Consumer Attitude Towards AI-Generated Advertisement

Consumer Attitude Towards AI-Generated Advertisement

Shaily Raj

Student Amity University Lucknow

Under Guidance Of:

Dr. Sabeeha Fatima

Assistant Professor, Amity Business School, Amity University Uttar Pradesh Lucknow

Abstract – Generative Artificial Intelligence and its increasing integration in the advertising market have transformed both the deployment and creation of marketing content. Although AI-generated advertisements provide competitive advantages like cost efficiency, scalability and personalization but, the effectiveness ultimately depends on consumer perception and attitude. This study helps to understand consumer awareness of AI-generated advertisements, the stimulus-based responses before and after disclosure of AI involvement, it also analyzes perceived efficiency and overall attitude. Data was collected first handed through a structured online questionnaire. As the world is moving towards AI, the findings indicate high awareness of AI-generated advertising and favorable psychological-based evaluations prior to disclosure except for the shifts in perception, particularly in relation to trust and authenticity. Perceived efficiency was evaluated positively in terms of message clarity and communication effectiveness, though emotional resonance remained a consideration. Respondents preferred transparency and had the overall cautiously positive attitude toward AI-generated with acknowledging future growth prospects. The study emphasizes the fact that consumer perception and attitude are crucial factors in determining the effectiveness of advertising in technologically mediated environments.

CHAPTER: 1 – INTRODUCTION

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. AI helps computers analyze and interpret data to perform various intellectual tasks, such as speech recognition, live translation, problem-solving, and even decision-making, almost imitating human-like behavior. Traditional AI was already a huge advancement in the digital world, and with the emergence of Generative AI (GenAI), it gave users the power to create new and unique content in seconds, popularizing its usage across different industries.

The public release of the Gen AI tool ChatGPT in late 2022 increased public awareness and use of generative AI in different fields, from using it for academic work to even for professional work. This increase in usage of AI tools has led to the adoption and acceptance of the integration of these tools in various marketing activities, including content targeting, optimization, personalization, and other activities, as AI's ability to identify patterns in large datasets helps marketers to be more relevant and effective.

Now, this integration of AI has also been adopted in the creative process of advertising, including the production of ads using Gen AI. This integration may help lower the cost and the production time, but it also raises concerns about the consumers' perception and their attitude. An automated ad might be viewed as less genuine, which can further negatively influence the consumer perception.

Consumer perception can directly influence the attitude of a consumer, which can further shape their purchasing behavior. Consumer attitude reflects the positive or negative evaluation of the consumer towards a product or brand.

A brand's advertising campaigns or even a single advertisement can help consumers shape their attitude towards the brand. Hence, studying the impact of this Gen AI integration in the creation process of these advertisements on the attitude of consumers becomes vital. This study aims to explore and evaluate this impact on the minds of todays consumers who are generally aware of AI and ethical concerns that comes with its usage.

CHAPTER: 2 – LITERATURE REVIEW

1. Baldassarre (2024). The article discusses how artificial intelligence shifted from being a supporting tool to becoming one of the primary tools in digital advertising strategy. The article talks about how AI is now shaping the entire advertising cycle, starting from targeting to performance optimization rather than just focusing on automation. The brands are able to deliver more relevant ads and can also adjust campaigns in real-time with the help of AI systems' ability to analyze large volumes of

   data quickly and even identify behavioral patterns that can be difficult to detect manually. A key takeaway from this article is that AI not only improves efficiency but also changes the way effectiveness is measured. An AI system enables advertisers to focus on engagement quality and predictive performance instead of simply tracking impressions or clicks. The article suggests that successful integration of AI tools helps brands optimize budgets, refine targeting precision, and respond more quickly to shifts in consumer behavior. The article also implies that while AI automation can increase speed and scale, human strategic thinking still remains important for setting objectives and interpreting results.

