Good News for You is Good News for Me: The Effect of Coworker Disclosure on Employee Vitality
Bibliographic record
Abstract
Research shows that coworkers can play a critical role in shaping employee vitality, but we have little insight into specific actionable behaviors that coworkers can take to affect the vitality of other employees. We draw insights from social information processing theory to argue that coworker positive work event disclosure has a positive indirect effect on employee vitality through the mediating role of employee career ambition. Drawing insights from role congruity theory, we further propose that this indirect effect is stronger for men compared to women. To test these arguments, we use a time-separated field dataset (n = 175), which provides support for these hypotheses. We conclude by discussing the theoretical contributions with respect to the important role of coworkers in sharing positive work stories with others as these stories elicit employee cognitions (career ambition) that foster employee vitality. In addition, we provide interesting gender-focused insights that suggest these coworker stories are particularly impactful for men. Practical insights for HR professionals, leaders, and employees are provided to meaningfully improve employee vitality in organizations.
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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 | 0.031 |
| 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 | 0.000 | 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".