How AI-Generated Images Impact Consumer Perceptions and Brand Identity
Open accessAbstract
This conceptual paper explores the impact of AI-generated images on consumer perceptions and brand identity within the advertising sector. Leveraging a comprehensive review of recent literature and theoretical frameworks, the study synthesizes insights on the advantages and challenges associated with AI-generated visuals. Findings indicate that while AI-generated images offer significant opportunities for personalization and creativity, they also present challenges related to authenticity, emotional engagement, and brand consistency. Specifically, AI-generated imagery can enhance visual appeal but may undermine perceived authenticity, affecting consumer trust. Emotional engagement with AI-generated content varies, with some consumers experiencing a disconnect due to the artificial nature of the visuals. Additionally, maintaining brand identity is critical, as AI-generated images can either support or disrupt brand coherence depending on their alignment with established brand values. The paper applies theories such as the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to understand consumer attitudes towards AI technology. The discussion provides recommendations for advertisers to balance innovation with authenticity, enhance emotional resonance, and ensure consistency in brand messaging. Future research is suggested to empirically validate these conceptual insights and further explore the impact of AI-generated imagery on consumer behavior and brand performance.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
3 of 10 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Interpretationcitation
When consumers view AI technology favorably, engagement and trust can rise if the technology is adopted in a thoughtful and transparent way.
“These theories suggest that positive consumer attitudes towards AI technology can enhance engagement and trust, provided that the technology is integrated thoughtfully and transparently (Jiao & Cao, 2024).”
- Also stated in: § 6.4 Theoretical Implications · plain
“Positive attitudes towards AI technology can enhance engagement and trust, provided that the technology is perceived as useful and easy to use (Mustofa et al., 2025).”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Jiao & Cao, 2024 source
The Discussion (TPB explains the intention to use AIAD; page unavailable) finds that designers’ positive attitudes correlate with their intention to use AI-aided design tools. The Practical implications section (page unavailable) suggests that making those tools transparent and explainable can improve designers’ trust and acceptance. The paper does not establish that positive consumer attitudes increase engagement or trust, or test thoughtful, transparent integration as a condition for that effect. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites Mustofa et al., 2025
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § 6.4 Theoretical Implications · plain
- Premisecitation
AI-generated visuals can reach a degree of detail and customization that conventional methods may find hard to equal.
“The research highlights how AI-generated visuals can achieve a level of detail and personalization that traditional methods may struggle to match (Karras et al., 2021).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Karras et al., 2021 source
The Results and Discussion compare optimized and conventional Invisalign attachments for tooth movement, not AI-generated visuals. Searches for AI-generated visuals, image generation, detail, and personalization found no passage addressing the attributed claim; the attachment comparison does not establish a contrary finding about visuals. (LLM, conf=0.78)
Experimental — accuracy not yet measured
- Interpretationreasoning
How emotionally engaged consumers become with AI-generated images depends on how the images are made and the setting in which they appear.
“AI-generated images can evoke varying levels of emotional engagement, depending on their execution and context.”
Quote in the paperSubstantial claim - Interpretationreasoning
The effect of AI-generated images on consumer trust and perceived authenticity is mixed.
“The literature suggests that AI-generated images have a nuanced impact on consumer trust and perceived authenticity.”
Quote in the paperNot a substantial claim - Interpretationreasoning
If AI-generated images fail to match a brand's existing identity, the result can be inconsistent visual style or messaging.
“On the other hand, inconsistency in visual style or messaging can arise if AI-generated images do not align with the brand's established identity.”
Quote in the paperSubstantial claim
- May combine two claims. 11
- Classified as probably not a substantial claim. 3
May combine two claims.
InterpretationcitationHow consumers feel about AI-generated images depends on how easy they find them to use, how useful they seem, and their sense of behavioral control.
“Applying theories such as the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) reveals that consumer attitudes towards AI-generated images are influenced by perceived ease of use, perceived usefulness, and perceived behavioral control.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Jiao & Cao, 2024 source
The Findings > Structural model section (page not provided) supports an association between perceived usefulness and designers’ attitudes toward AI-aided design. It does not support the full claim: perceived ease of use was not significantly associated with attitudes, and perceived behavioral control was associated with intention to use the tools, not attitudes. The study concerns designers using AI-aided design tools, not consumers’ attitudes toward AI-generated images. (LLM, conf=0.89)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationreasoningBecause AI can customize content, brands may adapt visuals for particular consumer groups, which could make the brand seem more relevant.
“On one hand, AI's capability for customization allows brands to tailor visuals to specific consumer segments, potentially enhancing brand relevance.”
