The Role of the Product-Process Matrix in the Consumption of Agricultural Finance by Smallholder Farmers: The Case of Centenary Bank in Uganda
Bibliographic record
Abstract
This study analyzes the contribution of the supply-based product and process matrix to smallholder farmers’ consumption of agricultural finance. The authors adopted a qualitative research design applying a semi structured interview guide to provide in-depth content about the products and processes of agricultural finance delivery. Data were collected from various bank staff, smallholder farmers, and documentary reviews. The results revealed that value chain financing is not a panacea for smallholder farmers to borrow and use funds to engage into higher agricultural value chain activities. In addition, product pricing and duration alongside inefficient and linear product delivery processes hinder smallholder farmers’ adoption of agricultural finance. Thus, inappropriate products and inefficient processes discourage smallholder farmers from exploiting agricultural finance. The methods of engagement are premised on respondents’ narratives implying that one person’s experience may limit the representation of multiple viewpoints. Central banks and financial institutions seeking to increase smallholder farmers’ use of agricultural finance must develop policies to understand end-user needs and create seamless delivery processes to deliver agricultural financing. This is one of the few studies that comprehensively and qualitatively assesses how the combination of pricing and products addresses smallholder farmers’ financing needs in the commercial banking context. Thus, this study contributes new ideas for increasing the consumption of agricultural finance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".