An investigation of optimal job characteristics for recruiting and retaining Science, Technology, Engineering, and Mathematics (STEM) professionals

Abstract

Motivated by the aspiration to extrapolate optimal combinations of job characteristics that may minimize employee turnover rate, this research investigates impacts of specific workplace policies in autonomy, performance feedback, skill and task variety, identity, and significance. A questionnaire is designed to discover what the most effective talent management strategies are to attract, develop and retain top tier talents in STEM fields. In this thesis, the targeted demographics are professionals who hold at least one bachelor's degree in STEM fields or work in STEM fields. By collecting, organizing, and analyzing the survey data set, the research attempts to identify series of workplace autonomy policies and work task characteristics that are appealing to the targeted demographics. The thesis analyzes the respondent dataset using three approaches. Firstly, chisquared tests suggest that the dataset exhibits similar job characteristic preference patterns within each demographic dimension (i.e. generation, gender, household composition, education and professional backgrounds). Secondly, conditional probability tests indicate respondents' acquisition and retention rates associated with specific policies. Lastly, the cross-tabulated contingency tables summarize the insights for optimizing performance review frequency and methods. After investigating questionnaire participants' responses, this thesis enriches the data set with literature review findings. This thesis proposes practical recommendations to improve existing workplace autonomy policies based on the research insights.