Linking workplace burnout theories to the project management discipline
Bibliographic record
Abstract
Purpose Given the demanding and stressful nature of project work, with a view to explore established concepts of burnout within the project management context, the purpose of this paper is to examine two instruments: the Maslach Burnout Inventory (MBI) and the Areas of Worklife Survey (AWS). Since there is a paucity of literature in project management anchored within the MBI and the Areas of Worklife Survey (AWS), this paper proposes a high-level model on burnout in project management, drawing on the literature underlying these two instruments. Design/methodology/approach Using a conceptual approach, the paper reviews the social psychology literature on burnout and then the narrow stream of literature on burnout in project management. The paper develops and proposes a conceptual model as a foundation to explore the links between the determinants of project manager burnout/engagement and turnover/retention. Findings This paper contributes to an improved understanding of the determinants of project manager burnout, engagement, turnover, and retention. Practical implications The driver for this research is to contribute to the emerging literature on burnout in project management and strategies to help improve engagement and retention of project managers in the discipline – specifically, their tenure in organizations and/or the profession. Originality/value This paper contributes to the topic of burnout in the project management context. An improved understanding of the stressors in project management contexts, and the mechanisms to mitigate the stress, can add to our understanding of project manager well-being, engagement and retention, improved project success, and healthier work environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".