Human-Centric AI Digital Human Advisors and Brand Trust: Social Presence versus Telepresence

Human-Centric AI Digital Human Advisors and Brand Trust: Social Presence versus Telepresence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Human-Centric AI Digital Human Advisors and Brand Trust: Social Presence versus Telepresence TANG YISHU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9084958/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract AI Digital Human Advisors have become increasingly prevalent as socially expressive frontline agents in digital financial platforms, yet how their design features translate into brand trust remains insufficiently understood. Drawing on the Stimulus–Organism–Response (S–O–R) framework, this study examines how form cues, behavioral cues, and interaction cues of AI Digital Human Advisors influence brand trust through presence-based experiential mechanisms. Using survey data from 487 experienced users of AI digital human advisors in financial applications and structural equation modeling, the results reveal a clear asymmetric pattern. While all three categories of design cues significantly enhance both social presence and telepresence, only social presence directly contributes to brand trust and serves as a robust mediating mechanism. Telepresence, although associated with immersive experiential engagement, does not independently foster trust in high-risk financial advisory contexts. These findings advance a human-centric perspective on digital transformation by demonstrating that trust in AI-mediated financial services is grounded more in socially responsive interaction than in immersive experiential vividness, offering implications for the design of trustworthy and sustainable AI-enabled financial platforms. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Digital Transformation Human-centric Artificial Intelligence AI Digital Human Advisors Social Presence Telepresence Brand Trust Figures Figure 1 Figure 2 1. Introduction The accelerating diffusion of artificial intelligence (AI) across financial services has fundamentally reshaped how individuals engage with digital platforms and financial institutions. Beyond back-end algorithmic decision systems, an increasing number of digital finance platforms are now deploying AI Digital Human Advisors—humanlike virtual agents equipped with embodied appearance, expressive behaviors, and interactive communication capabilities—to deliver advisory services, customer support, and investment guidance. These systems reflect a broader trajectory of digital transformation in which intelligent technologies are no longer confined to efficiency enhancement but are increasingly positioned at the interface between organizations and users ( 1 ). Unlike traditional robo-advisors that primarily emphasize computational accuracy and task automation, AI Digital Human Advisors are deliberately designed to assume social roles and function as symbolic representatives of financial brands through quasi-interpersonal interaction. Recent advances in embodied conversational agents and real-time animation technologies have further enhanced their capacity to simulate socially meaningful encounters within digital environments( 2 , 3 ). Within this evolving landscape of AI-enabled financial services, trust remains a central yet fragile foundation for sustainable digital transformation, particularly in platform-mediated service environments where user trust must be established without direct interpersonal contact ( 4 ). Financial decision making is inherently characterized by uncertainty, perceived risk, and information asymmetry, rendering brand trust a critical prerequisite for user acceptance, continued engagement, and long-term platform resilience ( 5 , 6 ). In conventional advisory contexts, trust is primarily cultivated through face-to-face interaction, where human advisors convey competence, benevolence, and integrity through rich social cues. As advisory functions are increasingly delegated to AI Digital Human Advisors, however, users can no longer rely on direct interpersonal contact. Instead, trust-related judgments must be inferred from design cues embedded in the AI-mediated interface itself, including visual presentation, behavioral expressiveness, and interaction quality. Despite the growing prevalence of such systems, existing research in digital finance and AI services has largely focused on adoption intentions, algorithm aversion, or comparative performance evaluations between human and automated advisors( 7 , 8 ). Consequently, limited attention has been devoted to understanding how specific AI design features shape users’ internal experiential states and, in turn, their trust in the financial brands represented by these technologies. To address this gap, the present study adopts the Stimulus–Organism–Response (S–O–R) framework to conceptualize brand trust formation as an experience-driven process within AI-mediated financial services. From this perspective, the design cues of AI Digital Human Advisors constitute external stimuli, users’ psychological experiences during interaction represent organismic states, and brand trust functions as the evaluative response. Rather than treating AI advisors as monolithic technological artifacts, this study differentiates design cues into three analytically distinct categories—form cues, behavioral cues, and interaction cues—capturing how AI Digital Human Advisors communicate social meaning through appearance, action, and dialogue. Central to this experiential pathway are two presence-related mechanisms: social presence and telepresence. Social presence refers to the extent to which users perceive the AI advisor as a socially responsive and engaging interaction partner, fostering a sense of interpersonal connection within mediated communication. Telepresence, by contrast, reflects users’ immersive psychological involvement in the digital advisory environment, characterized by the sensation of “being there” rather than merely interacting with a technological interface ( 9 ). Emerging evidence from AI-enabled service research suggests that these two forms of presence operate through complementary yet distinct mechanisms in shaping user evaluations of intelligent agents and the brands they represent ( 10 ). Nevertheless, empirical understanding remains limited regarding how different categories of AI design cues activate social presence and telepresence, and how these experiential states jointly contribute to brand trust in digital financial platforms. Accordingly, this study investigates how form, behavioral, and interaction cues of AI Digital Human Advisors influence brand trust through the mediating roles of social presence and telepresence. By foregrounding users’ experiential perceptions rather than system-level performance metrics, the study moves beyond outcome-oriented accounts of AI adoption and opens the experiential “black box” through which AI design features are translated into trust-related judgments. In doing so, it advances a human-centric understanding of AI-driven digital transformation in financial services and offers actionable insights for designing trustworthy and sustainable AI-mediated advisory systems. Based on these objectives, the study addresses the following research questions: RQ1 How do form cues, behavioral cues, and interaction cues of AI Digital Human Advisors influence users’ social presence and telepresence in digital financial services? RQ2 Do social presence and telepresence mediate the relationships between AI Digital Human Advisor design cues and brand trust? 2. Literature review and hypothesis development 2.1 AI Digital Human Advisors in Digital Finance AI Digital Human Advisors represent a distinctive evolution of intelligent service interfaces within digital finance, reflecting a broader transformation from purely algorithm-centered systems toward socially oriented, human-centric AI applications. These systems combine embodied digital avatars with natural language processing and adaptive communication capabilities, enabling them to deliver financial consultation, customer assistance, and investment guidance through humanlike interaction. Rather than functioning solely as back-end decision-support tools, AI Digital Human Advisors increasingly operate as frontline service agents embedded within digital platforms, directly mediating the relationship between financial institutions and users ( 2 , 11 ). This shift aligns with contemporary trajectories of digital transformation in which AI technologies are deployed not only to enhance operational efficiency but also to shape user experience, relational engagement, and platform trust. A defining feature that distinguishes AI Digital Human Advisors from earlier generations of text-based chatbots or rule-based robo-advisors lies in their explicit orientation toward social expressiveness. Advances in embodied agent technologies allow these systems to present visually recognizable human forms, display behaviorally expressive actions, and engage in responsive, dialogue-based interaction ( 3 ). Such design elements are not incidental aesthetic enhancements; rather, they are deliberately implemented to convey social cues that encourage users to perceive AI advisors as socially capable interaction partners. Prior research in human–AI interaction demonstrates that when AI systems exhibit humanlike appearance and socially contingent behaviors, users are more likely to engage social perception processes and evaluate these agents in terms of credibility, engagement, and relational appropriateness, rather than purely technical performance( 6 , 12 ). The significance of AI Digital Human Advisors is particularly pronounced in digital financial services, where user decisions are typically associated with elevated uncertainty, perceived risk, and long-term consequences. In conventional financial advisory contexts, trust is cultivated through interpersonal interaction, advisor demeanor, and perceived attentiveness during service encounters. As financial institutions increasingly delegate advisory and communicative functions to AI-driven agents, users can no longer rely on direct interpersonal contact to assess trustworthiness. Instead, they must infer the reliability and integrity of the institution behind the technology through design-mediated signals embedded in the AI interface itself. Empirical evidence suggests that when AI agents display humanlike appearance and socially responsive behaviors, users are more inclined to apply interpersonal heuristics and social norms, interpreting these systems as social actors participating in relational exchanges rather than as neutral technological artifacts ( 13 , 14 ). Despite their expanding deployment across banking applications, investment platforms, and insurance services, scholarly research on AI Digital Human Advisors has remained disproportionately focused on adoption outcomes, performance evaluations, or direct comparisons with human advisors. Comparatively limited attention has been devoted to examining how specific design characteristics of AI Digital Human Advisors shape users’ internal psychological experiences during interaction, particularly in trust-sensitive financial contexts. Moreover, existing studies rarely differentiate among distinct categories of design cues—such as visual form, behavioral expression, and interaction responsiveness—when analyzing their effects on user perception. Addressing this limitation, the present study conceptualizes AI Digital Human Advisors as socially expressive service agents embedded within digital platforms and foregrounds the experiential processes through which design cues influence trust-related evaluations. This perspective establishes a necessary foundation for examining presence-based mechanisms as central pathways linking AI design features to brand trust in digital finance. 2.2 Theoretical Foundations and Conceptual Framework Understanding how AI Digital Human Advisor design features shape brand trust in digital finance requires a process-oriented theoretical perspective that accounts for users’ internal experiential responses during interaction. In technology-mediated service environments, design characteristics seldom exert direct effects on evaluative outcomes. Instead, their influence is typically realized through users’ psychological interpretations and experiential perceptions formed while engaging with the system. To capture this indirect mechanism, the present study adopts the Stimulus–Organism–Response (S–O–R) framework as an overarching structure for modeling trust formation in AI-mediated financial services. Within the S–O–R perspective, external stimuli represent observable features of the service environment, organismic states reflect users’ internal experiential reactions, and responses denote subsequent evaluative judgments. Prior research in digital services and human–computer interaction consistently demonstrates that interface design, interaction quality, and system features shape user responses primarily through experiential states rather than through direct cognitive assessment of technological attributes ( 15 ). This mediating logic is particularly salient in AI-enabled contexts, where users must actively interpret system-generated cues to make sense of non-human service agents. In the context of AI Digital Human Advisors, design cues operate as socially expressive stimuli rather than neutral technological inputs. Visual form, behavioral expression, and interaction responsiveness collectively communicate social meaning and guide how users construe the role, capability, and intent of the AI advisor. These cues elicit internal experiential states that extend beyond functional evaluation, encompassing perceptions of social engagement and immersive involvement during interaction. Accordingly, the present study conceptualizes form cues, behavioral cues, and interaction cues as key stimuli within the S–O–R framework, while social presence and telepresence are modeled as central organismic states capturing users’ experiential responses in digital financial encounters. To explain why users respond to AI-generated cues in social and experiential terms, this study further draws on Social Response Theory and the Computers Are Social Actors (CASA) paradigm. These perspectives posit that individuals tend to apply social heuristics and interpersonal norms to technological systems whenever they display socially meaningful cues, such as humanlike appearance, natural language communication, or contingent interaction ( 16 , 17 ). Crucially, such responses occur largely automatically and persist even when users are fully aware that the interaction partner is non-human. In AI-mediated services, the presence of anthropomorphic and interactive design elements encourages users to perceive AI advisors as socially capable agents, thereby transforming technology-mediated encounters into socially meaningful experiences rather than purely instrumental exchanges. Within digital financial services—where advisory interactions are characterized by uncertainty, perceived risk, and high decision stakes—these presence-based experiential perceptions play a particularly consequential role. When AI Digital Human Advisors substitute for or complement human advisors, users increasingly rely on socially grounded experiential cues to infer credibility, reliability, and trustworthiness. From this perspective, social presence reflects users’ perception of interpersonal connection and social responsiveness during interaction, whereas telepresence captures their immersive psychological engagement with the advisory environment. Although conceptually distinct, these two experiential states operate through complementary mechanisms: social presence facilitates relational assurance, while telepresence enhances experiential credibility and situational involvement. Together, they provide parallel pathways through which AI design cues are translated into brand trust within digital financial platforms. Integrating the S–O–R framework with Social Response Theory and CASA, the present study advances a conceptual model in which AI Digital Human Advisor design cues influence brand trust indirectly through users’ perceptions of social presence and telepresence. By positioning presence-based experiences as central explanatory mechanisms, the model foregrounds the experiential foundations of trust formation in AI-mediated financial services, moving beyond outcome-oriented evaluations of system performance or algorithmic capability. The proposed relationships among design cues, experiential mechanisms, and brand trust are summarized in Fig. 1 . 2.3 Design Cues and Social Presence in AI-Mediated Financial Services In AI-mediated financial services, users rarely evaluate intelligent systems solely on the basis of algorithmic accuracy or functional performance. Instead, trust-related judgments are predominantly formed through experiential interpretations that emerge during interaction with AI agents ( 18 , 19 ). This experiential orientation is particularly salient in financial service contexts characterized by high uncertainty, perceived risk, and outcome irreversibility, where users tend to rely more heavily on interaction quality, social signals, and relational cues rather than purely technical assessments when forming evaluations of AI-enabled services ( 20 , 21 ). As AI Digital Human Advisors increasingly assume advisory and communicative roles traditionally fulfilled by human professionals, users become more dependent on agent-level design cues—such as anthropomorphic features, behavioral expressiveness, and interaction styles—to interpret the nature of the interaction and infer the credibility of the underlying financial institution ( 22 , 23 ). Prior research consistently indicates that socially expressive and humanlike design cues play a critical role in shaping users’ experiential perceptions during AI-mediated interaction, thereby constituting a key psychological mechanism through which evaluations are formed in digital service environments ( 18 , 23 ). Grounded in the Stimulus–Organism–Response framework, design cues can therefore be conceptualized as external stimuli that activate users’ internal experiential states, which subsequently influence evaluative responses in AI-driven financial services ( 19 , 24 ). Design cues embedded in AI Digital Human Advisors constitute critical triggers that activate social presence. When an AI advisor presents socially expressive cues through its appearance, behavior, and interaction patterns, users are more likely to apply interpersonal heuristics and construe the system as a socially capable interaction partner rather than as an impersonal technological interface ( 25 ). Form cues—such as humanlike visual appearance and embodied representation—serve as immediate perceptual anchors that shape first impressions and increase the likelihood that users categorize the AI advisor as a socially relevant entity ( 26 , 27 ). Behavioral cues—such as movement fluidity, response naturalness, and adaptive expressiveness—reinforce these social interpretations during ongoing interaction by signaling liveliness, intentionality, and responsiveness ( 28 ). Empirical evidence further suggests that behaviorally expressive AI agents are more likely to be perceived as socially capable and engaging, particularly in advice-oriented service contexts where attentiveness and responsiveness are central to users’ evaluative criteria ( 29 , 30 ). Interaction cues—such as timely feedback, bidirectional exchange, and contingent responses—further strengthen users’ perceptions of reciprocity and engagement, enhancing the sense that the AI advisor is socially attentive and involved. Collectively, these cue dimensions operate as socially meaningful stimuli that foster users’ perceptions of human contact, warmth, and interpersonal exchange, thereby enhancing social presence during AI-mediated financial interactions. Social presence reflects the extent to which users perceive interpersonal connection, attentiveness, and social awareness when engaging with an AI advisor, even in the absence of a human counterpart; this becomes especially salient in digital financial services characterized by high uncertainty and perceived risk ( 31 ). Based on the above reasoning, the following hypotheses are proposed: H1a The form cues of AI Digital Human Advisors positively influence users’ social presence. H1b The behavioral cues of AI Digital Human Advisors positively influence users’ social presence. H1c The interaction cues of AI Digital Human Advisors positively influence users’ social presence. 