Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 19, No. 3, 2025 58 A Conceptual Framework on AI-Driven Consumer Behavior in the Age of Digital Branding Xingye Pan Lee Kong Chian School of Business (LKCSB), Singapore Management University (SMU), 50 Stamford Road, 178899, Singapore Abstract: As artificial intelligence deepens into digital brand interaction, understanding its psychological and relational impact on consumers has become increasingly important. This conceptual paper proposes a comprehensive framework that links artificial intelligence perception, trust formation, and consumer behavior outcomes. This model goes beyond a purely technical perspective and emphasizes how consumers interpret artificial intelligence agents through perceived authenticity, intelligence, and social emotional cues. Trust - including cognitive trust and emotional trust - becomes a key mediating mechanism between perception and participation, loyalty, and co creation behaviors. This framework proposes five propositions and considers moderating factors such as digital literacy, perceptual control, and artificial intelligence labeling. This study is positioned at the intersection of marketing, consumer psychology, and human-computer interaction, filling the theoretical gap in the literature on artificial intelligence brands and providing practical guidance for designing AI experiences that resonate emotionally, are transparent, and enhance trust. It also lays the foundation for empirical work exploring how artificial intelligence technology can shape evolving consumer brand relationships in an increasingly digital environment in the future. Keywords: AI-driven consumer behavior; Digital brand strategies; Trust formation mechanisms. 1. Introduction Digital technologies are advancing rapidly, significantly transforming modern branding practices. Consequently, companies must reconsider their strategies for reaching consumers in online spaces that are getting more complicated and are always changing. Digital brand strategies aren’t just about having a website or running online ads anymore. Now, it involves complex systems that use AI for interactions, personalize things for users in the moment, and rely on data to communicate (Mikalef, Krogstie, & Enholm, 2022). As things keep changing, it's become vital to understand how people see and react to brand interactions that use AI. This knowledge is essential for brands to keep consumer trust, build loyalty, and stay ahead of the competition. Artificial intelligence (AI) is now integral to modern branding because it helps companies connect with consumers in a personalized way, right in the moment. Common AI tools, like chatbots and systems that recommend products, can really shape how people see a brand—whether they view it as genuine and a brand they can trust (Chae & Lee, 2024). However, the use of AI also creates some tricky issues around user privacy, how intrusive the technology might feel, and whether it makes people feel emotionally at ease. Recent studies highlight to an "intrusiveness paradox." This means that while greater AI-driven personalization can make brand interactions more relevant, it can also make consumers uncomfortable or resistant if they feel monitored or perceive a loss of control (Araujo, Moura, & Radaelli, 2024). Additionally, subtle details, like whether AI is called "smart" or "AI-powered," can noticeably impact how consumers feel about their own agency, control, and affective connection (Chae & Lee, 2024). These findings indicate that consumer reactions to AI depend not just on how well the technology works, but also on deeper psychological and affective factors. Despite this, much of the current research examines AI applications in isolated ways. Studies frequently focus on either how well AI performs technically or on general consumer attitudes. This approach tends to overlook the complex relational, emotional, and interpretive processes that occur during AI-mediated brand experiences. As a result, significant gaps in our understanding of how AI technologies affect the broader, long-term relationships between consumers and brands. To address this gap, our study puts proposes an integrated conceptual framework. This framework will help explore how AI perception, trust mechanisms, and consumer behaviors interact within digital brand settings. this study will use a mixed-methods approach, aiming to capture both measurable patterns and the deeper psychological processes behind AI- driven consumer behavior. Unlike previous research that often concentrated on just the technical aspects or how quickly AI is adopted, this study focuses on the relational and emotional sides of how consumers experience AI in brand interactions. This investigation seeks to contribute both theoretical and practical contributions. Theoretically, it aims to advance knowledge about AI's role in shaping consumer-brand relationships. Practically, it intends to provide actionable insights for brand managers. These insights should help them design AI experiences that feel more human, are trustworthy, and operate ethically in a marketplace increasingly influenced by AI. 2. Literature Review 2.1. Digital Brand Strategies and the Rise of AI Integration Traditional branding is increasingly moving towards digitally driven strategies, a shift accelerated by the