1. Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels.
- Author
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Dong, Beibei, Zhuang, Mengzhou, Fang, Eric, and Huang, Minxue
- Subjects
INTERNET advertising ,NATIVE advertising ,RECOMMENDER systems ,SUBSCRIPTION Internet services ,IN-stream advertising ,ARTIFICIAL intelligence ,CLICK through rate - Abstract
Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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