Examining the interconnection of job satisfaction and organizational commitment: an application of the bivariate probit model

Abstract

Links between employees' commitment to their organizations and satisfaction with their jobs have been the subject of a large amount of empirical research, and still there seems little agreement about the causal connections between these two important employee attitudes. Understanding these attitudes is important because they have an important effect on organizational performance, and these attitudes can be influenced by human resource policies and practices. This paper assesses the gains from the use of a bivariate probit approach in measuring the connections between job satisfaction and organizational commitment. This paper is the first to make use of the bivariate probit approach in this context, and it improves our understanding of the connections between HR policy and these important employee attitudes. The approach taken allows a direct test of the hypothesis that job satisfaction and organizational commitment are jointly determined by demographic and policy factors. The results are compared with the results from the more traditional binomial probit approach to illustrate the degree of bias corrected by the bivariate approach.

Keywords:

• Organizational commitment
• job satisfaction
• work attitudes
• human resource management
• bivariate probit

Notes

 1 Links between job satisfaction and pay satisfaction are discussed in the description of the data, below.

 2 Allen and Grisaffe (2001) provide an excellent review of the theory and evidence on the connections between commitment and customer reactions.

 3 O'Reilly and Caldwell (1980, 1981) and Staw (1980) discuss this perspective.

 4 These interviews were conducted with the support of the Chartered Institute of Personnel and Development, and the cooperation of the companies involved. Details are available in Purcell et al. (2003).

 5 Nunaly (1978) suggests 0.7 as an appropriate threshold value for Cronbach's alpha.

 6 Meyer and Herscovitch (2001: 311).

 7 The rotated factor solution is included as Appendix 2 to this paper.

 8 See DeGroot (1984: 449–51) for a discussion of choosing appropriate significance levels.

 9 The p-value can be thought of as the probability that any particular coefficient estimate is the result of chance.

10 See Currivan (1999: 517).

11 Recall, the difference between the binomial probit and the bivariate probit approaches is that the former assumes that the covariance between model errors is zero. The latter makes no such assumption.

12 In other words, those variables that are significant at the 5 per cent level in the bivariate results presented in Table 1 are still significant at the 5 per cent level in the binomial probit results.

13 Brown and Chin (2004) and Yoon et al. (2004) both demonstrate the impact that employees who are satisfied with their jobs can have on customer satisfaction.