recent spread of artificial intelligence (AI) technologies. Today, digital brand strategies include many AI-enhanced touchpoints. These allow brands to communicate with consumers, personalize experiences, and build relationships in real time (Chae & Lee, 2024). Traditional brand-consumer 59 interactions were often one-way and aimed at a mass audience. In contrast, AI now makes it possible to have dynamic, responsive, and personalized engagements using tools like recommendation engines, conversational agents, and AI- generated content (Araujo et al., 2024). For brand managers, these AI-embedded strategies open up unprecedented possibilities for understanding consumer behavior at a remarkably granular level. Rather than merely tracking transactional data, AI systems can reveal contextual factors, including consumer mood, intent, and micro- moments, thus supporting hyper-personalized communication efforts (Smith, 2024). Importantly, AI is now employed across both front-end consumer interfaces and back-end operational systems, from automated customer service and predictive analytics to adaptive, real-time content delivery. This evolution is driven by the growing consumer demand for seamless, customized, and convenient digital experiences (Lee & Shin, 2024). Despite the undeniable advantages AI provides, integrating it into branding strategies presents nontrivial challenges. A well-documented dilemma—often termed the “personalization–intrusiveness paradox”—illustrates how enhanced personalization can boost relevance and engagement while simultaneously raising concerns about privacy, data intrusiveness, or perceived algorithmic manipulation (Sharma & Gupta, 2024). This apparent contradiction highlights just how much contextual factors matter. Consumer reactions to AI are not fixed; instead, they are shaped by how transparent, trustworthy, and human-like the AI-mediated interaction feels. In this evolving landscape, brands need to do more than simply deploy technology. It’s increasingly important for them to explore the psychological and affective sides of how consumers interact with AI. AI often functions less as a neutral technical tool and more like a symbolic and relational agent. By influencing the formation of brand perception and trust, AI plays a crucial role in determining whether consumers are loyal in the long term. Therefore, it is necessary to adjust the way brands use AI: they can no longer simply choose between automation or authenticity, but must strike a balance between the two. That is to strike a cautious balance between automated, data-driven processes and real interpersonal relationships. Even when using data for hyper personalized experiences, it is important to enable consumers to use it instead of removing control. 2.2. AI-Enabled Consumer Interaction When AI enters brand consumer dynamics, it does more than just drive dialogue; It also facilitated a comprehensive rewrite of the fundamental principles of digital participation. We see it clearly with technologies like chatbots, virtual assistants, or recommendation engines: their operation introduces an unassimilable thread of non-human thought into the flow of interaction, radically refiguring such exchanges outside of the familiar realm of human-to-human talk. This altered landscape inevitably reshapes what consumers anticipate and compels a fresh evaluation of how they gauge a brand's value (Chen, Lu, Gong, & Xiong, 2023). Critically, these aren't just quick, functional exchanges. They are often rich with relational undertones and affective signals that profoundly influence whether a brand comes across as authentic, dependable, and capable of resonating emotionally (Zhang, Wang, & Chen, 2024). If we unpack how consumers respond to AI within brand interactions, a tapestry of complexity emerges. Obvious wins like increased efficiency and convenience are readily noted. However, the real story unfolds in the psychological undercurrents: aspects such as the AI’s perceived human- likeness (anthropomorphism), its apparent emotional intelligence, and the clarity of its operations are what truly steer consumer acceptance and the willingness to trust (Sargın, 2024). It’s common, for example, for people to warm up to AI agents that project human-like qualities, empathy, or a sense of warmth, particularly when the situation calls for social connection or emotional understanding (Zhang et al., 2024). Yet, this can be a double-edged sword. If an AI feels "too human," it can trigger discomfort, even the unsettling "uncanny valley" sensation, which can swiftly erode any budding trust (Koverola et al., 2023). Understanding the core of these dynamics through the concept of "algorithmic agents" is that people believe AI has autonomy and decision-making ability. The level of consumer awareness of algorithmic agency not only affects their interaction with AI interfaces, but also influences their ability to make more diverse judgments about a brand's capabilities, ethical issues, and true customer centricity (Musaiqer & Hamdan, 2025). When AI systems provide personalized results in a way that lacks transparency and user control, consumers often perceive these results as invasive or manipulative. This will undermine the original benefits and may lead to negative psychological reactions or even detachment. Therefore, in AI driven