How Does Coworker Job Crafting Affect Teammate Job Outcomes?
Bibliographic record
Abstract
Job crafting, the proactive changing of job demands and resources to better suit one’s needs and abilities, is on the rise within the modern workplace as individuals are experiencing greater autonomy and ownership over their careers. Research shows that job crafting is not only associated with increased employee job satisfaction, well-being, and work engagement, but also improves employee adaptability to change. In addition to current labour shortages, organizations need also to adapt to increasingly challenging and unpredictable work environments. Organizations therefore stand to benefit from the outcomes of individual job crafting. While limited research has shown some possible spillover benefits for teammates, recent research has revealed possible negative consequences of this self-targeted activity on the job crafter’s teammates, specifically because job crafting involves modifying tasks and relationships. In a collaborative environment, these changes risk negatively impacting teammates. The purpose of this study was therefore to examine whether coworker job crafting influences teammate job satisfaction and job stress in an interdependent work context. The results from a survey of 199 panel participants in Canada, USA, and the UK unexpectedly supported improved job satisfaction and reduced stress for teammates of coworkers who job craft. The data also supported the role of coworker social support as a mechanism by which coworker job crafting influenced teammate outcomes. These findings suggest that the effects that individual job crafting have on teammates depends on the work context. The theoretical implication indicates the importance of continued study of the effects of job crafting on others.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".