Quote in the paperNot a substantial claim- Rests on an assumption: AI-generated imagery permits customization and personalization, letting brands precisely adapt visuals for particular consumer segments.
May combine two claims.
PremisecitationConsumers often see AI-generated images as less authentic than traditional photography, and this can reduce brand trust.
“For instance, Zhang et al. (2023) find that consumers often perceive AIgenerated images as less authentic compared to traditional photography, which can affect their trust in the brand.”
- Also stated in: § 5.1 Impact on Consumer Trust and Authenticity · hedged
“Studies such as Zhang et al. (2023) highlight that consumers may view AI-generated images as less authentic, which can undermine trust in the brand.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhang et al., 2023 source
- Also stated in: § 5.1 Impact on Consumer Trust and Authenticity · hedged
May combine two claims.
InterpretationreasoningAI-generated images do not affect emotions consistently; some consumers feel detached because the visuals seem artificial.
“However, this engagement is not universally positive; the emotional impact of AI-generated images can be inconsistent, with some consumers feeling disconnected due to the images' artificial nature.”
- Also stated in: § Abstract · plain
“Emotional engagement with AI-generated content varies, with some consumers experiencing a disconnect due to the artificial nature of the visuals.”
Quote in the paperSubstantial claim- Also stated in: § Abstract · plain
Classified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningBecause this work is conceptual, it contains no original empirical study, so it cannot supply fresh quantitative or qualitative data.
“The conceptual nature of the paper means that it does not involve original empirical research, which limits the ability to provide new quantitative or qualitative data.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationAI-generated content may trigger powerful emotions, yet those reactions can be markedly different from ones caused by conventional photography.
“They found that while AI-generated content can evoke strong emotional responses, these responses may differ significantly from those elicited by traditional photography.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lee et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationAI may produce eye-catching visuals, yet without careful control it can also cause brand messages to become inconsistent.
“Their research highlights that while AI can create visually striking content, it can sometimes lead to inconsistencies in brand messaging if not carefully managed.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kim et al., 2023 source
May combine two claims.
InterpretationreasoningAI-generated imagery may either reinforce or undermine a brand's identity.
“The review of case studies and theoretical frameworks suggests that AI-generated imagery can both support and challenge brand identity.”
Quote in the paperSubstantial claim- Rests on an assumption: AI-generated imagery permits customization and personalization, letting brands precisely adapt visuals for particular consumer segments.
Classified as probably not a substantial claim.
PremisecitationBeing open about employing AI to generate images is essential for preserving consumer trust.
“Their study suggests that transparency about the use of AI in image creation is crucial for maintaining consumer trust (Zhang et al., 2023).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhang et al., 2023 source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe conclusions rest on prior literature and case examples, so they might miss some recent advances in AI-generated imagery.
“The findings are based on existing literature and case studies, which may not capture all current developments in AI-generated imagery.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationBecause AI-generated images are novel, they may at times increase emotional involvement, though this does not happen equally for every consumer group.
“The study suggests that the novelty of AI-generated images can sometimes lead to enhanced emotional engagement, but this effect is not uniform across all consumer segments (Lee et al., 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lee et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationKeeping a brand coherent matters because departures from its central values or look can weaken its identity and leave consumers confused.
“Kim et al. (2023) emphasize that maintaining brand coherence is crucial, as deviations from the brand's core values or aesthetics can dilute brand identity and confuse consumers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kim et al., 2023 source
May combine two claims.
InterpretationreasoningThe influence of AI-generated images on consumer perceptions and brand identity is multifaceted: they create chances for creativity and personalization but also raise difficulties involving authenticity, emotional engagement, and brand consistency.
“In summary, the conceptual analysis reveals that AI-generated images have a complex impact on consumer perceptions and brand identity.While they offer opportunities for creativity and personalization, they also pose challenges related to authenticity, emotional engagement, and brand consistency.”
Quote in the paperSubstantial claim- Rests on an assumption: AI-generated imagery permits customization and personalization, letting brands precisely adapt visuals for particular consumer segments.
The paper relies on this without support.
Visual content strongly shapes consumer perceptions and is central to how brand identity is formed.
“Consumer perceptions are deeply influenced by visual content, which plays a crucial role in shaping brand identity.”
The paper relies on this without support.
Current knowledge is insufficient about how AI-generated images influence consumer trust, emotional engagement, and brand loyalty.
“Specifically, there is a lack of comprehensive understanding regarding how these AIgenerated images affect consumer trust, emotional engagement, and brand loyalty.”
