Generative AI advertisements and Human–AI collaboration: The role of humans as gatekeepers of humanity

人性 生成语法 社会学 政治学 计算机科学 人工智能 法学
作者
Johnson Clement Madathil
出处
期刊:Journal of Retailing and Consumer Services [Elsevier BV]
卷期号:87: 104381-104381 被引量:11
标识
DOI:10.1016/j.jretconser.2025.104381
摘要

Generative AI technologies are rapidly advancing and are increasingly deployed by firms for marketing purposes. However, research on generative AI in the marketing domain remains limited, particularly regarding the balance of human-AI collaboration in generative AI video advertisements. This study explores and distinguishes between two types of generative AI video ads based on the balance of human-AI collaboration: (1) those featuring human-AI collaborative characters and (2) those comprising solely AI-generated characters. Using the elaboration likelihood model as a lens, this study proposes a conceptual framework with two parallel mediators: one at the cognitive level (perceived creativity) and the other at the emotional level (AI anxiety). A multi-method study was conducted, employing diverse data sources and analytical techniques. In Study 1, we analyze a real-world dataset of generative AI video advertisements on YouTube to identify the distinct types and examine audience reactions. In Study 2, an experimental design is used to causally evaluate the effects of these advertisements on the audience. The results indicate that generative AI advertisements featuring real human-AI character collaborations generate more favorable audience attitudes, as they are perceived to be more creative and evoke lower levels of AI-induced anxiety compared to advertisements composed entirely of AI-generated characters. The study underscores the critical role of humans as gatekeepers of humanity. • The paper provides a granular level understanding of how audiences view different generative AI ads. • Emphasize the role of humans while adopting new technologies. • Combines real-world dataset from YouTube and an experimental study to validate the hypotheses. • Extends prior research approaches in understanding video ads.
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