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Record W4402411890 · doi:10.1177/10591478241276133

How and Why does a Business-to-Business Firm's Corporate Social Responsibility Disclosure Impact its Dependence on its Major Customers and Major Suppliers?

2024· article· en· W4402411890 on OpenAlexaff
Min Bai, Vivek Astvansh

Bibliographic record

VenueProduction and Operations Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsMcGill University
FundersNational Social Science Fund of ChinaSouthern University of Science and TechnologyUniversity of Science and Technology Beijing
KeywordsBusinessCorporate social responsibilityIndustrial organizationSocial responsibilityAccountingMarketingPublic relations

Abstract

fetched live from OpenAlex

Prior research has documented that a firm's disclosure of corporate social responsibility (CSR) makes it a more attractive business partner, boosting its sales. The authors extend this finding to business-to-business (B2B) firms. Using a regulatory change in China as a quasi-natural experiment, they demonstrate that a firm's disclosure of its CSR lowers by 2.1% the firm's dependence (for sales revenue) on its major customers but raises by 3.7% its dependence (for purchases) on its major suppliers. They further show that the firm's production efficiency (marketing efficiency) is a mechanism underlying the effect of CSR disclosure on dependence on major customers (suppliers). Next, they demonstrate that the CSR report's emphasis on the firm's supply chain partners weakens (strengthens) the effect on dependence on major customers (suppliers). The findings contribute to the multidisciplinary evidence on the B2B value of CSR disclosure, and the operations and marketing literature streams on determinants of supply-chain dependence.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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