Entrepreneurial leadership and employee creativity: a multilevel mediation model of entrepreneurial self-efficacy
Bibliographic record
Abstract
Purpose The current study, which is based on social learning theory and social cognitive theory, intends to investigate the impact of entrepreneurial leadership on employee creativity at both the individual and team levels. In particular, the authors predict a mediating mechanism at both levels: employees’ entrepreneurial self-efficacy. Further, the authors consider whether the climate of support for innovation is a contextual element affecting the relationship between employees’ perceptions of entrepreneurial leadership and their own entrepreneurial self-efficacy. Design/methodology/approach The research hypotheses were tested using multilevel structural equation modeling on 191 employees nested in 49 entrepreneurial ventures in China. Findings The results indicated that entrepreneurial leadership positively correlates with employee creativity at individual level. Moreover, this study found that individual followers’ entrepreneurial self-efficacy partially mediates the association between individual perceptions of entrepreneurial leadership and employee creativity, whereas team members’ entrepreneurial self-efficacy fully mediates the association between team members’ perceptions of supervisors’ entrepreneurial leadership and employee creativity. Further, this research demonstrates the role of team-level climate of support for innovation as a boundary condition that strengthens the effect of entrepreneurial leadership on individual entrepreneurial self-efficacy. Originality/value Considering entrepreneurial self-efficacy to be a type of entrepreneurial context-specific self-efficacy, this study presents one of the first empirical examples of the mediating function of entrepreneurial self-efficacy in the association between entrepreneurial leadership and employee creativity. Additionally, this research demonstrates the role of team-level climate of support for innovation as a boundary condition that strengthens the effect of entrepreneurial leadership on individual entrepreneurial self-efficacy. Further, this study provides a methodological contribution by simultaneously assessing all three variables of the mediation process at the individual and team levels: entrepreneurial leadership, entrepreneurial self-efficacy and employee creativity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".