Relationship Between Job Embeddedness and Turnover Intentions Among Municipal Law Enforcement Officers

Relationship Between Job Embeddedness and Turnover Intentions Among Municipal Law Enforcement Officers by William Alonzo Forrester III MS, Bethel University, 2013 BM, Lambuth University, 2007 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Policy and Administration Walden University August 2019 Abstract Law enforcement organizations have been facing a challenge with maintaining an adequate level of personnel due to an increased rate of employee turnover, which has been shown to have a negative impact on an agency’s ability to reduce both property and violent crimes. The purpose of this cross-sectional, quantitative study was to examine job embeddedness as a predictor of turnover intentions among municipal law enforcement officers by using the human capital theory as the theoretical foundation. To collect the data, a survey link was distributed to all personnel of a large, municipal law enforcement agency in the southeastern United States; only full-time, commissioned law enforcement personnel were included in the study, which resulted in a sample size of 107. Linear regression was used to analyze the data. Job embeddedness and turnover intentions had a statistically significant and negative correlation (r = -.656, p < .001). In addition, the bivariate linear regression model significantly predicted turnover intentions, F(11, 106) = 79.135, p < . 001); job embeddedness was responsible for 42.4% (adjusted R2 = .424) of the variance in turnover intentions. When job embeddedness decreases, turnover intentions increase, and when job embeddedness increases, turnover intentions decrease. The study has implications for positive social change as it established the relationship between job embeddedness andLaw enforcement organizations have been facing a challenge with maintaining an adequate level of personnel due to an increased rate of employee turnover, which has been shown to have a negative impact on an agency’s ability to reduce both property and violent crimes. The purpose of this cross-sectional, quantitative study was to examine job embeddedness as a predictor of turnover intentions among municipal law enforcement officers by using the human capital theory as the theoretical foundation. To collect the data, a survey link was distributed to all personnel of a large, municipal law enforcement agency in the southeastern United States; only full-time, commissioned law enforcement personnel were included in the study, which resulted in a sample size of 107. Linear regression was used to analyze the data. Job embeddedness and turnover intentions had a statistically significant and negative correlation (r = -.656, p < .001). In addition, the bivariate linear regression model significantly predicted turnover intentions, F(11, 106) = 79.135, p < . 001); job embeddedness was responsible for 42.4% (adjusted R2 = .424) of the variance in turnover intentions. When job embeddedness decreases, turnover intentions increase, and when job embeddedness increases, turnover intentions decrease. The study has implications for positive social change as it established the relationship between job embeddedness and turnover intentions among law enforcement officers. The results provide support for using the concept of job embeddedness to inform retention programs aimed at reducing voluntary turnover. Reducing voluntary turnover has the potential to maximize the ability of an agency to meet its crime control mission and to reduce costs associated with recruitment and training new employees, which will allow for more funding to go directly to the provision of services. Relationship Between Job Embeddedness and Turnover Intentions Among Municipal Law Enforcement Officers by William Alonzo Forrester III MS, Bethel University, 2013 BM, Lambuth University, 2007 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Policy and Administration