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Record W4210664345 · doi:10.5267/j.uscm.2022.1.005

Knowledge integration and entrepreneurial capabilities for sustainable competitive advantage through supply chain management

2022· article· en· W4210664345 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 · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageBusinessEntrepreneurial orientationSmall and medium-sized enterprisesStructural equation modelingSupply chainSupply chain managementSampling (signal processing)Knowledge managementPopulationBusiness administrationOperations managementIndustrial organizationEntrepreneurshipProcess managementMarketingComputer scienceStatisticsMathematicsEconomics

Abstract

fetched live from OpenAlex

Sustainable Competitive Advantage (SCA) is very much needed in the development of the business world. This study aims to determine the model of increasing the SCA variable with Entrepreneurial Capability (EC) and Knowledge Integration Capability (KIC) directly or through Supply Chain Management (SCM) variables indirectly so that the objectives of SCA in small and medium enterprises (SMEs) can be achieved effectively. The research method used is a quantitative method with a structural model type using the SmartPLS version 3.2 program. The population in this study were all 2,296 administrators and members of IPEMI West Java. The sampling method used is random sampling. Data collection techniques using questionnaires were addressed to 360 respondents and 344 respondents were properly collected. The results show that EC influenced SCA with a T statistics score of 3.971, EC for SCM was 4.858, KIC for EC was 13.874, KIC for SCA was 1.886, KIC against SCM was 7.876, and SCM against SCA was 7.796 and it can be concluded that KIC to SCA can be significant if it is through SCM.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.274
Teacher spread0.259 · 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