A RESEARCH FRAMEWORK FOR CROSS-CULTURAL ADJUSTMENT AND JOB PERFORMANCE OF HIGHLY SKILLED IMMIGRANTS
Bibliographic record
Abstract
Given the importance of knowledge as a source of economic growth, Canada, like other industrialized countries, increasingly depends on a pool of highly skilled immigrants (HSIs), to remain competitive. However, the poor adaptation of some HSIs to their new cultural environment remains one major factor to their underperformance (Li, 2010). It is crucial for organizations to efficiently manage the cultural issues and to better help HSIs for their cross-cultural adjustment. This paper aims to propose a research framework with two predictors of cross-cultural adjustment from a theoretical perspective: transformational leadership (TL), and learning goal orientation (LGO). In fact, cross-cultural adjustment is a learning process during which an individual learns the norms and appropriate behaviors so as to function in a different culture. Several studies show that the LGO would constitute an important factor to explain how and why certain individuals adapt more than others in the context of cultural transition (Gong & Chang, 2007; Palthe, 2004). Furthermore, since adaptation is first and foremost a process through which incertitude (or stress) is reduced, the TL in diminishing this stress is a crucial factor (Gill and al., 2010; Gundersen and al., 2012). However, the studies that examine their relationships with cultural adaptation are still rather rare. In the light of existing researches in different fields of social sciences, we propose the following research framework for cross-cultural adjustment and job performance of HSIs in Canada:Hypothesis 1. HSIs who have high levels of cross-cultural adjustment will have high levels of job performance.Hypothesis 2a. LGO is positively correlated to the psychological adjustment of HSIs.Hypothesis 2b. LGO is positively correlated to the socio-cultural adjustment of HSIs.Hypothesis 2c. LGO is positively correlated to the work adjustment of HSIs.Hypothesis 3. LGO is positively correlated to the job performance of HSIs.Hypothesis 4a. TL is positively correlated to the psychological adjustment of HSIs.Hypothesis 4b. TL is positively correlated to the socio-cultural adjustment of HSIs.Hypothesis 4c. TL is positively correlated to the work adjustment of HSIs.Hypothesis 5. TL is positively correlated to the job performance of HSIs.Hypothesis 6: HSIs, under the influence of transformational leaders would have higher levels of LGO.Hypothesis 7. Cross-cultural adjustment moderates the relationship between TL and job performance of HSIs.Hypothesis 8. Cross-cultural adjustment moderates the relationship between LGO and job performance of HSIs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".