Cultural Differences in Perception and Engagement of AI-generated Online Ads

Abstract: AI-generated advertising media are fascinating for online advertising, as they can be used to achieve a high degree of personalization at a low cost. However, they also introduce unique challenges. The seamless integration of text and visuals, the ability to capture and retain audience attention, and the effectiveness of AI-generated content in diverse cultures are all areas that require in-depth understanding. This understanding is crucial as companies increasingly rely on AI to enhance their marketing efforts.In this study, we examine cultural differences in the perception of and interaction with Instagram advertising, which was created using generative AI. We used 75 people from Columbia and 41 from Austria to investigate how these two groups differ.For this purpose, an application was developed that generates ads for Instagram based on the GPT4 and DALLE-3 AI systems that can create Text and Images. To further define the intended demographic for the advertisements, a persona generator was developed to generate basic user profiles. Both target groups were then surveyed using a further application that presents a structured questionnaire. Six ads for five target groups, i.e., 30, were created and presented to the test persons, followed by the questionnaire.The questionnaire asks things such as the clarity of the message, the trustworthiness of the ad, whether it is visually appealing, whether it matches the interests, whether it attracts attention, whether it would be interacted with, etc., on a 5-point scale. In addition, free text questions are asked about which elements of the ad encourage interaction and which emotional responses there are. Specifically, the analysis aimed to investigate differences in engagement, visual appeal, relevance, and other factors that could influence the perception of the ads.To achieve this, a T-test was conducted to determine the significance of differences in the answers to each question in the questionnaire. Additionally, a separate analysis focused exclusively on ads related to “Skiing” and a “Hotel in the Alps.” This was done to see if filtering for elements culturally significant to Austrians would yield significant response differences.The cross-cultural analysis showed numerous significant results in how Colombians and Austrians evaluated AI-generated advertisements. For example, Colombians consistently determined that the ads were more visually appealing than Austrians. Colombians ranked culturally relevant advertising, such as those about skiing and the Alps, higher in visual appeal, catching attention more successfully. Colombians were more inclined to interact with the ads regarding engagement in the general comparison. However, this difference in interaction likelihood became less pronounced when only culturally specific ads were considered.Interestingly, while the general comparison showed no significant difference in overall quality ratings between the two groups, the filtered analysis for culturally specific ads revealed that Colombians rated the overall quality higher than Austrians. This suggests that Colombians found the culturally relevant ads more enjoyable overall. However, both groups' perceptions of clarity, credibility, and relevance remained similar, with no significant differences observed, indicating that these aspects were less influenced by cultural context. The capabilities of the AI models constrained the study used, specifically GPT 4 and DALL-E 3. These models, while advanced, still need to be improved in fully understanding and replicating human creativity, particularly in areas such as appropriate design, cultural references, and the integration of text and visuals. The lack of interaction between the text and image generation phases is a limitation that often results in inconsistencies between the content and visuals. Overusing certain words in the captions or over image text is a constraint of the model. It sticks to words like “Enhance” and “Elevate” regardless of the product or service, which deteriorates the quality of the final output. Integrating text and image generation models could significantly improve the coherence and quality of AI-generated ads. Using tools like ChatGPT-Vision to offer feedback on the generated DALL-E image to GPT could be a step forward in automating the whole process. The analysis was based on responses from participants from two cultural backgrounds (Austrian and Colombian). While this allowed for some cross-cultural insights, the sample size and cultural diversity were limited, which may affect the general result of the findings. Future studies with a more diverse participant pool could provide a broader understanding of how different cultures perceive AI-generated content.

Keywords: Generative AI, Online Ads, Cultural Differences

DOI: 10.54941/ahfe1005916

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