The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index, and 1 are not checked yet.
Understanding AI Advertising From the Consumer Perspective: What Factors Determine Consumer Appreciation of AI-Created Advertisements?
Abstract
In recent years, artificial intelligence (AI) technology has been used to create advertising messages. This study examined the factors that influence consumers’ overall appreciation of AI-created advertisements. The findings indicate that, in addition to its direct effect on consumer reactions to AI-created advertisements, consumers’ perceived objectivity of the general advertisement creation process positively influences machine heuristic—a rule of thumb that machines are more secure and trustworthy than humans. This effect boosted consumer appreciation of AI-created advertisements. Consumers’ perceived objectivity of advertisement creation negatively influenced perceived eeriness of AI advertising, which jeopardized consumer appreciation of AI-created advertisements. Consumers’ feelings of uneasiness with robots were found to have a positive influence on both machine heuristic and perceived eeriness of AI advertising.
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“In alignment with Wu and Wen (2021), the perceived novelty and creativity offered by AI-generated advertisements did not entirely offset concerns about reliability and authenticity, especially in high-involvement contexts.”
From AI-generated versus human-created advertising: Effects on consumer trust and purchase intent (2025) · cites this paper as Wu and Wen, 2021
Citations
Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Herne, H., Cooley, W. W., & Lohnes, P. R. (1973) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344428 · Cited by 35,801
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
Structural Equation Modelling: Guidelines for Determining Model Fit
Hooper, D., Coughlan, J., & Mullen, M. R. (2008) · Dublin Institute of Technology · doi:10.21427/d7cf7r · Cited by 8,746
Social psychology of intergroup relations
Tajfel, H. (1982) · Annual Review of Psychology · doi:10.1146/annurev.ps.33.020182.000245 · Cited by 7,949
Issues and opinion on structural equation modeling
Chin, W. W. (1998) · Journal of the Association for Information Systems · doi:10.5555/290231.290235 · Cited by 4,579
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022
Gray, H. M., Gray, K., & Wegner, D. M. (2007) · Science · doi:10.1126/science.1134475 · Cited by 2,342
Research works printed but not linked: 3
- Fiske, S. T., and S. E. Taylor. Social Cognition. New York: McGraw-Hill, 1991.
- Mazurek, K. Human vs. Artificial Intelligence (master’s thesis). Universitat Pompeu Fabra, 2019.
- Nunnally, J. C. Psychometric Theory. New York: McGraw-Hill, 1978.
Cited by
Sorted by citations
AI advertising: An overview and guidelines
Ford, J., Jain, V., Wadhwani, K., & Gupta, D. G. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114124 · Cited by 176
Effect of disclosing AI-generated content on prosocial advertising evaluation
Baek, T. H., Kim, J., & Kim, J. H. (2024) · International Journal of Advertising · doi:10.1080/02650487.2024.2401319 · Cited by 197
“Wu and Wen (2021) found that consumers’ perceptions of objectivity in ad creation increased trust in machines but negatively affected the perceived eeriness of AI advertising.”
Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (2022) · Journal of Advertising Research · doi:10.2501/jar-2022-017 · Cited by 156
Service ads in the era of generative AI: Disclosures, trust, and intangibility
Grigsby, J. L., Michelsen, M., & Zamudio, C. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104231 · Cited by 110
Wu, L., Dodoo, N. A., Wen, T. J., & Li, K. (2021) · International Journal of Advertising · doi:10.1080/02650487.2021.1920218 · Cited by 88
“Wu and Wen (2021) explored consumers’ overall appreciation of AI-created advertisements, and they found that consumers’ perceived objectivity of ad creation and feelings of uneasiness with robots are important predictors of how consumers react to advertisements created by AI.”
BackgroundSelf-citation
Metadata
- Authors
- Wu, L., & Jing Wen, T.
- Affiliations
- University of South Carolina (US)
- University of South Carolina
- ORCID
- Wu, Linwan
- Venue
- Journal of Advertising Research
- Publisher
- Informa UK Limited
- Published
- 2021-03-23
- Volume
- 61
- Issue
- 2
- Pages
- 133-146
- ISSN
- 0021-8499 (ISSN-L)
- 1740-1909
- OpenAlex
- W3138769209
- Semantic Scholar
- 9e11fdcc48b54eb29e60849ccb7529178250b8c9
- Cited by
- 127
- References
- 52
- Citations by year
- 2026: 55 · 2025: 47 · 2024: 13 · 2023: 7 · 2022: 4 · 2021: 1
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Advertising Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior in Brand Consumption and Identification
- Keywords
- AI-generated advertising, consumer perception, machine heuristic, perceived objectivity, trust in machines
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