How coworker undermining leads justice-sensitive employees to miss deadlines
Bibliographic record
Abstract
Purpose This study examines how employees’ exposure to coworker undermining may lead them to miss work deadlines. It offers a particular focus on the mediating role of diminished organization-based self-esteem and the moderating role of justice sensitivity in this connection. Design/methodology/approach The research hypotheses are tested with data collected among employees and supervisors who work in various industries. Findings Purposeful efforts by coworkers to cause harm translate into an increased propensity to fail to complete work on time, because the focal employees consider themselves unworthy organizational members. The extent to which employees feel upset with unfair treatments invigorates this process. Practical implications For employees who are frustrated with coworkers who deliberately compromise their professional functioning, diminished self-worth in relation to work and the subsequent reduced willingness to exhibit timely work efforts might make it more difficult to convince organizational leaders to do something about the negative coworker treatment. Pertinent personal characteristics can serve as a catalyst of this dynamic. Originality/value This study contributes to extant human resource management research by detailing the link between coworker undermining and a reduced propensity to finish work on time, pinpointing the roles of two hitherto overlooked factors (organization-based self-esteem and justice sensitivity) in this link.
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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 | 0.020 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".