How employees leverage psychological capital and perform, even in the presence of rude co-workers: an empirical study from Pakistan
Bibliographic record
Abstract
Purpose This study aims to unpack the link between co-worker incivility and job performance, by detailing a mediating role of psychological detachment and a moderating role of psychological capital. Design/methodology/approach The research hypotheses are tested with three-wave, time-lagged data collected from Pakistani-based employees and their supervisors. Findings An important reason that disrespectful co-worker treatment curtails job performance, with respect to both in-role and extra-role work efforts, is that employees detach from their work environment. This mediating role of psychological detachment is less salient to the extent that employees possess high levels of psychological capital. Practical implications For organizations, this study pinpoints a key mechanism, a propensity to distance oneself from work, by which convictions that co-workers do not show respect direct employees away from productive work activities. This study also shows how this mechanism can be subdued by ensuring that employees exhibit energy-enhancing personal resources. Originality/value This study expands extant research on the dark side of interpersonal co-worker relationships by revealing pertinent factors that explain why and when co-worker incivility can escalate into diminished performance-enhancing activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".