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.     

A COMPARISON OF UNIT,SUBJECTIVE, AND REGRESSION MEASURES OF SECOND-LEVEL VALENCES IN EXPECTANCY THEORY

Two different decision-making exercises are compared with unit weights and with traditional Likert scales as measures of second-level valences in expectancy theory. Second-level valences are measured across different levels of outcomes in each exercise and compared to the traditional subjective- and unit-weight measures. In the first experiment, the job-preference decisions of 21 undergraduates are examined using a decision-making exercise involving 24 hypothetical jobs described in terms of three intrinsic instrumentalities at two levels. In the second experiment, a different 21 undergraduates make job-preference decisions in a decision-making exercise involving 27 hypothetical jobs described in terms of three extrinsic instrumentalities at three levels. The β-weight (standardized regression coefficient) measures of the second-level valences are found to be more powerful predictors of job-preference decisions than either subjective-weight or unit-weight measures, both before and after shrinkage. They also had higher cross validations, exhibited higher test-retest reliability, and allowed testing of a quadratic motivational effect in the three-level exercise.     

THE PREDICTIVE ABILITY CRITERION IN EXPERIMENTAL JUDGMENT RESEARCH IN ACCOUNTING*

Previous experimental judgment research in accounting has been interpreted as supportive of the linear model as an appropriate representation of decision-making behavior in nearly all of the tasks investigated. Moreover, nonlinear models have come to be viewed as adding relatively little predictive power over that provided by the linear model, even in tasks considered to be inherently nonlinear. These conclusions were based largely upon evaluating the predictive ability of the linear model in terms of statistics measuring the proportion of variance accounted for by the model. In the present paper it is argued that, since these statistics are not independent of the experimental design, it is not clear whether the high correlations are indicative of the model's success in representing decision-making processes or instead are more the result of various features of the experimental design. It is suggested that correlational tests be supplemented with qualitative tests of the predictive ability of a model. Implications for accounting are discussed.     

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.     

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.     

Testing Prospect Theory in a Deterministic Multiple Criteria Decision-Making Environment*

Prospect theory by Kahneman and Tversky [7] is tested in a deterministic multiple criteria decision-making context. In two experiments conducted in classroom settings subjects made pairwise preference comparisons of condominiums for sale. The results of the experiments indicate that the traditional value model did not explain the subjects' revealed preferences as well as the prospect model. We conclude that prospect theory is a reasonable model of choice for many individuals in such a context.     

ORGANIZATIONAL CHOICE AND SOME CORRELATES OF INDIVIDUALS' EXPECTANCIES1

This study uses expectancy theory to predict the sorority choices of 121 undergraduate students. Results show that the complete, multiplicative expectancy model is only a slightly better predictor of choice behavior than other versions of the model. Several personal and situational correlates of individuals' expectancies of entering sororities also are examined. Results show significant relationships between expectancy and subjects' self-esteem, perception of value similarity with sorority members, and number of perceived friendships with sorority members.     

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.     

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.     

A Seasonal Index for Business

This paper explores the problem of obtaining a multiplicative seasonal index for forecasting sales from a small set of historical data (as is common in business applications) and in the presence of a trend. It is shown that the standard method for generating a seasonal index (from a centered moving average) contains a systematic error. This error is transmitted through to forecasts that use the seasonal index and causes higher than necessary safety stocks and other consequences. The paper presents two alternative consistent methods for estimating the seasonal index in the presence of a trend, one for a multiplicative (nonlinear) trend and one for an additive (linear) trend. These methods may be run easily on a spreadsheet program or on statistical software. The nonlinear method is suggested as a convenient alternative to the standard method in many circumstances.     

Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning

Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade-offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancing decision-maker learning are considered.     

Price-setting newsvendor with strategic consumers

We consider a newsvendor who sells a single product over a single season with the objective of determining both the selling price and stock quantity to maximize the expected profit. The customers are strategic and we consider two demand cases: additive and multiplicative. For each case, we derive the newsvendor׳s optimal decisions and demonstrate that neglecting the price-sensitivity of demand leads the newsvendor to make sub-optimal decisions. Moreover, we show that under certain conditions, strategic consumer behavior may positively affect the newsvendor׳s optimal expected profit in the additive demand case.     

