Exploring supply chain business bullying of small and medium-sized business suppliers by dominant buyers in the apparel retail sector in Gauteng
Bibliographic record
Abstract
Background: Issues relating to dominant behaviour and bullying practices in supply chains are mostly not reported. Some evidence exists of unfair business practices in the apparel retail sector, but the extent and nature of such practices in South Africa, as well as the business implications for small and medium-sized enterprises (SMEs), have not been researched. The reason could be the sensitive nature of these issues and possible adverse implications for the SMEs supplying apparel to the retailers in Gauteng. Aim: The primary research objective is to determine the incidence and type of supply chain dominance experienced by SMEs in the retail apparel sector in Gauteng, as well as the business implications of such dominant behaviour and how SMEs cope with it. Setting: The setting for this study was the business premises of SME apparel suppliers in Gauteng. Methods: Qualitative semi-structured in-depth interviews were conducted with seven SMEs that were apparel suppliers to the large retailers in Gauteng, to obtain the supplier perspective. Results: Six of the seven SMEs had experienced some form of bullying behaviour by large retailers in the apparel sector, such as late payments and long payment terms. For some of these SMEs, it had serious financial implications. Meaningful insight is provided into this supplier–buyer relationship between SMEs and large retailers in the apparel sector in Gauteng. Conclusion: This is the first study in South Africa investigating supply chain dominance, in particular supply chain bullying of SME suppliers through unfair business practices by dominant buyers in the apparel sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".