Linking managerial practices and leadership style to innovative work behavior
Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose and test a theoretical model linking individual perceptions of participative leadership style and managerial practices (i.e. teamwork and information sharing) to individual innovative behavior through the mediating mechanisms of: perceptions of team support for innovation and team vision; and psychological empowerment. Design/methodology/approach – Self-report data were collected from 394 employees working in five organizations. Structural equation models were conducted to empirically test the hypothesized research model. Findings – As hypothesized, participative leadership, teamwork and information sharing positively predicted perceptions of team support for innovation and team vision, which in turn fostered psychological empowerment. The latter was further positively associated with innovative performance. Practical implications – The results of the present study inform management of the group processes (i.e. team vision and support for innovation) that can mobilize employees to engage in effective innovative activities. Importantly, the findings indicate that for such processes to be developed and nurtured, teamwork activities should be promoted within work groups, effective communication systems should be implemented throughout the organization, and participatory skills should be developed among supervisors. Originality/value – The study represents one of the first attempts to investigate the perceived group and psychological processes that can explain how managerial practices and leadership style jointly benefit employee innovative behavior.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".