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The Antecedents of the Willingness to Adopt and Pay for Climate-Smart Agricultural Technology Among Cooperatives in China

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  • Xiaoxue Feng

    (Department of Decision Science, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Suhaiza Zailani

    (Department of Decision Science, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Climate-smart agricultural technology (CSAT) has been recognized as an effective agricultural technology for mitigating or eliminating climate change damages to agricultural production. The adoption of CSAT by Chinese farmers has also increased significantly, driven by the government. However, the range of technologies adopted is very limited to several types, such as piped irrigation, high-yielding seeds, and organic fertilizers. In this study, four reasons for the adoption of CSAT were summarized through a literature review and semi-structured interviews, and antecedents and outcomes were conceptualized through the Behavioral Reasoning Theory (BRT) model and the concept of willingness to pay (WTP). The data from 308 farmers’ cooperatives were analyzed and it was found that three reasons—policy/government support, recommendations from opinion leaders, and extension services—have positive significant impacts on the willingness of farmers’ cooperatives to adopt CSAT. However, perceived usefulness was also observed to have no significant impact on the willingness of farmers’ cooperatives to adopt CSAT because the significant level of the t-value was only 1.767. In addition, the value of openness to change was proved to have significant positive effects on policy/government support, recommendations from opinion leaders, extension services, and perceived usefulness, whereas it had no impact on willingness to adopt CSAT. However, reasons for the adoption (policy/government support, recommendations from opinion leaders, and extension services) were confirmed to have a mediating effect between the value of openness to change and willingness to adopt CSAT, which means that the value of openness to change can indirectly affect the willingness to adopt CSAT by influencing the views of farmers’ cooperatives on the variables of reasons for the adoption of CSAT. More widespread, diverse, and multi-level CSAT adoption requires in-depth publicity and promotion and multi-channel recommendations, both at the government level and at the technology company level.

Suggested Citation

  • Xiaoxue Feng & Suhaiza Zailani, 2024. "The Antecedents of the Willingness to Adopt and Pay for Climate-Smart Agricultural Technology Among Cooperatives in China," Sustainability, MDPI, vol. 17(1), pages 1-31, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:19-:d:1551705
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    1. Eliana Lima & Thomas Hopkins & Emma Gurney & Orla Shortall & Fiona Lovatt & Peers Davies & George Williamson & Jasmeet Kaler, 2018. "Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    2. Theodrose Sisay & Kindie Tesfaye & Mengistu Ketema & Nigussie Dechassa & Mezegebu Getnet, 2023. "Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
    3. Federica Caffaro & Eugenio Cavallo, 2019. "The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy," Agriculture, MDPI, vol. 9(5), pages 1-13, May.
    4. Alvin Chandra & Karen E. McNamara & Paul Dargusch, 2018. "Climate-smart agriculture: perspectives and framings," Climate Policy, Taylor & Francis Journals, vol. 18(4), pages 526-541, April.
    5. Sahu, Aditya Kumar & Padhy, R.K. & Dhir, Amandeep, 2020. "Envisioning the future of behavioral decision-making: A systematic literature review of behavioral reasoning theory," Australasian marketing journal, Elsevier, vol. 28(4), pages 145-159.
    6. Jiang, Meishan & Li, Jingrong & Mi, Yunsheng, 2024. "Farmers’ cooperatives and smallholder farmers’ access to credit: Evidence from China," Journal of Asian Economics, Elsevier, vol. 92(C).
    7. Jui-Hsiung Chuang & Jiun-Hao Wang & Yu-Chang Liou, 2020. "Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan," IJERPH, MDPI, vol. 17(19), pages 1-8, October.
    8. Gerli, Paolo & Clement, Jessica & Esposito, Giovanni & Mora, Luca & Crutzen, Nathalie, 2022. "The hidden power of emotions: How psychological factors influence skill development in smart technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    9. Leslie Lipper & David Zilberman, 2018. "A Short History of the Evolution of the Climate Smart Agriculture Approach and Its Links to Climate Change and Sustainable Agriculture Debates," Natural Resource Management and Policy, in: Leslie Lipper & Nancy McCarthy & David Zilberman & Solomon Asfaw & Giacomo Branca (ed.), Climate Smart Agriculture, pages 13-30, Springer.
    10. Gupta, Anil & Arora, Neelika, 2017. "Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 1-7.
    11. Daeseong An & Seonggoo Ji & Ihsan Ullah Jan, 2021. "Investigating the Determinants and Barriers of Purchase Intention of Innovative New Products," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    12. Cho, Youngsang & Hwang, Junseok & Lee, Daeho, 2012. "Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 97-106.
    13. Rosalia Filippini & Maria Elena Marescotti & Eugenio Demartini & Anna Gaviglio, 2020. "Social Networks as Drivers for Technology Adoption: A Study from a Rural Mountain Area in Italy," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    14. Yan Shi & Abu Bakkar Siddik & Mohammad Masukujjaman & Guangwen Zheng & Muhammad Hamayun & Abdullah Mohammed Ibrahim, 2022. "The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    15. Junfang Zhao & Dongsheng Liu & Ruixi Huang, 2023. "A Review of Climate-Smart Agriculture: Recent Advancements, Challenges, and Future Directions," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    16. Westaby, James D., 2005. "Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 98(2), pages 97-120, November.
    17. Qiao Liang & Rongrong Bai & Zhi Jin & Linlin Fu, 2023. "Big and strong or small and beautiful: Effects of organization size on the performance of farmer cooperatives in China," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 196-213, January.
    18. Streukens, Sandra & Leroi-Werelds, Sara, 2016. "Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results," European Management Journal, Elsevier, vol. 34(6), pages 618-632.
    19. Dash, Ganesh & Paul, Justin, 2021. "CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
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