Recruitment and Turnover in Public Organizations

Recruitment and retention of valuable employees in the public sector is crucial for public service delivery, implementation of policies, and effective allocation of resources. However, despite extensive research, there are still many unanswered questions, limiting our understanding of and ability to design effective and unbiased recruitment systems and reduce unwanted turnover. Across four articles, this dissertation aims to address central questions relating to these areas.

First, the dissertation examines the use of civil service exams in the recruitment of public employees. The dissertation develops a theoretical model to understand how exams can mitigate discrimination in candidate selection, while not shielding against discrimination later in the recruitment process. Next, the dissertation investigates the predictive power of established turnover antecedents by applying a predictive modeling approach and machine learning methods. The last part of the dissertation focuses on the methods and research designs of existing research on turnover intention. The dissertation examines the cumulative value of prior research and the correspondence between intentions and behavior which is a core assumption in the literature.

The results of the dissertation highlight the potential of using civil service exams in recruitment to secure an effective and fair selection of candidates but also show that exams do not prevent discrimination in later phases of the recruitment process. The dissertation also identifies a number of central turnover antecedents with high predictive power. These can guide future research and improve the retention efforts of public organizations. Finally, the dissertation uncovers notable methodological issues in existing turnover intention research and suggests that the cumulative evidence of this body of research has not substantially improved predictions of turnover intention.