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Roles of Multi-Dimensions of CSR in Brand Value: Moderating Effect of Firm Size

2019· article· en· W2966800248 on OpenAlexaff
Hyun Gon Kim, Wootae Chun, Zhan Wang

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCorporate social responsibilityBusinessCompetitive advantageIntangible assetValue (mathematics)MarketingBrand managementBrand equityGeneralizability theoryResource-based viewReputationBrand awarenessCorporate brandingPublic relationsPsychologyAccounting

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR) has become an important strategic tool for enhancing firms’ competitive advantage and legitimacy. Although CSR is a fundamental intangible asset that is rare and hard to imitate or substitute, research on its effect on firm performance has been inconclusive; studies have failed to examine how firm engagement in CSR influences brand value. The purpose of this study is to examine the potential moderating effect of multidimensional CSR activities on brand value. Using the resource-based view (RBV), the authors develop a conceptual framework in which investments in multidimensional CSR activities enhance corporate reputation and stakeholder satisfaction and thus lead to higher brand value. Firm size positively moderates the relationship between CSR activities and brand value. This study contributes to extant literature by affirming the generalizability of the relationship between CSR activities and brand value with cross-country and cross-industry data sets from 144 global brands across 17 countries.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.008
GPT teacher head0.234
Teacher spread0.225 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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