Team reflexivity and employee innovative behavior: the mediating role of knowledge sharing and moderating role of leadership
Bibliographic record
Abstract
Purpose Although team reflexivity has been identified as a potent tool for improving organizational performance, how and when it influences individual employee innovative behavior remains theoretically and conceptually underspecified. Taking a knowledge management perspective, this study aims to investigate the role of team-level knowledge sharing and leadership in transforming team reflexivity into innovative behavior at the individual level. Design/methodology/approach The paper follows a multilevel study design to collect data (n = 441) from 91 teams in 48 knowledge-based organizations. The paper tests our multilevel model using multinomial logistic techniques. Findings The overall results confirm that knowledge sharing in teams mediates the influence of team reflexivity on individual employee innovative behavior, and that leadership plays an important role in moderating these influences. Specifically, authoritarian leadership is found to attenuate the team reflexivity and knowledge sharing effect, whereas benevolent leadership is found to amplify this indirect effect. Originality/value The multilevel study design that explains how team-level processes translate into innovative behavior at the individual employee level is novel. Relatedly, our use of a multilevel analytical framework is also original.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".