2.4 Design Cues and Telepresence in AI-Mediated Financial Services Beyond relational perceptions, AI-mediated financial interactions also give rise to immersive experiential states that shape how users psychologically engage with advisory environments. Telepresence captures this dimension of experience and refers to users’ immersive psychological involvement in a mediated interaction space, characterized by experiential vividness, sustained attentional engagement, and the subjective sensation of “being there” rather than merely interacting through a technological interface ( 32 , 33 ). In digital financial services, telepresence reflects the extent to which users become experientially absorbed in the advisory encounter, even when interaction occurs remotely and through artificial agents. Design cues embedded in AI Digital Human Advisors play a central role in eliciting telepresence by structuring the perceptual and interactional qualities of the mediated environment. Embodied visual representation contributes to telepresence by situating the advisory interaction within a coherent perceptual space, enhancing spatial realism and reducing the perceived artificiality of the mediated setting ( 34 , 35 ). When the AI advisor’s appearance supports perceptual continuity and environmental coherence, users are more likely to experience a heightened sense of immersion within the advisory context. In addition to visual form, behavioral cues—such as natural movement, temporal coherence, and fluid responsiveness—further strengthen telepresence by minimizing cognitive disruption during interaction and enabling users to maintain experiential flow and psychological absorption ( 11 ). Interaction cues, including smooth turn-taking, timely feedback, and continuous dialogue, reinforce this immersive experience by sustaining attentional focus and reducing breaks in the interaction process. When AI Digital Human Advisors maintain fluent, responsive, and temporally coherent interaction, users are more likely to remain experientially embedded in the advisory encounter, thereby strengthening telepresence even in technology-mediated financial service settings ( 33 ). Through the combined influence of form cues, behavioral cues, and interaction cues, the mediated advisory environment becomes experientially vivid and engaging, allowing users to experience the interaction as psychologically involving rather than purely instrumental. Based on the above reasoning, the following hypotheses are proposed: H2a The form cues of AI Digital Human Advisors positively influence users’ telepresence. H2b The behavioral cues of AI Digital Human Advisors positively influence users’ telepresence. H2c The interaction cues of AI Digital Human Advisors positively influence users’ telepresence. 2.5 Presence and Brand Trust in AI-Mediated Financial Services In AI-mediated financial services, brand trust represents users’ confidence in the reliability, integrity, and competence of the financial institution represented by the AI advisor. Because users rarely interact directly with human representatives in digital financial platforms, trust-related judgments are increasingly shaped by experiential perceptions formed during AI-mediated interactions rather than by direct interpersonal contact. Within this context, presence-based experiences play a central role in translating interaction episodes into broader brand-level evaluations. Social presence contributes to brand trust primarily through relational assurance. When users perceive an AI Digital Human Advisor as socially attentive, responsive, and interpersonally engaged, the interaction signals benevolence, accountability, and concern for users’ interests—qualities that are especially salient in advisory contexts characterized by information asymmetry and elevated perceived risk ( 6 , 8 ). Experiencing social presence encourages users to attribute humanlike intentionality and normative responsibility to the AI advisor, which in turn facilitates the extension of trust judgments from the immediate interaction to the financial institution the advisor represents ( 36 ). In this sense, social presence functions as a relational bridge that links AI-mediated interaction experiences to brand-level trust evaluations. Telepresence, by contrast, is expected to influence brand trust through experiential credibility rather than through interpersonal bonding. Immersive psychological engagement reduces the perceived distance inherent in remote and technology-mediated services and enhances users’ perceptions of process continuity, system reliability, and operational stability ( 37 ). When advisory interactions feel vivid, coherent, and experientially grounded, users may infer that the underlying service infrastructure and organizational capabilities are competent and dependable. Prior research suggests that such immersive experiences can strengthen trust by reinforcing confidence in the technological and organizational foundations supporting the service encounter, even in the absence of strong interpersonal cues ( 38 ). Accordingly, telepresence is theorized as a complementary experiential pathway through which AI-mediated interactions may shape brand trust. Taken together, social presence and telepresence capture two distinct yet potentially convergent experiential routes linking AI-mediated interaction to brand trust in digital financial services. Social presence emphasizes relational engagement and interpersonal assurance, whereas telepresence emphasizes immersive involvement and experiential credibility. Based on this theoretical reasoning, the following hypotheses are proposed: H3 Social presence positively influences brand trust in AI-mediated financial services. H4 Telepresence positively influences brand trust in AI-mediated financial services. 2.6 The Mediating Role of Social Presence and Telepresence From a process-oriented perspective, the effects of AI Digital Human Advisor design cues on brand trust are unlikely to occur in a direct or mechanical manner. Instead, these effects are expected to be transmitted through users’ experiential perceptions formed during AI-mediated interaction. Within the Stimulus–Organism–Response framework, design cues function as external stimuli, brand trust represents the evaluative response, and presence-based experiences constitute the organismic states through which this translation occurs. Accordingly, social presence and telepresence are conceptualized as mediating mechanisms that link AI Digital Human Advisor design cues to brand trust in digital financial services. Social presence serves as a relational mediating mechanism by translating socially expressive design cues into trust-related evaluations. When form cues, behavioral cues, and interaction cues collectively foster perceptions of interpersonal connection, attentiveness, and social awareness, users are more likely to interpret the advisory interaction as socially grounded and normatively appropriate. Such relational experiences signal benevolence, accountability, and concern for users’ interests, which encourages users to extend trust judgments beyond the immediate interaction to the financial institution represented by the AI advisor ( 6 , 8 , 36 ). In this sense, social presence operates as a relational bridge that mediates the influence of AI design cues on brand trust by embedding trust judgments within socially meaningful interaction experiences. Telepresence, in contrast, is expected to function as an experiential mediating mechanism by translating design cues into perceptions of experiential credibility rather than interpersonal assurance. When AI Digital Human Advisor design cues enhance immersive psychological involvement, users may experience the advisory encounter as vivid, coherent, and experientially grounded. Such immersive experiences can reduce perceived distance and strengthen perceptions of system reliability, process continuity, and organizational competence ( 37 , 38 ). Through this pathway, telepresence is theorized to mediate the relationship between AI design cues and brand trust by reinforcing users’ confidence in the technological and institutional foundations underlying the AI-mediated service encounter. Taken together, social presence and telepresence represent two parallel yet conceptually distinct experiential mechanisms through which AI Digital Human Advisor design cues may influence brand trust in digital financial services. While social presence emphasizes relational assurance and interpersonal meaning, telepresence emphasizes immersive involvement and experiential credibility. On the basis of this mediating logic, the following hypotheses are proposed: H5a Social presence mediates the relationship between form cues and brand trust. H5b Social presence mediates the relationship between behavioral cues and brand trust. H5c Social presence mediates the relationship between interaction cues and brand trust. H6a Telepresence mediates the relationship between form cues and brand trust. H6b Telepresence mediates the relationship between behavioral cues and brand trust. H6c Telepresence mediates the relationship between interaction cues and brand trust. 3. Method 3.1 Research Design, Data Collection, and Sample This study employed a scenario-supported survey design to investigate users’ experiential responses to AI-enabled digital human advisory services in the context of digital finance. The research design was grounded in the premise that meaningful evaluations of socially expressive AI systems are most accurately elicited from users with prior, real-world interaction experience. Accordingly, participation was restricted to individuals who had previously used financial applications featuring AI-based digital human advisors for consultation, guidance, or customer service purposes. Rather than introducing participants to an unfamiliar or artificially constructed system, the survey was designed to activate respondents’ existing experiential memories of interacting with AI digital human advisors in their everyday financial app usage. At the beginning of the questionnaire, respondents were presented with a brief description and a representative video stimulus illustrating a typical AI digital human advisor interaction. This stimulus served as a contextual cue to anchor respondents’ evaluations in their own prior experiences, enabling them to map the questionnaire items onto familiar interaction patterns, service scripts, and interface features encountered in real-world digital finance environments. Participants were explicitly instructed to answer all items based on their own prior real-world experiences with AI digital human advisors in financial apps, rather than evaluating the specific system depicted in the video. To further reduce potential common method bias, several procedural remedies were implemented in the questionnaire design and data collection process ( 39 ). First, respondents were assured of anonymity and informed that there were no right or wrong answers, thereby reducing evaluation apprehension and social desirability bias. Second, screening questions and attention checks were incorporated to exclude careless or inexperienced respondents and to enhance response quality. Third, the questionnaire included reverse-coded and neutrally worded items to minimize acquiescence bias and common scale-related artifacts. Finally, the use of a scenario-supported design and experience-based screening helped anchor respondents’ evaluations in their own accumulated interaction experiences rather than in transient stimulus impressions, thereby mitigating the risk of systematic response inflation due to common measurement context. For users with prior experience, even relatively concise or abstract measurement items are sufficient to elicit stable and meaningful experiential judgments, as these items function as prompts that reactivate accumulated interaction schemas rather than as exhaustive descriptions of system features. Data were collected through a professional online survey platform Wenjuanxing in mainland China. Initial screening questions ensured that only respondents with verified experience using AI-enabled digital human advisors in financial applications were retained. Participation was voluntary and anonymous, and respondents were informed that the study was conducted solely for academic research purposes. No personally identifiable information was collected. A total of 527 questionnaires were distributed. After excluding incomplete responses and questionnaires that failed attention or consistency checks, 487 valid responses were retained for analysis, yielding an effective response rate of approximately 92.4%. The final sample exhibited substantial heterogeneity across demographic and usage-related characteristics, providing a robust empirical basis for examining experiential mechanisms in digital financial services. As reported in Table 1 , the gender distribution was balanced, and respondents covered a broad age range, with the majority concentrated between 30 and 49 years—an age cohort that represents core users of contemporary digital financial platforms. Educational attainment and income levels were well distributed, indicating representation across multiple socioeconomic strata. In addition, respondents varied considerably in their frequency of financial app usage, ranging from occasional users to daily users, reflecting different levels of familiarity and engagement with AI-mediated financial services. Occupational backgrounds and city tiers were likewise diverse, encompassing users from first-tier, new first-tier, and lower-tier cities. This diversity enhances the external validity of the findings and supports the generalizability of the results within the broader context of platform-based digital finance. Collectively, the sample characteristics align with the profile of experienced users who routinely interact with AI digital human advisors and are therefore capable of providing informed experiential evaluations of design cues, presence perceptions, and brand trust. Table 1 Sample Profile Variable Category Frequency Percentage (%) Gender Male 248 50.92 Female 239 49.08 Age 20–29 years 74 15.20 30–39 years 128 26.28 40–49 years 140 28.75 50–59 years 84 17.25 60 years and above 61 12.53 Highest Education Level High school or below 102 20.94 Junior college (Associate degree) 115 23.61 Bachelor’s degree 129 26.49 Master’s degree or above 141 28.95 Monthly Income Below RMB 5,000 125 25.67 RMB 5,000–10,000 113 23.20 RMB 10,001–20,000 137 28.13 Above RMB 20,000 112 23.00 Monthly Usage Frequency of Financial Apps Rarely use 69 14.17 5–10 times 76 15.61 11–15 times 71 14.58 16–20 times 56 11.50 21–25 times 73 14.99 26–30 times 67 13.76 Daily use 75 15.40 Occupation Employees of enterprises or public institutions 194 39.84 Self-employed / Freelancers 157 32.24 Students 19 3.90 Others 117 24.02 City Tier First-tier cities 115 23.61 New first-tier cities 174 35.73 Second-tier cities 122 25.05 Other cities 76 15.61 3.2 Measurement Quality To assess the reliability and validity of the measurement model, a series of confirmatory factor analyses (CFA) and construct validity tests were conducted using structural equation modeling. All latent constructs—form cues, behavioral cues, interaction cues, social presence, telepresence, and brand trust—were modeled as reflective constructs and estimated simultaneously. Given that all measurement items were derived from well-established scales and adapted to the digital finance context, the evaluation focused on overall model fit, convergent validity, and discriminant validity. The overall measurement model demonstrated a satisfactory fit to the data. As reported in Table 2 , the chi-square to degrees of freedom ratio was well below the recommended threshold, indicating an acceptable level of parsimony. Incremental and absolute fit indices, including GFI, AGFI, CFI, NFI, and TLI, all exceeded conventional cutoff values, while RMSEA remained well below the upper bound typically suggested for good model fit. Collectively, these indices indicate that the proposed six-factor measurement structure adequately captures the covariance structure among the observed variables and supports the distinctiveness of the underlying constructs. Importantly, the CFA results suggest that users were able to reliably differentiate between design cue dimensions (form, behavioral, and interaction cues), experiential states (social presence and telepresence), and brand trust evaluations, despite the conceptual relatedness of these constructs in AI-mediated service contexts. This finding aligns with the study’s theoretical premise that design cues and presence-based experiences represent analytically separable stages within the experiential trust formation process. Table 2 Confirmatory Factor Analysis (CFA) Model Fit Indices Fit Indices χ2 df χ²/df GFI RMSEA AGFI CFI NFI TLI Recommended Thresholds - - 0.9 0.9 > 0.9 > 0.9 > 0.9 Values 359.116 260 1.381 0.944 0.028 0.930 0.985 0.948 0.983 Note. χ²/df < 3, RMSEA 0.90 indicate an acceptable model fit. Convergent validity was evaluated by examining composite reliability (CR) and average variance extracted (AVE) for each construct. As shown in Table 3 , all CR values exceeded the recommended threshold of 0.70, indicating satisfactory internal consistency. In addition, AVE values for all constructs were above 0.50, demonstrating that a substantial proportion of variance in the observed indicators is captured by their respective latent constructs. These results provide evidence that the measurement items consistently reflect their intended constructs. This is particularly relevant given the experiential nature of the focal variables. Because respondents were selected based on prior experience with AI-enabled digital human advisory services, the measurement items functioned as cognitive and experiential anchors that activated accumulated interaction memories rather than requiring respondents to infer abstract system characteristics. Under such conditions, even concise or perceptually oriented items are sufficient to capture stable experiential judgments, thereby supporting the observed levels of convergent validity. Table 3 Convergent Validity of Constructs Construct CR AVE Form Cues 0.878 0.644 Behavioral Cues 0.844 0.643 Interaction Cues 0.899 0.598 Social Presence 0.865 0.681 Telepresence 0.887 0.611 Brand Trust 0.858 0.601 Note. CR = composite reliability; AVE = average variance extracted. All CR values exceed 0.70 and all AVE values exceed the recommended threshold of 0.50, indicating satisfactory convergent validity. Discriminant validity was assessed using the Fornell–Larcker criterion. As reported in Table 4 , the square roots of the AVE for each construct exceeded the corresponding inter-construct correlations. This pattern indicates that each construct shares more variance with its own indicators than with other constructs in the model. The results further confirm that conceptually adjacent constructs—such as social presence and telepresence, or behavioral cues and interaction cues—remain empirically distinct in users’ evaluations. This distinction is theoretically meaningful in the context of AI-mediated financial services, where relational perceptions and immersive experiences may co-occur but are not experienced as interchangeable. The establishment of discriminant validity therefore reinforces the appropriateness of modeling social presence and telepresence as parallel experiential mechanisms rather than as a single undifferentiated presence construct. Overall, the measurement model demonstrates satisfactory reliability and validity, providing a sound empirical foundation for subsequent hypothesis testing and structural model estimation. The full list of measurement items and their sources is provided in Appendix Table A1. Table 4 Discriminant Validity Based on Latent Variable Correlations (Fornell–Larcker Criterion) Construct Form Cues Behavioral Cues Interaction Cues Social Presence Telepresence Brand Trust Form Cues 0.802 Behavioral Cues 0.506*** 0.802 Interaction Cues 0.414*** 0.456*** 0.773 Social Presence 0.481*** 0.434*** 0.408*** 0.825 Telepresence 0.429*** 0.534*** 0.413*** 0.370*** 0.782 Brand Trust 0.420*** 0.416*** 0.380*** 0.464*** 0.227*** 0.776 Note. Diagonal elements (in bold) represent the square roots of AVE. Off-diagonal elements are correlations among latent constructs estimated from the confirmatory factor analysis (CFA). *** p < 0.001. Values are rounded to three decimal places. 