Work engagement, affective commitment, and career satisfaction: the mediating role of knowledge sharing in context of SIEs
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine linkages between work engagement, affective commitment, and career satisfaction, while probing the mediating role of knowledge sharing in context of self-initiated- expatriates (SIEs). Design/methodology/approach A mediation model was tested using survey data from 266 SIEs working in US information technology (IT) multinational corporations (MNCs). Findings The results revealed significant direct and indirect effects of work engagement on affective commitment and career satisfaction through knowledge sharing. Research limitations/implications Although common method bias and validity of measurement were assessed in this study, the survey data were cross-sectional. Rigorous testing of the proposed mediated model through longitudinal design must be undertaken to allow for stronger inferences about causation. Practical implications The results suggest that organizations must nurture a knowledge sharing culture to promote knowledge exchange amongst SIEs. This study also underscores the importance of SIEs' work engagement as an enabler of knowledge sharing. Managers have a critical role in creating the right work environment, where SIEs feel engaged in their work and motivated to share knowledge. Originality/value This is the first study to examine interlinkages between work engagement, knowledge sharing, affective commitment and career satisfaction in SIEs' context.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".