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Record W4211123160 · doi:10.3846/tede.2022.16321

THE TRADE-OFF BETWEEN CORPORATE SOCIAL RESPONSIBILITY AND COMPETITIVE ADVANTAGE: A BIFORM GAME MODEL

2022· article· en· W4211123160 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnological and Economic Development of Economy · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsCompetitive advantageIndustrial organizationCorporate social responsibilityBusinessBalance (ability)Context (archaeology)Investment (military)MicroeconomicsEconomicsMarketing

Abstract

fetched live from OpenAlex

This paper uses a biform game model to study firms’ trade-offs between corporate social responsibility (CSR) and competitive advantage. We focus on the context in which a competitive advantage may lead to a non-profitable scenario. It is possible that the first mover’s investment in competitive strength may deter itself from the market, which encourages firms’ investment in CSR over competitive strength. As a result, in some circumstances, firms may actively choose a CSR strategy over a competitive strategy. Our results show that (1) technological characteristics, (2) industrial structure, and (3) institutional environments are factors that influence the rational equilibrium of our model and the balance between competitive advantage and CSR. The mechanism and boundary on how firms make trade-offs between CSR activities and competitive strength are exhibited by our model, which provides a framework for decision-making and adds new insights into the strategic balance between market and non-market strategies.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.756

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 studies0.0010.001
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.245
Teacher spread0.199 · 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