Blockchain in Agricultural Cooperative Supply Chains
Description
Data Description for Repository Submission Research Hypothesis This study explores how socio-demographic and organizational factors affect the adoption of blockchain-based traceability systems in agricultural cooperatives. It hypothesizes that education level enhances perceived usefulness, age influences adoption priorities, and perceived usefulness mediates the relationship between supply chain challenges and adoption intention. Together, these hypotheses examine how trust, transparency, and capacity-building shape technology acceptance in cooperative structures. What the Data Show Evidence from the 15-month blockchain pilot at the S.S. Ödemiş Bademli Agricultural Development Cooperative (Türkiye) supports these assumptions. Education significantly increased perceived usefulness (η² = 0.545, p < .01), while younger members valued traceability and older ones prioritized efficiency. Capacity-building and infrastructure were rated more important than financial incentives. Perceived usefulness strongly correlated with adoption intention (r = 0.78, p < .01). System telemetry confirmed 99.7 % uptime, 94 % fewer manual errors, and 35 % faster task completion compared with pre-blockchain processes. Interpretation and Notable Findings Results emphasize that trust, transparency, and digital literacy are key to blockchain adoption in cooperatives. The proposed Community-Enhanced Technology Acceptance Model (CE-TAM) extends traditional TAM by integrating cooperative governance factors such as shared trust and participation. Findings suggest that social cohesion can be as critical as technical infrastructure in digital transformation. Data Nature and Use Data were collected through structured surveys (5-point Likert scales), semi-structured interviews, and blockchain system logs (Polygon Amoy testnet + MySQL backend). All participants provided informed consent, and no personal data were stored, ensuring KVKK and GDPR compliance. The dataset includes variable labels, codebook, and a README explaining structure and analysis methods. Researchers can reuse the data for replication, meta-analysis, or benchmarking blockchain performance in cooperative settings. Aggregated datasets are available at the designated repository (DOI forthcoming).
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Steps to reproduce
Data Collection and Methodological Outline The dataset was obtained through a mixed-method research design combining field surveys, semi-structured interviews, and system-generated operational data. Primary data were collected at the S.S. Ödemiş Bademli Agricultural Development Cooperative in Türkiye between 2023 and 2024 as part of the doctoral research project Efficiency-Led Digital Transformation of an Agricultural Cooperative via Blockchain. Surveys were conducted with 25 cooperative members and employees using a structured questionnaire built on 5-point Likert scales. The items measured perceived usefulness, supply-chain challenges, adoption intentions, and trust and transparency dimensions. The questionnaire was pre-tested for internal consistency (Cronbach’s α = 0.89). Responses were entered into a MySQL database through a web-based interface, with data validation routines to prevent duplication or missing entries. Interviews with eight participants provided qualitative insights into the cooperative’s organizational dynamics, challenges in digital adaptation, and expectations from blockchain systems. Interviews were recorded with consent, transcribed verbatim, and coded using NVivo 12 software. System data were collected directly from the blockchain traceability platform deployed on the Polygon Amoy testnet. Metrics such as transaction latency, block confirmation time, and uptime were retrieved using PHP-based Infura API calls and stored as JSON logs. These logs were synchronized with MySQL tables to align blockchain events with field observations. All analyses were performed using Python scripts (pandas, matplotlib) for data cleaning and visualization. No personally identifiable data were stored, ensuring compliance with KVKK (Turkish Personal Data Protection Law) and GDPR. This combination of quantitative surveys, qualitative interviews, and real-time blockchain telemetry enables full reproducibility of results and provides a comprehensive understanding of socio-technical adoption processes within agricultural cooperatives.
Institutions
- Ankang University