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Record W4402138995 · doi:10.1177/10591478241281495

How Do National Cultural Differences Affect Cross-Border Acquisitions? Cultural Dimensions, Learning From Supply Chain Partners, and Post-Acquisition Performance

2024· article· en· W4402138995 on OpenAlexaff

Bibliographic record

VenueProduction and Operations Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsAffect (linguistics)BusinessSupply chainChain (unit)Industrial organizationMarketingPsychologyCommunication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.301
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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