The paper relies on this without support.
AI-generated images are appearing more and more often in marketing campaigns.
“AI-generated images, created through advanced algorithms and machine learning techniques, have become increasingly prevalent in marketing campaigns.”
The paper relies on this without support.
AI-generated imagery permits customization and personalization, letting brands precisely adapt visuals for particular consumer segments.
“On one hand, AI-generated imagery allows for unprecedented customization and personalization, enabling brands to tailor visuals to specific consumer segments with precision.”
The paper relies on this without support.
AI is able to generate high-quality images quickly and in large quantities.
“The ability of AI to produce high-quality images rapidly and at scale presents both opportunities and challenges for advertisers.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
The cited Lee study concerns education rather than advertising images
MajorThe review says Lee et al. (2022) studied consumers' emotional responses to AI-generated images, but the corresponding reference identifies a study of informatics and AI education in Korea.
“A study by Lee et al (2022) explores how AI-generated images impact emotional responses and engagement levels among consumers.”
“2. Dagyeom Lee, Ji-Yeon Hwang, Youngjun Lee, and Seong-Won Kim (2022). Informatics and Artificial Intelligence (AI) Education in Korea: Situation Analysis Using the Darmstadt Model.”
The cited Zhang article is about sensors, not consumer authenticity
MinorThe paper attributes findings about consumers' reactions to AI-generated images to Zhang et al. (2023), while the listed reference is an article on artificial intelligence sensors.
“For instance, Zhang et al. (2023) find that consumers often perceive AIgenerated images as less authentic compared to traditional photography, which can affect their trust in the brand.”
“8. Zixuan Zhang, Luwei Wang, and Chengkuo Lee (2023). Recent Advances in Artificial Intelligence Sensors.”
The cited Kim study has a different stated subject
MinorThe paper presents Kim et al. (2023) as research on AI-generated visuals and brand coherence, whereas its reference describes a study of Instagram social capital, brand identification, trust, and purchase intention.
“A study by Kim et al. (2023) examines how the use of AI-generated visuals impacts brand coherence and consumer perception of brand values.”
“6. Kim, J., Leung, X. Y., & McKneely, B. (2023). The effects of Instagram social capital, brand identification and brand trust on purchase intention for small fashion brands: the generational differences.”
The cited Karras study is about orthodontics, not image generation
MinorThe literature review describes Karras et al. (2021) as a study of GAN-generated images, but the corresponding reference is a retrospective study of Invisalign attachments.
“A study by Karras et al. (2021) discusses the capabilities of GANs in generating highquality images and explores their potential applications in various fields, including advertising.”
“5. Karras, T., Singh, M., Karkazis, E., Liu, D., Nimeri, G., & Ahuja, B. (2021). Efficacy of Invisalign attachments: A retrospective study.”
Cites
The publisher hasn’t deposited this paper’s reference list.
Cited by
Sorted by citations
Fang, H., Li, B., Zhou, Z., Li, M., Lai, H., Zheng, Y., Hu, B., & Chen, W. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344501
“Source: Adapted from Choung et al. (2023) [31] and Nazrin et al. (2025) [22].”
A Sneak Peek into AI-Generated 3D Immersive Memories
Coll, P. D. T., Bukits, T., Ruiz, J., et al. (2026) · ACM International Conference on Interactive Media Experiences · doi:10.1145/3788851.3815035
“Recognisability, perceived authenticity, and identity preservation can dominate preference, and highly aesthetic renderings are not necessarily the most preferred [6, 15].”
- Authors
- College of Creative Art, U. T. M., Nazrin, N., Isahak Merican, F. M., Faculty of Business and Management, U. T. M. K. B., Taharuddin, N. S., College of Creative Arts, U. T. M. P. B., Ibrahim, N. I., & College of Creative Arts, U. T. M. P. B.
- Affiliations
- Universiti Teknologi MARA (MY)
- Universiti Teknologi MARA System (MY)
- Venue
- Idealogy Journal
- Published
- 2025-04-01
- Volume
- 10
- Issue
- 1
- ISSN
- 2550-214X (ISSN-L)
- OpenAlex
- W4413290701
- Semantic Scholar
- c55dbcb17af3e78b66fe971775515a048bbc0967
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Idealogy Journal (publishedVersion)
- Idealogy Journal (publishedVersion, cc-by-nc-nd)
- UiTM Institutional Repositories (Universiti Teknologi MARA) (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior in Brand Consumption and Identification
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- AI-generated images, consumer perception, brand identity, advertising, perceived authenticity, consumer trust, emotional engagement, brand consistency
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