Organizational behavior and human performance最新文献

A review of expectancy theory research concerning how individuals choose occupations or organizations is presented. Sixteen studies, conducted between 1966 and 1981, were found which used the within-subjects form of expectancy theory. The average within-person correlation between a valence—times—instrumentality index and an overall measure of occupational/organizatinal attractiveness is .72. The average “hit rate” for a valence—times—instrumentality index predicting which occupation/organization will be chosen is 63.4%. A method of “implicit weighting” is proposed as a way to combine valence and instrumentality while avoiding the questionable procedure of multiplying non-ratio scale variables. This implicit weighting method was used in a study of college graduates (N = 93) choosing an M.B.A. program. In contrast to previous studies, the implicit weighting method was able to detect empirical support for the multiplicative propositions of expectancy theory.

This paper employs a decision modeling approach to measure second level valences in Expectancy Theory. As proposed by J. C. Naylor, R. D. Pritchard and D. R. Ilgen (A theory of behavior in organizations, New York: Academic Press, 1980) second-level valences are measured across different levels of an outcome. In the first experiment, the job-preference decisions of 24 under-graduates were examined using a decision making exercise involving 24 hypothetical jobs described in terms of three intrinsic instrumentalities. In the second experiment, the job-preference decisions of 57 undergraduates were examined using a decision-making exercise involving 24 hypothetical jobs described in terms of four extrinsic instrumentalities. Factorial designs were used in both experiments to preserve orthogonality and allow a separate interpretation of each of the second-level valences. A regression model was derived for each subject to block on individuals and provide a within-person analyses of the data. In both experiments, the beta weight measures of the second level valences (1) conformed to the concept of Naylor et al. that second-level valence is a relationship across levels of an outcome; (2) operationalized the within-person property of Expectancy Theory; (3) allowed separate interpretations of each second-level valence; and (4) displayed stable, high internal consistency estimates. Therefore, it appears that decision modeling with beta weight measures of second-level valences offers an innovative approach for Expectancy Theory researchers.

Multiattribute Utility Measurement (MAUM) provides a set of tools and procedures that are designed to aid the decision maker who is faced with decision problems of such complexity and ambiguity that unaided, intuitive judgment is likely to lead to the selection of suboptimal alternatives. Attempts to validate MAUM procedures have been primarily of three types: (1) behavioral tests of axiom systems derived from assumptions about what constitutes reasonable behavior; (2) convergent validation, in which the results of different procedures or even different subjects are compared; and (3) criterion validation, in which judgments and their resultant decisions are compared with some external criterion. From a behavioral point of view, the last of these, criterion validity, is by far the strongest. Past efforts at criterion validation of MAUM have suffered from three limitations: the subjects were not experts, alternative weight elicitation procedures were not compared, and the strength of the criterion used in each case is open to question. The purpose of this experiment was to provide an empirical comparison of a number of alternative MAUM weight elicitation procedures in a situation that offered a meaningful external crtierion along with subjects expert in its use. High quality decisions resulted from weight judgments provided in response to all weight elicitation procedures as long as single dimensions were first individually scaled and then weighted for aggregation. A procedure in which alternatives were rated holistically and weights and single dimension utility functions derived statistically showed poorer quality decisions. Thus, the “divide and conquer” theme of MAUM was upheld.

This paper presents and experimentally tests a macro theory of leadership. The Leader—Environment—Follower Interaction theory contends that the effective leader is one who analyzes the deficiencies in the follower's ability, motivation, role perception, and work environment which inhibit performance, and then takes action to eliminate those deficiencies. Nine hypotheses derived from the theory are tested in two laboratory experiments using a paper “moon tent” construction task. ANOVA tests are used to analyze the effects of the independent variables and their interaction effects on performance. The hypotheses of the study are supported. Several significant interaction effects are obtained.

A very general approach to the analysis of job duration is developed. The data are allowed to contain varying start dates, incomplete durations, and certain common problems encountered when personnel files are reorganized. Empirical results on almost 25,000 employees for a large corporation are presented. Basic properties of these data, e.g., median duration on the job, cannot be calculated directly from these data. This formal modeling approach allows estimation of the important characteristics of the data. In addition some well-known nonparametric methods for estimating medians are compared to those parametric estimates. The approach used on the particular data set described should be of value to any organization involved in human resource planning activities.

