THE EFFECT OF SUPPLIER SELECTION, SUPPLIER DEVELOPMENT AND INFORMATION SHARING ON SME’s BUSINESS PERFORMANCE IN SEDIBENG
Bibliographic record
Abstract
In recent times, logistics and supply chain management (SCM) have become important sources of sustainable competitive advantage to firms. However, the roles of logistics and SCM are still influenced by the value chain approach. Consequently, there are factors that have not been given enough attention in the supply chain literature. Realising this issue, the study examines the influence of practices such as supplier selection, supplier development and information sharing on the SMEs business performances in the Sedibeng district. A quantitative research survey was conducted among 300 SME owners/ managers. SPSS 22.0 was used to analyse the data. AMOS 24.0 was used to perform confirmatory factor analysis. Structural path modelling (SEM) was conducted to assess the proposed model fit and to test the statistically significant relationship of the hypotheses. The results of the study show significant relationships amongst the practices: supplier selection, supplier development and information sharing to improve business performance within SMEs in the targeted FMCG industry. This study contributes to the body of knowledge by providing a research framework that can be adopted to enhance SMEs performance as well as providing practical recommendations based on the research findings for SMEs and for future research. Furthermore, as one of the first studies evaluating the influence of practices such as supplier selection, supplier development and information sharing on the SMEs business performances in the Sedibeng district, it has generated new insights and outlines strategic reasons for SME owners and managers to improve on their business relationships across the value chain. Key Words: Supplier selection, Information sharing, Supplier development, Business performance
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".