4. Results 4.1 Correlation and Structural Model Results Prior to testing the proposed hypotheses, descriptive statistics and bivariate correlations among the key constructs were examined. Descriptive statistics for all constructs are reported in Appendix Table A2. As shown in Table 5 , all design cue variables—form cues, behavioral cues, and interaction cues—were positively and significantly correlated with social presence, telepresence, and brand trust. Social presence exhibited moderate positive correlations with both design cues and brand trust, whereas telepresence showed weaker correlations with brand trust. These correlation patterns are consistent with the conceptual distinction between relational presence and immersive experience proposed in the theoretical framework and provide preliminary support for the hypothesized relationships. It should be noted that the correlations reported in Table 5 are based on observed scale means, whereas the inter-construct correlations reported in Table 4 are latent correlations estimated from the CFA. Differences in magnitude between the two tables therefore reflect differences in measurement level rather than inconsistency in results. Table 5 Pearson Correlation Matrix Based on Observed Scale Means Construct Form Cues Behavioral Cues Interaction Cues Social Presence Telepresence Brand Trust Form Cues 1 Behavioral Cues 0.440** 1 Interaction Cues 0.372** 0.398** 1 Social Presence 0.423** 0.373** 0.360** 1 Telepresence 0.385** 0.470** 0.378** 0.326** 1 Brand Trust 0.374** 0.364** 0.336** 0.407** 0.207** 1 Note. Pearson correlation coefficients are based on observed scale means of each construct. ** p < 0.01 (two-tailed). The structural model was then estimated using structural equation modeling to test the proposed direct effects among design cues, presence-based experiential states, and brand trust. Overall, the structural model demonstrated a satisfactory fit to the data. As reported in Table 6 , the chi-square to degrees of freedom ratio was well within acceptable limits, and all major goodness-of-fit indices—including GFI, AGFI, CFI, NFI, and TLI—exceeded recommended threshold values. The RMSEA value was also below the commonly accepted cutoff, indicating a good approximation of the data by the proposed model. These results suggest that the hypothesized structural relationships provide an adequate representation of the underlying data structure. Table 6 Structural Equation Modeling (SEM) Model Fit Indices Fit Indices χ2 df χ²/df GFI RMSEA AGFI CFI NFI TLI Recommended Thresholds - - 0.9 0.9 > 0.9 > 0.9 > 0.9 Values 402.192 264 1.523 0.937 0.033 0.922 0.979 0.942 0.976 Note. χ²/df < 3, RMSEA 0.90 indicate an acceptable model fit. The standardized path coefficients for the structural model are presented in Table 7 , and the simplified model with direct effects is illustrated in Fig. 2 . With respect to the effects of design cues on social presence, form cues ( β = 0.311, p < 0.001), behavioral cues ( β = 0.204, p < 0.001), and interaction cues ( β = 0.201, p < 0.001) all exerted significant positive effects. These findings support H1a , H1b , and H1c , indicating that visually embodied characteristics, behavioral expressiveness, and interaction responsiveness each contribute to users’ perceptions of social presence during AI-mediated financial service encounters. Similarly, all three categories of design cues were found to have significant positive effects on telepresence. Form cues positively influenced telepresence ( β = 0.174, p = 0.002), as did behavioral cues ( β = 0.371, p < 0.001) and interaction cues (β = 0.176, p < 0.001), supporting H2a , H2b , and H2c . Among these effects, behavioral cues exhibited the strongest association with telepresence, suggesting that movement fluidity, response naturalness, and behavioral realism play a particularly important role in fostering immersive experiential involvement. Regarding the relationships between presence-based experiential states and brand trust, social presence had a strong and significant positive effect on brand trust ( β = 0.459, p < 0.001), providing support for H3 . In contrast, the effect of telepresence on brand trust was not statistically significant ( β = 0.080, p = 0.114), leading to a rejection of H4 . This pattern indicates that while immersive experiential involvement is successfully elicited by AI design cues, it does not independently translate into higher levels of brand trust in the digital financial service context when relational perceptions are taken into account. Taken together, the structural model results demonstrate that AI design cues systematically shape users’ experiential perceptions of social presence and telepresence, but only social presence directly contributes to brand trust. These findings provide an empirical basis for the subsequent mediation analysis. Table 7 Structural Model Path Coefficients Hypothesis Path Estimate S.E. C.R. p Std. Coefficient Result H1a Form Cues → Social Presence 0.359 0.067 5.400 < 0.001 0.311 Supported H1b Behavioral Cues → Social Presence 0.226 0.066 3.442 < 0.001 0.204 Supported H1c Interaction Cues → Social Presence 0.218 0.058 3.759 < 0.001 0.201 Supported H2a Form Cues → Telepresence 0.165 0.052 3.147 0.002 0.174 Supported H2b Behavioral Cues → Telepresence 0.337 0.055 6.091 < 0.001 0.371 Supported H2c Interaction Cues → Telepresence 0.157 0.047 3.367 < 0.001 0.176 Supported H3 Social Presence → Brand Trust 0.417 0.050 8.320 < 0.001 0.459 Supported H4 Telepresence → Brand Trust 0.089 0.056 1.582 0.114 0.080 Not Supported Note. Std. Coefficient = standardized path coefficient. 4.2 Mediation Analysis To further examine the mediating roles of social presence and telepresence in the relationships between AI design cues and brand trust, a bootstrap mediation analysis was conducted. Indirect effects were estimated using bias-corrected bootstrap procedures with 95% confidence intervals. An indirect effect was considered statistically significant when the confidence interval did not include zero. As reported in Table 8 , social presence emerged as a significant mediator linking all three categories of AI design cues to brand trust. Specifically, the indirect effect of form cues on brand trust through social presence was positive and significant (indirect effect = 0.150, 95% CI [0.083, 0.236], p < 0.001), supporting H5a . Similarly, behavioral cues exerted a significant indirect effect on brand trust via social presence (indirect effect = 0.094, 95% CI [0.037, 0.171], p = 0.001), providing support for H5b . The indirect pathway from interaction cues to brand trust through social presence was also significant (indirect effect = 0.091, 95% CI [0.043, 0.157], p < 0.001), supporting H5c . These results indicate that socially expressive design features enhance brand trust primarily by fostering users’ perceptions of interpersonal connection and social responsiveness during interaction. In contrast, none of the indirect effects transmitted through telepresence were statistically significant. The indirect paths from form cues (indirect effect = 0.015, 95% CI [− 0.005, 0.051], p = 0.141), behavioral cues (indirect effect = 0.030, 95% CI [− 0.013, 0.085], p = 0.178), and interaction cues (indirect effect = 0.014, 95% CI [− 0.004, 0.049], p = 0.132) to brand trust via telepresence all included zero within their confidence intervals, leading to the rejection of H6a , H6b , and H6c . These findings suggest that although AI design cues successfully elicit immersive experiential involvement, telepresence does not function as an independent mediating mechanism in the formation of brand trust within the digital financial service context examined in this study. Taken together, the mediation analysis reveals a clear and asymmetric pattern. While both social presence and telepresence are significantly shaped by AI design cues, only social presence serves as a robust experiential pathway through which these cues are translated into brand trust. Telepresence, by contrast, does not transmit the effects of design cues to brand trust when relational perceptions are simultaneously accounted for. This pattern underscores the centrality of relational experiential mechanisms in AI-mediated financial services and provides a focused empirical foundation for the subsequent discussion of theoretical and practical implications. Table 8 Mediation Analysis Results (Bootstrap Method) Hypothesis Indirect Path Indirect Effect 95% CI (Lower) 95% CI (Upper) p Result H5a Form Cues → Social Presence → Brand Trust 0.150 0.083 0.236 < 0.001 Supported H5b Behavioral Cues → Social Presence → Brand Trust 0.094 0.037 0.171 0.001 Supported H5c Interaction Cues → Social Presence → Brand Trust 0.091 0.043 0.157 < 0.001 Supported H6a Form Cues → Telepresence → Brand Trust 0.015 −0.005 0.051 0.141 Not Supported H6b Behavioral Cues → Telepresence → Brand Trust 0.030 −0.013 0.085 0.178 Not Supported H6c Interaction Cues → Telepresence → Brand Trust 0.014 −0.004 0.049 0.132 Not Supported Note. Indirect effects were tested using the bootstrap method with bias-corrected 95% confidence intervals (CI). An indirect effect is considered significant when the confidence interval does not include zero. 5. Discussion This study set out to examine how design cues embedded in AI digital human advisors shape users’ brand trust through presence-based experiential mechanisms in digital financial services. The findings reveal a clear and asymmetric pattern. While all three categories of design cues—form, behavioral, and interaction cues—significantly enhanced both social presence and telepresence, only social presence exerted a direct effect on brand trust and functioned as a robust mediating mechanism. In contrast, telepresence, although strongly elicited by AI design cues, neither directly influenced brand trust nor mediated the effects of design cues on trust outcomes. This divergence between social presence and telepresence is theoretically meaningful rather than anomalous. It indicates that immersive experiential involvement alone is insufficient to foster brand trust in high-risk, advisory-oriented financial contexts ( 40 ). Instead, trust formation appears to rely more critically on relational and socially grounded perceptions that signal responsiveness, attentiveness, and interpersonal acknowledgment. While telepresence enhances the vividness and absorptive quality of AI-mediated interaction, such immersive engagement does not automatically translate into trust unless it is accompanied by cues that convey social intentionality and relational orientation. In this sense, immersive experience represents a necessary but insufficient condition for trust formation in AI-mediated financial services. These findings align with emerging research suggesting that trust in AI-enabled services is less contingent on perceptual immersion per se and more dependent on whether users feel socially recognized, normatively engaged, and relationally addressed during interaction. Collectively, the results underscore the importance of analytically distinguishing between different forms of presence when theorizing experiential trust formation in AI-mediated financial services. 5.1 Theoretical Implications This study offers several theoretical contributions to research on human–AI interaction, digital finance, and experiential trust formation. First, by differentiating AI digital human advisor design cues into form, behavioral, and interaction dimensions, the study advances a more granular understanding of how socially expressive AI systems function as experiential stimuli. Prior research has often treated anthropomorphism or human likeness as a monolithic construct. The present findings demonstrate that distinct categories of design cues systematically activate users’ experiential states, thereby supporting calls to move beyond undifferentiated treatments of AI design features and toward more fine-grained conceptualizations of socially expressive AI. Second, the study contributes to presence theory by empirically disentangling the roles of social presence and telepresence within a unified S–O–R framework. While existing studies frequently treat presence as a singular experiential construct, the current results show that social presence and telepresence operate through fundamentally different pathways in trust formation ( 10 ). Social presence functions as a relational mechanism that translates AI design cues into brand trust, whereas telepresence primarily enhances experiential immersion without directly affecting trust outcomes. This distinction extends recent work that highlights the multidimensional nature of presence in AI-enabled services and underscores the need to theorize relational and immersive experiences as parallel but non-substitutable organismic states. Third, the findings refine theoretical understanding of trust formation in digital financial services by demonstrating that experiential trust is anchored more strongly in perceived social responsiveness than in immersive engagement. In high-uncertainty and high-stakes contexts such as financial advisory services, users appear to prioritize cues that signal interpersonal attentiveness and social accountability over cues that merely enhance experiential vividness. This insight extends experiential perspectives on AI trust by positioning social presence—not telepresence—as the central experiential bridge between AI-mediated interaction and brand-level trust judgments. 5.2 Practical Implications The findings yield several practical implications for financial institutions and digital platform designers seeking to deploy AI digital human advisors in a trustworthy and sustainable manner. First, the results indicate that investments in AI design should prioritize socially expressive and interaction-oriented features over purely immersive enhancements. While advanced visual rendering and immersive environments may enhance telepresence, such features alone are unlikely to foster trust unless they are coupled with behaviors and interaction patterns that convey social attentiveness, responsiveness, and relational intent ( 41 ). Accordingly, financial platforms should focus on designing AI advisors that communicate warmth, responsiveness, and interpersonal awareness through natural dialogue, adaptive feedback, and coherent interaction flow, rather than relying solely on visual sophistication. Second, the study underscores the strategic importance of social presence as a core trust-building mechanism in AI-mediated financial services. Designers and managers should recognize that AI digital human advisors function not merely as technical service interfaces but as symbolic representatives of the financial brand embedded within the platform ( 42 ). As a result, the interaction style, responsiveness, and social expressiveness of AI advisors may shape users’ trust not only in the immediate service encounter but also in the institution behind the technology. Training AI systems to respond contingently, acknowledge user inputs, and maintain conversational coherence can help signal benevolence, accountability, and concern for user interests—qualities that are particularly critical in advisory contexts characterized by information asymmetry and elevated perceived risk. Finally, the findings caution platform managers against over-reliance on immersive technologies as a substitute for relational design. Although telepresence can enhance experiential engagement and interaction vividness, it does not independently generate trust in high-stakes financial contexts. Managers should therefore avoid assuming that more immersive or visually sophisticated AI interfaces will automatically lead to stronger trust outcomes. Instead, immersive features should be strategically integrated with socially responsive behaviors to ensure that experiential engagement translates into relational assurance and durable brand trust ( 43 ). From a platform governance perspective, prioritizing socially grounded interaction design over immersion-driven technological escalation may contribute to more stable, scalable, and sustainable trust relationships between users and AI-enabled financial services. 5.3 Limitations and Future Research Directions Despite its theoretical and empirical contributions, this study is subject to several limitations that should be acknowledged when interpreting the findings and that open up important avenues for future research. First, the empirical context of this study is confined to digital financial services in mainland China, which may limit the generalizability of the findings across institutional environments. The formation of trust in AI-mediated interactions is likely to be shaped by culturally embedded norms regarding uncertainty avoidance, interpersonal distance, and technology acceptance, as well as by differences in regulatory regimes governing financial services and AI deployment. As such, the relative salience of social presence and telepresence may not be invariant across contexts. Future research could adopt cross-cultural or cross-institutional comparative designs to examine whether the asymmetric role of social presence observed in this study persists under different cultural logics and regulatory conditions. Such extensions would contribute to a more context-sensitive understanding of presence-based trust formation in AI-mediated financial services. Second, the present study employs a cross-sectional survey design, which captures users' experiential perceptions at a single point in time and therefore constrains the ability to draw causal inferences regarding the relationships among design cues, presence-based experiences, and brand trust. Trust formation in AI-mediated service environments is inherently dynamic and may evolve as users accumulate interaction experience, update their mental models of AI capabilities, and recalibrate their expectations of system reliability. The observed relationships should thus be interpreted as associative rather than strictly causal. Future research could build on this work by employing longitudinal designs, panel data, or controlled experiments to trace how social presence and telepresence develop over repeated interactions and how their respective roles in trust formation shift across different stages of user–AI engagement. Third, the study relies on self-reported measures to capture users’ perceptions of design cues, experiential states, and brand trust, which may introduce potential biases related to subjective evaluation, recall processes, and common method variance. Although procedural remedies were implemented to mitigate these concerns, the possibility of residual bias cannot be fully excluded. Moreover, self-reported experiential measures may not fully capture behavioral manifestations of trust, such as actual usage decisions, financial commitment, or long-term platform engagement. Future research could complement self-reported data with behavioral, transactional, or platform-level data to provide a more comprehensive and multi-method assessment of trust formation in AI-mediated financial services. Fourth, while this study focuses on experienced users of AI digital human advisors, the findings may not fully generalize to novice users or to individuals with limited exposure to AI-enabled financial services. User experience, familiarity, and prior interaction history may fundamentally shape how design cues are interpreted and how presence-based experiences are formed. In addition, the effects observed in this study may be contingent on individual-level and situational boundary conditions, such as AI literacy, risk propensity, decision importance, and task complexity. Future research could explicitly model these moderating factors to identify when and for whom social presence or telepresence becomes more influential in shaping trust outcomes. Such efforts would help refine the theoretical boundary conditions of presence-based mechanisms and enhance the explanatory power of human–AI interaction models in high-stakes service contexts. 