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Record W2766022527 · doi:10.1108/ejmbe-10-2017-019

Reputation of multinational companies

2017· article· en· W2766022527 on OpenAlexfundno aff
Javier Aguilera‐Caracuel, Jaime Guerrero‐Villegas, Encarnación García Sánchez

Bibliographic record

VenueEuropean Journal of Management and Business Economics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersUniversidad de GranadaInnovation, Science and Economic Development Canada
KeywordsMultinational corporationInternationalizationDiversification (marketing strategy)BusinessReputationCorporate social responsibilityIndustrial organizationOriginalityContext (archaeology)MarketingInternational businessAccountingEconomic geographyPublic relationsInternational tradeEconomicsSociologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to use stakeholder theory as the theoretical reference framework to study the influence of internationalization (geographic international diversification) and social performance on multinational companies’ (MNCs) reputation. Design/methodology/approach The authors confirm the research hypotheses using a sample of 113 US MNCs in the chemical, energy and industrial machinery sectors during the period 2005-2010. Findings This study contributes to the literature in three ways. First, it incorporates literature on internationalization to study the possible connection between geographic international diversification and social performance in MNCs. Second, it sheds light on the debate between corporate social responsibility (CSR) and the reputation of MNCs in a very diverse transnational context in which MNCs must meet the needs of stakeholders at both local and global levels. Third, it incorporates the mediating role of social performance in the relationship between geographic international diversification and the firm’s reputation. Originality/value Prior studies have hardly analyzed this relationship, which becomes especially relevant for MNCs, since their implementation of advanced CSR practices in the different markets in which they operate will gain them a good reputation, not only in specific local contexts but also globally, benefitting the organization as a whole and enabling it to gain internal consistency (improvement in internal efficiency), transparency and legitimacy.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.241
Teacher spread0.203 · 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 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

Citations60
Published2017
Admission routes1
Has abstractyes

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