THE EFFECT OF SUPPLIER SELECTION, SUPPLIER DEVELOPMENT AND INFORMATION SHARING ON SME’s BUSINESS PERFORMANCE IN SEDIBENG
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it