Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement

In this study of burnout and engagement, we address three major themes. First, we offer a review of prior studies of burnout among IT professionals and link these studies to the Job Demands-Resources (JD-R) model. Informed by the JD-R model, we identify three factors that are organizational job resources and posit that these (a) increase engagement and (b) decrease burnout. Second, we extend the JD-R by considering software professionals’ intention to stay as a consequence of these two affective states, burnout and engagement. Third, we focus on the importance of factors for intention to stay, and actual retention behavior. We use a unique dataset of over 13,000 respondents at one global IT organization, enriched with employment status 90 days after the initial survey. Leveraging partial-least squares structural quation modeling and machine learning, we find that the data mostly support our theoretical model, with some variation across different subgroups of respondents. An importance-performance map analysis suggests that managers may wish to focus on interventions regarding burnout as a predictor of intention to leave. The Machine Learning model suggests that engagement and opportunities to learn are the top two most important factors that explain whether software professionals leave an organization.