AI Influencers in Advertising: The Role of AI Influencer-Related Attributes in Shaping Consumer Attitudes, Consumer Trust, and Perceived Influencer–Product Fit

Abstract

The purpose of this research is to develop an artificial intelligence (AI) influencer attributes scale (AIAS), which consists of key measures of AI influencers’ perceived attributes, as well as to unveil the relationship between each attribute and consumers’ acceptance of AI influencers as product/brand endorsers. Given the two properties of AI influencers (i.e., consumers perceive AI influencers as having humanlike personas, consumers perceive AI influencers as products of new technology), we reviewed literature on anthropomorphism and technology acceptance. Guided by previous literature and through a mixed-methods approach (i.e., machine learning, qualitative analysis, and survey), we identified seven key attributes of AI influencers (i.e., anthropomorphism, artificiality, attractiveness, luminary, quality, trendiness, and robophobia). Results indicated that six of these key attributes (i.e., anthropomorphism, attractiveness, luminary, quality, trendiness, and robophobia) significantly affected consumers’ acceptance of AI influencers as product/brand endorsers.

Keywords:

• AI influencers
• anthropomorphism
• consumer acceptance
• mixed methods
• technology

Note

Disclosure Statement

No potential conflicts of interest were reported by the authors.

Ethical Statement

This research has Southern Methodist University IRB Committee approval (Protocol ID: 22-008).

Notes

1 After finalizing the model presented in Figure 3, we explored whether each of these variables in Figure 3 varies across different AI influencers. In particular, we performed a multivariate analysis of covariance (MANCOVA) test in which we adopted AI influencer (i.e., Lil Miquela versus Bermuda versus Blawko) as the independent variable, the six relevant attributes (i.e., anthropomorphism, attractiveness, luminary, quality, trendiness, and robophobia) and the three consumer acceptance variables (i.e., influencer attitude, influencer trust, and influencer–product fit) as the dependent variables, and sex, age, race, influencer follow, preexisting brand attitude, and influencer involvement as the covariates. Results from the MANCOVA showed that after controlling for the impact of all the covariates, there was a significant main effect of AI influencer on the dependent variables (Wilks’s lambda = .88, F (18, 2278) = 8.62, p < .001). In particular, univariate test results revealed a significant main effect of AI influencer on attractiveness (F (2, 1147) = 26.05, p < .001), luminary (F (2, 1147) = 9.43, p < .001), quality (F (2, 1147) = 4.12, p < .05), trendiness (F (2, 1147) = 4.91, p < .01), robophobia (F (2, 1147) = 11.43, p < .001), influencer attitude (F (2, 1147) = 23.67, p < .001), influencer trust (F (2, 1147) = 9.00, p < .001), and influencer–product fit (F (2, 1147) = 23.13, p < .001). However, univariate test results showed no significant impact of AI influencer on anthropomorphism (F (2, 1147) = 1.68, p = 1.88). Consequently, future research can utilize these identified attributes to assess the characteristics of a specific AI influencer. The table that follows provides the descriptive statistics regarding each AI influencer:

Table

Additional information

Funding

This research was financially supported by the General Board of Higher Education and Ministry of the United Methodist Church under the Sam Taylor Fellowship Fund.

Notes on contributors

Yang Feng

Yang Feng (PhD, Southern Illinois University Carbondale) is Advertising Associate Professor in Artificial Intelligence, Consortium on Trust in Media and Technology/Department of Advertising, College of Journalism and Communications, University of Florida.

Huan Chen

Huan Chen (PhD, University of Tennessee) is Associate Professor of Advertising, Department of Advertising, College of Journalism and Communications, University of Florida.

Quan Xie

Quan Xie (PhD, Ohio University) is Assistant Professor of Advertising, Temerlin Advertising Institute, Meadows School of the Arts, Southern Methodist University.