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Data-Driven Volunteer Matching for Rural E-Commerce Service Delivery

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  • Weiqiang Zou

    (Guangdong Polytechnic Normal University, China)

Abstract

Smallholder farmers in remote regions face challenges accessing e-commerce services due to limited digital literacy and inadequate support. Volunteer-based programs can bridge this gap, but inefficient resource allocation hinders effectiveness. This study proposes an integrated agricultural information system leveraging big data analytics to optimize volunteer resource matching in rural e-commerce. Using multi-source data from e-commerce platforms, volunteer registries, and farmer requests, the authors developed a dynamic model incorporating weighted similarity, skill-demand alignment, and priority queuing. A pilot in central and western China showed significant improvements: 82.3% matching accuracy, 41% reduction in response time, and up to 89% user satisfaction. The findings demonstrate the potential of data-driven systems to enhance service delivery in agricultural value chains, improving last-mile support for smallholder farmers and contributing to rural digital transformation.

Suggested Citation

  • Weiqiang Zou, 2025. "Data-Driven Volunteer Matching for Rural E-Commerce Service Delivery," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global Scientific Publishing, vol. 16(1), pages 1-15, January.
    • Handle: RePEc:igg:jaeis0:v:16:y:2025:i:1:p:1-15
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