Digital capital and food agricultural SMEs: Examining the effects on SME performance, inequalities and government role
Bibliographic record
Abstract
This paper provides an explorative and interrogative profile of digital capital on SMEs within the agricultural food sector, focusing on SME farmers. Digital capital is deemed the new capital essential for farmers. The paper examines the opportunities and threats offered by digital capital and explores how it influences agricultural SME performance and how it leads to digital inequalities. The study purposively sampled three South African agricultural provinces and adopted a purposive sampling technique to collect quantitative and qualitative data. With the undoubted contribution of SMEs to social and economic fronts, the study chronicled how digital capital has improved the value chain processes while unearthing the barriers to digital tools access. It emerged that SMEs face many adoption challenges; hence it is debatable to link positive SME performance to digital capital adoption. It emerged that agricultural SMEs mostly adopt complimentary service digital tools, indicating that digital capital is a catalyst for inequalities. While the government has implemented some initiatives to promote digital capital adoption, such interventions remain inadequate. The study contemplates other initiatives that could be adopted to address the barriers SMEs face in this digital era, hence closing the inequalities gap within the industry. SMEs should be subject to public policy support and protection, particularly on digital capital incentives and sponsorship. The government must regulate some digital capital tools which are more harmful than productive.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".