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Record W3004193764 · doi:10.5267/j.uscm.2020.1.001

Nonlinear impact of supply chain finance on the performance of seafood firms: A case study from Vietnam

2020· article· en· W3004193764 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUncertain Supply Chain Management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainBusinessNonlinear systemChain (unit)Industrial organizationFinanceMarketing

Abstract

fetched live from OpenAlex

Supply chain finance has become an interesting research topic which attracts lots of attention from scholars recently, particularly after the global financial crisis. However, only few studies have examined the causal relationship between supply chain finance and firm performance. More specially, there is a big research gap when almost none of existing research has analysed the nonlinear impact of supply chain finance on firm performance. With this aim, this paper succeeds in giving first empirical evidence on the U-shaped nonlinear relationship between supply chain finance and the performance of seafood firms in Vietnam. Specifically, a bad performance of supply chain finance (the increase in cash conversion cycle -CCC) causes a lower firm performance (FP). Nevertheless, if any decrease in firm performance reaches its minimum (CCC*), the restructuring of the firm will gradually improve it. In addition, firm performance is significantly influenced by controlled variables of firm-specific, firm size (SIZE) and capital structure (CAP), and macroeconomic, economic growth (EG), factors. The findings are valuable for the management as well as scholars in bringing a more comprehensive perspective on the causal relationship between supply chain finance and firm performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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