Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology—Mediating Effects of Attitude
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Based on the technology acceptance model and technology–organization–environment framework, this study has developed a new model to explain the adoption of agricultural information technology (AIT) by farmer cooperatives from the combination perspective of individual and organization using four factors: cooperatives’ factors, environmental factors, technological factors, and leaders’ attitude towards AIT. Data was collected from a survey of farmer cooperatives and was analyzed using structural equation modelling. The model showed a good fit with findings indicating that the cooperatives’ and environmental factors positively influence the intentions of farmer cooperatives to adopt AIT. Again, the results depict that the technological factors have no significant effect. Leaders’ attitude towards AIT has a mediating effect on the relationships between the environmental, the cooperatives’, as well as the technological factors and the intentions to adopt AIT. The results will guide promotional strategies by technology providers and help government agencies better optimize policies to promote widespread use of AIT.
KeywordsFarmer cooperatives Intention to adopt Agricultural information technology Structural equation modelling
This work was financially supported by the Key projects of the National Social Science Fund of China (No. 16AJL006); Jiangsu Provincial Education Department Project(No. 2017SJB1064); a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No.PAPD-2014-37).
- Chong, A., & Chan, F. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry.Expert Systems with Applications 39, 8645–8654.Google Scholar
- Dong, H., Wang, Y.Q. (2018). Analysis of the status and problems concerning the development of farmer professional cooperatives in China and possible countermeasures. Journal of Yunnan Minzu University (Social Sciences), 35(2), 106–109 (Chinese). https://doi.org/10.13727/j.cnki.53-1191/c.2018.02.015.
- Fishbein M, Ajen I. (1975). Theory and Research[M]. MA: Belief Attitude Intention and Behavior: An Introduction to Addison-Wesley.Google Scholar
- Gholami, R., Molla, A., Goswami, S., & Brewster, C. (2018). Green information systems use in social enterprise: The case of a community-led eco-localization website in the west midlands region of the UK. Information Systems Frontiers, 20, 1345–1361. https://doi.org/10.1007/s10796-016-9733-z.CrossRefGoogle Scholar
- Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling. Thousand Oaks: Sage Publications.Google Scholar
- Higgins, C. (1995). The partial least squares (pls) approach to causal modeling: Personal computer adoption and use an illustration. Technology Studies, 2, 284–324.Google Scholar
- Kim, K., & Prabhakar, B.. (2000). Initial Trust, Perceived Risk, and the Adoption of Internet Banking. Twenty First International Conference on Information Systems. Association for Information Systems. https://dl.acm.org/citation.cfm?id=359809
- Lei, L. (2015). An Empirical Study of the Impact of the Local Government Behavior upon the Development of Regional Brands. Journal of Lanzhou University (Social Sciences), 43(01), 112–119 (Chinese). https://doi.org/10.13885/j.issn.1000-2804.2015.01.015.
- Lian J W, Yen D C, Wang Y T (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital.Google Scholar
- Liang, H. (2009). Innovation and transformation of agricultural science and technology based on farmers' professional cooperatives. Social Scientist, 02, 69–72 (Chinese).Google Scholar
- Lin, D., Lee, C. K. M., & Lin, K. (2016). Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain. IEEE International Conference on Industrial Engineering & Engineering Management. https://doi.org/10.1109/IEEM.2016.7797948.
- Rogers, E. M. (1983). Diffusion of innovations [M]. New York: The Free Press.Google Scholar
- Scupola, A. . (2004). Adoption of E-commerce in small and medium size Enterprises in Australia. Americas Conference on Information Systems. DBLP. https://aisel.aisnet.org/amcis2004/49/.
- Seeman, E., & Gibson, S. (2009). Predicting acceptance of electronic medical records: Is the technology acceptance model enough? SAM Advanced Management Journal, 74(4), 21–26 http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=5&sid=3135bcc7-ec06-42f7-9379-95e624c62d33%40sessionmgr4009.
- Shalini, C., & Nanda, K. K. (2018). Exploring factors influencing organizational adoption of augmented reality in e-commerce: Empirical analysis using technology-organization-environment model. Journal of Electronic Commerce Research, 19(3), 237–265.Google Scholar
- Tian, Z. F. (2013). Application of information Technology in Agriculture in developed countries and its enlightenment. World agriculture, 06, 45–48 (Chinese). https://doi.org/10.13856/j.cn11-1097/s.2013.06.013.CrossRefGoogle Scholar
- Tornatzky, L. G., & Fleischer, M. O. (1990). The process technological innovation [M]. Lexington Mass: Lexington books.Google Scholar
- Xian, J., & Zhaoyou, L. (2008). The basic connotation and behavior of Enterprise technology innovation adoption. Science and Technology Management Research, 6, 13–15 (Chinese).Google Scholar
- Zhao, C., Yang, X., Li, B., Li, M., & Yan, H. (2018). The retrospect and Prospect of agricultural information Technology in China. Agricultural Science and Engineering in China, 30(04), 3–7 (Chinese). https://doi.org/10.19518/j.cnki.cn11-2531/s.2018.0083.CrossRefGoogle Scholar