brand architecture, user control and system transparency are the foundation for maintaining trust (Sargın, 2024). AI interactions can also have significant long-term impacts. They not only influence initial contact, but also shape lasting loyalty and emotional connections. This article found that when AI agents provide emotional rewards, consumers are more likely to view the brand as trustworthy and socially relevant, thereby establishing longer-term relationships (Zhang et al., 2024). Similarly, although personalized experiences can enhance relevance and attract attention, good results can only be achieved on the premise of respecting consumer privacy and autonomy (Moore, McKechnie, & Ryan, 2023). Although there are many detailed studies, the academic discussions within them are still disjointed, with most studies focusing on individual AI features or specific consumer responses, without incorporating them into a unified framework. What we lack is a big picture perspective: understanding the interaction supported by AI as a rich social technology, interweaving practical information, emotional experiences, and symbolic meanings. Our research aims to start piecing together this picture. We propose a theoretical framework aimed at establishing the connection between the perception of AI, the establishment (or destruction) of trust, and the resulting behavioral outcomes. This involves a systematic examination of both the cognitive arithmetic and the emotional responses that define today's digital brand landscapes. 3. Theoretical Framework The research creates links between consumer psychology, communication theory and information systems research, forming a rich interdisciplinary foundation. Due to this groundwork, we can take an integrated conceptual framework that can help to illuminate these intricate interactions. Our framework seeks to primarily clarify the psychological and 60 behavioral processes whereby AI-driven brand strategies shape consumers responses. A core idea in our framework is that consumers interact with AI systems not just functionally. They also perceive and interpret these systems as symbolic and relational entities. We suggest that these interpretations are processed through three interconnected stages: (1) AI perception, (2) trust formation, and (3) behavioral outcomes. 3.1. AI Perception Consumers’ evaluations of brand touchpoints involving AI will depend on many factors other than use or use performance. Their evaluations will involve perceptions of AI system (and its outputs) on variables like authenticity, intelligence, and socio-emotional traits. (Chae & Lee, 2024). These evaluations will of course be informed by a range of established constructs, including; perceived anthropomorphism (which refers to the degree that an AI seems or acts human like), algorithmic agency (Araujo et al., 2024), and social presence theory (Zhang et al., 2024). The characteristics perceived ultimately determine whether the consumer views AI as an authentic representation of the brand, or a cold, un-emotional automaton. For instance, an AI chatbot that utilizes natural language, demonstrates empathy, or adapts to consumer tone is more likely to be perceived as socially intelligent and brand-authentic. 3.2. Trust Mechanism The second stage centers on the development of consumer trust, which acts as a mediating mechanism between AI perception and behavioral outcomes. Trust in AI involves both cognitive trust (e.g., system reliability, accuracy) and affective trust (e.g., warmth, goodwill), drawing from the parasocial interaction theory and trust transfer theory (Sargın, 2024). When Consumers perceive AI systems as emotionally aware and competent, they are more likely to extend their trust to the brand itself, reinforcing relational depth and reducing perceived risk (Lee & Shin, 2024). However, trust is fragile in AI contexts, particularly when personalization is perceived as intrusive or opaque. The personalization–intrusiveness paradox (Araujo et al., 2024) indicates that even well-designed AI interactions can erode trust if they violate consumers’ sense of control or privacy. Therefore, for trust to be sustained in AI-driven branding efforts, consumers need to feel that the system operates with transparency and fairness. 3.3. Behavioral Outcomes The final stage encompasses key consumer behavioral responses, including engagement, loyalty, willingness to co- create, and word-of-mouth intentions. They are a result of cumulative assessments during both trust and perception stages. Importantly, trust establishment has tangible results (Zhang et al., 2024). Specifically, they report that if trust is high, consumers are significantly more likely to interact intensely with AI systems. Also, it leads to emotional relationships with the brand and establishes long-term loyalty. All of this comes out best in interactions with virtual agents. For instance, emotionally fulfilling interactions in this regard can enhance repeat use intentions and induce favorable brand advocacy among digital-native consumers. 