Credit supervision and trading strategy of rural e-commerce based on evolutionary game

PurposeThis study investigates the credit supervision issue in rural e-commerce. By studying the trading strategies of buyers and sellers under different credit supervision measures and the impact of different pricing strategies on the trading strategies of both parties, this paper proposes regulatory suggestions for the increasingly severe credit problems in rural e-commerce.Design/methodology/approachIn the online agricultural product transaction between farmers and consumers, both parties' decision-making is a dynamic process. Using the copying dynamic model of the evolutionary game, this study establishes two evolutionary game models to explore the factors affecting credit supervision in the rural e-commerce transaction process. Then, the study provides corresponding countermeasures and suggestions.FindingsFirst, credit supervision measures implemented by rural e-commerce platforms and the Government's legal system construction and infrastructure construction guarantees influence both parties' trust choices in rural e-commerce transactions. Second, price is a key factor affecting both parties' trading strategies. In the case of relatively fair prices, the higher the proportion of farmers who choose “low price” and “honest transaction” strategies, the easier that is for consumers to choose to trust farmers. In contrast, the higher the price, the higher the proportion of consumers who choose the “trust farmers” strategy, and the more willing farmers are to choose honest transactions.Originality/valueThis work develops a new approach for analyzing rural e-commerce credit supervision. Moreover, this study helps establish and improve the credit supervision mechanism of rural e-commerce and further realize the long-term sustainable development of the rural economy.

[1]  Bingzhen Sun,et al.  Optimal pricing model for non-instantaneous deterioration items with price and freshness sensitive demand under the e-commerce environment in China , 2021, Kybernetes.

[2]  Karine Haji.  E-commerce development in rural and remote areas of BRICS countries , 2021 .

[3]  Lin Chen,et al.  Risk evaluation for C2C E‐commerce via an improved credit counting method , 2020, Internet Technol. Lett..

[4]  Jiafu Su,et al.  Blockchain and edge computing technology enabling organic agricultural supply chain: A framework solution to trust crisis , 2021, Comput. Ind. Eng..

[5]  Li Cong,et al.  Introduction of stochastic evolutionary stability , 2020 .

[6]  Jin Zhu,et al.  Informality and rural industry: Rethinking the impacts of E-Commerce on rural development in China , 2020 .

[7]  Zhiao Liu.  Research on Information Asymmetry in C2C E-Commerce: Based on the Case of Alibaba , 2020 .

[8]  Weifen Wu,et al.  ICT Empowers the Formation and Development of Rural E-Commerce in China , 2020, IEEE Access.

[9]  Y. Wei,et al.  E-Commerce, Taobao Villages and Regional Development in China* , 2020 .

[10]  Chen Ji,et al.  Enhancing consumer trust in short food supply chains , 2019 .

[11]  Tian Fang,et al.  Transaction credit in the unstructured crowd transaction network , 2019, International Journal of Crowd Science.

[12]  Chun Xia,et al.  Study on the governance mechanism of rural e-commerce service centers in rural China: agency problems and solutions , 2019, International Food and Agribusiness Management Review.

[13]  Xiaoyong Zheng,et al.  The formation of Taobao villages in China , 2019, China Economic Review.

[14]  Yiqing Lu,et al.  Research on the Problems and Strategies of Rural E-Commerce in the Age of Internet + Agriculture , 2018, 2018 14th International Conference on Semantics, Knowledge and Grids (SKG).

[15]  Qingxiang Wang.  Study on the Development of Rural E-Commerce against the Backdrop of Rural Revitalization , 2018 .

[16]  Decheng Wen,et al.  Quality supervision game between government and online shopping platforms , 2018, Total Quality Management & Business Excellence.

[17]  Zhiquan Hu,et al.  Considerations of constructing quality, health and safety management system for agricultural products sold via e-commerce , 2018 .

[18]  Wei Liu,et al.  Analytic of B2C E - Commerce Credit Mechanism Mixed Strategy Risk Behavior Based on Logical Game Petri Nets , 2018, IEEE Access.

[19]  Berlilana Berlilana,et al.  Understanding of Antecedents to Achieve Customer Trust and Customer Intention to Purchase E-Commerce in Social Media, an Empirical Assessment , 2017 .

[20]  P. Ji,et al.  Developing green purchasing relationships for the manufacturing industry: An evolutionary game theory perspective , 2015 .

[21]  Islam H. El-adaway,et al.  Evolutionary stable strategy for post-disaster insurance : a game theory approach , 2015 .

[22]  Ali Akbar Jalali,et al.  A new applicable model of Iran rural e-commerce development , 2011, WCIT.

[23]  Liwei Li,et al.  Research on fuzzy comprehensive evaluation of enterprise websites , 2010, 2010 IEEE International Conference on Information Theory and Information Security.

[24]  Mingming Wang,et al.  Credit Rating System in C2C E-Commerce: Verification and Improvement of Existent Systems with Game Theory , 2009, 2009 International Conference on Management of e-Commerce and e-Government.

[25]  Wang Xiao-yan.  Trust Issue in C-C:An Analysis Model by Evolutionary Game Theory , 2005 .

[26]  F. E. Sistler,et al.  Robotics and intelligent machines in agriculture , 1987, IEEE J. Robotics Autom..

[27]  I. Bomze.  Non-cooperative two-person games in biology: A classification , 1986 .

[28]  J. Harsanyi.  Oddness of the number of equilibrium points: A new proof , 1973 .