Psychological contract breach and organizational cynicism and commitment among self-initiated expatriates vs. host country nationals in the Chinese and Malaysian transnational education sector
Bibliographic record
Abstract
Abstract In today’s global economy, self-initiated expatriates (SIEs) and host country nationals (HCNs) both represent critical human resources for organizations operating globally. Yet, because these two groups of employees have been studied separately, little is known about how SIEs’ and HCNs’ perceptions of, and attitudes towards the organization compare and diverge (vs. converge) in terms of implications for human resource management. This study aims to contribute to fill this gap by examining psychological contract breach, organizational cynicism, and organizational commitment components (i.e., affective, normative, and continuance) among a sample of 156 SIEs and HCNs working in the Chinese and Malaysian transnational education sector. Using a one-year time-lagged study, we found that compared to HCNs, SIEs experienced more organizational cynicism and less affective, normative, and continuance commitment. Moreover, the breach-organizational cynicism relationship was stronger (i.e., more positive) among SIEs than HCNs. The indirect relationships between breach and affective and continuance commitment, as mediated by organizational cynicism, were also stronger (i.e., more negative) among SIEs than HCNs. Implications for human resource management are discussed under the lens of Conservation of Resources theory.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".