Research On The Influencing Factors Of User Privacy Protection Behavior Under Cross-Platform Precision Recommendation

Recently,the e-commerce platforms of Taobao,JD and Pinduoduo have carried out strategic cooperation with the third-party platforms of Today Headline,Tiktok and We Chat.It has become a new trend of cross-border integration in the field of e-commerce to push precise advertising with the help of third-party platforms.E-commerce platforms obtain super traffic entry through third-party platforms to help monetize the advertising revenue flow of thirdparty platforms.Different from the traditional recommendation model,this kind of crossplatform accurate recommendation can achieve accurate big data portrait by connecting the user system of e-commerce platform and third-party platform,integrating user data of both platforms,and also arousing strong concerns about user privacy.However,there is little academic research on this issue.Therefore,this study first analyzes the game mechanism between cross-platform accurate recommendation Chinese e-commerce platforms and thirdparty content platforms and users;Secondly,considering the cross-platform accurate recommendation of user information sharing between platforms,analyze the influencing factors of user privacy protection behavior in this context.This study provides novel theoretical and methodical support for privacy management of network platforms and improving users’ adoption of cross-platform precise recommendation services.This study is based on a literature review and systematic analysis of relevant topics,such as precision advertising,privacy protection behavior,and privacy management,to summarize the current status and shortcomings of research.Then,using the theories of privacy computing and social cognition,a model of the influencing factors of privacy protection behavior for cross-platform precision recommendation is constructed from the perspectives of individuals and social environments.The study also explores how two types of platforms in cross-platform precision recommendation can choose user privacy protection strategies to obtain greater benefits.To test the research model,this study employed a questionnaire survey method,distributing an online survey via the online platform.A total of 357 valid questionnaires were collected,and data analysis software including SPSS26.0、AMOS26.0 and fs QCA3.0 was used.Data analysis methods included reliability and validity analysis,path analysis,and the application of fuzzy set qualitative comparative analysis(fs QCA)to explore the configurational effects of the condition variables involved on the outcome variable of user privacy protection behavior.The empirical results show that:(1)Data privacy sensitivity,self-efficacy,descriptive norms and subjective norms have a significant positive impact on perceived risk,while platform trust has a significant negative impact on perceived risk;(2)Data privacy sensitivity,self-efficacy and descriptive norms have a significant negative correlation with perceived benefits,while platform trust has a significant positive correlation with perceived benefits;(3)Perceived risk has a significant positive impact on user privacy protection behavior,and perceived benefit has a significant negative impact on user privacy protection behavior;(4)User subject factors(data privacy sensitivity,self-efficacy)and social environment factors(descriptive norms,subjective norms)are the necessary conditions for the generation of user privacy protection behavior;(5)There are five conditional combination paths for high-level user privacy protection behaviors,and the above seven conditional variables have complex causal relationships with user privacy protection behaviors.Finally,based on the results of data analysis and in combination with the empirical findings and actual situation of cross-platform precision recommendation,this paper suggests that ecommerce platforms and third-party platforms should pay more attention to users’ emotional experiences and reduce their perceived risks when providing cross-platform precision recommendation services.Companies should simplify privacy settings,optimize privacy protection technologies,and increase user trust.The conclusions of this study have certain theoretical contributions to the research on mobile commerce and provide some guidance for e-commerce platforms and third-party content platforms to analyze users and increase users’ acceptance of cross-platform precision recommendation.