The influence of AI Recommendation Algorithm on Social Media Users' Consumption Decisions - A Case Study of Mainstream Short Video Platforms in China

Abstract

With the deep application of Artificial Intelligence technology in the field of social media, algorithmic recommendations have become a major factor that influences social media users’ content browsing and product purchases. This study focuses on the impact path of AI recommendation algorithm on consumption decision, focusing on the three dimensions of user perceived recommendation precision, privacy concerns, and intergenerational differences. An online questionnaire survey was designed following the principles of “high cohesion, low coupling”, and distributed on an online survey platform in China. Results showed a positive relationship between user-perceived recommendation precision and purchase frequency, meaning that with more precise recommendations, the user would have a higher frequency of purchase. Privacy concerns weaken the effects of the recommendation. Users who are more sensitive about their privacy have a lower purchase frequency. Different intergenerational groups have obvious differences in consumption response. Generation Z has the highest probability of purchasing behaviour influenced by algorithm recommendations. The research shows that AI recommendation algorithms can promote business transformation, while the needs of user privacy protection are essential. Recommendation strategies can be optimized by combining user characteristics of different generations. This study provides an empirical reference for understanding the consumption behaviour in the algorithm environment.