3.4. Conceptual Model Overview Figure 1. Conceptual Framework of AI-Driven Consumer Behavior 4. Conceptual Propositions Proposition 1: Perceived authenticity, intelligence, and socio-emotional cues in AI-driven brand interactions positively influence consumer trust. When consumers engage with brand AI, their evaluations extend well beyond mere functionality, keenly capturing the symbolic meaning and social clues embodied by AI. The perceived authenticity of AI - whether its interactions are consistent with the brand image and give people a sense of authenticity - is crucial for brand credibility (Chae & Lee, 2024). Meanwhile, the perceptual intelligence and social emotional responsiveness of AI reflect brand competence and relationship adaptability (Zhang et al., 2024), jointly shaping consumers' cognitive and emotional trust, which often extends from AI to the entire brand. Proposition 2: Consumer trust in AI mediates the relationship between AI perception and behavioral outcomes. Trust, as a vital relationship bond, transforms consumers' initial perception of AI into subsequent behaviors such as brand engagement, loyalty, and affective connection (Araujo et al., 2024). Its central intermediary role not only echoes the theory of trust transfer, but also highlights the fundamental position of relational mechanisms in AI-driven brand experience (Sargın, 2024). Proposition 3: AI-driven personalization positively influences engagement and loyalty, conditional on perceived transparency and control. AI-driven personalization is often viewed as a significant advantage, but its success depends crucially on user perception. Excessive personalization, especially when users feel a lack of data control, can lead to negative experiences such as monitoring or manipulation (Araujo et al., 2024). Therefore, for AI personalization to foster genuine user engagement and lasting loyalty, it is necessary to ensure that users can feel both clear algorithm operations and their subjective initiative during the interaction process. Proposition 4: Digital literacy strengthens the positive relationship between AI perception and consumer trust. Digital literacy has a pivotal influence on consumers' perception and trust in AI: a tech savvy individual generally supplies much more agreeable explanations of AI behavior and thus trusts, and individuals with digital skills, as Smith (2024) articulated, can be readily confused or distrustful of AI. 61 Proposition 5: AI labeling (e.g., “AI-powered” vs. “smart assistant”) influences the effectiveness of AI on behavioral outcomes via perceived agency. AI labels can have strong effects on audiences' perceptions of institutions implicated in AI systems and therefore how they interpret or expect them. For example, labels with more anthropomorphic traits or agency, can elicit increased emotional response, potentially eliciting psychological resistance if the users' agency is perceived to be reduced - as discussed in prior research (Chae & Lee, 2024). Table 1. Variable Relationships and Theoretical Basis Proposition Relationship Focal Variables Moderators (if any) Theoretical Basis P1 Direct AI Perception → Trust — Perceived authenticity, emotional cues (Chae & Lee, 2024; Zhang et al., 2024) P2 Mediation AI Perception → Trust → Behavior — Trust transfer theory (Sargın, 2024) P3 Moderated Effect Personalization → Engagement Transparency, Control Intrusiveness paradox (Araujo et al., 2024) P4 Moderated Effect AI Perception → Trust Digital Literacy Cognitive processing theory P5 Moderated Effect Trust → Behavior AI Labeling Social cue & anthropomorphism (Chae & Lee, 2024) 5. Discussion 5.1. Theoretical Contributions The major contribution of this research is a multi-level framework that depicts how consumers develop the perception of AI, which is a precursor to their trust development and subsequent behavioral outcomes. Our framework is different than those that use a utilitarian view to solely explain technical efficiency (Varshini & Venotha, 2025) by including symbolic and relational dimensions explaining the effect of perceived authenticity, social emotional cues, and algorithmic proxies, thus advancing recent academic conversation (Chae & Lee, 2024; Zhang et al., 2024; Maeda & Quan-Haase, 2024). Furthermore, the framework articulates two important aspects as: cognition (performance confidence) and emotion. Both serve as important mediating dimensions in consumer responses (Sargın, 2024), expanding the trust transfer theory when discussing the role of AI. Viewing AI as a social agent of brands, this perspective on dual trust fits with the theories for parasitic social interaction and for social existence, thus contributing to consumer communication with humans and AI, and elaborating on the complex nature of trust (Fakhimi, Garry, & Biggemann, 2023). Additionally, the model incorporates moderating factors (digital literacy and perceptual control) along with key contingency logic. There are so many more ways it enhances cross contextual interpretation accuracy. For instance, digital literacy may encourage greater acceptance of personalization, but it also increases expectations around transparency (Dogruel et al., 2025). An appreciation for context like this provides a wealth of contextual richness that enhances intermediate range theory development, particularly between laws of nature for more generalizable inferences and the relevance of context, (Wang & Zhang, 2024). From a methodological perspective, the framework provides multiple definable and verifiable propositions (P1- P5) for empirical inquiry into AI brands. More critically, this framework creates a pathway for researchers to meld psychological abstractions like anthropomorphism, perceptual agency, and affective perception into measurable variables, providing clarity and direction for both linking to theory and measuring empirically. 