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Job Embeddedness Demonstrates Incremental Validity When Predicting Turnover Intentions for Australian University Employees

Overview of attention for article published in Frontiers in Psychology, April 2016
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Title
Job Embeddedness Demonstrates Incremental Validity When Predicting Turnover Intentions for Australian University Employees
Published in
Frontiers in Psychology, April 2016
DOI 10.3389/fpsyg.2016.00582
Pubmed ID
Authors

Brody Heritage, Jessica M. Gilbert, Lynne D. Roberts

Abstract

Job embeddedness is a construct that describes the manner in which employees can be enmeshed in their jobs, reducing their turnover intentions. Recent questions regarding the properties of quantitative job embeddedness measures, and their predictive utility, have been raised. Our study compared two competing reflective measures of job embeddedness, examining their convergent, criterion, and incremental validity, as a means of addressing these questions. Cross-sectional quantitative data from 246 Australian university employees (146 academic; 100 professional) was gathered. Our findings indicated that the two compared measures of job embeddedness were convergent when total scale scores were examined. Additionally, job embeddedness was capable of demonstrating criterion and incremental validity, predicting unique variance in turnover intention. However, this finding was not readily apparent with one of the compared job embeddedness measures, which demonstrated comparatively weaker evidence of validity. We discuss the theoretical and applied implications of these findings, noting that job embeddedness has a complementary place among established determinants of turnover intention.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 73 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 18%
Student > Master 10 14%
Student > Doctoral Student 8 11%
Student > Bachelor 4 5%
Professor > Associate Professor 4 5%
Other 9 12%
Unknown 26 35%
Readers by discipline Count As %
Business, Management and Accounting 21 28%
Psychology 12 16%
Social Sciences 6 8%
Arts and Humanities 2 3%
Economics, Econometrics and Finance 2 3%
Other 3 4%
Unknown 28 38%