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Record W4404145333 · doi:10.5267/j.uscm.2024.7.014

The role of supply chain management, firm value, and competitive advantage in the food sector

2024· article· en· W4404145333 on OpenAlexvenueno aff
Miguel Angel Esquivias, I Made Laut Mertha Jaya, Mar’a Elthaf Ilahiyah

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIndustrial organizationCompetitive advantageValue (mathematics)Supply chain managementSupply chainValue chainMarketingComputer science

Abstract

fetched live from OpenAlex

The adoption of supply chain management (SCM) is pivotal in boosting organizational competitiveness and performance. This study examines the relationships among SCM, firm value, company performance, and competitive advantage within the context of micro, small, and medium enterprises (MSMEs) in Surabaya, East Java, Indonesia. Employing a quantitative approach, we analyzed data collected from a sample of 100 MSMEs using SmartPLS to test various hypotheses. The findings indicate that effective SCM significantly influences a firm’s value, performance, and competitive advantage. This study supports the mediating role of firm value by showcasing its robust influence on the relationship between SCM and competitive advantage. However, the mediating role of company performance on competitive advantage appears weaker. The discussion integrates relevant literature, highlighting the pivotal role of SCM strategies in enhancing productivity, fulfilling customer desires, and increasing competitiveness. This study also underscores the critical link between financial performance, value creation, and competitive advantage. Overall, this study contributes to the ongoing development of practical SCM knowledge by providing valuable insights for MSMEs seeking to navigate the challenges of an evolving business landscape.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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