The effects of psychological contract violation on Chinese executives
Bibliographic record
Abstract
We investigate the influence that a violation of psychological contracts can have on organizational commitment both in main and moderating effects. Although there have been many studies relating to the negative effects of psychological contracts on organizational outcomes, our study is the first to examine these potentially negative effects on executives. As well, few studies have used the Chinese landscape to determine if Chinese employees develop psychological contracts and if so, to delve into whether these violations have similar impacts on Chinese employees as on their Western counterparts. Our sample consists of 200 Chinese executives from Mainland China. The sample includes CEOs, executive vice presidents, and general managers, all of which are powerful decision makers. We found that a violation of psychological contracts for Chinese executives has a strong negative relationship with organizational commitment. Our results also show the interactional effects of both job and person related variables and psychological contract violations on organizational commitment. More specifically, job involvement, job satisfaction, and hope decrease the negative effects of psychological contract violations, while job demand and locus of control heighten the negative effects of psychological contract violations. Thus, psychological contract research is applicable not only for the Western employee but is also relevant within the Asian context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".