How and when high-involvement work practices influence employee innovative behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it