2. Babatunde et al. (2024). The study examines how personalization strategies can be enhanced with the use of artificial intelligence in the modern marketing environments. The authors argue that with the ability to analyze large-scale consumer data, hyper-personalized communication can be achieved, which further results in an increase in relevance and customer engagement. They also emphasized on how AI-powered personalization strengthens customer-brand interaction, enhances consumer experience, and contributes to higher engagement rates. Lastly, they also highlighted the concerns regarding data privacy, ethical transparency, and algorithmic bias.

3. Gao et al. (2023). The study focuses on four critical elements of AI advertising: Targeting, Personalization, Content Creation, and Optimization uncovering mutual influences among these elements. It explains how AI can improve advertising precision by helping marketers better identify and understand consumer needs. Personalization helps increase user engagement and is closely related to content creation, for which AI can be used to produce advertising content that aligns with the consumer preferences. The study also discusses about how optimization can also be automated by AI through machine learning and big data analysis. In this process, targeting helps in identifying the main audience, the type of advertisement to be displayed is determined by personalization and lastly optimization helps adjust the factors, including timing and frequency, in order to improve advertising outcomes.

4. Buder and Unfried (2025). This report revealed a gap between the familiarity with generative AI and actual trust of consumer in the use of AI within marketing communications. The survey conducted by them showed that half of the consumers showed awareness towards the ability of AI to generate marketing content whereas only one-quarter were aware that AI uses personal data for personalized content or were confident in recognizing AI-generated materials. Furthermore, the survey also showed very low trust levels of consumers towards use of AI in marketing and in AI itself. These low trust levels indicated that awareness does not translate into acceptance. They also highlighted that policies aiming for transparency may lead in increase in consumer skepticism instead of reducing it. Lastly, the authors suggested that businesses to proactively shape cnsumer attitudes towards AI in order to face this challenge of skepticism.

5. Zhang and Hur (2025). The study explored the impact of disclosure of the use of AI in the creation of an advertisement on consumers' perception. They revealed that under conditions of non-disclosure, the consumers could not identify any significant differences between AI-generated and human- made images and also found them comparable. They also highlighted the concern of consumers about ethical issues, including racial and gender stereotyping and possible job displacement, which can be associated with the usage of AI, further increasing these concerns. Finally. Their findings revealed that disclosure need not inevitably undermine advertising effectiveness. When AI use is strategically framed with appropriate justifications, consumer acceptance can be preserved even under full disclosure . Suggesting that approaches like using AI within framework of consumer benefit and ethical responsibility rather than concealing AI use helps create a balance between innovation and consumer protection needs potentially transforming disclosure from a defensive necessity into a trust-building opportunity .

6. Wang (2025). The article explores the main reason for the failure of high profile AI-Generated holiday advertising campaigns by major global brands such as McDonalds and Coca-Cola in resonating with the audiences. The author highlighted the criticism, for lacking an emotional depth, collected by these ads in context, like the holiday season, in which emotional connection and cultural relevance are crucial elements for audience engagement. The article talks about how the online backlash and criticism were focused on a mix of factors like aesthetic, ethical, emotional and economical factors. Even with the involvement human in creative process for giving the prompts they still cant control the translation of the ideas in the final output. This obscured human involvement in the creative process might lead to a work that is more likely to be perceived as impersonal and inauthentic. Wang concludes that the framing of the content with the guidance and integration with human insight, preserving emotional authenticity while using AI strengths, are the factors that decide the success of AI in creative fields.

CHAPTER: 3

1. Research Objectives

   The main purposes of this study are:

   • To analyze the level of consumers awareness with integration of AI in advertising and in AI itself.

   • To examine consumer perception towards AI and use of AI in advertising.

   • To access change in consumer perception post-disclosure of AI integration.

   • To examine perceived efficiency of AI-generated ads.

   • To evaluate overall consumer attitude towards AI-generated ads and its future scope.

