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Record W4402069727 · doi:10.1002/cjas.1760

International experience, growth opportunities, and repatriate job satisfaction

2024· article· en· W4402069727 on OpenAlexaffvenue
Michael Roberts, Etayankara Muralidharan, Adam H. Cave

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsMacEwan UniversityMount Royal University
Fundersnot available
KeywordsJob satisfactionBusinessPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Multinational Enterprises expect to retain repatriates (i.e., returning employees from international assignments) and to gain from the new knowledge and experience they bring back from international assignments. Drawing insights from social exchange theory and using the perspective of psychological contracts, our study findings demonstrate that repatriate perception of recognition of foreign experience influences their job satisfaction, and this influence is mediated by perceived growth opportunities in the organization. Further, this mediation is nuanced in that it is stronger when there is an organization repatriation plan in place. The study contributes to the theoretical underpinnings of the antecedents of repatriate job satisfaction. Moreover, it demonstrates the need to have a strong repatriation plan in place along with growth opportunities within the organization to heighten job satisfaction of these highly valued employees upon their return from an international assignment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.165
GPT teacher head0.377
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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