Facilitating Cross-Cultural Adaptation: A Meta-Analytic Review of Dispositional Predictors of Expatriate Adjustment
Bibliographic record
Abstract
This meta-analytic review examines the relationship between various dispositional characteristics and expatriate adjustment, including the Big Five constructs and other characteristics that have garnered more recent empirical attention (i.e., cultural empathy/flexibility, cognitive intelligence, emotional intelligence). Using 62 primary studies ( n = 13,060), we found that the Big Five traits play an important role in expatriate adjustment; however, when assessing the relative influence of these predictors, characteristics such as cultural empathy, cultural intelligence (e.g., motivational CQ), and emotional intelligence appear to exert a stronger influence on adjustment outcomes. Various cultural variables (cultural distance, cultural tightness, gender inequality in the host country) and year of publication were found to moderate some relationships, indicating that sociocultural factors may temper some of these effects.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".