Human Resource Management Practices and Employee Retention in the Teaching Hospitals in Ghana

Abstract

The drive of the study was to examine human resource management practices and employee retention in the teaching hospitals in Ghana. The specific objectives were to assess Human Resource Recruitment on Employee Retention, Human Resource Training on Employee Retention, Human Resource Employee Appraisal on Employee Retention, Human Resource Data Management on Employee Retention, with a moderating variable of HR policy. The study draws from resource-based view theory, human capital theory, equity theory of motivation, organizational information processing theory, transformational leadership theory, and job embeddedness theory. The study deployed quantitative research design with a population of 605 and a sample size of 240 respondents from the HR unit in the teaching hospitals in Ghana. With the use of SPSS 2.0, data was examined quantitatively drawing through descriptive and inferential statistics. Inferential analyses included correlation analysis, linear and multiple linear regression analysis to test hypotheses at a significance level of 0.05, descriptive analyses were used to determine frequencies, percentages mean, maximums, minimums and standard deviations. The study concluded that HR recruitment, HR training and HR employee appraisal statistically were significant to predict employee retention in the teaching hospitals. The recommendations of the study were that the teaching hospitals review the recruitment of medics from 4 years to 2 years. That the allocated budget of training be increased to cater for the training of all cadres of staff, specifically the nurses, for further education to become physician assistants. That HR team should implement a continuous feedback practice as a strategy for communicating employee appraisal issues and retention. That management invests in technological advancement to upskill employees. That a compensation review committee to oversee the compensation plan by incorporating the workload analysis for fair compensation.