2. Research Methodology

   As prior research has investigated various aspects of consumer perception toward usage of AI tools for generating marketing content, including emotional connect of consumers, their perception, trust and ethical concerns, and disclosure effects. There still remains a gap in observed evidence on consumer awareness, stimulus evaluation and perceived efficiency.

   Hence, an exploratory primary research design with a pilot-scale sample is adopted for the research to generate additional evidence within the existing but still emergent field of research.

   • Sampling Technique

     A pilot-sized sample of consumers from tier 2 and tier 3 cities, those are digitally active, regularly exposed to online advertising and aware of AI.

   • Data Collection Instrument

     A structured questionnaire was developed for the study, and an online survey was conducted using the questionnaire. The questionnaire was divided into four sections, with each section having its own main objective. The first section included questions collecting demographic details, and the second section collected data regarding the awareness of AI-generated ads and questions regarding an AI-generated ad stimulus that was attached to record the first reaction of the consumers before disclosing the use of AI. In the third section, the disclosure of the Ad being AI-generated was given, and then questions regarding change in perception were asked. The last section was focused on exploring the perceived efficiency and overall attitude of the consumers. The stimulus provided pre-disclosure of AI use, allowing respondents to evaluate the advertisement without any influence of pre-existing bias. Further change in perception of the respondents after the disclosure of the use of AI was measured. Coca- Cola Holidays are coming ad, which is a disclosed AI generated ad, was used as the stimulus for the study.

   • Variable and Measurement

     The study focused on measuring constructs including awareness of AI-generated advertisements; stimulus-based evaluation to evaluate visual appeal, credibility, creativity and purchase interest; post-disclosure perception to analyze trust and ethical concerns of the consumers; perceived efficiency, analyzing the message clarity, influence on decision-making, communication effectiveness; and overall attitude of consumers. The assessment of all constructs was done using multiple five point likert-scale items.

   • Data Analysis Techniques

   Statistical tools, including percentage analysis were used for demographic profiling and mean-score analysis for the evaluation of construct-level perceptions was employed for the analysis of the collected data.

3. Limitations

1. Relatively small sample size: Although the study is pilot in nature, the small sample size can limit the generalizability of the results.

2. Generalizability of the findings: The sample was largely composed of respondents of Generation Z, which may affect the ability to estimate and generalize the perception of other age groups. Further, the respondents were shown only one advertisement as a stimulus for evaluation, which limits the generalizability of the results for other types of advertisements or product categories.

3. Reliability of the responses: As all the responses collected were self-reported, this may decrease the reliability of representing actual behavior.

CHAPTER: 4 – RESULTS

The final analysis was done on a total of 48 responses collected. A strong representation of Generation Z was observed as the majority of respondents, 70.8 per cent, were between the ages of 18 and 25. The remaining 16.7 per cent were aged 26 to 31, and

12. Percent fell within the age group of 45 to 55. The predominance of younger respondents is significant, as prior research suggests that younger cohorts demonstrate higher digital media engagement and greater exposure to algorithm-driven content (Pew Research Center, 2022). The presence of responses of Generation X helps explore the perspective of individuals with the

experience of both traditional and digital advertising paradigms, which also adds a comparative depth to the sample.

The first part of the examination included consumer awareness of AI-generated advertisements, and to ensure a meaningful evaluation, familiarity with the concept is essential. A mean score of 4.17 on the five-point Likert scale was recorded. In interpreting 5-scale Likert data, mean values above the scale mid-point, 3.0, are generally interpreted as reflecting agreement, while higher mean values indicates stronger level of agreement. Therefore, a mean of 4.17 reflects a clearly positive level of awareness. This positive awareness of AIgenerated ads is also supported by the response distribution, as 7 per cent of respondents either agreed or strongly agreed to having an understanding of AI-generated advertisements, while 83 per cent reported having encountered such ads in digital spaces. A minority of only 6 per cent indicated disagreement regarding familiarity. This relativity low proportion of negative or neutral responses suggests that the strong agreement or higher mean is not driven by extreme outliners but it rather reflects the consistent agreements across the responders. This evaluation indicates that digitally active consumers are more likely to recognize and understand AI-generated ads rather than perceiving it as an unfamiliar or abstract innovation.