6. Conclusions This study examines how design cues embedded in AI digital human advisors are associated with brand trust through presence-based experiential mechanisms in digital financial services. Grounded in a human-centric design perspective and the Stimulus–Organism–Response (S–O–R) framework, the findings indicate that socially expressive design cues are consistently related to users’ experiential perceptions, while only social presence shows a statistically significant direct and mediating relationship with brand trust in the tested model. By contrast, telepresence, although positively associated with immersive experiential engagement, does not exhibit a statistically significant direct or mediating effect on brand trust in this context. These findings suggest that, within high-risk and advisory-oriented financial service environments, relationally grounded experiential perceptions may play a more prominent role in shaping trust than immersive experiential involvement alone, and that users’ perceptions of social responsiveness, attentiveness, and interpersonal acknowledgment are more closely associated with trust formation than the intensity of immersive experience per se. At the same time, these findings should be interpreted with caution, as the cross-sectional design, reliance on self-reported measures, and the focus on users in mainland China imply that the observed relationships should be understood as associative rather than strictly causal, and their generalizability to other contexts remains to be further examined. Within these boundaries, this study contributes to a more differentiated understanding of how distinct dimensions of presence-based experience relate to trust outcomes in AI-mediated financial services and provides a theoretically grounded basis for future research on the design and governance of human-centric AI systems in digital finance. Declarations Ethics approval This study was conducted in accordance with the Ethical Review Measures for Life Sciences and Medical Research Involving Humans issued by the National Health Commission of China (2023). According to these guidelines, ethical approval was not required because the study involved an anonymous questionnaire survey, posed minimal risk to participants, and did not collect any personally identifiable information. The relevant regulation can be accessed at: http://www.nhc.gov.cn/ . Informed consent Informed consent was obtained electronically from all participants prior to their participation in the study. Participants were informed that participation was voluntary and anonymous, and that they could withdraw at any time before submitting their responses. Consent for publication Not applicable. Competing interests The author declares that there are no competing interests. Author Contribution T.Y. solely conceptualized the study, designed the research, conducted data collection and analysis, and wrote the manuscript. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Panori A (2025) Platforms as Proximity Enablers for Regional Development. Platforms 3(1):4. https://doi.org/10.3390/platforms3010004 Belanche D, Casaló LV, Flavián C (2019) Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Manage Data Syst 119(7):1411–1430. https://doi.org/10.1108/imds-08-2018-0368 Ho C-C, MacDorman KF (2016) Measuring the Uncanny Valley Effect. Int J Soc Robot 9(1):129–139. https://doi.org/10.1007/s12369-016-0380-9 Turnšek M, Radivojević V (2025) Platformization in Tourism: Typology of Business Models, Evolution of Market Concentration and European Regulation Responses. Platforms 3(1):1. https://doi.org/10.3390/platforms3010001 McKnight DH, Choudhury V, Kacmar C (2002) Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Inform Syst Res 13(3):334–359. https://doi.org/10.1287/isre.13.3.334.81 Glikson E, Woolley AW (2020) Human Trust in Artificial Intelligence: Review of Empirical Research. Acad Manag Ann 14(2):627–660. https://doi.org/10.5465/annals.2018.0057 Castelo N, Bos MW, Lehmann DR (2019) Task-Dependent Algorithm Aversion. J Mark Res 56(5):809–825. https://doi.org/10.1177/0022243719851788 Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ Behav Hum Decis Process 151:90–103. https://doi.org/10.1016/j.obhdp.2018.12.005 Lombard M, Ditton T (2006) At the Heart of It All: The Concept of Presence. J Computer-Mediated Communication 3(2):0–0. https://doi.org/10.1111/j.1083-6101.1997.tb00072.x Flavián C, Belk RW, Belanche D, Casaló LV (2024) Automated social presence in AI: Avoiding consumer psychological tensions to improve service value. J Bus Res 175:114545. https://doi.org/10.1016/j.jbusres.2024.114545 Flavián C, Pérez-Rueda A, Belanche D, Casaló LV (2021) Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness. J Service Manage 33(2):293–320. https://doi.org/10.1108/josm-10-2020-0378 Chi NTK, Hoang Vu N (2022) Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Trans Intell Technol 8(1):260–273. https://doi.org/10.1049/cit2.12133 Adam M, Wessel M, Benlian A (2020) AI-based chatbots in customer service and their effects on user compliance. Electron Markets 31(2):427–445. https://doi.org/10.1007/s12525-020-00414-7 Janson A (2023) How to leverage anthropomorphism for chatbot service interfaces: The interplay of communication style and personification. Comput Hum Behav 149:107954. https://doi.org/10.1016/j.chb.2023.107954 Kim J, Lennon SJ (2013) Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention. J Res Interact Mark 7(1):33–56. https://doi.org/10.1108/17505931311316734 Reeves B, Nass C (1996) The media equation: How people treat computers, television, and new media like real people. Camb UK 10(10):19–36 Nass C, Moon Y (2002) Machines and Mindlessness: Social Responses to Computers. J Soc Issues 56(1):81–103. https://doi.org/10.1111/0022-4537.00153 Munnukka J, Talvitie-Lamberg K, Maity D (2022) Anthropomorphism and social presence in Human–Virtual service assistant interactions: The role of dialog length and attitudes. Comput Hum Behav 135:107343. https://doi.org/10.1016/j.chb.2022.107343 Ng SWT, Zhang R (2025) Trust in AI chatbots: A systematic review. Telemat Inf 97:102240. https://doi.org/10.1016/j.tele.2025.102240 Alagarsamy S, Mehrolia S (2023) Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon 9(5):e16074. https://doi.org/10.1016/j.heliyon.2023.e16074 Ltifi M (2023) Trust in the chatbot: a semi-human relationship. Future Bus J 9(1):109. https://doi.org/10.1186/s43093-023-00288-z Calahorra-Candao G, Martín-de Hoyos MJ (2024) The effect of anthropomorphism of virtual voice assistants on perceived safety as an antecedent to voice shopping. Comput Hum Behav 153:108124. https://doi.org/10.1016/j.chb.2023.108124 Wut TM, Lee SW, Xu J, Kwok MLJ (2025) Do Trusting Belief and Social Presence Matter? Service Satisfaction in Using AI Chatbots: Necessary Condition Analysis and Importance-Performance Map Analysis. Informatics 12(3):91. https://doi.org/10.3390/informatics12030091 Dalvi-Esfahani M, Barati-Ahmadabadi H, Ramayah T, Turner JJ, Iahad A, Azar N (2025) Stimulus-organism-response framework of decision-makers intention to adopt generative AI to replace entry-level jobs: The moderating impact of personality traits. Technol Forecast Soc Change 219:124291. https://doi.org/10.1016/j.techfore.2025.124291 Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to Medical Artificial Intelligence. J Consum Res 46(4):629–650. https://doi.org/10.1093/jcr/ucz013 Martini MC, Gonzalez CA, Wiese E (2016) Seeing Minds in Others - Can Agents with Robotic Appearance Have Human-Like Preferences? PLoS ONE 11(1):e0146310. https://doi.org/10.1371/journal.pone.0146310 Moussawi S, Koufaris M, Benbunan-Fich R (2020) How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electron Markets 31(2):343–364. https://doi.org/10.1007/s12525-020-00411-w Li Q, Luximon Y, Zhang J (2023) The Influence of Anthropomorphic Cues on Patients' Perceived Anthropomorphism, Social Presence, Trust Building, and Acceptance of Health Care Conversational Agents: Within-Subject Web-Based Experiment. J Med Internet Res 25:e44479. https://doi.org/10.2196/44479 De Keyser A, Köcher S, Alkire L, Verbeeck C, Kandampully J (2019) Frontline Service Technology infusion: conceptual archetypes and future research directions. J Service Manage 30(1):156–183. https://doi.org/10.1108/josm-03-2018-0082 Luo X, Tong S, Fang Z, Qu Z, Frontiers (2019) Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Mark Sci 38(6):937–947. https://doi.org/10.1287/mksc.2019.1192 Huang M-H, Rust RT (2020) A strategic framework for artificial intelligence in marketing. J Acad Mark Sci 49(1):30–50. https://doi.org/10.1007/s11747-020-00749-9 Flavián C, Ibáñez-Sánchez S, Orús C (2019) The impact of virtual, augmented and mixed reality technologies on the customer experience. J Bus Res 100:547–560. https://doi.org/10.1016/j.jbusres.2018.10.050 Schultze U (2010) Embodiment and Presence in Virtual Worlds: A Review. J Inform Technol 25(4):434–449. https://doi.org/10.1057/jit.2009.25 Radianti J, Majchrzak TA, Fromm J, Wohlgenannt I (2020) A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Comput Educ 147:103778. https://doi.org/10.1016/j.compedu.2019.103778 Riva G, Davide F, IJsselsteijn WA (2003) Being there: The experience of presence in mediated environments. Being there: Concepts Eff Meas user presence Synth Environ 5:2003 Puntoni S, Reczek RW, Giesler M, Botti S (2020) Consumers and Artificial Intelligence: An Experiential Perspective. J Mark 85(1):131–151. https://doi.org/10.1177/0022242920953847 Shankar V, Kleijnen M, Ramanathan S, Rizley R, Holland S, Morrissey S (2016) Mobile shopper marketing: Key issues, current insights, and future research avenues. J Interact Mark 34(1):37–48 Hilken T, Chylinski M, Keeling DI, Heller J, de Ruyter K, Mahr D (2021) How to strategically choose or combine augmented and virtual reality for improved online experiential retailing. Psychol Mark 39(3):495–507. https://doi.org/10.1002/mar.21600 Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903. https://doi.org/10.1037/0021-9010.88.5.879 Gursoy D, Chi OH, Lu L, Nunkoo R (2019) Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int J Inf Manag 49:157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008 Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T et al (2021) Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag 57:101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Rheu M, Shin JY, Peng W, Huh-Yoo J (2020) Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design. Int J Human–Computer Interact 37(1):81–96. https://doi.org/10.1080/10447318.2020.1807710 Komiak SYX, Benbasat I (2006) The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1. MIS Q 30(4):941–960. https://doi.org/10.2307/25148760 Bartneck C, Kulić D, Croft E, Zoghbi S (2008) Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. Int J Social Robot 1(1):71–81. https://doi.org/10.1007/s12369-008-0001-3 Liu Y, Shrum LJ (2002) What is Interactivity and is it Always Such a Good Thing? Implications of Definition, Person, and Situation for the Influence of Interactivity on Advertising Effectiveness. J Advertising 31(4):53–64. https://doi.org/10.1080/00913367.2002.10673685 Verhagen T, van Nes J, Feldberg F, van Dolen W (2014) Virtual Customer Service Agents: Using Social Presence and Personalization to Shape Online Service Encounters. J Computer-Mediated Communication 19(3):529–545. https://doi.org/10.1111/jcc4.12066 Dagger TS, Sweeney JC, Johnson LW (2007) A Hierarchical Model of Health Service Quality. J Service Res 10(2):123–142. https://doi.org/10.1177/1094670507309594 Additional Declarations No competing interests reported. Supplementary Files HSSCPreReviewMaterials.zip AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 04 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9084958","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619664574,"identity":"4c0a418d-271c-417d-b066-588ba4242b76","order_by":0,"name":"TANG YISHU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3RMUvDQBTA8RcCyfLANXHpVzgJHIgF/Sg5hEzhEAKOIVO6iF0tCP0KyTeIHNQldU4oCMXRCOciDh16BdvtDt0K3n883o973AHYbEcbgRj8ApzicNLoh/FAsPkTAUWC+Jfk0l+Kd7zJOek/FuGsHPPRlbuW0L7qb0GeXCARGVnxJKzLJDtrvCiALjMsltIIScOqVUrDdSlYXQAFkLGenAw7krOqb/fE/zKTII3ekLis6pCqxQSbA6pbOgPpBuo8EsFmLb8+f3hJWOViFsStnvjTNJLDJmf3z8un/u52zOaTSS3lQk9U3in+vAQ4nvpWAbtvMuZ+fu8JbABGhXncZrPZ/l9bbWxazQh8VwMAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University of Engineering and Vocational Technology","correspondingAuthor":true,"prefix":"","firstName":"TANG","middleName":"","lastName":"YISHU","suffix":""}],"badges":[],"createdAt":"2026-03-10 14:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9084958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9084958/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725638,"identity":"76bbf0e8-d27e-4a92-929b-b585a18492e3","added_by":"auto","created_at":"2026-04-12 18:33:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47084,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model of Design Cues and Presence-Based Trust Formation in AI-Mediated Financial Services\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9084958/v1/759bb638b8fea4b3fb1953bb.png"},{"id":106617923,"identity":"a0c0516f-2870-496d-8d67-cdad14946d0f","added_by":"auto","created_at":"2026-04-10 13:30:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107217,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified Structural Model with Direct Effects\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9084958/v1/d339bc5a05e64ff28aa808ae.png"},{"id":106727781,"identity":"3f453250-1ad1-4ba2-97d8-204dc2030473","added_by":"auto","created_at":"2026-04-12 18:40:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9084958/v1/0d654844-ea2b-4230-aa32-f149cd01998a.pdf"},{"id":106617920,"identity":"be7e3900-f9aa-4c60-81b9-9a21006b8412","added_by":"auto","created_at":"2026-04-10 13:30:50","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3579809,"visible":true,"origin":"","legend":"","description":"","filename":"HSSCPreReviewMaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-9084958/v1/6429919d4079dd64b71df29d.zip"},{"id":106726628,"identity":"a702f345-cd53-4d6f-bc27-794aee349a28","added_by":"auto","created_at":"2026-04-12 18:36:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21587,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-9084958/v1/766ca9fbd9ef3724a29ab339.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human-Centric AI Digital Human Advisors and Brand Trust: Social Presence versus Telepresence","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerating diffusion of artificial intelligence (AI) across financial services has fundamentally reshaped how individuals engage with digital platforms and financial institutions. Beyond back-end algorithmic decision systems, an increasing number of digital finance platforms are now deploying AI Digital Human Advisors\u0026mdash;humanlike virtual agents equipped with embodied appearance, expressive behaviors, and interactive communication capabilities\u0026mdash;to deliver advisory services, customer support, and investment guidance. These systems reflect a broader trajectory of digital transformation in which intelligent technologies are no longer confined to efficiency enhancement but are increasingly positioned at the interface between organizations and users (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Unlike traditional robo-advisors that primarily emphasize computational accuracy and task automation, AI Digital Human Advisors are deliberately designed to assume social roles and function as symbolic representatives of financial brands through quasi-interpersonal interaction. Recent advances in embodied conversational agents and real-time animation technologies have further enhanced their capacity to simulate socially meaningful encounters within digital environments(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this evolving landscape of AI-enabled financial services, trust remains a central yet fragile foundation for sustainable digital transformation, particularly in platform-mediated service environments where user trust must be established without direct interpersonal contact (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Financial decision making is inherently characterized by uncertainty, perceived risk, and information asymmetry, rendering brand trust a critical prerequisite for user acceptance, continued engagement, and long-term platform resilience (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In conventional advisory contexts, trust is primarily cultivated through face-to-face interaction, where human advisors convey competence, benevolence, and integrity through rich social cues. As advisory functions are increasingly delegated to AI Digital Human Advisors, however, users can no longer rely on direct interpersonal contact. Instead, trust-related judgments must be inferred from design cues embedded in the AI-mediated interface itself, including visual presentation, behavioral expressiveness, and interaction quality. Despite the growing prevalence of such systems, existing research in digital finance and AI services has largely focused on adoption intentions, algorithm aversion, or comparative performance evaluations between human and automated advisors(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Consequently, limited attention has been devoted to understanding how specific AI design features shape users\u0026rsquo; internal experiential states and, in turn, their trust in the financial brands represented by these technologies.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study adopts the Stimulus\u0026ndash;Organism\u0026ndash;Response (S\u0026ndash;O\u0026ndash;R) framework to conceptualize brand trust formation as an experience-driven process within AI-mediated financial services. From this perspective, the design cues of AI Digital Human Advisors constitute external stimuli, users\u0026rsquo; psychological experiences during interaction represent organismic states, and brand trust functions as the evaluative response. Rather than treating AI advisors as monolithic technological artifacts, this study differentiates design cues into three analytically distinct categories\u0026mdash;form cues, behavioral cues, and interaction cues\u0026mdash;capturing how AI Digital Human Advisors communicate social meaning through appearance, action, and dialogue. Central to this experiential pathway are two presence-related mechanisms: social presence and telepresence. Social presence refers to the extent to which users perceive the AI advisor as a socially responsive and engaging interaction partner, fostering a sense of interpersonal connection within mediated communication. Telepresence, by contrast, reflects users\u0026rsquo; immersive psychological involvement in the digital advisory environment, characterized by the sensation of \u0026ldquo;being there\u0026rdquo; rather than merely interacting with a technological interface (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Emerging evidence from AI-enabled service research suggests that these two forms of presence operate through complementary yet distinct mechanisms in shaping user evaluations of intelligent agents and the brands they represent (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Nevertheless, empirical understanding remains limited regarding how different categories of AI design cues activate social presence and telepresence, and how these experiential states jointly contribute to brand trust in digital financial platforms.