One of the most consistent criticisms of expectancy theory research for the prediction of effort is that it has not been tested using the within-subjects choice model that the theory requires. The choice model proposed by the theory generates a motivational force score (MFS) for each of several effort levels for each subject and predicts that each will choose the level of effort which has the highest MFS for that subject. This study operationalizes the choice model and compares it with a difference model (derived from subtracting the MFS for low effort from the MFS for high effort) and a single-alternative model (MFS for high effort only). Subjects were 74 undergraduates who estimated valences and expectancies for the outcomes of six general student activities. The difference model, not the choice model, was generally the best predictor, while the single-alternative model predicted least well. Within-subjects predictions were of greater magnitude than between-subjects predictions. The results indicated that sufficiently designed future studies should gather data for three levels of effort (high, medium, and low) and then compare the models to determine the most effective predictor for that particular situation.

In order for predictions derived from judgment models to effectively represent or replace an individual's decision-making behavior, they must show adequate levels of temporal reliability. This study examined the test—retest reliability of judgment models. Thirty-three undergraduates were asked to rate the acceptability of constructing 50 hypothetical public utility plants and to describe their judgment strategy. This decision task was replicated two weeks later. Test—retest reliabilities of (a) actual judgments, (b) predictions from a statistically derived judgment model, and (c) predictions from a subjectively reported judgment model were computed. All average test—retest reliability estimates were significantly different from zero; the average reliability of judgments predicted by regression models was significantly larger than the average reliability of actual judgments and the average reliability of judgments predicted by subjective weighting strategies. Large individual differences in all three indices of reliability were noted. Further analyses showed that groups who completed the decision task before describing their subjective policies showed significantly higher reliabilities for predictions based upon their subjective policies than did most individuals who described their subjective policies before making judgments. Overall, the results support the implicit assumption that most individuals harbor temporally stable judgment policies across time, although some caution is suggested in the use of subjectively described decision strategies.

The effects of delayed exposure to relevant configural information were studied. Three groups were run for 480 trials in a two-cue nonmetric multiple-cue probability learning task. All groups had one relevant cue dimension. One group had no relevant configural information, while another always had relevant configural information. The third group began without relevant configural information and was switched to relevant configural information after trial 120. The switched group did learn to utilize the configural information, but not nearly as strongly as the group which always had relevant configural information. A follow-up study explored the effects of the length of the delay before exposure to the relevant configural information. Four groups were run under the above conditions but were switched at trial 40, 80, 120, or 200, respectively. The results replicated the effect for the group switched at trial 120 and showed no differences between the four groups, indicating that the effect of delayed exposure is constant and requires little delay.

As part of a method for assessing health risks associated with primary National Ambient Air Quality Standards, T. B. Feagans and W. F. Biller (Research Triangle Park, North Carolina. EPA Office of Air Quality Planning and Standards, May 1981) developed a technique for encoding experts' subjective probabilities regarding dose—response functions. The encoding technique is based on B. O. Koopman's (Bulletin of the American Mathematical Society, 1940, 46, 763–764; Annals of Mathematics, 1940, 41, 269–292) probability theory, which does not require probabilities to be sharp, but rather allows lower and upper probabilities to be associated with an event. Uncertainty about a dose—response function can be expressed either in terms of the response rate expected at a given concentration or, conversely, in terms of the concentration expected to support a given response rate. Feagans and Biller (1981, cited above) derive the relation between the two conditional probabilities, which is easily extended to upper and lower conditional probabilities. These relations were treated as coherence requirements in an experiment utilizing four ozone and four lead experts as subjects, each providing judgments on two separate occasions. Four subjects strongly satisfied the coherence requirements in both conditions, and three more did so in the second session only. The eighth subject also improved in Session 2. Encoded probabilities were highly correlated between the two sessions, but changed from the first to the second in a manner that improved coherence and reflected greater attention to certain parameters of the dose—response function.

Two studies (a longitudinal field survey and a laboratory simulation) were designed to test the effects of realistic expectations and value attainment on job-related attitudes and perceptions. In each study, value attainment (the match between job values and job experiences) accounted for considerably more variance in facet satisfaction than did realistic expectations. In the simulation, value attainment and realistic expectations interacted to predict the level of trust toward the organization. In addition, there was some evidence that realistic expectations may dampen the importance of a facet that is unattained on the job. The implications of these findings for the recruitment process were discussed.