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Record W4328025638 · doi:10.5267/j.uscm.2023.1.005

Digital supply chain adoption: An empirical result from food industry

2023· article· en· W4328025638 on OpenAlexvenueno aff
Basem Y. Barqawi, Motteh S. Al Shibly, Mahmoud Hussein Abu Joma, Malek Alharafsheh, Salman M Abulehyeh

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainBusinessSupply chain managementSupply chain risk managementMarketingIndustrial organizationService managementDemand chainSample (material)

Abstract

fetched live from OpenAlex

The aim of this study is to identify the benefits of digital supply chain and explore the effects of these benefits of the adoption of digital supply chain. The study was conducted using data collected by a questionnaire from a sample consisting of supply chain informant employees from companies in the food industry. Three benefits were selected for the current study, which are supply chain agility, organizational performance, and supply chain risk management. The results showed that digital supply chains have numerous benefits from which these three benefits have significant positive effects on digital supply chain adoption. Therefore, it was concluded that companies’ adoption of digital supply chains depends on a bundle of benefits not only related to the supply chain itself such as supply chain agility and risk management but also incorporates the company as a whole in terms of its organizational performance. It was recommended that companies should recognize the benefits of digital supply chains and make their decisions based on the desired outcomes of DSCs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.259
Teacher spread0.227 · 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 designNot applicable
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

Citations4
Published2023
Admission routes1
Has abstractyes

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