The implications of knowledge hiding at work for recovery after work: A diary study
Bibliographic record
Abstract
Past research on at-work predictors of after-work recovery mainly focused on what happened to someone at work. Yet, employees also act at work, and their own behavior and its consequences likely affect their ability to recover as well. Based on this premise, we bring together recovery research and research on moral behavior in organizations, examining the intrapersonal consequences of knowledge hiding, the intentional attempt to withhold knowledge that others have requested, for employee recovery. Specifically, we propose that knowledge hiding poses a moral dilemma, and thus has both positive (lower exhaustion) and negative (lower performance) intraindividual consequences that represent two opposing pathways to recovery in terms of work-related remorse in the evening and vigor the next morning. To test our hypotheses, we conducted a diary study across ten workdays, analyzing 517 daily reports from 152 participants. The results of multilevel path modeling suggest that day-specific knowledge hiding (in the form of playing dumb) can have both good (i.e., saving energy resources) and bad (i.e., low immediate performance) outcomes that cancel each other out in predicting evening work-related remorse. Evening remorse was negatively related to next-morning vigor. By considering how employees’ remorse affects their knowledge hiding, we meaningfully extend recovery research, showing that employees’ reflections on their own actions affect their post-work recovery processes and outcomes.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".