How Do National Cultural Differences Affect Cross-Border Acquisitions? Cultural Dimensions, Learning From Supply Chain Partners, and Post-Acquisition Performance
Bibliographic record
Abstract
This research examines how national cultural differences between the acquirer and target firms affect post-acquisition performance in cross-border acquisitions. We focus on two dimensions of national culture—individualism/collectivism (IDV) and power distance (PDI)—for their close relevance to structural changes that occur during post-acquisition integration. We find that while differences in PDI are negatively associated with post-acquisition performance, differences in IDV positively affect such performance. We also find that the acquirer's cultural learning from supply chain partners helps mitigate the negative impact of PDI differences on post-acquisition performance, especially when the partner has a similar national culture in PDI to the target. Our theoretical development and empirical findings contribute to the operations and supply chain management research by illuminating the differential effects of national cultural differences on post-acquisition integration outcomes. Also, our study sheds new light on the possibility that working with supply chain partners may provide an opportunity for cultural learning that can be utilized in a post-acquisition integration setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".