Fitting in: Different Types of Person-Environment Fit as Drivers of Career Self-management in Kuwait
Bibliographic record
Abstract
Contributing to emerging efforts to integrate the understudied career self-management (CSM) with person- environment fit research, this study aims to assess the unique effects of person-organization, person-coworkers and needs-supplies fits on employees’ deployment of career advancement strategies. A questionnaire was completed by 548 highly educated young Kuwaitis and self-initiated expatriates (Arab and South Asian) working in medium and large Kuwaiti organizations. The simultaneous effects of the three types of fits is assessed and the findings demonstrate that an increase in person-coworkers fit and decrease in person-organization and needs-supplies fits consistently encourage the deployment of career advancement strategies concerning accessing influential networks, self-promotion, competence building and psychological boundaryless. A relatively more robust effect of person-coworker fit has been detected. It is attributed to the Arab collectivistic culture and to the construct being a career competency and a contextual factor as per the “intelligent career” theory. Interventions should be mindful of the differential effects of different types of fit on CSM. Developing organizational ‘standards of fit’ and CSM skills are essential for individual career development and organizational success. The study provides unique information about the understudied constructs of CSM and person-environment fit in a traditional and high inequality Arab Middle Eastern country, and infers the causes of the inconsistent effects of contextual factors reported by previous studies.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".