Generating Explanations for Goal-Based Decision Making*

As information systems make the transition from decision support to decision making, there will be a concomitant need for the programs to explain or justify their actions. Without such explanations, humans will not readily cede authority to a machine. This paper describes an automated decision-making program, VOTE, which generates natural language explanations for its decisions in both English and French. The program domain is Congressional roll call voting. VOTE simulates voting decisions of specific members of the United States House of Representatives on given bills. VOTE's natural language generation facility is also used by the underlying databases to interpret the knowledge representations. These underlying knowledge representations are described and applications of this decision making model to the general business domains of strategic planning, investment, and marketing are suggested.     

Beyond expectancy theory: an integrative motivational model from health care

Expectancy theory has been criticized for its omission of normative, habitual, and other motivational elements. This paper describes the sources and features of an integrative motivational model from the field of preventive health care that combines expectancy/valence factors with habitual, normative, and conative motivational elements. The model is viewed as having implications for work motivation 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.     

Cancer Risk Estimation of Genotoxic Chemicals Based on Target Dose and a Multiplicative Model

A mechanistic model and associated procedures are proposed for cancer risk assessment of genotoxic chemicals. As previously shown for ionizing radiation, a linear multiplicative model was found to be compatible with published experimental data for ethylene oxide, acrylamide, and butadiene. The validity of this model was anticipated in view of the multiplicative interaction of mutation with inherited and acquired growth-promoting conditions. Concurrent analysis led to rejection of an additive model (i.e. the model commonly applied for cancer risk assessment). A reanalysis of data for radiogenic cancer in mouse, dog and man shows that the relative risk coefficient is approximately the same (0.4 to 0.5 percent per rad) for tumours induced in the three species.Doses in vivo, defined as the time-integrated concentrations of ultimate mutagens, expressed in millimol × kg^–1 × h (mMh) are, like radiation doses given in Gy or rad, proportional to frequencies of potentially mutagenic events. The radiation dose equivalents of chemical doses are, calculated by multiplying chemical doses (in mMh) with the relative genotoxic potencies (in rad × mMh^–1) determined in vitro. In this way the relative cancer incidence increments in rats and mice exposed to ethylene oxide were shown to be about 0.4 percent per rad-equivalent, in agreement with the data for radiogenic cancer.Our analyses suggest that values of the relative risk coefficients for genotoxic chemicals are independent of species and that relative cancer risks determined in animal tests apply also to humans. If reliable animal test data are not available, cancer risks may be estimated by the relative potency. In both cases exposure dose/target dose relationships, the latter via macromolecule adducts, should be determined.     

TESTING THE ROBUSTNESS OF MULTIATTRIBUTE UTILITY THEORY IN AN APPLIED SETTING*

Multiattribute utility theory (MAUT) was employed to model the professional judgments of external auditors. Fully developed MAUT models elicited from each subject according to keeney and Raiffa's [6] procedures were used to predict the internal control systems evaluations made by auditor-subjects. Correlation analyses were used to compare the predictive ability of the “correct” MAUT models to the accuracy of models developed under simplifications of the MAUT procedures. One simplified model resulted from relaxing the requirements for attribute independence that determine the functional forms. A second modified MAUT function was formed using unitary weightings on conditional utility functions instead of elicited scaling constants. Tests showed essentially no significant differences in predictive accuracy among the models in the contact of this study.     

On the Benefit of Inventory‐Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price‐sensitive demand, focusing on the benefit of the inventory‐based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.     

DUAL-METRIC MEASUREMENT OF THE IMPACT OF COMPLEXITY ON DECISION QUALITY*

Both sophisticated and unsophisticated subjects were provided either a simple or a complex cue set at random on which to base decisions. A dual-metric approach that consisted of measuring interrater consistency and cross-item variance for each subject category was used to compare the decision quality of the subjects. Although complexity and decision quality were inversely related, the sophisticated subjects performed relatively better using the complex cue set; the unsophisticated subjects performed better using the simple cue set. These findings support the notion that decision-maker and task compatibility are important to the assessment of decision quality.