THE ESCALATION OF COMMITMENT IN SEQUENTIAL DECISION MAKING: SITUATIONAL AND PERSONAL MODERATORS AND LIMITING CONDITIONS*

Two laboratory studies manipulated variables in order to observe their effect on the escalation of individuals' commitment to earlier investment decisions. The experimental results indicated that escalation of commitment to a failing course of action is not a general phenomenon. Information pertinent to the future prospects of reinvestment and to decision alternatives provided main effects. Neither study showed a main effect for initial success vs. failure. Decision-maker gender consistently influenced decisions in both studies through interactions with success/failure feedback and through the communicated attributions of “powerful others” regarding the causes of previous decision outcomes. Implications for theory and research are discussed.     

Exploring Contrasting Formulations of Expectancy Theory*

The explanatory potential of four forms of expectancy theory with additive and multiplicative expectancy terms and linear and nonlinear valence functions were contrasted. A behavioral decision-making theory approach was used when 101 subjects were asked to make 128 hypothetical job-choice decisions. More than 25,800 decisions under a within-subjects framework were analyzed. Results indicate that the majority (83 percent) of subjects employed additive or multiplicative expectancy models with linear valence functions. However, the predictive efficacy of the expectancy theory model was improved for 17 percent of the subjects when nonlinear valence terms were introduced. The findings imply that different functional forms of expectancy theory may be needed to model individuals' decision-making processes.     

INFLUENCE OF POWER BASES IN ORGANIZATIONAL DECISION MAKING: A CONTINGENCY MODEL*

The relative influence of four power bases was investigated in four decision-making contexts using data from forty successfully implemented decisions. The analysis suggests that position power decreases in influence while resource power and political power increase in influence as decision contexts vary from less to more uncertainty. Expert power derives its influence when organizational uncertainty is low and when technical uncertainty is reduced, whereas political power thrives when organizational uncertainty is high and when technical uncertainty remains high. Indexing the influence of power bases provides greater understanding of decision processes than the sociological or social-psychological approaches of studying participation.     

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.     

EFFECTS OF INQUIRY METHODS AND AMBIGUITY TOLERANCE ON PREDICTION PERFORMANCE*

This study examined the comparative effectiveness of four inquiry methods on a financial prediction task. Inquiry methods included the expert (E), devil's advocate (DA), and dialectical inquiry (DI) techniques as well as a combined devil's advocate-dialectical inquiry (DADI) technique. Also, the moderating effects of ambiguity tolerance were considered. Results indicated that when the state of the world was opposite to the assumptions underlying a plan, the DA, DI, and DADI approaches were all superior to the E approach. Further, for high ambiguity-tolerance subjects, the DA and DADI approaches led to significantly better prediction performance than the E approach, and the superiority of the DADI approach over the DI approach was marginally significant. Results suggest that decision makers' levels of ambiguity tolerance should be considered before recommending their use of either the DA or DADI approach as an ill-structured decision-making aid.     

Committee Decision Making in Organizations: An Experimental Test of the Core

One concept that has been widely supported in experiments on predicting the outcome of majority-rule voting committees is the Core, the set of alternatives that cannot be defeated by any other in a binary contest. Unfortunately, the applicability of these experiments to organizations is limited by a set of rigid controls intended to ensure that the experiments are internally valid. This paper tests the Core in a laboratory environment that relaxes these controls. The Core is found to be highly predictive under these relaxed conditions, thereby enhancing confidence in its external validity.     

Linear and Nonlinear Valence Functions: A Behavioral Decision-Making Assessment

In Vroom's [45] original formulation of expectancy theory, the relationship between affect and perceived instrumentality was assumed to be linear. Others have suggested that such a relationship may be better modeled by a nonlinear, utility-type function [30]. The current research contrasts the predictive ability of two linear and four nonlinear functions. Using four levels of McClelland's [26] needs for achievement, affiliation, and power as instrumentalities, 101 subjects provided more than 12,900 decisions on the valences of jobs in a behavioral decision-making experiment. Nearly 40 percent of the subjects exhibited nonlinear valence functions. The results emphasize the need to specify the appropriate functional form of the valence component to enhance predictive accuracy and to prevent misspecification problems.     