\u003c/p\u003e \u003cp\u003eAccordingly, this study investigates how form, behavioral, and interaction cues of AI Digital Human Advisors influence brand trust through the mediating roles of social presence and telepresence. By foregrounding users\u0026rsquo; experiential perceptions rather than system-level performance metrics, the study moves beyond outcome-oriented accounts of AI adoption and opens the experiential \u0026ldquo;black box\u0026rdquo; through which AI design features are translated into trust-related judgments. In doing so, it advances a human-centric understanding of AI-driven digital transformation in financial services and offers actionable insights for designing trustworthy and sustainable AI-mediated advisory systems.\u003c/p\u003e \u003cp\u003eBased on these objectives, the study addresses the following research questions:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ1\u003c/strong\u003e \u003cp\u003eHow do form cues, behavioral cues, and interaction cues of AI Digital Human Advisors influence users\u0026rsquo; social presence and telepresence in digital financial services?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ2\u003c/strong\u003e \u003cp\u003eDo social presence and telepresence mediate the relationships between AI Digital Human Advisor design cues and brand trust?\u003c/p\u003e \u003c/p\u003e"},{"header":"2. Literature review and hypothesis development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 AI Digital Human Advisors in Digital Finance\u003c/h2\u003e \u003cp\u003eAI Digital Human Advisors represent a distinctive evolution of intelligent service interfaces within digital finance, reflecting a broader transformation from purely algorithm-centered systems toward socially oriented, human-centric AI applications. These systems combine embodied digital avatars with natural language processing and adaptive communication capabilities, enabling them to deliver financial consultation, customer assistance, and investment guidance through humanlike interaction. Rather than functioning solely as back-end decision-support tools, AI Digital Human Advisors increasingly operate as frontline service agents embedded within digital platforms, directly mediating the relationship between financial institutions and users (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This shift aligns with contemporary trajectories of digital transformation in which AI technologies are deployed not only to enhance operational efficiency but also to shape user experience, relational engagement, and platform trust.\u003c/p\u003e \u003cp\u003eA defining feature that distinguishes AI Digital Human Advisors from earlier generations of text-based chatbots or rule-based robo-advisors lies in their explicit orientation toward social expressiveness. Advances in embodied agent technologies allow these systems to present visually recognizable human forms, display behaviorally expressive actions, and engage in responsive, dialogue-based interaction (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Such design elements are not incidental aesthetic enhancements; rather, they are deliberately implemented to convey social cues that encourage users to perceive AI advisors as socially capable interaction partners. Prior research in human\u0026ndash;AI interaction demonstrates that when AI systems exhibit humanlike appearance and socially contingent behaviors, users are more likely to engage social perception processes and evaluate these agents in terms of credibility, engagement, and relational appropriateness, rather than purely technical performance(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significance of AI Digital Human Advisors is particularly pronounced in digital financial services, where user decisions are typically associated with elevated uncertainty, perceived risk, and long-term consequences. In conventional financial advisory contexts, trust is cultivated through interpersonal interaction, advisor demeanor, and perceived attentiveness during service encounters. As financial institutions increasingly delegate advisory and communicative functions to AI-driven agents, users can no longer rely on direct interpersonal contact to assess trustworthiness. Instead, they must infer the reliability and integrity of the institution behind the technology through design-mediated signals embedded in the AI interface itself. Empirical evidence suggests that when AI agents display humanlike appearance and socially responsive behaviors, users are more inclined to apply interpersonal heuristics and social norms, interpreting these systems as social actors participating in relational exchanges rather than as neutral technological artifacts (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their expanding deployment across banking applications, investment platforms, and insurance services, scholarly research on AI Digital Human Advisors has remained disproportionately focused on adoption outcomes, performance evaluations, or direct comparisons with human advisors. Comparatively limited attention has been devoted to examining how specific design characteristics of AI Digital Human Advisors shape users\u0026rsquo; internal psychological experiences during interaction, particularly in trust-sensitive financial contexts. Moreover, existing studies rarely differentiate among distinct categories of design cues\u0026mdash;such as visual form, behavioral expression, and interaction responsiveness\u0026mdash;when analyzing their effects on user perception. Addressing this limitation, the present study conceptualizes AI Digital Human Advisors as socially expressive service agents embedded within digital platforms and foregrounds the experiential processes through which design cues influence trust-related evaluations. This perspective establishes a necessary foundation for examining presence-based mechanisms as central pathways linking AI design features to brand trust in digital finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical Foundations and Conceptual Framework\u003c/h2\u003e \u003cp\u003eUnderstanding how AI Digital Human Advisor design features shape brand trust in digital finance requires a process-oriented theoretical perspective that accounts for users\u0026rsquo; internal experiential responses during interaction. In technology-mediated service environments, design characteristics seldom exert direct effects on evaluative outcomes. Instead, their influence is typically realized through users\u0026rsquo; psychological interpretations and experiential perceptions formed while engaging with the system. To capture this indirect mechanism, the present study adopts the Stimulus\u0026ndash;Organism\u0026ndash;Response (S\u0026ndash;O\u0026ndash;R) framework as an overarching structure for modeling trust formation in AI-mediated financial services. Within the S\u0026ndash;O\u0026ndash;R perspective, external stimuli represent observable features of the service environment, organismic states reflect users\u0026rsquo; internal experiential reactions, and responses denote subsequent evaluative judgments. Prior research in digital services and human\u0026ndash;computer interaction consistently demonstrates that interface design, interaction quality, and system features shape user responses primarily through experiential states rather than through direct cognitive assessment of technological attributes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This mediating logic is particularly salient in AI-enabled contexts, where users must actively interpret system-generated cues to make sense of non-human service agents.\u003c/p\u003e \u003cp\u003eIn the context of AI Digital Human Advisors, design cues operate as socially expressive stimuli rather than neutral technological inputs. Visual form, behavioral expression, and interaction responsiveness collectively communicate social meaning and guide how users construe the role, capability, and intent of the AI advisor. These cues elicit internal experiential states that extend beyond functional evaluation, encompassing perceptions of social engagement and immersive involvement during interaction. Accordingly, the present study conceptualizes form cues, behavioral cues, and interaction cues as key stimuli within the S\u0026ndash;O\u0026ndash;R framework, while social presence and telepresence are modeled as central organismic states capturing users\u0026rsquo; experiential responses in digital financial encounters. To explain why users respond to AI-generated cues in social and experiential terms, this study further draws on Social Response Theory and the Computers Are Social Actors (CASA) paradigm. These perspectives posit that individuals tend to apply social heuristics and interpersonal norms to technological systems whenever they display socially meaningful cues, such as humanlike appearance, natural language communication, or contingent interaction (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Crucially, such responses occur largely automatically and persist even when users are fully aware that the interaction partner is non-human. In AI-mediated services, the presence of anthropomorphic and interactive design elements encourages users to perceive AI advisors as socially capable agents, thereby transforming technology-mediated encounters into socially meaningful experiences rather than purely instrumental exchanges.\u003c/p\u003e \u003cp\u003eWithin digital financial services\u0026mdash;where advisory interactions are characterized by uncertainty, perceived risk, and high decision stakes\u0026mdash;these presence-based experiential perceptions play a particularly consequential role. When AI Digital Human Advisors substitute for or complement human advisors, users increasingly rely on socially grounded experiential cues to infer credibility, reliability, and trustworthiness. From this perspective, social presence reflects users\u0026rsquo; perception of interpersonal connection and social responsiveness during interaction, whereas telepresence captures their immersive psychological engagement with the advisory environment. Although conceptually distinct, these two experiential states operate through complementary mechanisms: social presence facilitates relational assurance, while telepresence enhances experiential credibility and situational involvement. Together, they provide parallel pathways through which AI design cues are translated into brand trust within digital financial platforms.\u003c/p\u003e \u003cp\u003eIntegrating the S\u0026ndash;O\u0026ndash;R framework with Social Response Theory and CASA, the present study advances a conceptual model in which AI Digital Human Advisor design cues influence brand trust indirectly through users\u0026rsquo; perceptions of social presence and telepresence. By positioning presence-based experiences as central explanatory mechanisms, the model foregrounds the experiential foundations of trust formation in AI-mediated financial services, moving beyond outcome-oriented evaluations of system performance or algorithmic capability. The proposed relationships among design cues, experiential mechanisms, and brand trust are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Design Cues and Social Presence in AI-Mediated Financial Services\u003c/h2\u003e \u003cp\u003eIn AI-mediated financial services, users rarely evaluate intelligent systems solely on the basis of algorithmic accuracy or functional performance. Instead, trust-related judgments are predominantly formed through experiential interpretations that emerge during interaction with AI agents (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This experiential orientation is particularly salient in financial service contexts characterized by high uncertainty, perceived risk, and outcome irreversibility, where users tend to rely more heavily on interaction quality, social signals, and relational cues rather than purely technical assessments when forming evaluations of AI-enabled services (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs AI Digital Human Advisors increasingly assume advisory and communicative roles traditionally fulfilled by human professionals, users become more dependent on agent-level design cues\u0026mdash;such as anthropomorphic features, behavioral expressiveness, and interaction styles\u0026mdash;to interpret the nature of the interaction and infer the credibility of the underlying financial institution (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Prior research consistently indicates that socially expressive and humanlike design cues play a critical role in shaping users\u0026rsquo; experiential perceptions during AI-mediated interaction, thereby constituting a key psychological mechanism through which evaluations are formed in digital service environments (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Grounded in the Stimulus\u0026ndash;Organism\u0026ndash;Response framework, design cues can therefore be conceptualized as external stimuli that activate users\u0026rsquo; internal experiential states, which subsequently influence evaluative responses in AI-driven financial services (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDesign cues embedded in AI Digital Human Advisors constitute critical triggers that activate social presence. When an AI advisor presents socially expressive cues through its appearance, behavior, and interaction patterns, users are more likely to apply interpersonal heuristics and construe the system as a socially capable interaction partner rather than as an impersonal technological interface (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Form cues\u0026mdash;such as humanlike visual appearance and embodied representation\u0026mdash;serve as immediate perceptual anchors that shape first impressions and increase the likelihood that users categorize the AI advisor as a socially relevant entity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Behavioral cues\u0026mdash;such as movement fluidity, response naturalness, and adaptive expressiveness\u0026mdash;reinforce these social interpretations during ongoing interaction by signaling liveliness, intentionality, and responsiveness (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Empirical evidence further suggests that behaviorally expressive AI agents are more likely to be perceived as socially capable and engaging, particularly in advice-oriented service contexts where attentiveness and responsiveness are central to users\u0026rsquo; evaluative criteria (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Interaction cues\u0026mdash;such as timely feedback, bidirectional exchange, and contingent responses\u0026mdash;further strengthen users\u0026rsquo; perceptions of reciprocity and engagement, enhancing the sense that the AI advisor is socially attentive and involved. Collectively, these cue dimensions operate as socially meaningful stimuli that foster users\u0026rsquo; perceptions of human contact, warmth, and interpersonal exchange, thereby enhancing social presence during AI-mediated financial interactions. Social presence reflects the extent to which users perceive interpersonal connection, attentiveness, and social awareness when engaging with an AI advisor, even in the absence of a human counterpart; this becomes especially salient in digital financial services characterized by high uncertainty and perceived risk (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the above reasoning, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1a\u003c/strong\u003e \u003cp\u003eThe form cues of AI Digital Human Advisors positively influence users\u0026rsquo; social presence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1b\u003c/strong\u003e \u003cp\u003eThe behavioral cues of AI Digital Human Advisors positively influence users\u0026rsquo; social presence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1c\u003c/strong\u003e \u003cp\u003eThe interaction cues of AI Digital Human Advisors positively influence users\u0026rsquo; social presence.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Design Cues and Telepresence in AI-Mediated Financial Services\u003c/h2\u003e \u003cp\u003eBeyond relational perceptions, AI-mediated financial interactions also give rise to immersive experiential states that shape how users psychologically engage with advisory environments. Telepresence captures this dimension of experience and refers to users\u0026rsquo; immersive psychological involvement in a mediated interaction space, characterized by experiential vividness, sustained attentional engagement, and the subjective sensation of \u0026ldquo;being there\u0026rdquo; rather than merely interacting through a technological interface (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In digital financial services, telepresence reflects the extent to which users become experientially absorbed in the advisory encounter, even when interaction occurs remotely and through artificial agents.\u003c/p\u003e \u003cp\u003eDesign cues embedded in AI Digital Human Advisors play a central role in eliciting telepresence by structuring the perceptual and interactional qualities of the mediated environment. Embodied visual representation contributes to telepresence by situating the advisory interaction within a coherent perceptual space, enhancing spatial realism and reducing the perceived artificiality of the mediated setting (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). When the AI advisor\u0026rsquo;s appearance supports perceptual continuity and environmental coherence, users are more likely to experience a heightened sense of immersion within the advisory context. In addition to visual form, behavioral cues\u0026mdash;such as natural movement, temporal coherence, and fluid responsiveness\u0026mdash;further strengthen telepresence by minimizing cognitive disruption during interaction and enabling users to maintain experiential flow and psychological absorption (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Interaction cues, including smooth turn-taking, timely feedback, and continuous dialogue, reinforce this immersive experience by sustaining attentional focus and reducing breaks in the interaction process.\u003c/p\u003e \u003cp\u003eWhen AI Digital Human Advisors maintain fluent, responsive, and temporally coherent interaction, users are more likely to remain experientially embedded in the advisory encounter, thereby strengthening telepresence even in technology-mediated financial service settings (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Through the combined influence of form cues, behavioral cues, and interaction cues, the mediated advisory environment becomes experientially vivid and engaging, allowing users to experience the interaction as psychologically involving rather than purely instrumental.\u003c/p\u003e \u003cp\u003eBased on the above reasoning, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2a\u003c/strong\u003e \u003cp\u003eThe form cues of AI Digital Human Advisors positively influence users\u0026rsquo; telepresence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2b\u003c/strong\u003e \u003cp\u003eThe behavioral cues of AI Digital Human Advisors positively influence users\u0026rsquo; telepresence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2c\u003c/strong\u003e \u003cp\u003eThe interaction cues of AI Digital Human Advisors positively influence users\u0026rsquo; telepresence.