Job embeddedness versus traditional models of voluntary turnover: A test of voluntary turnover prediction.
Description: Voluntary turnover has historically been a problem for today's organizations. Traditional models of turnover continue to be utilized in a number of ways in both academia and industry. A newer model of turnover, job embeddedness, has recently been developed in an attempt to better predict voluntary turnover than existing models. Job embeddedness consists of organizational fit, organizational sacrifice, and organizational links. The purpose of this study is to two fold. First, psychometric analyses were conducted on the job embeddedness model. Exploratory factor analyses were conducted on the dimensions of job embeddedness, which revealed a combined model consisting of five factors. This structure was then analyzed using confirmatory factor analysis, assessing a 1, 3, and 5 factor model structure. The confirmatory factor analysis established the use of the 5 factor model structure in subsequent analysis in this study. The second purpose of this study is to compare the predictive power of the job embeddedness model versus that of the traditional models of turnover. The traditional model of turnover is comprised of job satisfaction, organizational commitment, and perceived job alternatives. In order to compare the predictive power of the job embeddedness and traditional model of voluntary turnover, a series of structural equation model analyses were conducting using LISREL. The job embeddedness model, alone, was found to be the best fit with the sample data. This fit was improved over the other two models tested (traditional model and the combination of the traditional and job embeddedness model). In addition to assessing which model better predicts voluntary turnover, it was tested which age group and gender is a better fit with the job embeddedness model. It was found that the job embeddedness model better predicts turnover intention for older respondents and males.
Date: December 2005
Creator: Besich, John