Empirical Study On The Impact Of Artificial Intelligence Personalized Recommendation On Willingness To Continue Using APP In E-commerce Platform

With the increasing popularity of big data in transactions,the marketing communication role of artificial intelligence technology in e-commerce platforms is becoming increasingly prominent.On the one hand,artificial intelligence-based precise marketing has stronger learning ability and faster computing speed,which can push customers to products or services that they are interested in,reducing the cost of searching and processing customer information,and enhancing the willingness to continue using the APP.On the other hand,precise marketing communication can also make customers feel that their decision-making freedom and autonomy are being violated,or cause a series of problems such as perceived intrusiveness and privacy leakage,which can lower the willingness to continue using the APP.Although research has already focused on the influence of artificial intelligence personalized recommendations on the willingness to continue using the APP,it has not identified the theoretical boundary conditions of this influence.That is,in what circumstances does artificial intelligence personalized recommendations have a positive effect on the willingness to continue using the APP,and in what circumstances does it have a negative effect.In addition,previous studies have not used a comprehensive framework to explore the specific mechanisms underlying these effects.Therefore,this study intends to explore the direction and path of the influence of artificial intelligence personalized recommendations on the willingness to continue using the APP,as well as the extent to which the strength of their relationship changes under the mediating effects of perceived usefulness and perceived intrusiveness,and the moderating effect of recommendation timing.Based on the theories of expectation confirmation model of IS continuance and two-stage decision theory,this paper reviews relevant literature on AI precision marketing,personalized recommendations,perceived usefulness,perceived intrusiveness,and customers’ willingness to continue using the APP,in order to analyze the mechanism and theoretical boundaries of the impact of AI personalized recommendations on customers’ willingness to continue using the APP.Through an online questionnaire survey and quasi-experiment,first-hand experimental data from ecommerce platform customers were collected,and the data were analyzed using SPSS software for homogeneity testing,reliability and validity analysis,manipulation checks,regression analysis,and mediation analysis to verify the theoretical model.The following analysis results were obtained:(1)the higher the level of personalization of artificial intelligence recommendation,the higher the perceived usefulness and perceived intrusiveness of consumers.(2)perceived usefulness has a significantly positive effect on customers’ willingness to continue using the APP,while perceived intrusiveness has a significantly negative effect,and the two mediating variables have a mutually offsetting effect,resulting in no significant effect of AI personalized recommendations on customers’ willingness to continue using the APP.(3)the timing of the recommendation moderates the impact of AI personalized recommendations on customers’ willingness to continue using the APP.When the recommendation timing is based on active searching by the customer,as the level of AI personalized recommendation increases,customers perceive greater usefulness,which in turn increases their willingness to continue using the APP.When the recommendation timing is based on passive display to the customer,as the level of AI personalized recommendation increases,customers perceive greater intrusiveness,which in turn reduces their willingness to continue using the APP.According to the findings,it can be inferred that perceived usefulness and perceived intrusiveness are two important factors that affect consumers’ continuous usage intention of e-commerce platforms.Moreover,these two factors have a counterbalancing effect on each other,while the recommendation timing plays a crucial moderating role in this relationship.Drawing on the experimental results and taking into account the current development status of e-commerce in China,concrete and feasible recommendations are proposed on how to enhance consumers’ continuous usage intention of e-commerce platform APPs.Firstly,e-commerce platforms should optimize their recommendation algorithms and improve their overall capabilities.This ensures that the accuracy and operability of AI-based recommendations are controllable,and the shopping website system can quickly and accurately respond to consumer needs,reduce cognitive load,and improve decision-making efficiency.Secondly,e-commerce platforms should identify task scenarios and improve communication efficiency.For consumers who actively search for products,AI-based recommendations should be as accurate as possible,quickly responding to their needs,providing relevant product information,enhancing their perceived usefulness,and ultimately increasing their continuous usage intention.