Multinational Enterprises within Cultural Space and Place: Integrating Cultural Distance and Tightness–Looseness
Bibliographic record
Abstract
Prior research into the effects of cultural differences between multinational enterprises’ (MNEs’) home and host countries on expatriate staffing decisions in foreign subsidiaries has produced a large number of conflicting findings. We address some of these conflicting findings and aim to advance theory in two ways. First, we draw on transaction cost economics to explain why and how the effects of cultural distance on the proportion of expatriate parent-country nationals form a curvilinear relationship, instead of a linear one as commonly proposed. Second, we integrate the values-based cultural distance concept with the norms-based tightness–looseness concept. This allows us to simultaneously account for cultural differences between countries and location-bound normative cultural effects within countries, which cannot be overcome solely through expatriate learning and adaptation. Using a large global dataset of Japanese MNEs, we find support for a convex relationship between cultural distance and the proportion of expatriate parent-country nationals. We also find a moderating (steepening) effect of tightness–looseness on this relationship. The results reconcile some of the tensions between the subjectivists’ values-based approach, which positions culture in the shared cognitions realm, and the structuralists’ approach, which places culture in a normative situational environment.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".