The authors statistically studied why MIS professionals change jobs. Their analysis was based on an attitude survey of professionals currently working, focusing on the intent to change jobs rather than on actual job changes. They surveyed ACM members in the mid-Atlantic states; after responses from retirees and academics were eliminated, the remaining 464 responses reflected the general demographics of the ACM. (Had I been surveyed, my response would have been used although I work in software engineering on systems for space satellites and not in MIS. Either the declared subject area should have been broader, or the filtering of unsuitable responses should have been more discriminating than the paper indicates.)

The authors developed six hypotheses regarding the relationships of 11 attitude variables to each other and to turnover intent. By acting on both each other and the intent, these variables have both direct or indirect effects on that intent. Half of the survey responses were used to validate the hypotheses. The result caused the authors to revise their hypotheses, which were then validated by the remaining responses. Both validations used the partial least squares (PLS) method of multivariate analysis, which this paper describes well while presenting minimal mathematics.

The authors conclude that job satisfaction is the most significant variable directly affecting turnover intent, followed by the employee’s commitment to the organization. The other variables--in the categories of demographics, role stressors, and career considerations--were found to operate more strongly on those two variables than directly on turnover intent. Recognizing that the model encompassed by their hypotheses is limited, they describe potential future studies. They finish with praise for PLS. The paper is footnoted well, with 56 references on statistics, surveying, and employee attitudes.

As noted above, the use of “MIS” to describe the subject career environment seems to be a misnomer. Another deficiency in the paper is the failure to consider such variables as the perceived commitment of the employer to its employees and economic and social trends external to the employer (both of which could affect employee fears of layoffs and motivate people to change jobs). I hope the authors address these deficiencies in their future studies.

This paper may be valuable for both those concerned with personnel practices and those interested in statistical analyses of attitude surveys. When fully developed, the authors’ techniques could be used in either area, well beyond studying the career environment of software professionals.