5.2. Managerial Implications The findings of this study are underscoring that consumers' evaluations of artificial intelligence go beyond functional attributes because the symbolic human-like attributes it displays are the most important. When using AI tools that convey warmth, resonance, and consistency with brand personality, these can translate into enhanced emotional trust and perceived authenticity (Chae & Lee, 2024; Maeda & Quan-Haase, 2024). The implications for managers are clear: do not use generic or overly mechanical AI, rather build emotional intelligence systems that offer a true reflection of the brand's voice and the principles of the relationship. In addition, the framework recognizes the dual positives of personalized AI: while up-to-the-minute customization can raise user engagement, it can also incite user discomfort where the user feels invaded, or the process of how algorithms developed the personalized AI model is less clear to the consumer. Brands need to support the balance between personalization personalizing relevant content for consumers and that personalization providing user autonomy. This requires once again providing user control options and transparency options in what was recommended (Araujo et al., 2024; Soni, 2025). Allowing users to change recommendation settings or understand why one or another content was recommended ("Why Am I seeing This?") can ease any sentiment of being manipulated. Moreover, marketers ought to be mindful on the effects on AI labeling and framing. Evidence demonstrates that consumers behave in disparate ways according to how the AI agent is described, such as whether they are called "smart assistants" or "AI-driven technologies" (Chae & Lee, 2024). Misaligned labeling—especially those that overpromise anthropomorphic qualities—may backfire and elicit feelings of discomfort, or distrust (Maeda & Quan-Haase, 2024). Therefore, the communication of AI capabilities should be purposefully calibrated to account for target customer segment's digital maturity and psychology. Lastly, with the identification of the differing AI interaction styles between different populations (for example, Generation Z "digital natives" and older "digital immigrants"), our framework opens a new channel for developers to engage in better segmentation and personalization. To mitigate the problem, managers can enact artificial intelligence design variation in relation to users, and their digital skills and relationship expectations, utilizing A/B testing and data- 62 driven segmentation to inform product design (Jorzik, Krüger, & Zschech, 2024). 5.3. Positioning Within Existing Literature This research is situated between AI-marketing, consumer psychology, and digital brand strategy, providing a conceptually distinctive perspective to understand the humanization of AI in consumer-brand relationships. While previous studies have often focused on specific aspects such as adoption intentions (Varshini & Venotha, 2025), service automation efficiency (Dogruel, Masur, & Joeckel, 2025), or consumer trust in recommendation agents (Smith, 2024), they frequently treat AI as a black-box variable. This research moves beyond such abstractions by articulating the internal psychological processes through which consumers make sense of AI, focusing on perceived authenticity, affective resonance, and symbolic trust transfer. By design, model of this study addresses a substantial void within the literature: a lack of models that comprehensively link together the dots from consumers' perceptions of AI within brand contexts, through whether or when and how trust is built or destroyed, and then on to what actions are taken. Most of the previous work presents insightful yet piecemeal observations, focusing on, for example, AI trust, ethical issues, or affective personalization separately, without a synthesis of what makes up the entire AI-mediated consumer journey. In contrast, we present a thoroughly designed and easily testable model, with a plan to serve simultaneously both as a solid platform for empirical studies and a point of critical reference for future theoretical debates within this field. Furthermore, the proposed framework complements and extends emerging work on consumer-AI relationships (Truong & Ta, 2025), adding depth to the discussion of AI's symbolic function as a relational brand interface, rather than a mere technological tool. It also opens important avenues for future research exploring cross-cultural, generational, and platform-specific dynamics—areas that remain relatively underexplored yet possess considerable theoretical significance. 5.4. Future Empirical Pathways The conceptual inquiry is not meant to remain theoretical; rather, it leads into proposals that are carefully designed as dynamic launch points for actual, empirical investigation within the intricate digital brand world. To really turn them into reality, discussion now turns to mapping out separate methodological paths and feasible research frameworks. The aim is to equip the scholarly community with tools needed to interrogate seriously the model, operationalize its constructs into empiricizable variables, and probe incisively into the boundaries of AI's impact on shopper behavior. 