Overwork: A Review of the Literature and Importance to Management Accounting Research*
Bibliographic record
Abstract
ABSTRACT In this paper, I examine the literature on overwork, and I discuss its significance to the management accounting literature. Overwork, or working long hours, is a prevalent phenomenon, particularly among professional workers. Despite its pervasiveness, overwork has seldom been addressed in the accounting literature or studied as a distinct construct in managerial accounting research. Consequently, there is limited understanding of the management controls that contribute to overwork and the consequences of overwork in a management accounting context. This is despite overwork directly relating to an assumption in the management accounting literature that management controls are needed to motivate otherwise effort‐averse employees. In this paper, the existing literature on employee overwork, primarily sourced from outside of accounting, is systematically reviewed and categorized into reasons for overwork and consequences of overwork. I discuss reasons at the economic, individual, job, and organization levels that are proposed as antecedents of overwork in the literature. I also find that there are negative consequences to overwork in the work domain as well as outside of work and to employee health, in addition to important macro‐level consequences. Additionally, some relevant findings from the accounting literature are discussed. This paper also identifies significant gaps in the overwork literature from a management accounting perspective and discusses future research opportunities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".