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Record W1917417629 · doi:10.1111/basr.12061

The Impact of Stakeholder Management on Corporate International Diversification

2015· article· en· W1917417629 on OpenAlexaff
Jijun Gao, Natalie Slawinski

Bibliographic record

VenueBusiness and Society Review · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMemorial University of NewfoundlandUniversity of Manitoba
Fundersnot available
KeywordsStakeholderDiversification (marketing strategy)BusinessStakeholder analysisStakeholder theoryMultinational corporationStakeholder managementKnowledge managementPublic relationsMarketingPolitical scienceFinanceComputer science

Abstract

fetched live from OpenAlex

Abstract This article explores the relationship between stakeholder management and international diversification. We differentiate between strengths and concerns in stakeholder management to demonstrate the differential effects of the two aspects of stakeholder management. Previous research on stakeholder theory often focuses on the importance of stakeholder relations, trying to build a business case of relational capital that results from strong stakeholder management. Such a relational approach, however, overlooks the process of managing stakeholders, a process that allows firms with strengths in stakeholder management to develop an important capability of managing tensions. In this study, we argue that this capability, an inherent part of stakeholder management, can be critical when firms face increased complexity during the process of international diversification. We therefore propose that strengths in stakeholder management are positively related to international diversification, while concerns in stakeholder management are negatively related to international diversification. Using panel data for 169 US multinational firms over a 10‐year period, we find support that strengths in stakeholder management facilitate international diversification. We discuss the contributions of our findings to stakeholder theory and international business research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.293

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.120
GPT teacher head0.287
Teacher spread0.166 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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