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Record W4312185343 · doi:10.5267/j.uscm.2022.9.016

The effect of key user capability on supply chain digital and flexibility in improving financial performance

2022· article· en· W4312185343 on OpenAlexvenueno aff
Sautma Ronni Basana, Sahnaz Ubud, Mariana Ing Malelak, Zeplin Jiwa Husada Tarigan

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Supply chainKey (lock)Process managementSupply chain managementComputer scienceService managementBusinessFunctional integrationRisk analysis (engineering)Industrial organizationMarketingOperating systemEconomics

Abstract

fetched live from OpenAlex

Organizational competitiveness is enhanced by implementing supply chain integration. Organizations, through information technology, can integrate internal and external cross-functional. The team assigned to run the integration system in its function is designated as the key user who can implement and maintain an ongoing basis. Key user capabilities are needed to maintain a digital supply chain in information technology systems to integrate internally and externally. Data analysis using Partial least squares (PLS) on 89 hotel organizations with a one-star category or more shows that key user capability significantly affects internal cross-functional integration (β = 0.728) and external cross-functional integration (β = 0.127). Key user capability has an impact on supply chain flexibility (β = 0.370) while internal cross-functional integration influences increasing supply chain flexibility (β = 0.373) and financial performance (β = 0.421). External cross-functional integration increases supply chain flexibility (β = 0.316) and financial performance (β = 0.441). Lastly, supply chain flexibility impacts increasing financial flexibility (β = 0.192). The research contributes enrichment to the theory of digital supply chain and practical contribution to enlighten top management in information technology investment.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2022
Admission routes1
Has abstractyes

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