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Record W4403882253 · doi:10.1080/00036846.2024.2421457

The impact of supply chain finance on financial constraints of small and medium-sized enterprises and the moderating factors

2024· article· en· W4403882253 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

VenueApplied Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsTrent University
FundersNational Natural Science Foundation of China
KeywordsEconomicsSupply chainFinanceBusinessMarketing

Abstract

fetched live from OpenAlex

Supply chain finance (SCF) has gained significant popularity and has been extensively implemented in practical applications to facilitate cash flow by involving financial institutions. However, there are very few empirical studies on SCF. This study examines whether and how much SCF can alleviate financial constraints of small and medium-sized enterprises (SMEs). Furthermore, we examine the impact of information asymmetry, financial development depth, and economic policy uncertainty on the effectiveness of SCF. These three moderating factors correspond to the influence of firms, local financial development, and national government on SCF effectiveness, respectively. The empirical analysis shows that SCF can alleviate SMEs’ financial constraints significantly, and this effect is more pronounced for firms with higher information disclosure quality and located in financially developed areas. Our findings have important implications for firms and authorities, such as understanding the dynamics of SCF and developing appropriate measures and frameworks to promote the broad adoption and effective utilization of SCF.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.300

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 communication0.0000.000
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 head0.009
GPT teacher head0.190
Teacher spread0.182 · 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