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Record W4407003123 · doi:10.1002/csr.3141

What Drives Consumer Willingness to Pay for Environmental, Social, and Governance Initiatives? A Choice Experiment

2025· article· en· W4407003123 on OpenAlexaff
Seong Ok Lyu, Changwook Kim

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsBrock University
FundersNational Research Foundation of KoreaMinistry of EducationKorea UniversityNational Research Foundation
KeywordsWillingness to payBusinessCorporate governanceCorporate social responsibilityMarketingEnvironmental economicsStakeholder engagementPublic economicsEconomicsPublic relationsFinanceMicroeconomicsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT This study explores consumer preferences for Environmental, Social, and Governance (ESG) initiatives, utilizing a choice experiment within the context of sports organizations. This research aims to identify how each ESG dimension influences consumer decision‐making and the economic implications for organizations implementing these practices. By examining gender differences in preferences for ESG practices, this study provides nuanced insights into consumer attitudes toward diverse sustainability initiatives. Results indicate consumer willingness to pay for improved ESG practices, with distinctive variations across demographic segments. These findings suggest that consumers prioritize sustainability and ethical considerations in their choices, offering valuable implications for sports organizations seeking to enhance their ESG performance. This study contributes to the expanding body of literature on consumer behavior toward ESG, highlighting the economic benefits of aligning corporate strategies with consumer values. Our research underscores the importance of ESG initiatives in driving consumer engagement and promoting sustainable business practices.

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)0.006
metaresearch head score (Gemma)0.019
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.086
GPT teacher head0.265
Teacher spread0.179 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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