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Record W4391062651 · doi:10.5267/j.uscm.2024.1.004

Linking supply chain management practices with supply chain performance and food and beverage: Evidence from SMEs' competitive advantage

2024· article· en· W4391062651 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 · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingSupply chainSupply chain managementCompetitive advantageBusinessLikert scaleData collectionMarketingSample (material)Descriptive statisticsStructural equation modelingSmall and medium-sized enterprisesComputer scienceStatistics

Abstract

fetched live from OpenAlex

The development of technology and science in the Industrial Revolution 4.0 era requires companies to increase effectiveness and efficiency to maintain a competitive advantage. Supply chain management is the integration of business processes involving end customers and key suppliers whose function is to provide value to customers and stakeholders by providing products, services, and information. Implementation of supply chain management is considered an operational function or company activity that greatly determines the effectiveness and efficiency of the supply chain. This research aims to analyze the relationship between the implementation of supply chain management and the performance of small and medium enterprises (SMEs), the relationship between supply chain management, and the relationship between competitive advantage and the performance of SMEs. The research uses a quantitative descriptive approach. The quantitative approach is data in the form of numbers which are generally arranged through structured questions. The questionnaire contains statement items designed using a Likert scale from 1 to 7. The data in this study uses cross-sectional data because the data collection was carried out in a certain period. The data was obtained from distribution of online questionnaires via social media. The unit of analysis used in this research is the owners/managers of SMEs in Indonesia. The sampling technique used in this research is a non-probability sampling technique, namely purposive sampling. The total sample for this research was 432 respondents. The data management used in this research is the Structural Equation Model (SEM) method, which is a collection of statistical testing techniques on a series of relatively complex relationships, simultaneously. The data processing tool is SmartPLS 3.0. The SEM technique is used to examine and justify different hypotheses of the survey. Hypothesis testing is carried out by comparing the p-value with a confidence level (alpha) of 5% (𝛼 = 0.05). The results of this research show that the implementation of supply chain management has a positive effect on the performance of SMEs. In addition, supply chain management has a significant effect on competitive advantage while competitive advantage has a significant effect on SME performance. This research shows that supply chain management has a positive influence on competitiveness both with performance and competitiveness. Descriptive analysis found that supply chain management indicators have sufficient value and have a big impact on performance and competitiveness.

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), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0000.001
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 head0.244
Teacher spread0.230 · 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