Whither business-to-business electronic commerce in developing economies? The case of the South African manufacturing sector
Bibliographic record
Abstract
Business-to-business electronic commerce has become a priority area for many international development organisations, particularly since concerns about the ‘digital divide’ have put the policymaking spotlight on the connection between ICTs and industrial development policies. This paper aims to explore the current state and likely future direction of B2B e-commerce in the South African manufacturing sector. The empirical research is based on 120 firm-level interviews, and 31 personal interviews with industry experts. The results suggest that B2B e-commerce is in an embryonic stage in the South African manufacturing sector, and technology and market dynamics are still casting its basic shape. The ability to realise efficiency gains in the B2B electronic marketplace will largely hinge on the climate of confidence and trust that businesses are able to create in their relations with their suppliers and customers. We argue that policy decisions will have a major impact on the kind of environment in which e-commerce will develop and should therefore be crafted with due recognition of its fragile and evolving nature.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".