How COVID‐19 can promote workplace cheating behavior via employee anxiety and self‐interest – And how prosocial messages may overcome this effect
Bibliographic record
Abstract
Summary While scholars have debated whether environmental factors (e.g., air pollution) can prompt unethical behavior (e.g., crime), we argue that the COVID‐19 pandemic provides a unique opportunity to inform this theoretical debate by elaborating onwhythese effects may occur, identifyinghowthey can be overcome, and addressing methodological issues. Drawing on appraisal theories of emotion, we argue that appraising COVID‐19 (i.e., an environmental factor) as a threat can elicit anxiety. This can focus employees on their own self‐interest and prompt cheating behavior (i.e., unethical workplace behavior). However, we propose that these detrimental effects can be attenuated by prosocial messages (i.e., highlighting the meaningful and positive impact that employees' work can have on others). Our predictions were supported using a two‐wave survey (N = 396) and an experiment (N = 163) with samples of full‐time employees during the COVID‐19 pandemic. Theoretically, our studies inform this ongoing debate by highlighting the importance of state anxiety and self‐interest as key mechanisms and that drawing peoples' attention towards others can serve as a boundary condition. Practically, we provide insight into the ethical costs of COVID‐19 in the workplace and identify a simple yet effective strategy that organizations can use to curtail workplace cheating behavior.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".