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Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement
Abstract
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.
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“However, we grounded our survey design in well-established productivity frameworks and prior validated measures used in related work [4, 16, 36].”
From The Fast and Spurious: Developer Productivity with GenAI (2025) · cites this paper as [36]
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Early predictors of job burnout and engagement.
Maslach, C., & Leiter, M. P. (2008) · Journal of Applied Psychology · doi:10.1037/0021-9010.93.3.498 · Cited by 2,397
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Measures of Clinical Significance
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Franca, C., da Silva, F. Q. B., & Sharp, H. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2842201 · Cited by 79
Technostress Creators and Burnout: A Job Demands-Resources Perspective
Mahapatra, M., & Pati, S. P. (2018) · Proceedings of the 2018 ACM SIGMIS Conference on Computers and People Research · doi:10.1145/3209626.3209711 · Cited by 62
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Cited by
The Fast and Spurious: Developer Productivity with GenAI
Afroz, S., Feng, Z., Menezes, T., Kimura, K., Trinkenreich, B., Steinmacher, I., & Sarma, A. (2025) · arXiv · doi:10.48550/arxiv.2510.24265 · Cited by 11
Self-citationAI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work
Choudhuri, R., Badea, C., Bird, C., Butler, J. L., DeLine, R., & Houck, B. (2026) · Proceedings of the IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice · doi:10.1145/3786583.3786885 · Cited by 1
Metadata
- Authors
- Trinkenreich, B., Santos, F., & Stol, K.-J.
- Affiliations
- Colorado State University, Fort Collins, CO, USA
- Lero, Cork, Ireland and University College Cork, Cork, Ireland
- Venue
- ACM Transactions on Software Engineering and Methodology
- Published
- 2024-11-30
- Volume
- 33
- Issue
- 8
- Pages
- 1-45
- ISSN
- 1049-331X
- 1557-7392
- Cited by
- 17
- References
- 6
- Language
- en
- Funders
- Sponsor Science Foundation Ireland (#15/SIRG/3293 and #13/RC/2094-P2)
- European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero—the Irish Software Research Centre
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