The following stage of the analysis explored stimulus based evaluation pre-disclosure. Respondents were presented with an AI- generated advertisement as a stimulus without disclosing the production method used to avoid bias. The calculated mean score of the overall evaluation was 3.94 on likert five-point scale; this indicates a generally favorable perception as the value is lying above the neutrality. A further insight can be gathered through percentage distributions with 72 percent of respondents either agreeing or strongly agreeing to the advertisement having a visual appeal while 68 percent of the respondents also found the ad creative, and percent verified its effectiveness in capturing their attention. About 59 percent of responders even agreed that the advertisement is capable to influence their purchase consideration, whereas 17 percent expressed disagreement and 24 percent remained neutral. These figures highlight higher rating for aesthetic and attention- related aspects than persuasive influence. The slightly lower agreement regarding purchase influence, despite the majority viewing the advertisement favorably, suggests that consumers may differentiate between creative appreciation and behavioral persuasion. The overall pattern can be interpreted as positive in terms of aesthetic and communication performance.

After the initial evaluation, respondents were informed about the advertisement being AI-generated. This disclosure helped to measure any change in perception after the revelation of the involvement of AI. The mean score was calculated to be 3.56 for the post disclosure related responses. Although following the established likert interpretation conventions, values between 3.5 and 4.0 indicate moderate agreement, the reduction of the score from 3.94 in pre- disclosure to 3.6 post disclosure suggests a measurable moderation effect. This can also be further verified through percentage distribution, as 42 percent that the disclosure changed their perception, while 25 percent respondents disagreed, and 33 percent remained neutral. Moving to trust, 34 percent respondents agreed that their trust was reduced after disclosure, whereas 37 percent expressed to have no change in perception regarding trust, and 29 per cent were neutral. At the same time, 61 percent respondent expressed that a clear disclosure of AI involvement should be provided, reflecting a preference for transparency. These distributions clearly indicate that disclosure did not produce blatant rejection but introduced critical reassessment.

The evaluation of perceived efficiency of AI generated ads factors, including message clarity, influence on decision making and communication effectiveness, were taken into consideration. The mean score of perceived efficiency was calculated to be 3.55, indicating a moderate agreement suggesting that AI-generated ads are generally considered as functional by the respondents. This interpretation can be The mean score for overall attitude toward supported by the response distribution, with 65 percent agreeing with AI generated ads having ability to communicate marketing message clearly, while 57 percent even expressing that such advertisement may influence consumer decision.

The mean score of 3.74 was calculated regarding the overall attitude toward AI generated advertisement. The proportion distribution showed that almost 62 percent of respondents were open to engage with such advertisement while 70 percent agreed that AI generated advertisement will become more common in the coming future, while only 8 percent clearly disagreed with statements suggesting future acceptance. These figures indicate that immediate evaluation may be influenced by ethical concerns and trust considerations, the future expectations regarding the integration of AI in advertising are not diminished by them and transparency remains as the priority. Lastly Ai-generated advertising appeared to be normalized as a component of marketing communication by the consumers.

CHAPTER: 5 – CONCLUSION

This study examines consumer awareness, attitude perception, and perceived efficiency of AI-generated advertisements in the era of increasing technological integration in marketing activities. It aims to provide a more refined understanding of how consumers evaluate AI-generated marketing content and what factors these consumers priorities while evaluating such content.

The findings indicate a high level of awareness of consumers towards AI-generated ads, and when evaluated without disclosure, the advertisement was perceived positively in terms of visual appeal and communicative clarity. The disclosure did not drastically change the responses as they remain moderate rather than severely negative, suggesting that these ads are not inherently rejected by consumers, while trust and transparency-related concern may arise with this Ai integration.

