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Record W4400619474 · doi:10.1016/j.jbusres.2024.114816

The moderating effect of national culture on board interlocks’ impact on firm performance: A meta-analysis

2024· article· en· W4400619474 on OpenAlexaff
Teng Ying, Zhenzhong Ma, Dapeng Liang, Shenyi Song, Yuhang Zheng

Bibliographic record

VenueJournal of Business Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInterlockHofstede's cultural dimensions theoryUncertainty avoidanceCollectivismBusinessModerationMasculinityEmpirical researchPsychologyMarketingIndividualismSocial psychologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Globalization has made it essential to examine the effectiveness of management practices in different cultural contexts. This study employs a meta-analysis method to explore the effects of board interlocks across cultures. Based on 56 empirical studies with 121 correlations between board interlocks and firm performance, this meta-analytic study shows a positive relationship between board interlocks and firm performance. It further demonstrates that the widely examined cultural dimensions including individualism vs. collectivism, uncertainty avoidance, masculinity vs. femininity, and long-term vs. short-term orientation do not influence the impact of board interlocks on firm performance across cultures. The exception to this is that power distance and indulgence negatively moderate the relationship between board interlocks and firm performance: the impact of board interlocks on firm performance is stronger in cultures characterized by high power distance and low restraint. The findings of this study can help enrich the research on board interlocks and provide insights for international management.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.394
Teacher spread0.300 · 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 designSimulation or modeling
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

Citations9
Published2024
Admission routes1
Has abstractyes

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