process. The implications of our findings for future strategic decision-making research are discussed.     

An Empirical Investigation of Information Search Strategies with Implications for Decision Support System Design

Understanding how people search through and combine information before making decisions is an important concern in the study of decision making and in the design of decision support systems (DSS). The purpose of this study is to examine DSS search strategies in relation to the body of empirical research on information load. Prior research is examined in relation to basic empirically-testable hypotheses and compared to the results of this study as a way of validating the procedures used here. The transfer of information load empirical research to a DSS scenario is confirmed statistically. Then, results of an analysis of information search patterns under DSS conditions are described. Finally, implications for DSS design are discussed in terms of possible search support mechanisms for specific search strategies.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

When Restrictive Constraints Are Nonbinding: Illustrations and Implications

For convex and concave mathematical programs restrictive constraints (i.e., their deletion would change the optimum) will always be binding at the optimum, and vice versa. Less well-known is the fact that this property does not hold more generally, even for problems with convex feasible sets. This paper demonstrates the latter fact using numerical illustrations of common classes of problems. It then discusses the implications for public policy analysis, econometric estimation, and solution algorithms.     

COGNITIVE STYLE AND THE USEFULNESS OF INFORMATION*

The desirability of a merger/acquisition alternative depends in part on the perceptions of the decision maker. What sources of information are “useful” to the decision maker & Does the set of useful information remain constant for all decision makers; if not, do individuals using similar information sets have similar information processing characteristics? Do these sets vary as feedback is obtained during the decision process? To answer these questions, graduate students participated in a modified Delphi experiment, and the resulting data were analyzed by the two-way aligned-ranks nonparametric test. These test results affirm that in a merger/acquisition scenario, decision makers with different cognitive styles prefer different sets of information and these sets vary dynamically as feedback is incorporated in the decision-making process. Furthermore, information that contains worker and community welfare considerations is identified as “useful” five times more frequently by decision makers with a “feeling” cognitive style than those with a “thinking” style.     

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.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.