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Presence and Brand Trust in AI-Mediated Financial Services\u003c/h2\u003e \u003cp\u003eIn AI-mediated financial services, brand trust represents users\u0026rsquo; confidence in the reliability, integrity, and competence of the financial institution represented by the AI advisor. Because users rarely interact directly with human representatives in digital financial platforms, trust-related judgments are increasingly shaped by experiential perceptions formed during AI-mediated interactions rather than by direct interpersonal contact. Within this context, presence-based experiences play a central role in translating interaction episodes into broader brand-level evaluations.\u003c/p\u003e \u003cp\u003eSocial presence contributes to brand trust primarily through relational assurance. When users perceive an AI Digital Human Advisor as socially attentive, responsive, and interpersonally engaged, the interaction signals benevolence, accountability, and concern for users\u0026rsquo; interests\u0026mdash;qualities that are especially salient in advisory contexts characterized by information asymmetry and elevated perceived risk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Experiencing social presence encourages users to attribute humanlike intentionality and normative responsibility to the AI advisor, which in turn facilitates the extension of trust judgments from the immediate interaction to the financial institution the advisor represents (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In this sense, social presence functions as a relational bridge that links AI-mediated interaction experiences to brand-level trust evaluations.\u003c/p\u003e \u003cp\u003eTelepresence, by contrast, is expected to influence brand trust through experiential credibility rather than through interpersonal bonding. Immersive psychological engagement reduces the perceived distance inherent in remote and technology-mediated services and enhances users\u0026rsquo; perceptions of process continuity, system reliability, and operational stability (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). When advisory interactions feel vivid, coherent, and experientially grounded, users may infer that the underlying service infrastructure and organizational capabilities are competent and dependable. Prior research suggests that such immersive experiences can strengthen trust by reinforcing confidence in the technological and organizational foundations supporting the service encounter, even in the absence of strong interpersonal cues (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Accordingly, telepresence is theorized as a complementary experiential pathway through which AI-mediated interactions may shape brand trust.\u003c/p\u003e \u003cp\u003eTaken together, social presence and telepresence capture two distinct yet potentially convergent experiential routes linking AI-mediated interaction to brand trust in digital financial services. Social presence emphasizes relational engagement and interpersonal assurance, whereas telepresence emphasizes immersive involvement and experiential credibility. Based on this theoretical reasoning, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eSocial presence positively influences brand trust in AI-mediated financial services.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eTelepresence positively influences brand trust in AI-mediated financial services.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 The Mediating Role of Social Presence and Telepresence\u003c/h2\u003e \u003cp\u003eFrom a process-oriented perspective, the effects of AI Digital Human Advisor design cues on brand trust are unlikely to occur in a direct or mechanical manner. Instead, these effects are expected to be transmitted through users\u0026rsquo; experiential perceptions formed during AI-mediated interaction. Within the Stimulus\u0026ndash;Organism\u0026ndash;Response framework, design cues function as external stimuli, brand trust represents the evaluative response, and presence-based experiences constitute the organismic states through which this translation occurs. Accordingly, social presence and telepresence are conceptualized as mediating mechanisms that link AI Digital Human Advisor design cues to brand trust in digital financial services.\u003c/p\u003e \u003cp\u003eSocial presence serves as a relational mediating mechanism by translating socially expressive design cues into trust-related evaluations. When form cues, behavioral cues, and interaction cues collectively foster perceptions of interpersonal connection, attentiveness, and social awareness, users are more likely to interpret the advisory interaction as socially grounded and normatively appropriate. Such relational experiences signal benevolence, accountability, and concern for users\u0026rsquo; interests, which encourages users to extend trust judgments beyond the immediate interaction to the financial institution represented by the AI advisor (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In this sense, social presence operates as a relational bridge that mediates the influence of AI design cues on brand trust by embedding trust judgments within socially meaningful interaction experiences.\u003c/p\u003e \u003cp\u003eTelepresence, in contrast, is expected to function as an experiential mediating mechanism by translating design cues into perceptions of experiential credibility rather than interpersonal assurance. When AI Digital Human Advisor design cues enhance immersive psychological involvement, users may experience the advisory encounter as vivid, coherent, and experientially grounded. Such immersive experiences can reduce perceived distance and strengthen perceptions of system reliability, process continuity, and organizational competence (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Through this pathway, telepresence is theorized to mediate the relationship between AI design cues and brand trust by reinforcing users\u0026rsquo; confidence in the technological and institutional foundations underlying the AI-mediated service encounter.\u003c/p\u003e \u003cp\u003eTaken together, social presence and telepresence represent two parallel yet conceptually distinct experiential mechanisms through which AI Digital Human Advisor design cues may influence brand trust in digital financial services. While social presence emphasizes relational assurance and interpersonal meaning, telepresence emphasizes immersive involvement and experiential credibility. On the basis of this mediating logic, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5a\u003c/strong\u003e \u003cp\u003eSocial presence mediates the relationship between form cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5b\u003c/strong\u003e \u003cp\u003eSocial presence mediates the relationship between behavioral cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5c\u003c/strong\u003e \u003cp\u003eSocial presence mediates the relationship between interaction cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6a\u003c/strong\u003e \u003cp\u003eTelepresence mediates the relationship between form cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6b\u003c/strong\u003e \u003cp\u003eTelepresence mediates the relationship between behavioral cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6c\u003c/strong\u003e \u003cp\u003eTelepresence mediates the relationship between interaction cues and brand trust.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design, Data Collection, and Sample\u003c/h2\u003e \u003cp\u003eThis study employed a scenario-supported survey design to investigate users\u0026rsquo; experiential responses to AI-enabled digital human advisory services in the context of digital finance. The research design was grounded in the premise that meaningful evaluations of socially expressive AI systems are most accurately elicited from users with prior, real-world interaction experience. Accordingly, participation was restricted to individuals who had previously used financial applications featuring AI-based digital human advisors for consultation, guidance, or customer service purposes.\u003c/p\u003e \u003cp\u003eRather than introducing participants to an unfamiliar or artificially constructed system, the survey was designed to activate respondents\u0026rsquo; existing experiential memories of interacting with AI digital human advisors in their everyday financial app usage. At the beginning of the questionnaire, respondents were presented with a brief description and a representative video stimulus illustrating a typical AI digital human advisor interaction. This stimulus served as a contextual cue to anchor respondents\u0026rsquo; evaluations in their own prior experiences, enabling them to map the questionnaire items onto familiar interaction patterns, service scripts, and interface features encountered in real-world digital finance environments. Participants were explicitly instructed to answer all items based on their own prior real-world experiences with AI digital human advisors in financial apps, rather than evaluating the specific system depicted in the video. To further reduce potential common method bias, several procedural remedies were implemented in the questionnaire design and data collection process (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). First, respondents were assured of anonymity and informed that there were no right or wrong answers, thereby reducing evaluation apprehension and social desirability bias. Second, screening questions and attention checks were incorporated to exclude careless or inexperienced respondents and to enhance response quality. Third, the questionnaire included reverse-coded and neutrally worded items to minimize acquiescence bias and common scale-related artifacts. Finally, the use of a scenario-supported design and experience-based screening helped anchor respondents\u0026rsquo; evaluations in their own accumulated interaction experiences rather than in transient stimulus impressions, thereby mitigating the risk of systematic response inflation due to common measurement context. For users with prior experience, even relatively concise or abstract measurement items are sufficient to elicit stable and meaningful experiential judgments, as these items function as prompts that reactivate accumulated interaction schemas rather than as exhaustive descriptions of system features. Data were collected through a professional online survey platform Wenjuanxing in mainland China. Initial screening questions ensured that only respondents with verified experience using AI-enabled digital human advisors in financial applications were retained. Participation was voluntary and anonymous, and respondents were informed that the study was conducted solely for academic research purposes. No personally identifiable information was collected. A total of 527 questionnaires were distributed. After excluding incomplete responses and questionnaires that failed attention or consistency checks, 487 valid responses were retained for analysis, yielding an effective response rate of approximately 92.4%.\u003c/p\u003e \u003cp\u003eThe final sample exhibited substantial heterogeneity across demographic and usage-related characteristics, providing a robust empirical basis for examining experiential mechanisms in digital financial services. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the gender distribution was balanced, and respondents covered a broad age range, with the majority concentrated between 30 and 49 years\u0026mdash;an age cohort that represents core users of contemporary digital financial platforms. Educational attainment and income levels were well distributed, indicating representation across multiple socioeconomic strata. In addition, respondents varied considerably in their frequency of financial app usage, ranging from occasional users to daily users, reflecting different levels of familiarity and engagement with AI-mediated financial services. Occupational backgrounds and city tiers were likewise diverse, encompassing users from first-tier, new first-tier, and lower-tier cities. This diversity enhances the external validity of the findings and supports the generalizability of the results within the broader context of platform-based digital finance. Collectively, the sample characteristics align with the profile of experienced users who routinely interact with AI digital human advisors and are therefore capable of providing informed experiential evaluations of design cues, presence perceptions, and brand trust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest Education Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior college (Associate degree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow RMB 5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMB 5,000\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMB 10,001\u0026ndash;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove RMB 20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Usage Frequency of Financial Apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;25 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;30 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployees of enterprises or public institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed / Freelancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity Tier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst-tier cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew first-tier cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond-tier cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measurement Quality\u003c/h2\u003e \u003cp\u003eTo assess the reliability and validity of the measurement model, a series of confirmatory factor analyses (CFA) and construct validity tests were conducted using structural equation modeling. All latent constructs\u0026mdash;form cues, behavioral cues, interaction cues, social presence, telepresence, and brand trust\u0026mdash;were modeled as reflective constructs and estimated simultaneously. Given that all measurement items were derived from well-established scales and adapted to the digital finance context, the evaluation focused on overall model fit, convergent validity, and discriminant validity.\u003c/p\u003e \u003cp\u003eThe overall measurement model demonstrated a satisfactory fit to the data. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the chi-square to degrees of freedom ratio was well below the recommended threshold, indicating an acceptable level of parsimony. Incremental and absolute fit indices, including GFI, AGFI, CFI, NFI, and TLI, all exceeded conventional cutoff values, while RMSEA remained well below the upper bound typically suggested for good model fit. Collectively, these indices indicate that the proposed six-factor measurement structure adequately captures the covariance structure among the observed variables and supports the distinctiveness of the underlying constructs.\u003c/p\u003e \u003cp\u003eImportantly, the CFA results suggest that users were able to reliably differentiate between design cue dimensions (form, behavioral, and interaction cues), experiential states (social presence and telepresence), and brand trust evaluations, despite the conceptual relatedness of these constructs in AI-mediated service contexts. This finding aligns with the study\u0026rsquo;s theoretical premise that design cues and presence-based experiences represent analytically separable stages within the experiential trust formation process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory Factor Analysis (CFA) Model Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecommended Thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote. \u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08, and GFI, AGFI, CFI, NFI, and TLI\u0026thinsp;\u0026gt;\u0026thinsp;0.90 indicate an acceptable model fit.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConvergent validity was evaluated by examining composite reliability (CR) and average variance extracted (AVE) for each construct. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all CR values exceeded the recommended threshold of 0.70, indicating satisfactory internal consistency. In addition, AVE values for all constructs were above 0.50, demonstrating that a substantial proportion of variance in the observed indicators is captured by their respective latent constructs.\u003c/p\u003e \u003cp\u003eThese results provide evidence that the measurement items consistently reflect their intended constructs. This is particularly relevant given the experiential nature of the focal variables. Because respondents were selected based on prior experience with AI-enabled digital human advisory services, the measurement items functioned as cognitive and experiential anchors that activated accumulated interaction memories rather than requiring respondents to infer abstract system characteristics. Under such conditions, even concise or perceptually oriented items are sufficient to capture stable experiential judgments, thereby supporting the observed levels of convergent validity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConvergent Validity of Constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. CR\u0026thinsp;=\u0026thinsp;composite reliability; AVE\u0026thinsp;=\u0026thinsp;average variance extracted. All CR values exceed 0.70 and all AVE values exceed the recommended threshold of 0.50, indicating satisfactory convergent validity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was assessed using the Fornell\u0026ndash;Larcker criterion. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the square roots of the AVE for each construct exceeded the corresponding inter-construct correlations. This pattern indicates that each construct shares more variance with its own indicators than with other constructs in the model. The results further confirm that conceptually adjacent constructs\u0026mdash;such as social presence and telepresence, or behavioral cues and interaction cues\u0026mdash;remain empirically distinct in users\u0026rsquo; evaluations. This distinction is theoretically meaningful in the context of AI-mediated financial services, where relational perceptions and immersive experiences may co-occur but are not experienced as interchangeable. The establishment of discriminant validity therefore reinforces the appropriateness of modeling social presence and telepresence as parallel experiential mechanisms rather than as a single undifferentiated presence construct. Overall, the measurement model demonstrates satisfactory reliability and validity, providing a sound empirical foundation for subsequent hypothesis testing and structural model estimation. The full list of measurement items and their sources is provided in Appendix Table A1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity Based on Latent Variable Correlations (Fornell\u0026ndash;Larcker Criterion)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBehavioral Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteraction Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial Presence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTelepresence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrand Trust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.506***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.414***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.456***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.481***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.434***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.408***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.429***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. Diagonal elements (in bold) represent the square roots of AVE. Off-diagonal elements are correlations among latent constructs estimated from the confirmatory factor analysis (CFA). *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Values are rounded to three decimal places.