Evaluating blockchain technology for contract farming in Tanzania: A task-technology fit analysis
Bibliographic record
Abstract
This study employs Task-Technology Fit (TTF) theory to evaluate the alignment between blockchain technology capabilities and contract farming tasks in Tanzania’s Singida District, examining technological suitability and implementation requirements for improving agricultural operations. The study utilizes a mixed-methods approach, combining quantitative and qualitative data from 100 stakeholders (60 farmers, 20 agricultural officers, 15 agribusiness representatives, and 5 government officials). Data collection involved structured surveys, in-depth interviews, and focus group discussions, analyzed through the TTF framework to assess technology-task alignment and implementation factors. Results reveal strong technology-task fit in contract creation (9/10), payment processing (9/10) and record-keeping (9/10), with blockchain’s smart contracts and immutable ledger capabilities effectively addressing current operational inefficiencies. However, significant implementation challenges exist, including infrastructure gaps (45%) and varying readiness levels between urban (7.8/10) and rural (5.2/10) areas. Stakeholder acceptance ranges from 92% (farmers) to 78% (government officials), indicating the need for targeted implementation strategies. This research presents the first comprehensive TTF analysis of blockchain technology in Tanzania’s agricultural context, integrating technical alignment assessment with implementation readiness evaluation. The findings provide evidence-based guidance for policymakers and stakeholders considering blockchain adoption in developing agricultural economies.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".