What Comes Around, Goes Around: Effects of Unethical Pro-Organizational Behaviour on Time Theft
Bibliographic record
Abstract
In recent decades, China's rapid economic expansion has been accompanied by a rise in corporate ethical scandals, which have drawn increasing attention to unethical pro-organizational behaviour (UPB)—actions intended to benefit the organization but violating ethical norms. While often justified by employees as organizationally beneficial, UPB carries significant psychological and behavioural consequences. Using cognitive dissonance theory, we examine how UPB leads to time theft through the chain mediation of ego depletion and moral sensitivity, with managerial recognition serving as a key boundary condition. We collected data from 432 randomly selected retail employees through a structured questionnaire, including demographic data and measures of UPB, ego depletion, moral sensitivity, managerial recognition and time theft. Statistical analyses were performed using SPSS 27.0 for preliminary analyses (e.g., reliability, correlation) and MPLUS 8.3 for path analysis and hypothesis testing, including confirmatory factor analysis (CFA) and mediation analysis. The results show that UPB increases ego depletion and directly promotes time theft, while also reducing moral sensitivity indirectly through ego depletion. Ego depletion and moral sensitivity sequentially mediate the relationship between UPB and time theft. Importantly, managerial recognition moderates the mediation path by strengthening the positive relationship between ego depletion and time theft. These findings reveal not only the underlying psychological mechanism of UPB but also how organizational feedback can unintentionally reinforce deviant behaviour. We conclude that organizations should carefully evaluate recognition practices and implement ethics-oriented support systems to mitigate the hidden costs of UPB.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".