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Record W4404918721 · doi:10.1016/j.econlet.2024.112098

Does supply chain finance improve firms’ ESG performance?

2024· article· en· W4404918721 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

VenueEconomics Letters · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsSaint Mary's University
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsBusinessSupply chainIndustrial organizationFinancial systemMarketing

Abstract

fetched live from OpenAlex

• Firms’ participation in SCF (supply chain finance) enhances their ESG performance. • SCF participation improves ESG performance by alleviating financial constraints. • SCF participation enhances ESG performance by increasing monitoring and oversight. • This study uses textual analysis to identify SCF participation from annual reports. • The positive SCF-ESG relationship remains robust after addressing endogeneity. This study investigates the impact of supply chain finance (SCF) participation on firms’ ESG performance by studying Chinese companies between 2011 and 2021. We find a significant positive association between SCF participation and ESG performance. Additional analysis indicates that SCF improves ESG by alleviating financial constraints and enhancing operational scrutiny. The results are consistent across various ESG and SCF measures and remain robust after endogeneity concerns are addressed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.005
GPT teacher head0.160
Teacher spread0.155 · 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