How and when team reflexivity influences employee innovative behavior
Bibliographic record
Abstract
Purpose Based on affective events theory, this study explores the cross-level effect of team reflexivity on employee innovative behaviors. Specifically, the authors examine the mediating effects of affective and normative commitment on this relationship, as well as the moderating effects of benevolent leadership. Design/methodology/approach The authors surveyed 341 employees and their direct supervisors in 74 work units and utilized multilevel path analysis to test a model of cross-level moderated mediation. Findings The study analysis results suggest that team reflexivity significantly contributes to employee innovative behavior. Both affective commitment and normative commitment mediate this relationship. Benevolent leadership not only enhances the relationship between team reflexivity and affective/normative commitment, but also reinforces the linkage of team reflexivity→affective commitment→employee innovative behavior. Practical implications The current study suggests that organizations should invest more in promoting team reflexivity and benevolent leadership in workplace. Furthermore, managers need to develop appropriate employees training programs and pay more attention to employees' work and personal lives. They need to make efforts to enhance employees' affective and normative commitment, thereby facilitating their innovative behavior. Originality/value This research identifies affective commitment and normative commitment as key mediators that link team reflexivity to employee innovative behavior and reveals the moderating role of benevolent leadership in the process.
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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 | 0.013 |
| 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) | 0.002 | 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".