5.4.1. Quantitative Validation via Structural Equation Modeling (SEM) To test the proposed relationships among AI perception, trust mechanisms, and behavioral outcomes, future researchers can adopt survey-based quantitative methods. Constructs such as perceived authenticity (e.g., Kumar & Sharma, 2024), cognitive and affective trust (e.g., Shang, Hsieh, & Shah, 2024), and engagement or loyalty (e.g., Chen et al., 2022) have validated measurement scales that can be adapted to AI contexts. Using Structural Equation Modeling (SEM), scholars can examine the direct, mediated, and moderated relationships posited in the framework— especially the role of digital literacy and AI labeling as moderators. Participants can be recruited from online consumer panels, platforms like Amazon MTurk or Prolific, or brand-specific communities that regularly interact with AI systems such as chatbots, recommendation engines, or virtual assistants. 5.4.2. Experimental Approaches to Test AI Framing and Labeling Effects Given the growing interest in psychological framing of AI (Chae & Lee, 2024), controlled experiments can be employed to examine how different AI labels (“AI-powered” vs. “smart assistant”), degrees of anthropomorphism, or personalization depth affect trust and consumer response. Between-subjects designs could manipulate the AI interface characteristics and measure subsequent shifts in perceived warmth, authenticity, or trust. This approach is particularly effective for testing Proposition 5, which posits that AI labeling moderates the relationship between trust and behavioral outcomes. Eye- tracking or neuroimaging tools (e.g., EEG) could be used in future studies to examine subconscious processing of AI cues. 5.4.3. Qualitative Insights Through In-Depth Interviews and Thematic Analysis To investigate how consumers interpret and emotionally respond to AI agents, future research could utilize employ semi-structured interviews combined with thematic analysis using tools like NVivo. This qualitative approach is particularly suitable for investigating complex constructs like perceived emotional comfort, intrusiveness, and psychological control, which may not be fully captured through structured surveys. The qualitative findings can help refine the framework by identifying emergent dimensions of AI perception and extending theoretical constructs (e.g., trust, perceived control) through the lens of lived consumer narratives. Additionally, conducting cross-cultural interviews may further illuminate how cultural norms and technological orientations shape consumer interpretations of AI across regions. 5.4.4. Mixed Methods Approaches for Contextual Richness and Scale Considering the multi-dimensional nature of AI-brand interactions, mixed methods research combining quantitative modeling with qualitative exploration can yield a more comprehensive understanding. For example, researchers might first conduct qualitative interviews to surface context- specific themes in AI perception and then develop quantitative surveys informed by these insights for large-scale testing. Such mixed methods designs enhance construct validity by anchoring measurement instruments in real-world consumer experiences, thereby bolstering both theoretical rigor and practical relevance. This approach is particularly valuable for refining theories and for deriving actionable insights for the design of human-centric AI systems. 5.4.5. Longitudinal and Cross-Platform Research Designs Lastly, future research would benefit from adopting longitudinal designs to capture how consumer trust and AI- related behaviors evolve over time. Given that trust formation is inherently dynamic, repeated-measures studies can shed light on how factors such as user satisfaction, accumulated experience, or system updates shape ongoing engagement or disengagement. 63 Moreover, comparative analyses across various industry sectors—such as e-commerce, financial services, healthcare, and entertainment—can reveal domain-specific variations that influence the applicability and generalizability of the proposed framework. 6. Conclusion The rise of artificial intelligence (AI) within digital branding ecosystems has fundamentally reshaped the landscape of consumer-brand interactions, compelling both scholars and practitioners to reconsider how trust, personalization, and emotional engagement unfold in AI- mediated environments. This study presents a comprehensive conceptual framework that weaves together consumer perceptions of AI, underlying trust mechanisms, and resultant behavioral outcomes, offering a psychologically rich and relationally grounded lens through which to interpret AI- driven brand experiences. Our multi-step model reveals a pivotal change among consumers toward AI agents: it's no longer solely judging functional utility. Rather, AI is being viewed more and more as a symbolic performer, actively conveying brand identity, evoking affective atmospheres, and creating a sense of social presence. We discovered that such things as consumers' perceptions of an AI's genuineness, whether it's able to convey socio-emotional signals, and users' extent of agency attribution are all determinants. These factors are