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Correlation and Structural Model Results\u003c/h2\u003e \u003cp\u003ePrior to testing the proposed hypotheses, descriptive statistics and bivariate correlations among the key constructs were examined. Descriptive statistics for all constructs are reported in Appendix Table A2. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all design cue variables\u0026mdash;form cues, behavioral cues, and interaction cues\u0026mdash;were positively and significantly correlated with social presence, telepresence, and brand trust. Social presence exhibited moderate positive correlations with both design cues and brand trust, whereas telepresence showed weaker correlations with brand trust. These correlation patterns are consistent with the conceptual distinction between relational presence and immersive experience proposed in the theoretical framework and provide preliminary support for the hypothesized relationships. It should be noted that the correlations reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are based on observed scale means, whereas the inter-construct correlations reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e are latent correlations estimated from the CFA. Differences in magnitude between the two tables therefore reflect differences in measurement level rather than inconsistency in results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Correlation Matrix Based on Observed Scale Means\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBehavioral Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteraction Cues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial Presence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTelepresence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrand Trust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.440**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.372**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.398**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.423**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.385**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.470**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.374**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.364**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.407**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.207**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. Pearson correlation coefficients are based on observed scale means of each construct. ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (two-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe structural model was then estimated using structural equation modeling to test the proposed direct effects among design cues, presence-based experiential states, and brand trust. Overall, the structural model demonstrated a satisfactory fit to the data. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the chi-square to degrees of freedom ratio was well within acceptable limits, and all major goodness-of-fit indices\u0026mdash;including GFI, AGFI, CFI, NFI, and TLI\u0026mdash;exceeded recommended threshold values. The RMSEA value was also below the commonly accepted cutoff, indicating a good approximation of the data by the proposed model. These results suggest that the hypothesized structural relationships provide an adequate representation of the underlying data structure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Equation Modeling (SEM) Model Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecommended Thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote. \u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08, and GFI, AGFI, CFI, NFI, and TLI\u0026thinsp;\u0026gt;\u0026thinsp;0.90 indicate an acceptable model fit.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe standardized path coefficients for the structural model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and the simplified model with direct effects is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. With respect to the effects of design cues on social presence, form cues (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.311, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), behavioral cues (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.204, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and interaction cues (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.201, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all exerted significant positive effects. These findings support \u003cb\u003eH1a\u003c/b\u003e, \u003cb\u003eH1b\u003c/b\u003e, and \u003cb\u003eH1c\u003c/b\u003e, indicating that visually embodied characteristics, behavioral expressiveness, and interaction responsiveness each contribute to users\u0026rsquo; perceptions of social presence during AI-mediated financial service encounters. Similarly, all three categories of design cues were found to have significant positive effects on telepresence. Form cues positively influenced telepresence (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.174, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), as did behavioral cues (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.371, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and interaction cues (β\u0026thinsp;=\u0026thinsp;0.176, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting \u003cb\u003eH2a\u003c/b\u003e, \u003cb\u003eH2b\u003c/b\u003e, and \u003cb\u003eH2c\u003c/b\u003e. Among these effects, behavioral cues exhibited the strongest association with telepresence, suggesting that movement fluidity, response naturalness, and behavioral realism play a particularly important role in fostering immersive experiential involvement.\u003c/p\u003e \u003cp\u003eRegarding the relationships between presence-based experiential states and brand trust, social presence had a strong and significant positive effect on brand trust (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.459, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), providing support for \u003cb\u003eH3\u003c/b\u003e. In contrast, the effect of telepresence on brand trust was not statistically significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.114), leading to a rejection of \u003cb\u003eH4\u003c/b\u003e. This pattern indicates that while immersive experiential involvement is successfully elicited by AI design cues, it does not independently translate into higher levels of brand trust in the digital financial service context when relational perceptions are taken into account. Taken together, the structural model results demonstrate that AI design cues systematically shape users\u0026rsquo; experiential perceptions of social presence and telepresence, but only social presence directly contributes to brand trust. These findings provide an empirical basis for the subsequent mediation analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Model Path Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues \u0026rarr; Social Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral Cues \u0026rarr; Social Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction Cues \u0026rarr; Social Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues \u0026rarr; Telepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral Cues \u0026rarr; Telepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction Cues \u0026rarr; Telepresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Presence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelepresence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. Std. Coefficient\u0026thinsp;=\u0026thinsp;standardized path coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mediation Analysis\u003c/h2\u003e \u003cp\u003eTo further examine the mediating roles of social presence and telepresence in the relationships between AI design cues and brand trust, a bootstrap mediation analysis was conducted. Indirect effects were estimated using bias-corrected bootstrap procedures with 95% confidence intervals. An indirect effect was considered statistically significant when the confidence interval did not include zero. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, social presence emerged as a significant mediator linking all three categories of AI design cues to brand trust. Specifically, the indirect effect of form cues on brand trust through social presence was positive and significant (indirect effect\u0026thinsp;=\u0026thinsp;0.150, 95% CI [0.083, 0.236], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting \u003cb\u003eH5a\u003c/b\u003e. Similarly, behavioral cues exerted a significant indirect effect on brand trust via social presence (indirect effect\u0026thinsp;=\u0026thinsp;0.094, 95% CI [0.037, 0.171], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), providing support for \u003cb\u003eH5b\u003c/b\u003e. The indirect pathway from interaction cues to brand trust through social presence was also significant (indirect effect\u0026thinsp;=\u0026thinsp;0.091, 95% CI [0.043, 0.157], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting \u003cb\u003eH5c\u003c/b\u003e. These results indicate that socially expressive design features enhance brand trust primarily by fostering users\u0026rsquo; perceptions of interpersonal connection and social responsiveness during interaction.\u003c/p\u003e \u003cp\u003eIn contrast, none of the indirect effects transmitted through telepresence were statistically significant. The indirect paths from form cues (indirect effect\u0026thinsp;=\u0026thinsp;0.015, 95% CI [\u0026minus;\u0026thinsp;0.005, 0.051], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.141), behavioral cues (indirect effect\u0026thinsp;=\u0026thinsp;0.030, 95% CI [\u0026minus;\u0026thinsp;0.013, 0.085], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.178), and interaction cues (indirect effect\u0026thinsp;=\u0026thinsp;0.014, 95% CI [\u0026minus;\u0026thinsp;0.004, 0.049], p\u0026thinsp;=\u0026thinsp;0.132) to brand trust via telepresence all included zero within their confidence intervals, leading to the rejection of \u003cb\u003eH6a\u003c/b\u003e, \u003cb\u003eH6b\u003c/b\u003e, and \u003cb\u003eH6c\u003c/b\u003e. These findings suggest that although AI design cues successfully elicit immersive experiential involvement, telepresence does not function as an independent mediating mechanism in the formation of brand trust within the digital financial service context examined in this study.\u003c/p\u003e \u003cp\u003eTaken together, the mediation analysis reveals a clear and asymmetric pattern. While both social presence and telepresence are significantly shaped by AI design cues, only social presence serves as a robust experiential pathway through which these cues are translated into brand trust. Telepresence, by contrast, does not transmit the effects of design cues to brand trust when relational perceptions are simultaneously accounted for. This pattern underscores the centrality of relational experiential mechanisms in AI-mediated financial services and provides a focused empirical foundation for the subsequent discussion of theoretical and practical implications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis Results (Bootstrap Method)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect Path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI (Lower)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI (Upper)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues \u0026rarr; Social Presence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral Cues \u0026rarr; Social Presence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction Cues \u0026rarr; Social Presence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForm Cues \u0026rarr; Telepresence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral Cues \u0026rarr; Telepresence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction Cues \u0026rarr; Telepresence \u0026rarr; Brand Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. Indirect effects were tested using the bootstrap method with bias-corrected 95% confidence intervals (CI). An indirect effect is considered significant when the confidence interval does not include zero.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"5. Discussion","content":"\u003cp\u003eThis study set out to examine how design cues embedded in AI digital human advisors shape users\u0026rsquo; brand trust through presence-based experiential mechanisms in digital financial services. The findings reveal a clear and asymmetric pattern. While all three categories of design cues\u0026mdash;form, behavioral, and interaction cues\u0026mdash;significantly enhanced both social presence and telepresence, only social presence exerted a direct effect on brand trust and functioned as a robust mediating mechanism. In contrast, telepresence, although strongly elicited by AI design cues, neither directly influenced brand trust nor mediated the effects of design cues on trust outcomes.\u003c/p\u003e \u003cp\u003eThis divergence between social presence and telepresence is theoretically meaningful rather than anomalous. It indicates that immersive experiential involvement alone is insufficient to foster brand trust in high-risk, advisory-oriented financial contexts (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Instead, trust formation appears to rely more critically on relational and socially grounded perceptions that signal responsiveness, attentiveness, and interpersonal acknowledgment. While telepresence enhances the vividness and absorptive quality of AI-mediated interaction, such immersive engagement does not automatically translate into trust unless it is accompanied by cues that convey social intentionality and relational orientation. In this sense, immersive experience represents a necessary but insufficient condition for trust formation in AI-mediated financial services. These findings align with emerging research suggesting that trust in AI-enabled services is less contingent on perceptual immersion per se and more dependent on whether users feel socially recognized, normatively engaged, and relationally addressed during interaction. Collectively, the results underscore the importance of analytically distinguishing between different forms of presence when theorizing experiential trust formation in AI-mediated financial services.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis study offers several theoretical contributions to research on human\u0026ndash;AI interaction, digital finance, and experiential trust formation. First, by differentiating AI digital human advisor design cues into form, behavioral, and interaction dimensions, the study advances a more granular understanding of how socially expressive AI systems function as experiential stimuli. Prior research has often treated anthropomorphism or human likeness as a monolithic construct. The present findings demonstrate that distinct categories of design cues systematically activate users\u0026rsquo; experiential states, thereby supporting calls to move beyond undifferentiated treatments of AI design features and toward more fine-grained conceptualizations of socially expressive AI.\u003c/p\u003e \u003cp\u003eSecond, the study contributes to presence theory by empirically disentangling the roles of social presence and telepresence within a unified S\u0026ndash;O\u0026ndash;R framework. While existing studies frequently treat presence as a singular experiential construct, the current results show that social presence and telepresence operate through fundamentally different pathways in trust formation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Social presence functions as a relational mechanism that translates AI design cues into brand trust, whereas telepresence primarily enhances experiential immersion without directly affecting trust outcomes. This distinction extends recent work that highlights the multidimensional nature of presence in AI-enabled services and underscores the need to theorize relational and immersive experiences as parallel but non-substitutable organismic states.\u003c/p\u003e \u003cp\u003eThird, the findings refine theoretical understanding of trust formation in digital financial services by demonstrating that experiential trust is anchored more strongly in perceived social responsiveness than in immersive engagement. In high-uncertainty and high-stakes contexts such as financial advisory services, users appear to prioritize cues that signal interpersonal attentiveness and social accountability over cues that merely enhance experiential vividness. This insight extends experiential perspectives on AI trust by positioning social presence\u0026mdash;not telepresence\u0026mdash;as the central experiential bridge between AI-mediated interaction and brand-level trust judgments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings yield several practical implications for financial institutions and digital platform designers seeking to deploy AI digital human advisors in a trustworthy and sustainable manner. First, the results indicate that investments in AI design should prioritize socially expressive and interaction-oriented features over purely immersive enhancements. While advanced visual rendering and immersive environments may enhance telepresence, such features alone are unlikely to foster trust unless they are coupled with behaviors and interaction patterns that convey social attentiveness, responsiveness, and relational intent (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Accordingly, financial platforms should focus on designing AI advisors that communicate warmth, responsiveness, and interpersonal awareness through natural dialogue, adaptive feedback, and coherent interaction flow, rather than relying solely on visual sophistication.\u003c/p\u003e \u003cp\u003eSecond, the study underscores the strategic importance of social presence as a core trust-building mechanism in AI-mediated financial services. Designers and managers should recognize that AI digital human advisors function not merely as technical service interfaces but as symbolic representatives of the financial brand embedded within the platform (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). As a result, the interaction style, responsiveness, and social expressiveness of AI advisors may shape users\u0026rsquo; trust not only in the immediate service encounter but also in the institution behind the technology. Training AI systems to respond contingently, acknowledge user inputs, and maintain conversational coherence can help signal benevolence, accountability, and concern for user interests\u0026mdash;qualities that are particularly critical in advisory contexts characterized by information asymmetry and elevated perceived risk.\u003c/p\u003e \u003cp\u003eFinally, the findings caution platform managers against over-reliance on immersive technologies as a substitute for relational design. Although telepresence can enhance experiential engagement and interaction vividness, it does not independently generate trust in high-stakes financial contexts. Managers should therefore avoid assuming that more immersive or visually sophisticated AI interfaces will automatically lead to stronger trust outcomes. Instead, immersive features should be strategically integrated with socially responsive behaviors to ensure that experiential engagement translates into relational assurance and durable brand trust (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). From a platform governance perspective, prioritizing socially grounded interaction design over immersion-driven technological escalation may contribute to more stable, scalable, and sustainable trust relationships between users and AI-enabled financial services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eDespite its theoretical and empirical contributions, this study is subject to several limitations that should be acknowledged when interpreting the findings and that open up important avenues for future research. First, the empirical context of this study is confined to digital financial services in mainland China, which may limit the generalizability of the findings across institutional environments. The formation of trust in AI-mediated interactions is likely to be shaped by culturally embedded norms regarding uncertainty avoidance, interpersonal distance, and technology acceptance, as well as by differences in regulatory regimes governing financial services and AI deployment. As such, the relative salience of social presence and telepresence may not be invariant across contexts. Future research could adopt cross-cultural or cross-institutional comparative designs to examine whether the asymmetric role of social presence observed in this study persists under different cultural logics and regulatory conditions. Such extensions would contribute to a more context-sensitive understanding of presence-based trust formation in AI-mediated financial services.