Maximizing the Benefits of Internationalization: The Moderating Role of Labour Flexibility
Bibliographic record
Abstract
Using the large-scale Korean Workplace Panel Survey, this study examines the interplay between international diversification, labour flexibility, and workplace-level performance in the context of advanced emerging markets. Filling the gap in the literature on the international diversification-performance (IDP) relationship, which focuses primarily on firm-level characteristics and overlooks the role of labour factors as contingent variables, we draw attention to the workplace level dynamics by exploring how the two types of labour flexibility—functional and numerical flexibility—moderate the impact of international diversification on performance. The results show that when workplaces invest in training for job enlargement and employee involvement programs that lead to the enhancement of functional flexibility, the link between international diversification and performance can be strengthened. This finding supports the assertion in the international HRM literature that, in the ever-globalized business environment, investment in human capital is a better strategy for improving financial performance in the long run. Furthermore, we find that numerical flexibility, as measured by in-house subcontracting arrangements, has a negative impact on the IDP relationship. Overall, our study suggests that the quality of human resources and a well-designed workplace configuration may still help improve performance in the context of international diversification, whereas excessive dependence on employment externalization for cost reduction is likely to hurt not only financial performance but also long-term sustainability. We also believe that our findings on the advanced emerging market economy complement insights from previous studies, which are largely based on Western developed economies, thus enriching current theories on labour flexibility.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".