responsible for building cognitive and affective trust—trust that serves as a bridge to such consequential outcomes as greater engagement, sustained loyalty, and deep emotional attachment. In all of this, our paradigm intentionally avoids strictly technology-driven perspectives, heightening contrast regarding deeply human and meaning-making features of human-AI interaction. Notably, this work adds finer-grained insight into the delicate tradeoff of personalization and intrusiveness. Plainly, AI-driven personalization has the potential to pull people into a relationship with a brand and make brand interaction seem more meaningful. However, this same ability invites backlash if it is experienced as overly controlling or invisible. By including moderating variables such as a user's digital literacy, a sense of control over interaction, and explicit AI labeling, our research provides a more context-sensitive and adjustable theoretical toolbox. This makes it possible for a far more nuanced understanding of all of the varied ways that different people, in different contexts and on different platforms, will react. Theoretically, our model doesn't simply coexist with what's currently known; it actually converses with, and builds upon, core conceptions of trust transfer theory and parasocial interaction theory, supplemented by new findings from an emerging field of AI anthropomorphism. For practitioners, that manifests in hard, usable takeaways for brand leaders seeking to integrate AI into operations. The objective is efficiency, certainly, but deploying AI that is ethically sound, emotionally savvy, and intuitively aligned with core brand values. Through mapping AI's intricate paths of influence over consumer psychographics, this research forges new paths toward designing AI initiatives that can artfully balance the strength of automation with a genuine human touch. Ultimately, while conceptual in nature, the framework is structured around five empirically testable propositions that directly invite future research. Suggested empirical pathways—including quantitative modeling, experimental designs, qualitative interviews, mixed methods, and longitudinal studies—highlight the versatility and applicability of the model across disciplines and industries. These directions also reflect a growing need for interdisciplinary collaboration, bridging marketing, psychology, information systems, and human-computer interaction. To conclude, this paper responds to the call for theory- driven understanding in the rapidly evolving field of AI branding. By articulating a relational and emotionally anchored approach to AI–consumer dynamics, it contributes a timely and robust foundation for both scholarly advancement and practical innovation in digital brand strategy. As AI continues to evolve in form and function, so too must our conceptual tools evolve to capture its impact on the future of brand relationships. Acknowledgements The author would like to express his sincere gratitude to Prof. Sabine Benoit for her invaluable guidance throughout the writing of this paper. Her insights and suggestions were instrumental in refining the theoretical framework and research propositions. The author also thanks the reviewers for their time and constructive comments. References [1] Araujo, T., Moura, M., & Radaelli, F. (2024). Navigating the intrusiveness paradox: A comprehensive literature review on AI marketing and consumer perception. Advances in Consumer Research. https://acr- journal.com/article/navigating-the-intrusiveness-paradox-a- comprehensive-literature-review-on-ai-marketing-and- consumer-perception-870/ [2] Chae, B. K., & Lee, S. Y. (2024). Consumer preferences for smart vs. AI-powered products: The role of framing in digital marketing. Computers in Human Behavior, 151, 107213. https://doi.org/10.1016/j.chb.2024.107213 [3] Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 33(6), 2205–2243. https://doi.org/10.1108/intr-09-2021-0686 [4] Chen, Q., Prentice, C., Weaven, S., & Hisao, R. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty in home-sharing platforms. Frontiers in Psychology, 13, Article 912339. https://doi.org/10.3389/fpsyg.2022.912339 [5] Dogruel, L., Masur, P. K., & Joeckel, S. (2025). What do we know about algorithmic literacy? The status quo and a research agenda for media studies. New Media & Society, 27(2), 345– 367. https://doi.org/10.1177/14614448231182662 [6] Fakhimi, A., Garry, T., & Biggemann, S. (2023). The effects of anthropomorphised virtual conversational assistants on consumer engagement and trust during service encounters. Australasian Marketing Journal, 31(4), 314–324. https://doi.org/10.1177/14413582231181140 [7] Jorzik, E., Krüger, J., & Zschech, P. