Modeling managerial promotion decisions using Bayesian networks: an exploratory study

Purpose – A review of the academic literature on managerial promotions reveals that there has been a limited number of studies conducted on this subject. This study aims to identify key determinants used by managers in making managerial promotion decisions via Bayesian networks. It also seeks to explore the effects these determinants have on managerial promotion outcomes. Design/methodology/approach – The researchers surveyed MBA students with significant work experience to assess the effect levels for 13 managerial promotion factors derived from a research study by Service and Lockamy. The participants were asked to assign a percentage effect level to these factors. Factor analysis was used to determine the most influential factors, and Bayesian networks were constructed to determine the probability of receiving a promotion based on these factors. Findings – The results indicate that there are five key determinants which have the most influence on managerial promotions. They also indicate that managerial promotion outcomes were not significantly influenced by either the promoting manager's years of work experience, or the number of promotions witnessed. Originality/value – The paper focuses on managerial and professional career advancement research, managerial promotion processes, and personnel development.