Involving Information System Users in Defining System Requirements: The Influence of Procedural Justice Perceptions on User Attitudes and Performance

This study incorporates the theoretical framework of procedural justice into the information system (IS) user participation research paradigm. In a field setting, 726 subjects representing 59 local newspaper sites took part in a fully randomized, longitudinal experiment. Four increasing levels of participation concerning specifying system requirements were manipulated: mute, voice, choice, and voice plus choice. As the participation level increased, procedural justice judgments increased correspondingly. Perceptions of control over the development process, satisfaction with the outcome, and objective measures of performance increased from the mute to voice conditions, were unchanged from the voice to choice conditions, and increased again as participation was manipulated at the highest level—voice plus choice. The rich contextual field setting in which this experiment was conducted, combined with strong attitudinal and performance results, encourage researchers to integrate the psychology of procedural justice into the study of IS user participation.     

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.     

TOWARD UNDERSTANDING THE CONTENTS OF THE “BLACK BOX” FOR PREDICTING COMPLEX DECISION-MAKING OUTCOMES

The purpose of this research is to show the usefulness of three relatively simple nonlinear classification techniques for policy-capturing research where linear models have typically been used. This study uses 480 cases to assess the decision-making process used by 24 experienced national bank examiners in classifying commercial loans as acceptable or questionable. The results from multiple discriminant analysis (a linear technique) are compared to those of chi-squared automatic interaction detector analysis (a search technique), log-linear analysis, and logit analysis. Results show that while the four techniques are equally accurate in predicting loan classification, chi-squared automatic interaction detector analysis (CHAID) and log-linear analysis enable the researcher to analyze the decision-making structure and examine the “human” variable within the decision-making process. Consequently, if the sole purpose of research is to predict the decision maker's decisions, then any one of the four techniques turns out to be equally useful. If, however, the purpose is to analyze the decision-making process as well as to predict decisions, then CHAID or log-linear techniques are more useful than linear model techniques.     

A Framework for the Analysis of Error in Conjunctive,Multi-Criteria,Satisficing Decision Processes*

This paper examines the impact of certain types of error on ad hoc, unstructured decision making that incorporates a satisficing strategy in a multi-criteria context using the conjunctive rule. The impacts of error in evaluation and of error in minimal or satisficing levels are systematically modelled for each of the decision criteria. Error functions, analytical expressions, and procedures are developed to obtain information such as the likelihood of correct decision making in the presence of evaluation and minimal criteria level error. These are applied to a specific research design, which is then analyzed. This highlights certain features of decision making in a satisficing context, such as the importance of keeping the number of decision criteria to a minimum. The results yield various insights, some of which confirm information obtained from previous analyses. A major advantage of the framework presented is that it can provide quantitative measurements which support ideas previously couched in qualitative terms only. Although the framework is applied in the context of decision making that uses the conjunctive rule in the case of multiple criteria, other satisficing strategies can be accommodated as well.     

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.     

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.     

SMALL PANELS OF EXPERTS IN DICHOTOMOUS CHOICE SITUATIONS*

This paper analyzes an expert resolution problem under an uncertain dichotomous choice situation. The experts share a common system of norms and therefore they all prefer the alternative that best suits their purpose. The selection of such an alternative is referred to as a correct choice. Our analysis of optimal decision rules for panels of independent experts is pursued for n-member decision-making bodies, n≤ 5. The suggested optimality criterion is the maximization of the probability of the panel's making the correct choice. Within our framework, this criterion is equivalent to the more common criterion of expected-utility maximization. For three-member panels of experts, the expert resolution problem is solved and illustrated by means of a medical application. For four-member panels, we list the three relevant decision rules, specify the conditions for all possible rankings of these rules, and, finally, present an extended consulting application. We conclude by listing seven relevant decision rules in the case of five-member decision-making bodies.     

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.