时 间：6月25日下午14:30-16:30
地 点：九里校区零号楼0218室
主 题：Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels
内 容：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, regarding click-through rate (CTR) and conversion rate (CR), 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 CTRs but lower CRs 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.
主讲人简介：庄梦舟，博士毕业于美国伊利诺伊大学香槟分校，现为香港大学经管学院助理教授。他的研究专注于在新的媒体、新情景、新的渠道模式和新的社交环境中，企业的广告、定价以及销售策略的设计与优化。他参与了多个与可口可乐，淘宝，苏宁，网易游戏，百度等国内外知名企业的咨询项目。他在研究中主要采用计量模型、现场实验、非参数估计、机器学习等方法分析企业或消费者层面的数据，从而提出对企业，消费者和政策制定者有价值的建议。现已在工商管理领域的一流期刊发表多篇文章，包括Information Systems Research, Journal of Marketing, Journal of Business Research等。
主办：经济管理学院 数字化营销与消费者行为科研团队
承办：服务科学与创新四川省重点实验室
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