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 5, 100062. https://colab.ws/articles/10.1016/j.chbah.2024.100062 [8] Kumar, A., & Sharma, S. (2024). Analytical modeling of perceived authenticity in AI assistants: Application of PLS- Predict algorithm and importance-performance map analysis. https://acr-journal.com/article/navigating-the-intrusiveness-paradox-a-comprehensive-literature-review-on-ai-marketing-and-consumer-perception-870/ https://acr-journal.com/article/navigating-the-intrusiveness-paradox-a-comprehensive-literature-review-on-ai-marketing-and-consumer-perception-870/ https://acr-journal.com/article/navigating-the-intrusiveness-paradox-a-comprehensive-literature-review-on-ai-marketing-and-consumer-perception-870/ https://acr-journal.com/article/navigating-the-intrusiveness-paradox-a-comprehensive-literature-review-on-ai-marketing-and-consumer-perception-870/ https://doi.org/10.1016/j.chb.2024.107213 https://doi.org/10.1108/intr-09-2021-0686 https://doi.org/10.3389/fpsyg.2022.912339 https://doi.org/10.1177/14614448231182662 https://doi.org/10.1177/14413582231181140 https://colab.ws/articles/10.1016/j.chbah.2024.100062 64 South Asian Journal of Business Studies, 13(2), 123–145. https://doi.org/10.1108/SAJBS-04-2023-0102 [9] Koverola, M., et al. (2023). Mitigating the Uncanny Valley Effect in Hyper-Realistic Robots: A Student-Centered Study on LLM-Driven Conversations. arXiv preprint. https://arxiv.org/html/2503.16449v1 [10] Lee, J., & Shin, D. (2024). Integrating Artificial Intelligence and Customer Experience. Asia Pacific Journal of Marketing and Logistics. https://journals.sagepub.com/doi/10.1177/1441358224125290 4 [11] Moore, S., McKechnie, S., & Ryan, L. (2023). Unpacking the personalisation-privacy paradox in the context of AI-enabled retail offers. Frontiers in Psychology, 14, Article 9840426. https://pmc.ncbi.nlm.nih.gov/articles/PMC9840426/ [12] Mikalef, P., Krogstie, J., & Enholm, I.M. (2022b). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w [13] Maeda, T., & Quan-Haase, A. (2024). When human-AI interactions become parasocial: Agency and anthropomorphism in affective design. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24) (pp. 1–13). https://facctconference.org/static/papers24/facct24-71.pdf [14] Musaiqer, A., & Hamdan, A. (2025). Impact of artificial intelligence on branding: A bibliometric review and future research agenda. Humanities and Social Sciences Communications, 12, Article 4488. https://www.nature.com/articles/s41599-025-04488-6 [15] Smith, J. (2024). Beyond algorithms: A comprehensive analysis of AI-driven personalization in strategic communications. Open Journal of Business and Management, 12(4), 1234–1267. https://www.scirp.org/journal/paperinformation?paperid=136 812 [16] Sharma, R., & Gupta, S. (2024). Balancing Customized Marketing with Consumer Data Protection. International Journal of Computer Trends and Technology, 72(9), 57–65. https://www.ijcttjournal.org/2024/Volume-72%20Issue- 9/IJCTT-V72I9P105.pdf [17] Sargın, S. (2024). Antecedents and consequences of consumers’ attitudes towards artificial intelligence in social media. Business and Economics Research Journal, 15(3), 229–256. https://www.berjournal.com/antecedents-and-consequences- of-consumers-attitudes-towards-artificial-intelligence-in- social-media [18] Soni, V. (2025). AI and the personalization-privacy paradox: Balancing customized marketing with consumer data protection. IRE Journals, 7(4), 411–418. https://www.irejournals.com/formatedpaper/1705983.pdf [19] Truong, T. H., & Ta, H. H. (2025). Impact of artificial intelligence on branding: A bibliometric review and future research directions. Humanities and Social Sciences Communications, 12, Article 4488. https://doi.org/10.1057/s41599-025-04488-6 [20] Varshini, A., & Venotha, A. S. (2025). AI in sustainable marketing and consumer behaviour: A transformational perspective. International Journal of Business and Economics Research, Special Issue, 165–168. https://drbgrpublications.in/wp-content/uploads/2025/Special- Issue/SJC_041.pdf [21] Wang, X., & Zhang, Y. (2024). Mechanism-based middle- range theories: Using realist synthesis to bridge generalizability and context in qualitative research. Qualitative Health Research, 34(11), 1234–1250. https://doi.org/10.1177/10497323251316401 [22] Zhang, Y., Wang, S., & Chen, X. (2024). Exploring the mechanism of sustained consumer trust in AI chatbots: The role of social interaction cues and anthropomorphic factors. Humanities and Social Sciences Communications, 11, Article 347. https://doi.org/10.1057/s41599-024-03879-5 https://doi.org/10.1108/SAJBS-04-2023-0102 https://arxiv.org/html/2503.16449v1 https://journals.sagepub.com/doi/10.1177/14413582241252904 https://journals.sagepub.com/doi/10.1177/14413582241252904 https://pmc.ncbi.nlm.nih.gov/articles/PMC9840426/ https://doi.org/10.1007/s10796-021-10186-w https://facctconference.org/static/papers24/facct24-71.pdf https://www.nature.com/articles/s41599-025-04488-6 https://www.scirp.org/journal/paperinformation?paperid=136812 https://www.scirp.org/journal/paperinformation?paperid=136812 https://www.ijcttjournal.org/2024/Volume-72%20Issue-9/IJCTT-V72I9P105.pdf https://www.ijcttjournal.org/2024/Volume-72%20Issue-9/IJCTT-V72I9P105.pdf https://www.berjournal.com/antecedents-and-consequences-of-consumers-attitudes-towards-artificial-intelligence-in-social-media https://www.berjournal.com/antecedents-and-consequences-of-consumers-attitudes-towards-artificial-intelligence-in-social-media https://www.berjournal.com/antecedents-and-consequences-of-consumers-attitudes-towards-artificial-intelligence-in-social-media https://www.irejournals.com/formatedpaper/1705983.pdf https://doi.org/10.1057/s41599-025-04488-6 https://drbgrpublications.in/wp-content/uploads/2025/Special-Issue/SJC_041.pdf https://drbgrpublications.in/wp-content/uploads/2025/Special-Issue/SJC_041.pdf https://doi.org/10.1177/10497323251316401 https://doi.org/10.1057/s41599-024-03879-5