Unravelling the relationship between perceived values-congruence with organizational change readiness: A moderated mediation model
Bibliographic record
Abstract
Recent studies have demonstrated that organizations often fail to execute organizational changes effectively due to a lack of their employees' organizational change readiness (OCR). However, the absence of employees' OCR is rooted in whether their values align with their organizations. The research aims to clarify when and why employees' perceived values-congruence with their organizations, supervisors, and workgroups (PVC-O, PVC-S, and PVC-G) helps stimulate their organizational change readiness (OCR). Specifically, it Integrates the self-categorization theory and social information processing theory and proposes a moderated mediation model to investigate the roles of perceived insider status (PIS) and the quality of change communication (QCC). This study gathered a valid sample of 252 employees from six Chinese companies at three different time points, and performed the structural equation modeling and multiple regression to test the proposed research model. The results demonstrate that PVC-O, PVC-S, and PVC-G are all positively related to employees' PIS, which further promotes their readiness for organizational change. Additionally, QCC strengthens not only the positive effect of employees' PVC-O and PVC-G (except for PVC-S) on PIS but also the indirect effects of PIS. This study offers valuable implications for practitioners implementing their organizational change practices in China. Moreover, this study can contribute to the organizational change literature by uncovering the underlying mechanism between perceived values-congruence and employees' OCR in the light of the person-environment interaction.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".