Impacts Of Artificial Intelligence Recommendation Tags On Consumers’ Feeds Advertising Effectiveness

Under the developing background of "artificial intelligence,Internet of everything",the news feed content based on the artificial intelligence recommendation system is deployed in most mobile application software,from social media platform to short video media platform,as well as other application software with functions of distributing content and media channels,such as financial management and life services applications.News feed advertising comes into being,with three significant characteristics: native,dynamic and social.AI recommendation tags are an important component of tags in the user interface design of feeds content and advertising.These tags not only are meaningful in visual display but also undertake functions such as transmitting information,classifying content,promoting interaction and attracting attention.AI recommendation tags directly or indirectly disclose the recommendation intent of the feed content,and may explain to consumers the reason for the recommendation,the source of the recommendation,the recommendation algorithm and other relevant clues.This study constructs a mediated mediation model and through three experiments it finds that the AI recommendation tags evoke consumers’ perceived personalization,thereby positively influencing advertising effectiveness.The results of experiment 1verified the positive impact of AI recommendation tags on the feeds advertising effectiveness and tested the mediating role of perceived personalization.The specific AI recommendation tags with algorithm description evoke perceived personalization,thereby positively influencing the advertising effectiveness,while the vague AI recommendation tags simply suggesting recommendations don’t have such effect.The results of experiment 2 verify the positive influence of AI recommendation tags on feeds advertising effectiveness and discover an interaction effect between recommendation fitness and AI recommendation tags.The results of experiment 3 confirm that privacy concerns play a negative moderate role in perceived personalization influencing advertising effectiveness.Finally,this paper elaborates on theoretical contribution to AI recommendation tags research field and discusses the marketing implications obtained from the research.Enterprises should choose AI recommendation tags display strategy according to different market conditions based on explicit feedback or implicit feedback data source.AI recommendation based on explicit feedback data should let consumers know AI recommendation sources as soon as possible.For AI recommendations based on implicit feedback data,when recommendation fitness or privacy concern is uncertain,it is suggested not to display AI recommendation tags,otherwise it may cause personalized recommendation failures or non-target market effects and other issues.