University of Illinois at Urbana-Champaign
Persuasiveness of artificial intelligence versus human influencers: The interaction between agent knowledge and message construal through mentalizing
Abstract
dc:descriptionThe recent emergence of artificial intelligence (AI) influencers has risen in prominence. As they gain popularity, it is expected that AI influencer marketing will shape the future of social media advertising. However, little empirical research has examined the persuasiveness of AI influencers compared to human influencers. To understand the nascent trend of AI influencer marketing, the present research examined the interplay between the types of influencers and persuasive messages by focusing on the concept of agent knowledge—lay beliefs that consumers hold about influencers as persuasion agents—and construal levels at which the agents and messages are mentally represented. Employing 2 (agent knowledge: AI influencer vs. human influencer) × 2 (message construal: low-level vs. high-level) between-subjects factorial experimental designs across two studies, the findings showed that AI influencers are as similarly effective in persuasion as human influencers when low-level messages are represented for utilitarian products, but human influencers outperformed their AI influencer counterparts when high-level messages are represented (Study 1). However, the interaction effects did not occur in hedonic products (Study 2). Additionally, the study revealed that mentalizing plays a mediating role between agent knowledge and persuasion coping outcomes, contingent on the levels of message construal. The moderated mediation analysis demonstrated that high-level messages facilitate, whereas low-level messages inhibit the underlying mentalizing mechanism. Based on the findings, theoretical and managerial implications are discussed.
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Advertising
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Lee, Dongchan
- Contributors dc:contributor
-
- Ham, Chang-Dae
- Sar, Sela
- Su, Leona Yi-Fan
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- Copyright 2023 Dongchan Lee
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/121321