The Research Of Influencing Factors Of The Adoption Of Personalized E-commerce Recommendation Information

Coinciding with the remarkable evolution of the Internet, business opportunity and the size of the Chinese e-commerce user base is growing. An increasing number of online goods, while offering consumers more choice has the potential to lead to information overload. Buyers are wasting precious time on archaic search functions which has inevitably resulted in inaccurate and slow decision making. Personalized recommendation services have emerged in order to tackle the user information overload problem. In China, most online shopping websites have been using personalized recommendation with varying degree. Although the personalized recommendation algorithm is consistently evolving, and its accuracy gradually improving, we are still a fair way from the ideal.From the perspective of the online shopper, personalized recommendation has an influence on users’ purchasing behavior. Users utilize recommendations selectively. Personalized recommendation services assist the user by sending them information about the product. Whether users accept this information or not is still an uncertain issue. We have to establish the factors, which influence information adoption.This paper defines the connotation of the adoption of recommended information firstly. And then based on the analysis of the theories about personalized recommendation services and information adoption, this paper think that credibility of recommendation source, personalized recommendation results, recommendation reasons, recommendation timing, information’s presentation format and information overload are the influencing factors to online buyers. We also propose a novel model of information adoption combined IAM with UTAUT. Through the questionnaires, previous assumptions of our model are verified. According to the study, we can find that subjective norms, trust, performance expectations and usability expectations have direct positive influence on the intention of information adoption; And credibility of recommendation source, personalized recommendation results, recommendation reasons, subjective norm affected the intention of information adoption indirectly through influenced the trust; The credibility of recommendation source, personalized recommendation results and recommendation reasons affected the intention of information adoption indirectly through influenced performance expectations; The recommendation timing and information’s presentation format affected the intention of information adoption indirectly through influenced usability expectations. In order to provide more effective personalization services and offer better recommendations to users, some suggestions will be present in the end according to our results.