The impact of manager's animosity and ethnocentrism on multinational enterprise (MNE) international entry‐mode decision
Bibliographic record
Abstract
For MNEs, entry‐mode decision is undeniably critical. While the literature has identified several determinants of foreign entry‐mode choice, few have examined the impact of manager's animosity and ethnocentrism. This article is an attempt to fill this gap by examining the independent effect of manager animosity and ethnocentrism as well as the moderating effect of cultural intelligence on entry mode. Data were collected through surveys targeting upper‐level managers in the U.S., U.K., and Germany to examine how animosity and ethnocentrism would shape their decision to enter Iran, a host market largely perceived negatively in the West. Multivariate multiple regression was used to test the hypothesized effects and to capture the multidimensional aspect of entry modes. We found that, in general, manager's animosity and ethnocentrism do negatively affect the choice of an entry mode and that cultural intelligence has the potential to neutralize that negative effect.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".