Keywords
digital agriculture
digital governance
rural transformation
structural equation modeling (SEM)
transaction costs
How to Cite
Abstract
This study advances the agricultural systems literature by theorizing and empirically validating the co-evolution of digital technologies and institutional governance in rural transformation. While prior research has examined precision agriculture, rural e-commerce, and digital governance separately, this paper develops a unified Technological-Institutional Co-Evolution Model that positions digital governance as an endogenous, mediating force within agricultural innovation systems. Using a stratified multi-actor dataset (N = 320) of farmers, agri-tech entrepreneurs, and rural officials, the study applies a mixed-methods approach combining instrumental variable (2SLS) estimation and structural equation modeling (SEM) to address endogeneity and estimate both direct and indirect effects. Results show that digital technology adoption significantly increases perceived agricultural productivity (β = 0.64, p < 0.01) and reduces perceived operational costs (β = −0.51, p < 0.01). However, its impact on market integration is not independent; it depends on institutional capacity. Digital governance plays a significant mediating role (indirect β = 0.22, p < 0.01), acting as a “trust infrastructure” that lowers transaction costs, reduces information asymmetries, and bridges institutional gaps in rural economies. These findings challenge techno-deterministic perspectives by demonstrating that technology diffusion alone cannot ensure inclusive agricultural transformation. Instead, outcomes depend on the alignment between technological adoption, governance modernization, and human capital development, particularly in contexts with substantial digital skills gaps (60%). The study contributes to Agricultural Innovation Systems theory by integrating institutional and technological dimensions and offers policy insights that emphasize coordinated socio-technical interventions over fragmented, technology-driven approaches.
References
Barrett, C. B., Benton, T., Fanzo, J., Herrero, M., Nelson, R. J., Bageant, E., Buckler, E., Cooper, K., Culotta, I., Fan, S., Gandhi, R., James, S., Kahn, M., Lawson-Lartego, L., Liu, J., Marchall, Q., Mason-D’Croz, D., Mathys, A., Mathys, C., … Wood, S. (2022). Socio-technical innovation bundles for agri-food systems transformation. Nature Sustainability, 5, 837–845.
https://doi.org/10.1038/s41893-022-00963-5
Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
Deininger, K., & Feder, G. (2009). Land registration, governance, and development: Evidence and implications for policy. The World Bank Research Observer, 24(2), 233–266. https://doi.org/10.1093/wbro/lkp007
Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.
https://doi.org/10.1126/science.1183899
Hall, A., Mytelka, L., & Oyeyinka, B. (2005). Innovation systems: Implications for agricultural policy and practice. Institutional Learning and Change Initiative. https://doi.org/10.22004/ag.econ.52512
Heeks, R. (2006). Implementing and managing eGovernment: An international text. SAGE Publications.
https://doi.org/10.4135/9781446220191
Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New
contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences, 90–91(1), 100315. https://doi.org/10.1016/j.njas.2019.100315
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Ma, W., Nie, P., Zhang, P., & Renwick, A. (2020). Impact of Internet use on economic well-being of rural households: Evidence from China. Review of Development Economics, 24(2), 503–523. https://doi.org/10.1111/rode.12645
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple-mediator models. Behavior Research Methods, 40, 879–891. https://doi.org/10.3758/BRM.40.3.879
Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research and innovations. Agricultural Systems, 172, 47–59. https://doi.org/10.1016/j.agsy.2018.01.022
Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews, & J. H. Stock (Eds.), Identification and inference for econometric models: Essays in honor of Thomas Rothenberg (pp. 80–108). Cambridge University Press. https://doi.org/10.1017/CBO9780511614491.006
van Dijk, J. (2020). The digital divide. Polity Press.
Williamson, O. E. (1985). The economic institutions of capitalism: Firms, markets, relational contracting. Free Press.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
World Bank Group. (2021). World development report 2021: Data for better lives.
https://doi.org/10.1596/978-1-4648-1600-0
Zeng, Y., Jia, F., Wan, L., & Guo, H. (2017). E-commerce in agri-food sector: A systematic literature review. International Food and
Agribusiness Management Review, 20(4), 439–459. https://doi.org/10.22434/IFAMR2016.0156
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Paraschos Maniatis