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Learn more about the Lab's research work.
The AI Ad Design Lab's mission is to expand our current understanding of AI technologies through academic research, accessible tutorials, and education. Our focus is on new, accessible, practical findings that businesses and academics can easily implement, regardless of budget.
Below is a list of our published research and abstracts.
Below is a list of our published research and abstracts.
RAISE: A New Method to Develop Experimental Stimuli for Advertising Research with Image Generative Artificial Intelligence
👩🏻💻 Authors: César Zamudio (Virginia Commonwealth University), Jamie L. Grigsby (Missouri State University), and Meg Michelsen (Longwood University)
📝 Published in: Journal of Advertising (ABDC A)
Advertising research widely uses visual stimuli. Stimuli development, whether by researchers or by hired designers, requires considerable time, funding, and know-how. Image generative artificial intelligence (iGenAI) allows faster and more cost-effective stimuli production, but whether this technology can produce rigorous experimental stimuli comparable to researcher-generated stimuli remains an open question addressed herein. First, we review publications in three advertising and marketing journals to identify relevant domains where iGenAI can be applied. Second, we present RAISE (Rapid Artificial Intelligence Stimuli for Experiments), a new methodology to generate AI stimuli, which requires no programming and relies on commercially available tools, increasing accessibility for researchers. Five studies (1,785 participants) directly compare visual stimuli generated using RAISE and iGenAI to stimuli generated by researchers and show that participants cannot differentiate them. Moreover, AI-generated stimuli satisfy the same manipulation checks in and replicate the effects of existing research. Three additional studies (N = 368) lend additional robustness, indicating that iGenAI and RAISE are valuable tools to complement traditional methods for producing visual experimental stimuli in advertising research.
👩🏻💻 Authors: César Zamudio (Virginia Commonwealth University), Jamie L. Grigsby (Missouri State University), and Meg Michelsen (Longwood University)
📝 Published in: Journal of Advertising (ABDC A)
Advertising research widely uses visual stimuli. Stimuli development, whether by researchers or by hired designers, requires considerable time, funding, and know-how. Image generative artificial intelligence (iGenAI) allows faster and more cost-effective stimuli production, but whether this technology can produce rigorous experimental stimuli comparable to researcher-generated stimuli remains an open question addressed herein. First, we review publications in three advertising and marketing journals to identify relevant domains where iGenAI can be applied. Second, we present RAISE (Rapid Artificial Intelligence Stimuli for Experiments), a new methodology to generate AI stimuli, which requires no programming and relies on commercially available tools, increasing accessibility for researchers. Five studies (1,785 participants) directly compare visual stimuli generated using RAISE and iGenAI to stimuli generated by researchers and show that participants cannot differentiate them. Moreover, AI-generated stimuli satisfy the same manipulation checks in and replicate the effects of existing research. Three additional studies (N = 368) lend additional robustness, indicating that iGenAI and RAISE are valuable tools to complement traditional methods for producing visual experimental stimuli in advertising research.
Service Ads in the Era of Generative AI: Disclosures, Trust, and Intangibility
👩🏻💻 Authors: Jamie L. Grigsby (Missouri State University), Meg Michelsen (Longwood University), and César Zamudio (Virginia Commonwealth University)
📝 Published in: Journal of Retailing and Consumer Services (ABDC A)
Generative AI (GenAI) is a new tool allowing marketers to quickly and cost-effectively develop advertisements. However, concerns about deception and misinformation voiced by consumers, ad agencies, and governments have led to mandates to disclose AI-generated content. Given the importance of visual advertising for service tangibilization, whether services marketers should use GenAI to advertise services, and how, is a pressing question that this paper investigates. We apply a source credibility framework to explore factors in GenAI service ad design that influence trust toward the service provider and ad attitudes. Three experiments uncover that AI disclosures result in lower trust and less positive ad attitudes. Ads designed to focus on intangible attributes (e.g., a dentist's image) are less effective relative to ads focusing on tangible attributes (e.g., a dentist's equipment) when an AI disclosure is present. However, trust and ad attitudes can be restored when AI is selectively used to generate an ad's tangible attributes, but not the intangible (e.g., a real dentist at an AI-generated office). Our findings thus provide concrete guidance on how services marketers can use AI to enjoy the cost and speed benefits of AI ad development while preserving trust and ad attitudes.
👩🏻💻 Authors: Jamie L. Grigsby (Missouri State University), Meg Michelsen (Longwood University), and César Zamudio (Virginia Commonwealth University)
📝 Published in: Journal of Retailing and Consumer Services (ABDC A)
Generative AI (GenAI) is a new tool allowing marketers to quickly and cost-effectively develop advertisements. However, concerns about deception and misinformation voiced by consumers, ad agencies, and governments have led to mandates to disclose AI-generated content. Given the importance of visual advertising for service tangibilization, whether services marketers should use GenAI to advertise services, and how, is a pressing question that this paper investigates. We apply a source credibility framework to explore factors in GenAI service ad design that influence trust toward the service provider and ad attitudes. Three experiments uncover that AI disclosures result in lower trust and less positive ad attitudes. Ads designed to focus on intangible attributes (e.g., a dentist's image) are less effective relative to ads focusing on tangible attributes (e.g., a dentist's equipment) when an AI disclosure is present. However, trust and ad attitudes can be restored when AI is selectively used to generate an ad's tangible attributes, but not the intangible (e.g., a real dentist at an AI-generated office). Our findings thus provide concrete guidance on how services marketers can use AI to enjoy the cost and speed benefits of AI ad development while preserving trust and ad attitudes.