Leadership and Employees’ Innovative Work Behavior: Test of a Mediation and Moderation Model
Bibliographic record
Abstract
The aim of this study is to determine the relationship between Transformational (TFL)/ Transactional (TSL) leadership and employees’ Innovative Work Behavior (IWB), through a mediation and moderation model. The proposed model postulates that Organizational Climate for Innovation (OCI) and Organizational Absorptive Capacity (OAC) exert a mediating role whereas Employees´ Work Engagement (EWE) has a moderating effect in such relationship. A total of 267 Colombian workers from different kind of companies completed a reliable battery of questionnaires. The sample was collected through the MBA programs from two recognized universities located in Bogotá, Colombia. Structural equation modeling and hierarchical regression analyses were used to test the proposed model. According to the results, there is a direct and positive relationship between TFL and IWB as was expected. However, contrary to what had been hypothesized, TSL demonstrated to exert the same positive linkage. OCI and OAC showed its mediator effect in the relationship between TFL/TSL and IWB, nevertheless, this effect was less strong than when the relationship between theses variables was direct. On the contrary, EWE does not exert a moderator effect in this relationship as it was posited, but shows a significant and direct relationship with IWB. This research allows assert that leadership influences IWB, either directly or mediated by organizational variables. These results contribute to extent the literature in a scarcely studied field, by testing an empirical model.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.008 | 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".