How and when high-involvement work practices influence employee innovative behavior
Bibliographic record
Abstract
Purpose Based on social information processing (SIP) theory, this study explores the cross-level effect of high-involvement work practices (HIWPs) on employee innovative behavior by studying the mediating role of self-reflection/rumination and the moderating role of transactive memory system (TMS). Design/methodology/approach This study collects data from 452 employees and their direct supervisors in 94 work units, and tests a cross-level moderated mediation model using multilevel path analysis. Findings The results suggest that HIWPs significantly contribute to employee innovative behavior. Both self-reflection and self-rumination mediate the above relationship. TMS not only positively moderates the relationship between HIWPs and self-reflection, but also reinforces the linkage of HIWPs. →self-reflection→employee innovative behavior. Furthermore, TMS negatively moderates the relationship between HIWPs and self-rumination, and attenuates the mediating effect of self-rumination. Practical implications The study suggests that enterprises should invest more in promoting HIWPs and TMS in the workplace. Furthermore, managers should provide employees training programs to enhance their self-reflection, as well as lower self-rumination, in order to facilitate employee innovative behavior. Originality/value This research identifies self-reflection and self-rumination as key mediators that link HIWPs to employee innovative behavior and reveals the moderating role of TMS in the process.
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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 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".