Blockchain technology and power asymmetries in Tanzanian agricultural supply chains
Bibliographic record
Abstract
This study investigates the role of power relations in agricultural supply chains and their impact on sustainability, market access, and technology adoption. Using the Resource Dependence Theory (RDT), this study explains the influence of power asymmetries between stakeholders, such as large buyers and smallholder farmers, on the diffusion of emerging technologies in the Agricultural Supply Chain (ASC) in Tanzania. A mixed-methods approach was employed, combining field surveys in three regions of Tanzania with a comprehensive literature review. The surveys targeted key groups in the ASC, including smallholder farmers, large-scale farmers, buyers, transporters, and local and national leaders. Quantitative data were analysed using descriptive statistics and correlation analyses. The findings show that buyers exert disproportionate influence over pricing (mean score: 5.61), while smallholders, youth, and women remain marginalised (mean scores below 4.0). Large-scale farmers and buyers heavily influence pricing decisions and technology adoption. Correlation analysis reveals a significant disparity, with smallholders and buyers exhibiting almost no pricing alignment (r = 0.002), whereas buyers and national leaders show a close alignment (r = 0.396). Strong AMCOS reported higher stakeholder influence than weak ones (mean 5.76 vs. 4.85). The results signal critical barriers to equitable technology adoption. We argue that blockchain technology can intervene by decentralising information access to rebalance these power asymmetries and enhance market inclusion. The study offers empirical insights for designing inclusive, technology-enabled agricultural markets. The results help policymakers develop targeted interventions to enable smallholders, women, and other disadvantaged groups to access and adopt technologies. Comparative research in different regions could further expand the understanding of how power dynamics and technology adoption evolve.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".