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Social embeddedness and agricultural technology diffusion from the perspective of scale differentiation – a case study from China

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  • Li, Kai
  • Li, Qi

Abstract

Social embeddedness always plays important role in facilitating agricultural technology diffusion. However, in China, dramatic changes have occurred in the social embeddedness of rural households in the transition from ‘acquaintance society’ to ‘semi-acquaintance society’. Could this be the reason for the debate over the role of social embeddedness? What are the differences in the role of social embeddedness between farmers with different land scales? Based on survey data from 583 rural households from Zhejiang Province, China, we used an endogenous switching regression model to answer these questions. The results indicated there are significant differences in social embeddedness between large- and small-scale households. Although the influence of social embeddedness on technology adoption remains significant, its function is significantly different between small- and large-scale farmers. To avoid technological lock-in for small-scale farmers, the government should strengthen the information push and expand the coverage of environmental-friendly agricultural subsidies for them.

Suggested Citation

  • Li, Kai & Li, Qi, 2022. "Social embeddedness and agricultural technology diffusion from the perspective of scale differentiation – a case study from China," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 26(01), August.
    • Handle: RePEc:ags:ifaamr:335082
    DOI: 10.22004/ag.econ.335082
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    1. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    2. Patrick S. Ward & Valerien O. Pede, 2015. "Capturing social network effects in technology adoption: the spatial diffusion of hybrid rice in Bangladesh," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), pages 225-241, April.
    3. Gloy, Brent A. & Akridge, Jay T. & Whipker, Linda D., 2000. "Sources Of Information For Commercial Farms: Usefulness Of Media And Personal Sources," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 3(2), pages 1-16.
    4. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    5. Senyolo, Mmapatla Precious & Long, Thomas B. & Omta, Onno, 2021. "Enhancing the adoption of climate-smart technologies using public-private partnerships: lessons from the WEMA case in South Africa," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5), May.
    6. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    7. Mary Thuo & Alexandra Bell & Boris Bravo-Ureta & Michée Lachaud & David Okello & Evelyn Okoko & Nelson Kidula & Carl Deom & Naveen Puppala, 2014. "Effects of social network factors on information acquisition and adoption of improved groundnut varieties: the case of Uganda and Kenya," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 31(3), pages 339-353, September.
    8. Magnan, Nicholas & Spielman, David J. & Lybbert, Travis J. & Gulati, Kajal, 2015. "Leveling with friends: Social networks and Indian farmers' demand for a technology with heterogeneous benefits," Journal of Development Economics, Elsevier, vol. 116(C), pages 223-251.
    9. Kallis, Giorgos & Norgaard, Richard B., 2010. "Coevolutionary ecological economics," Ecological Economics, Elsevier, vol. 69(4), pages 690-699, February.
    10. Awudu Abdulai & Wallace Huffman, 2014. "The Adoption and Impact of Soil and Water Conservation Technology: An Endogenous Switching Regression Application," Land Economics, University of Wisconsin Press, vol. 90(1), pages 26-43.
    11. Michael Lokshin & Zurab Sajaia, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 282-289, September.
    12. Hammond Wagner, Courtney & Cox, Michael & Bazo Robles, José Luis, 2016. "Pesticide lock-in in small scale Peruvian agriculture," Ecological Economics, Elsevier, vol. 129(C), pages 72-81.
    13. Jinhua Xie & Gangqiao Yang & Ge Wang & Wei Xia, 2021. "How Do Network Embeddedness and Environmental Awareness Affect Farmers’ Participation in Improving Rural Human Settlements?," Land, MDPI, vol. 10(10), pages 1-20, October.
    14. Jeffrey H. Dorfman, 1996. "Modeling Multiple Adoption Decisions in a Joint Framework," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 547-557.
    15. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    16. Pramila Krishnan & Manasa Patnam, 2014. "Neighbors and Extension Agents in Ethiopia: Who Matters More for Technology Adoption?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 308-327.
    17. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
    18. Tepic, M. & Trienekens, Jacques H. & Hoste, R. & Omta, S.W.F. (Onno), 2012. "The Influence of Networking and Absorptive Capacity on the Innovativeness of Farmers in the Dutch Pork Sector," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 15(3), pages 1-34, September.
    19. Mark Granovetter, 2005. "The Impact of Social Structure on Economic Outcomes," Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 33-50, Winter.
    20. Abrham Tezera Gessesse & Houjian Li & Ge He & Araya Alemie Berhe, 2018. "Study on farmers land consolidation adaptation intention," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(4), pages 666-682, August.
    21. Ma, Wanglin & Abdulai, Awudu, 2016. "Does cooperative membership improve household welfare? Evidence from apple farmers in China," Food Policy, Elsevier, vol. 58(C), pages 94-102.
    22. Wilson, Paul N. & Kennedy, Ana M., 1999. "Trustworthiness As An Economic Asset," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 2(2), pages 1-15.
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