How exploitative leadership influences employee innovative behavior: the mediating role of relational attachment and moderating role of high-performance work systems
Bibliographic record
Abstract
Purpose The purpose of this research is to examine the relationship between exploitative leadership and employee innovative behavior and explore the mediating role of relational attachment and the moderating role of high-performance work systems (HPWSs). Design/methodology/approach This research collected data from 374 employees and their direct supervisors in 75 teams and tested a cross-level moderated mediation model using multilevel path analysis. Findings The results suggest that (1) exploitative leadership has a negative impact on employee innovative behavior; (2) relational attachment mediates the relationship between exploitative leadership and employee innovative behavior; (3) HPWS positively moderates the relationship between exploitative leadership and relational attachment and (4) HPWS moderates the mediating mechanism from exploitative leadership to employee innovative behavior. Practical implications The empirical findings suggest that organizations should make efforts to prevent exploitative leadership. Moreover, managers should pay attention to the important role of relational attachment in promoting employee innovative behavior and realize the role of HPWSs in facilitating the negative effects of exploitative leadership. Originality/value This research identifies relational attachment as a key mediator that links exploitative leadership to innovative behavior and reveals the role of HPWSs in strengthening the negative effects of exploitative leadership on 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 | 0.008 |
| 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 | 0.001 | 0.001 |
| 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".