Is digital transformation the key to agricultural strength? A novel approach to productivity and supply chain resilience

被引:2
作者
Sargani, Ghulam Raza [1 ,2 ]
Wang, Bowen [1 ]
Leghari, Shah Jahan [3 ]
Ruan, Junhu [1 ]
机构
[1] Northwest A&F Univ, Coll Econ & Management, Xianyang, Peoples R China
[2] Sindh Agr Univ Tandojam, Dept Agr Econ, Tandojam, Pakistan
[3] Northwest A&F Univ, Coll Mech & Elect Engn, Xianyang, Peoples R China
来源
SMART AGRICULTURAL TECHNOLOGY | 2025年 / 10卷
基金
中国国家社会科学基金;
关键词
Digital technology adoption; Supply chain resilience; Agricultural productivity; Structural equation modeling; Market access; Agricultural infrastructure; FUTURE; TECHNOLOGY; PRINCIPLES; ADOPTION;
D O I
10.1016/j.atech.2025.100838
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
This study addresses the critical gap in understanding how digital technology adoption effects supply chain resilience and agricultural productivity within China's evolving agrarian sector. While digital technologies promise transformative benefits, their specific impacts on key factors such as infrastructure, market access, government policies, and environmental conditions remain underexplored. Our research aims to uncover the interrelationships between Digital Technology Adoption Rates (DTAR), Agricultural Infrastructure (AI), Market Access (MA), Government Support (GS), and Environmental Factors (EF) to provide actionable insights for enhancing Agricultural Productivity (AP) and Supply Chain Resilience (SCR) in regional contexts. We employed SEM path analysis modeling to evaluate data from agricultural stakeholders across five Chinese provinces (Shaanxi, Sichuan, Anhui, Hubei, and Hunan), representing diverse age groups, farm sizes, and practices. The model assessed hypotheses linking DTAR, AI, MA, GS, and EF to AP and SCR, enabling a systematic assessment of direct and indirect effects. DTAR and AI emerged as pivotal drivers of AP (68% variance explained), which subsequently strengthened MA (73% variance) and SCR (62% variance). GS and EF were also critical, directly enhancing SCR and underscoring the role of policy and environmental stability. Regional disparities were evident: Shaanxi (24.95%), Sichuan (20.79%), and Hubei (19.75%) exhibited higher contributions, attributed to varying technology adoption rates, farm sizes, and production scales, signaling the need for region-specific digital strategies. This study is among the first to apply SEM in analyzing digital agriculture's role in supply chain resilience, offering a holistic framework that integrates technological, infrastructural, policy, and environmental dimensions. It advances theory by elucidating how digital integration mediates productivity and resilience, while practical contributions include targeted recommendations for scaling digital infrastructure and tailoring policies to regional conditions. Policymakers and stakeholders can leverage these insights to fortify supply chains against global and local disruptions, fostering sustainable agricultural transformation.
引用
收藏
页数:12
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