Farmers' green technology adoption: Implications from government subsidies and information sharing

Author:

Hong Xianpei1ORCID,Chen Ying‐Ju2ORCID,Gong Yeming3ORCID,Wang Hua1

Affiliation:

1. College of Economics and Management Huazhong Agricultural University Wuhan China

2. Department of Information Systems, Business Statistics and Operations Management The Hong Kong University of Science and Technology Hong Kong

3. Artificial Intelligence in Management Institute Emlyon Business School Écully France

Abstract

AbstractWhile the previous literature on green technology adoption has not fully considered information sharing, we consider the impact of demand information sharing on the adoption of green technologies by risk‐averse farmers in a vertical agricultural supply chain. We find that government subsidies and information sharing do not always promote farmers' adoption of green technologies. The accuracy of the information plays a vital role in promoting farmers' adoption of green technologies; however, the increased green technology adoption induced by more accurate information may be detrimental to farmer welfare in the presence of production diseconomies. Information sharing can reduce the amount of government subsidies for promoting green technology adoption, thereby suggesting the substitutable role of information and monetary instruments. Nonetheless, information‐sharing may lead to lower water savings and thus should be adopted with caution. Risk aversion has a nontrivial impact on agricultural technology adoption: farmers are more likely to adopt traditional agricultural technologies when their risk aversion is either very low or very high. Finally, we validate our decision model with U.S. Department of Agriculture cotton production data and propose management insights to help farmers make appropriate adoption decisions under information asymmetry and risk‐averse attitudes.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

Management Science and Operations Research,Ocean Engineering,Modeling and Simulation

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