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Record W2592862706 · doi:10.1002/csr.1420

The Inverted U‐Shaped Relationship between Corporate Philanthropy and Spending on Research and Development: A Case of Complementarity and Competition Moderated by Firm Size and Visibility

2017· article· en· W2592862706 on OpenAlexaff
Yongqiang Gao, Wu Jian, Taı̈eb Hafsi

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité de MontréalHEC Montréal
FundersNational Natural Science Foundation of China
KeywordsComplementarity (molecular biology)Corporate social responsibilityCompetition (biology)VisibilityBusinessStakeholderStakeholder theoryMarketingIndustrial organizationEconomicsPublic relationsPolitical scienceManagement

Abstract

fetched live from OpenAlex

Abstract Prior studies on corporate social responsibility (CSR) and innovation suggest either a competing or a complementary relationship between CSR and spending on research and development (R&D) activities. In this study, we unravel this puzzle by theorizing an inverted U‐shaped relationship between CSR in general and corporate philanthropy (CP) in particular, and R&D spending. Drawing mainly on stakeholder theory, we suggest that CP, by securing stakeholders’ support differently at different levels of spending, would first increase and then reduce R&D spending. Evidence from Chinese publicly traded companies during 2006–2015 well supports our arguments. In addition, we find this inverse U‐shaped non‐linear relationship between CP and R&D spending to be strengthened by firm visibility and weakened by firm size. This study has important theoretical and practical implications. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.043
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.183
GPT teacher head0.335
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2017
Admission routes1
Has abstractyes

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