PSYCHOLOGICAL CONTRACTS AND JOB SATISFACTION: CLUSTERING ANALYSIS USING EVIDENTIAL C‐MEANS AND COMPARISON WITH OTHER TECHNIQUES

Malcolm J. Beynon, Margaret Heffernan and Aoife M. McDermott

Intelligent Systems in Accounting, Finance and Management, 2012, vol. 19, issue 4, 247-273

Abstract: The psychological contract refers to an individual employee's belief in mutual obligations between them and their employer. Psychological contracts are a key management concern, as they can impact employees' attitudes and behaviors in ways that influence organizational efficiency and effectiveness. In this paper, we analyse the relationship between the psychological contract and facets of job satisfaction among non‐profit sector employees, using the nascent non‐hierarchical evidential c‐means (ECM) clustering technique. To date, this technique has been theoretically discussed but not widely applied. Based on the Dempster–Shafer theory of evidence, ECM is novel in facilitating the assignment of objects, not only to single clusters, but to sets of clusters, and no clusters (outliers). The paper compares the theoretical underpinnings and findings from ECM with those of three other well‐known clustering techniques, namely (1) the hierarchical Ward's method, (2) the non‐hierarchical crisp k‐means and (3) the non‐hierarchical fuzzy c‐means approaches. We present and interpret the cluster solutions from each clustering technique. We establish three clusters differentiated by the content of the employees' psychological contracts. These clusters are validated by considering their relationship with facets of job satisfaction, to ensure the clusters are theoretically meaningful. Comparisons of the findings from each technique: (1) provide insights into the relationship between the psychological contract and job satisfaction; (2) reveal what ECM encompasses, relative to other clustering techniques; (3) inform the selection of an appropriate clustering technique for a specific research problem; and (4) demonstrate potential future directions in the development of cluster analysis. Copyright © 2012 John Wiley & Sons, Ltd.

Date: 2012
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