The findings confirm that AI-generated advertisements are not inherently rejected by consumers. The results of this study both slightly support and slightly challenge the existing narratives. While the prior research emphasized strong skepticism and a decrease in effectiveness with perceived artificiality, the findings of this study suggested a phase of cautious normalization. With only a moderate decline in trust post-disclosure, instead of a purely negative decline. The increase in consumer familiarity with AI technologies may be the reason for a more rational evaluation observed than in the earlier studies.

This slight divergence from the strongly negative perception in prior research to somewhat moderate perception may be attributed to the increase in familiarity and rapid normalization of AI tools in everyday contexts, suggesting that with more exposure and strategic application, the perceived threat of AI-generated content may diminish. This also indicated a dynamic nature of consumer attitude toward AI in advertising, rather than a static one.

Importantly, this study reinforces the importance of consumer attitude and perception while determining advertising effectiveness, just as technological efficiency does not guarantee success. The findings suggested that even with the agreement of the respondents with the functional effectiveness of the ad, the issues related to authenticity, trust, and transparency remain the priority of the consumers.

AI-generated advertising appears to be entering a stage of conditional acceptance rather than blatant rejection. While the concerns regarding transparency, trust and authenticity remain, digitally active consumers still demonstrate openness toward a continued integration of AI. The long-term effectiveness of AI integration in advertising hence will depend not only on improving algorithmic sophistication but also on ethical integration and the alignment with consumer expectation of transparency, credibility, and emotional resonance.

REFERENCES

1. AI in Advertising: Use Cases, Benefits, & Challenges. (n.d.). Salesforce Web Site:

2. https://www.salesforce.com/media/artificial-intelligence/ai-in-advertising/

3. AI ads work best when they do not look like AI: Study. (2026, Jan 30). Brand Equity Web Site:

4. https://brandequity.eonomictimes.indiatimes.com/news/digital/ai-generated-ads-why-they-need-to-look-human-to-succeed/127793624

5. Artificial Intelligence In Marketing Market (2025 – 2030): Size, Share & Trends Analysis Report By Component (Software, Services), By Application (Social Media Advertising, Search Engine Marketing), By Technology, By End User Industry, By Region, And Segment Forecasts. (n.d.). Grand View Research Web Site:

6. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-marketing-market-report/methodology

7. Babatunde, S., Odejide, O., Edunjobi, T., and Ogundipe, D. (2024, Mar 27). THE ROLE OF AI IN MARKETING PERSONALIZATION: A THEORETICAL EXPLORATION OF CONSUMER ENGAGEMENT STRATEGIES. International Journal of Management & Entrepreneurship Research:

8. file:///C:/Users/honda/Downloads/THE_ROLE_OF_AI_IN_MARKETING_PERSONALIZATION_A_THEO.pdf

9. Baldassarre, R. (2024, Apr 09). How AI Is Revolutionizing Digital Advertising In 2024. Forbes:

10. https://www.forbes.com/councils/forbesagencycouncil/2024/04/09/how-ai-is-revolutionizing-digital-advertising-in-2024/

11. Burder, F., and Unfried, M. (2025). Transparency Without Trust

12. The Impact of Consumer Skepticism of AI-Generated Marketing Content. NIM:

13. https://www.nim.org/en/research/projects-overview/detail-research-project/transparency-without-trust

14. Chui, M., Roberts, R., Yee, L., Hazan, E., Singla, A., Smaje, K., Sukharevsky, A., and Zemmel, R. (2023, Jun 14). The economic potential of generative AI: The next productivity frontier. McKinsey & Company:

15. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier#business-value

16. Coca-Cola. (2025, Nov 3). Coca-Cola | Holidays Are Coming [Video]. YouTube: https://www.youtube.com/watch?v=Yy6fByUmPuE

17. Consumer Behaviour: What is perception in Consumer Behaviour?. (2025, Oct 10). Kentrix.ai Web Site:

18. https://kentrix.ai/what-is-perception-in-consumer-behaviour/

19. Dimitrieska, S. (2024, Jun 25). Generative Artificial Intelligence and Advertising. Trends in Economics, Finance and Management Journal:

20. https://tefmj.ibupress.com/uploads/2024/07/ibu_journal_tefmj-3.pdf

21. Duggal, R. (2019, Apr 19). Consumer Attitudes: A Small Factor That Makes A Big Impact. Forbes:

22. https://www.forbes.com/councils/forbescommunicationscouncil/2019/04/19/consumer-attitudes-a-small-factor-that-makes-a-big-impact/

23. Estévez, M. and Fabrizio, D. (2014) Advertising Effectiveness: An Approach Based on What Consumers Perceive and What Advertisers Need. Open Journal

    of Business and Management:

24. https://pdfs.semanticscholar.org/acb5/86561076dd56652ffc2aee8caeeee385b444.pdf

25. Exner, Y., Hartmann, J., Netzer, O., Zhang, S., and Ding, Z. (2025, Dec 30). AI in Disguise Quasi-Experimental Analysis of a Large-Scale Deployment of AI-Generated Display Ads. Marketing Science Institute Working Paper Series:

26. https://www.msi.org/working-paper/ai-in-disguise-quasi-experimental-analysis-of-a-large-scale-deployment-of-ai-generated-display-ads/

27. Gao, B., Wang, Y., Xie, H., Hu, Y., and Hu, Y. (2023, Nov). Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization. Sage Publications:

28. https://www.researchgate.net/publication/376094347_Artificial_Intelligence_in_Advertising_Advancements_Challenges_and_Ethical_Considerations_in_Ta rgeting_Personalization_Content_Creation_and_Ad_Optimization

29. Gu, C., Jia, S., Lai, J., Chen, R., and Chang, X. (2024, Sep 3). Exploring Consumer Acceptance of AI-Generated Advertisements: From the Perspectives of Perceived Eeriness and Perceived Intelligence. MDPI:

30. https://www.mdpi.com/0718-1876/19/3/108

31. Niosi, A. (2021). Introduction to Consumer Behaviour: Understanding Attitudes. BCcampus:

32. Understanding Attitudes

33. Routley, N. (2023, Feb 6). What is generative AI? An AI explains. World Economic Forum:

34. https://www.weforum.org/stories/2023/02/generative-ai-explain-algorithms- work/?gad_source=1&gad_campaignid=22228224717&gbraid=0AAAAAoVy5F7rp1FP1b7zu-

    TVaIQQls1gZ&gclid=Cj0KCQiAhtvMBhDBARIsAL26pjHWbPvqTMbUFiG2kG727VqNkHpC-DaGDPJYbV-oYN70a9Q63qqEfEcaAjQAEALw_wcB

35. Stryker, C. and Kavlakoglu, E. (2025, Nov 17). What is artificial intelligence (AI)?. IBM: https://www.ibm.com/think/topics/artificial-intelligence

36. Tanwar, P., Antonyraj, S., and Shrivastav, R. (2024, May 5). A Study of Rise of AI in Digital Marketing. IJMRSET:

37. file:///C:/Users/honda/Downloads/A_Study_of_Rise_of_AI_in_Digital_Marketing.pdf

38. Wang, C., Liu, T., Zhu, Y., Wang, H., Wang, X., and Zhao, S. (2023, Nov). The influence of consumer perception on purchase intention: Evidence from cross-border E-commerce platforms. Heliyon:

39. https://www.sciencedirect.com/science/article/pii/S2405844023088254

40. Wang, H. (2025, Dec 19). Why AI-Generated Holiday Ads Fail And What They Teach Us About Using AI in UX Work. NN Group:

41. https://www.nngroup.com/articles/ai-ad/

42. Zhang, L. and Hur, C. (2025, Oct 16). The Impact of Generative AI Images on Consumer Attitudes in Advertising. Administrative Sciences:

43. https://www.mdpi.com/2076-3387/15/10/395