Impact of farmers’ benefits linking stability on cloud farm platform of company to farmer model
China has formed a new C2F (company-to-farmer) model of internet and agriculture. How to build a sustainable linkage of the C2F platform is important for promoting agricultural industrialization. Based on the cognition theory and internet thinking, we characterized the linkage mechanism and stability framework of the C2F regarding default proportion, benefits fairness and benefits gap. Using the logistic regression method, we constructed the impact effect model of benefit links stability based on the farmers’ characteristics, platform cognition and social environment. We found that in the C2F, optimizing farmers’ age structure (17.93%, impact effect), increasing farmers’ income level (16.79%), as well as improving farmers’ education level (14.33%), policy support (11.35%), platform service quantity (9.82%), market volatility (9.11%), platform transaction transparency (9.07%), farmers’ risk tolerance (7.93%), and platform technical guidance effect (3.67%) had a significant impact on reducing default proportion (28.13%) and benefits gap (36.55%), thus heightening benefits fairness (35.32%). The research suggested, we should promote the sustainability of C2F by improving the farmers’ digital ability and platform function, developing innovative linkage mechanisms between companies and farmers, strengthening government guidance, and protecting the policy environment.
Botsman R. (2010): What’s Mine Is Yours: The Rise of Collaborative Consumption". New York, HarperBusiness: 21–23.
Cristobal E., Montegut-Salla Y., Ferrer-Rosell B., Daries-Ramon N. (2020): Rural cooperatives in the digital age: an analysis of the internet presence and degree of maturity of agri-food cooperatives’ e-commerce. Journal of Rural Studies, 74: 55–66. https://doi.org/10.1016/j.jrurstud.2019.11.011
Davis F.D. (1989): Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13: 319–340. https://doi.org/10.2307/249008
Duan J.T. (2017): Thoughts on establishing benefit linking mechanism to promote the development of agricultural industrialization. Farm Economic Management, 6: 32–33.
Espinosa Goded M., Barreiro Hurlé J., Ruto E. (2010): What do farmers want from agri-environmental scheme design? A choice experiment approach. Journal of Agricultural Economics, 61: 259–273. https://doi.org/10.1111/j.1477-9552.2010.00244.x
Faham E., Asghari H. (2019): Determinants of behavioral intention to use e-textbooks: A study in Iran’s agricultural sector. Computers and Electronics in Agriculture, 165: 104935. https://doi.org/10.1016/j.compag.2019.104935
Greene W.H. (2008): Econometric Analysis. New Jersey, Prentice Hall: 983–984.
Hao Z.H. (2004): Analysis on the benefit mechanism of leading enterprises in agricultural industrialization and farmers. Rural Economy, 7: 45–47.
Henten A.H., Windekilde I.M. (2016): Transaction costs and the sharing economy. Digital Policy, Regulation and Governance, 18: 1–15. https://doi.org/10.1108/info-09-2015-0044
Lowder S.K., Skoet J., Raney T. (2016): The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Development, 87: 16–29. https://doi.org/10.1016/j.worlddev.2015.10.041
Liu Y.J., Chen J., Chu X.M. (2016): Analysis of the business mode of "internet + farmer + company": experience from Taobao village. Journal of Northwest Agriculture and Forestry University (Social Sciences), 16: 87–93.
Lin L., Guo H., Bijman J., Heerink N. (2017): The influence of uncertainty on the choice of business relationships: the case of vegetable farmers in china. Agribusiness, 1: 1–18.
Mathur A.K., Lu Y. (1997): Re: "Estimating relative risk functions in case-control studies using a nonparametric logistic regression". American Journal of Epidemiology, 146: 882–883. https://doi.org/10.1093/oxfordjournals.aje.a009207
Muriithi D.K., Kihoro J., Waititu A. (2012): Ordinal logistic regression versus multiple binary logistic, regression model for predicting student loan allocation. Journal of Agriculture Ence & Technology, 14: 191–204.
Marcelo J.C.F., de Souza Filho H.M., Batalha M.O., Rossi F.R. (2015): Farm management information systems (FMIS) and technical efficiency: an analysis of citrus farms in Brazil. Computers and Electronics in Agriculture, 119: 105–111.
Ma W., Abdulai A. (2017): The economic impacts of agricultural cooperatives on smallholder farmers in rural china. Agribusiness, 33: 1–15. https://doi.org/10.1002/agr.21522
Porter M.E., Kramer M.R. (2011): The big idea: creating shared value. Harvard Business Review, 89: 2–17.
Peter W., Franz S., Fabian U., Andreas N., Denisa F., Martin K., Melcher M., Kantelhardt J. (2019): Exploring the relationship between farmers’ innovativeness and their values and aims. Sustainability, 11: 5571.
Taylor S., Todd P. (1995): An integrated model of waste management behavior: A test of household recycling and composting intentions. Environment and Behavior, 27: 603–630. https://doi.org/10.1177/0013916595275001
Williamson O.E. (1987): The economic institution of capitalism: firms, markets, relational contracting. Journal of Comparative Economics, 11: 282–285.
Wilson T.D. (1999): Models in information behavior research. Journal of Documentation, 55: 249–270. https://doi.org/10.1108/EUM0000000007145
Zahri I., Wildayana E., Ak A.T., Adriani D., Harm M.U. (2019): Impact of conversion from rice farms to oil palm plantations on socio-economic aspects of ex-migrants in Indonesia. Agricultural Economics – Czech, 65: 579–586. https://doi.org/10.17221/349/2018-AGRICECON