Patrick R. Pinto

A cluster analytic approach to the study of organizations

Abstract Two hundred twenty-seven organizational units drawn from a variety of industries were cluster-analyzed on the basis of their similarities across 18 behavioral dimensions of effectiveness. Using a multivariate subgrouping procedure, eight homogeneous clusters of units were found, varying in size from eight to 65 units, and each characterized by a distinct behavioral profile. Within-cluster differences were described in terms of the 18 dimensions, overall unit effectiveness, and in terms of other “demographic” variables which were external to the clustering process. These variables included: unit function, work-setting dispersion, growth, technology employed, and skill level of the unit members. Significant relationships were found between the behavioral styles of the clusters on the one hand, and each of the demographic variables and the overall criterion of effectiveness, on the other. The rationale and benefits of clustering organizational units into groups were discussed, and further research ideas were proposed.

Describing Organizations as Reinforcer Systems: A New Use for Job Satisfaction and Employee Attitude Surveys.

Abstract Job satisfaction data on managerial personnel were used to illustrate how organizations can be differentiated and described as reinforcer systems. Twenty-seven specific satisfaction scales were regressed on overall job satisfaction, separately for each of five groups of managers from five organizations. For all groups, overall satisfaction was found to be determined mainly by scales concerning challenge of the job and prospects of ones chosen career. Application of decremental stepwise multiple regression procedures to obtain minimum-variable-set equations resulted in different, though overlapping, sets of variables in the equations for the different organizations. These results suggest a way of describing organizations as reinforcer systems, in terms of the specific determinants of overall satisfaction for the organization.

The Moderating Effect of Need Type on the Prediction of Overall Job Satisfaction

Abstract Recent investigations concerned with the analysis of work motivation and job satisfaction constructs have combined the logic of subgrouping with an emphasis on the importance of individual differences. The present study is based on a need-fulfillment model, which places a major emphasis on individual differences in needs. A hierarchical clustering method was used to subgroup a sample of 570 managers on their need profiles. Need type was tested as a moderator of the relationship between specific dimensions of job satisfaction and overall job satisfaction. The findings showed that need type moderated the relative contributions of specific dimensions of satisfaction in the prediction of overall job satisfaction, and in addition the prediction level was found to be greater for the need types than for the total sample.

Computer based strategies for clustering jobs

This paper is a review of some practical procedures for clustering jobs into homogeneous groups. The techniques are mainly statistical, and all have in common the collection of multivariate data on several jobs. Since the storage and processing requirements are well beyond the capabilities of any manual system of computation, I think it is appropriate that these procedures be reviewed before a group such as yours that has such a keen interest in using the computer in personnel research.

A Cost Model for Evaluating Alternative Manpower Input Strategies

The question of how to obtain a specified quantity of satisfactory manpower inputs for a given time period is a problem which confronts almost any personnel decisionmaker. The staffing model proposed in this paper defines selection, training, and any selection/training combination as being alternative staffing strategies which are available for coping with an organizations manpower requirement problems. Very few personnel decisionmakers, however, approach such problems with any appreciable knowledge of the relative costs associated with different alternative input strategies and of the costs of different misclassification errors which can result. The staffing model described here incorporates cost and utility considerations and suggests that the relative costs of alternative staffing strategies are amenable to systematic analysis. The model specifies the probability estimates for success and failure in both selection and training, the direct costs for each, and the potential costs which result from errors of misclassification. Then, an expression and rationale for determining the minimum total cost associated with various combinations of selection and training strategies are presented.