Research Of Multi-objective Decision-making Of User Adoption Behavior In Cross-Border E-Commerce Based On Game Theory

In 2014,cross-border electronic commerce(hereinafter referred to as "cross-border e-commerce")with its advocacy "putting cross-border online shopping in the sun" service concept gradually into the broad public view,and increasingly attracting many participants(sellers,buyers,government,service providers)to form a complex network of relationships(transactions,supervision,services).It is true that the emergence of every new thing is bound to go through the the stages of adoption,promotion,and innovation,the importance of cross-border e-commerce adoption is self-evident.Cross-border e-commerce only by the multi-user active adoption,its powerful potential benefits can burst out in the follow-up stage.However,from the current situation of application,our users’ adoption of cross-border e-commerce did not meet expectations,mainly embodied follow issues as:the user’s adoption scale is far smaller,the user’s adoption behavior does not standard.The collaborative optimization of cross-border e-commerce adoption need to consider multiple-objective optimization goals,such as the buyer C utility value,the seller B profit value,and the government G social welfare value,and these goals are often contradictory,competing with each other.Therefore,the decision-making of cross-border e-commerce adoption is a multi-objective decision-making problem.The traditional solution method of multi-objective decision-making problem has the disadvantages of low efficiency,high time cost,poor optimization effect and so on,so it is necessary to study a more efficient multi-objective decision-making method to implement the process of cross-border e-commerce user adoption multi-objective collaborative optimization,and it will highlight the unique theoretical and practical value to further improve the benign operation of cross-border e-commerce.In view of this,this paper analyzes the adoption behavior of China’s cross-border e-commerce users,and puts forward the multi-objective decision-making model of the adoption behavior of cross-border e-commerce users with reference to the research results and practical experience of relevant theories at home and abroad,using game theory and multi-objective decision theory as a tool.Then based on the Nash equilibrium strategy to optimize the game analysis,in order to optimize the performance of the method,at the same time to put the adoption behavior of cross-border e-commerce users to a greater extent into a better specification,and then dig out the potential benefits of cross-border e-commerce deeper.The main contents of this paper are as follows.Firstly,according to whether the adoption of the main actors directly from the subjective will,and ultimately the object of this study locked as the buyer-seller-government.Second,taking the three categories of users’ adoption behavior to in-depth analysis,and to further explore the various types of users’ interest appeal and the relevance of the targets,thus defining cross-border e-commerce users’ behavior as a multi-objective decision problem.Thirdly,on the basis of the original TAM model,the key factors such as market factors,consumer utility,corporate profits and social welfare are added to the model,which constructs a more comprehensive cross-border e-commerce users’ adoption conceptual model.Then,taking the cross-border transaction of the imported milk powder of Fujian Province as the object of application,and constructing the correlation multi-objective decision-making model,the optimal solution of the multi-objective decision-making problem is solved from the perspectives of AHP and game theory,and the optimization results and the method performance of the two are compared and analyzed.The results show that the users’adoption of cross-border e-commerce is actually a multi-objective decision-making problem,compared with the traditional multi-objective method,game optimization method shows more powerful computing power,more efficient convergence rate and more satisfied with the optimization results.