How and When Does Psychological Wellbeing Contribute to Proactive Performance? The Role of Social Resources and Job Characteristics
Bibliographic record
Abstract
Are psychologically healthy employees more proactive at work? Surprisingly, responses to this question are lacking as empirical research has overlooked the wellbeing-proactive performance relationship. Drawing insights from the conservation of resources theory and the motivational fit perspective, this study proposes that leader-member exchange and team-member exchange act as social resources that convey the benefits of psychological wellbeing to subsequent proactive performance. Moreover, job complexity and task interdependence-two job characteristics that enhance the motivational potential of social resources-are expected to amplify these positive indirect relationships. Data from a three-wave, time-lagged study conducted among employees (N = 318) from French-Canadian organizations were used to test our hypothesized model. The results indicated that leader-member exchange mediated a positive relationship between wellbeing and proactive performance and that the contribution of wellbeing to proactive performance via leader-member exchange was increased when job complexity was higher. We also found a negative indirect relationship between wellbeing and proactive performance via team-member exchange when team interdependence was lower. Theoretical and practical implications of this research are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".