\u003c/p\u003e \u003cp\u003eSecond, the present study employs a cross-sectional survey design, which captures users' experiential perceptions at a single point in time and therefore constrains the ability to draw causal inferences regarding the relationships among design cues, presence-based experiences, and brand trust. Trust formation in AI-mediated service environments is inherently dynamic and may evolve as users accumulate interaction experience, update their mental models of AI capabilities, and recalibrate their expectations of system reliability. The observed relationships should thus be interpreted as associative rather than strictly causal. Future research could build on this work by employing longitudinal designs, panel data, or controlled experiments to trace how social presence and telepresence develop over repeated interactions and how their respective roles in trust formation shift across different stages of user\u0026ndash;AI engagement.\u003c/p\u003e \u003cp\u003eThird, the study relies on self-reported measures to capture users\u0026rsquo; perceptions of design cues, experiential states, and brand trust, which may introduce potential biases related to subjective evaluation, recall processes, and common method variance. Although procedural remedies were implemented to mitigate these concerns, the possibility of residual bias cannot be fully excluded. Moreover, self-reported experiential measures may not fully capture behavioral manifestations of trust, such as actual usage decisions, financial commitment, or long-term platform engagement. Future research could complement self-reported data with behavioral, transactional, or platform-level data to provide a more comprehensive and multi-method assessment of trust formation in AI-mediated financial services.\u003c/p\u003e \u003cp\u003eFourth, while this study focuses on experienced users of AI digital human advisors, the findings may not fully generalize to novice users or to individuals with limited exposure to AI-enabled financial services. User experience, familiarity, and prior interaction history may fundamentally shape how design cues are interpreted and how presence-based experiences are formed. In addition, the effects observed in this study may be contingent on individual-level and situational boundary conditions, such as AI literacy, risk propensity, decision importance, and task complexity. Future research could explicitly model these moderating factors to identify when and for whom social presence or telepresence becomes more influential in shaping trust outcomes. Such efforts would help refine the theoretical boundary conditions of presence-based mechanisms and enhance the explanatory power of human\u0026ndash;AI interaction models in high-stakes service contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study examines how design cues embedded in AI digital human advisors are associated with brand trust through presence-based experiential mechanisms in digital financial services. Grounded in a human-centric design perspective and the Stimulus\u0026ndash;Organism\u0026ndash;Response (S\u0026ndash;O\u0026ndash;R) framework, the findings indicate that socially expressive design cues are consistently related to users\u0026rsquo; experiential perceptions, while only social presence shows a statistically significant direct and mediating relationship with brand trust in the tested model. By contrast, telepresence, although positively associated with immersive experiential engagement, does not exhibit a statistically significant direct or mediating effect on brand trust in this context. These findings suggest that, within high-risk and advisory-oriented financial service environments, relationally grounded experiential perceptions may play a more prominent role in shaping trust than immersive experiential involvement alone, and that users\u0026rsquo; perceptions of social responsiveness, attentiveness, and interpersonal acknowledgment are more closely associated with trust formation than the intensity of immersive experience per se. At the same time, these findings should be interpreted with caution, as the cross-sectional design, reliance on self-reported measures, and the focus on users in mainland China imply that the observed relationships should be understood as associative rather than strictly causal, and their generalizability to other contexts remains to be further examined. Within these boundaries, this study contributes to a more differentiated understanding of how distinct dimensions of presence-based experience relate to trust outcomes in AI-mediated financial services and provides a theoretically grounded basis for future research on the design and governance of human-centric AI systems in digital finance.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the Ethical Review Measures for Life Sciences and Medical Research Involving Humans issued by the National Health Commission of China (2023). According to these guidelines, ethical approval was not required because the study involved an anonymous questionnaire survey, posed minimal risk to participants, and did not collect any personally identifiable information. The relevant regulation can be accessed at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nhc.gov.cn/\u003c/span\u003e\u003cspan address=\"http://www.nhc.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003e Informed consent was obtained electronically from all participants prior to their participation in the study. Participants were informed that participation was voluntary and anonymous, and that they could withdraw at any time before submitting their responses.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.Y. solely conceptualized the study, designed the research, conducted data collection and analysis, and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePanori A (2025) Platforms as Proximity Enablers for Regional Development. Platforms 3(1):4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/platforms3010004\u003c/span\u003e\u003cspan address=\"10.3390/platforms3010004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelanche D, Casal\u0026oacute; LV, Flavi\u0026aacute;n C (2019) Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Manage Data Syst 119(7):1411\u0026ndash;1430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/imds-08-2018-0368\u003c/span\u003e\u003cspan address=\"10.1108/imds-08-2018-0368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo C-C, MacDorman KF (2016) Measuring the Uncanny Valley Effect. Int J Soc Robot 9(1):129\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12369-016-0380-9\u003c/span\u003e\u003cspan address=\"10.1007/s12369-016-0380-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurnšek M, Radivojević V (2025) Platformization in Tourism: Typology of Business Models, Evolution of Market Concentration and European Regulation Responses. Platforms 3(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/platforms3010001\u003c/span\u003e\u003cspan address=\"10.3390/platforms3010001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKnight DH, Choudhury V, Kacmar C (2002) Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Inform Syst Res 13(3):334\u0026ndash;359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1287/isre.13.3.334.81\u003c/span\u003e\u003cspan address=\"10.1287/isre.13.3.334.81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlikson E, Woolley AW (2020) Human Trust in Artificial Intelligence: Review of Empirical Research. Acad Manag Ann 14(2):627\u0026ndash;660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5465/annals.2018.0057\u003c/span\u003e\u003cspan address=\"10.5465/annals.2018.0057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelo N, Bos MW, Lehmann DR (2019) Task-Dependent Algorithm Aversion. J Mark Res 56(5):809\u0026ndash;825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022243719851788\u003c/span\u003e\u003cspan address=\"10.1177/0022243719851788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ Behav Hum Decis Process 151:90\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.obhdp.2018.12.005\u003c/span\u003e\u003cspan address=\"10.1016/j.obhdp.2018.12.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombard M, Ditton T (2006) At the Heart of It All: The Concept of Presence. J Computer-Mediated Communication 3(2):0\u0026ndash;0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1083-6101.1997.tb00072.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1083-6101.1997.tb00072.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlavi\u0026aacute;n C, Belk RW, Belanche D, Casal\u0026oacute; LV (2024) Automated social presence in AI: Avoiding consumer psychological tensions to improve service value. J Bus Res 175:114545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2024.114545\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2024.114545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlavi\u0026aacute;n C, P\u0026eacute;rez-Rueda A, Belanche D, Casal\u0026oacute; LV (2021) Intention to use analytical artificial intelligence (AI) in services \u0026ndash; the effect of technology readiness and awareness. J Service Manage 33(2):293\u0026ndash;320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/josm-10-2020-0378\u003c/span\u003e\u003cspan address=\"10.1108/josm-10-2020-0378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi NTK, Hoang Vu N (2022) Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Trans Intell Technol 8(1):260\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1049/cit2.12133\u003c/span\u003e\u003cspan address=\"10.1049/cit2.12133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdam M, Wessel M, Benlian A (2020) AI-based chatbots in customer service and their effects on user compliance. Electron Markets 31(2):427\u0026ndash;445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12525-020-00414-7\u003c/span\u003e\u003cspan address=\"10.1007/s12525-020-00414-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanson A (2023) How to leverage anthropomorphism for chatbot service interfaces: The interplay of communication style and personification. Comput Hum Behav 149:107954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2023.107954\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2023.107954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Lennon SJ (2013) Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention. J Res Interact Mark 7(1):33\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/17505931311316734\u003c/span\u003e\u003cspan address=\"10.1108/17505931311316734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReeves B, Nass C (1996) The media equation: How people treat computers, television, and new media like real people. Camb UK 10(10):19\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNass C, Moon Y (2002) Machines and Mindlessness: Social Responses to Computers. J Soc Issues 56(1):81\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/0022-4537.00153\u003c/span\u003e\u003cspan address=\"10.1111/0022-4537.00153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunnukka J, Talvitie-Lamberg K, Maity D (2022) Anthropomorphism and social presence in Human\u0026ndash;Virtual service assistant interactions: The role of dialog length and attitudes. Comput Hum Behav 135:107343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2022.107343\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2022.107343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg SWT, Zhang R (2025) Trust in AI chatbots: A systematic review. Telemat Inf 97:102240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tele.2025.102240\u003c/span\u003e\u003cspan address=\"10.1016/j.tele.2025.102240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlagarsamy S, Mehrolia S (2023) Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon 9(5):e16074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e16074\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e16074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLtifi M (2023) Trust in the chatbot: a semi-human relationship. Future Bus J 9(1):109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s43093-023-00288-z\u003c/span\u003e\u003cspan address=\"10.1186/s43093-023-00288-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalahorra-Candao G, Mart\u0026iacute;n-de Hoyos MJ (2024) The effect of anthropomorphism of virtual voice assistants on perceived safety as an antecedent to voice shopping. Comput Hum Behav 153:108124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2023.108124\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2023.108124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWut TM, Lee SW, Xu J, Kwok MLJ (2025) Do Trusting Belief and Social Presence Matter? Service Satisfaction in Using AI Chatbots: Necessary Condition Analysis and Importance-Performance Map Analysis. Informatics 12(3):91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/informatics12030091\u003c/span\u003e\u003cspan address=\"10.3390/informatics12030091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalvi-Esfahani M, Barati-Ahmadabadi H, Ramayah T, Turner JJ, Iahad A, Azar N (2025) Stimulus-organism-response framework of decision-makers intention to adopt generative AI to replace entry-level jobs: The moderating impact of personality traits. Technol Forecast Soc Change 219:124291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2025.124291\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2025.124291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLongoni C, Bonezzi A, Morewedge CK (2019) Resistance to Medical Artificial Intelligence. J Consum Res 46(4):629\u0026ndash;650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jcr/ucz013\u003c/span\u003e\u003cspan address=\"10.1093/jcr/ucz013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartini MC, Gonzalez CA, Wiese E (2016) Seeing Minds in Others - Can Agents with Robotic Appearance Have Human-Like Preferences? PLoS ONE 11(1):e0146310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0146310\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0146310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoussawi S, Koufaris M, Benbunan-Fich R (2020) How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electron Markets 31(2):343\u0026ndash;364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12525-020-00411-w\u003c/span\u003e\u003cspan address=\"10.1007/s12525-020-00411-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Luximon Y, Zhang J (2023) The Influence of Anthropomorphic Cues on Patients' Perceived Anthropomorphism, Social Presence, Trust Building, and Acceptance of Health Care Conversational Agents: Within-Subject Web-Based Experiment. J Med Internet Res 25:e44479. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/44479\u003c/span\u003e\u003cspan address=\"10.2196/44479\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Keyser A, K\u0026ouml;cher S, Alkire L, Verbeeck C, Kandampully J (2019) Frontline Service Technology infusion: conceptual archetypes and future research directions. J Service Manage 30(1):156\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/josm-03-2018-0082\u003c/span\u003e\u003cspan address=\"10.1108/josm-03-2018-0082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo X, Tong S, Fang Z, Qu Z, Frontiers (2019) Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Mark Sci 38(6):937\u0026ndash;947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1287/mksc.2019.1192\u003c/span\u003e\u003cspan address=\"10.1287/mksc.2019.1192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang M-H, Rust RT (2020) A strategic framework for artificial intelligence in marketing. J Acad Mark Sci 49(1):30\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11747-020-00749-9\u003c/span\u003e\u003cspan address=\"10.1007/s11747-020-00749-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlavi\u0026aacute;n C, Ib\u0026aacute;\u0026ntilde;ez-S\u0026aacute;nchez S, Or\u0026uacute;s C (2019) The impact of virtual, augmented and mixed reality technologies on the customer experience. J Bus Res 100:547\u0026ndash;560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2018.10.050\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2018.10.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchultze U (2010) Embodiment and Presence in Virtual Worlds: A Review. J Inform Technol 25(4):434\u0026ndash;449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/jit.2009.25\u003c/span\u003e\u003cspan address=\"10.1057/jit.2009.25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadianti J, Majchrzak TA, Fromm J, Wohlgenannt I (2020) A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Comput Educ 147:103778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2019.103778\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2019.103778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiva G, Davide F, IJsselsteijn WA (2003) Being there: The experience of presence in mediated environments. Being there: Concepts Eff Meas user presence Synth Environ 5:2003\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuntoni S, Reczek RW, Giesler M, Botti S (2020) Consumers and Artificial Intelligence: An Experiential Perspective. J Mark 85(1):131\u0026ndash;151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022242920953847\u003c/span\u003e\u003cspan address=\"10.1177/0022242920953847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankar V, Kleijnen M, Ramanathan S, Rizley R, Holland S, Morrissey S (2016) Mobile shopper marketing: Key issues, current insights, and future research avenues. J Interact Mark 34(1):37\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHilken T, Chylinski M, Keeling DI, Heller J, de Ruyter K, Mahr D (2021) How to strategically choose or combine augmented and virtual reality for improved online experiential retailing. Psychol Mark 39(3):495\u0026ndash;507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mar.21600\u003c/span\u003e\u003cspan address=\"10.1002/mar.21600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0021-9010.88.5.879\u003c/span\u003e\u003cspan address=\"10.1037/0021-9010.88.5.879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGursoy D, Chi OH, Lu L, Nunkoo R (2019) Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int J Inf Manag 49:157\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2019.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T et al (2021) Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag 57:101994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2019.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRheu M, Shin JY, Peng W, Huh-Yoo J (2020) Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design. Int J Human\u0026ndash;Computer Interact 37(1):81\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10447318.2020.1807710\u003c/span\u003e\u003cspan address=\"10.1080/10447318.2020.1807710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomiak SYX, Benbasat I (2006) The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1. MIS Q 30(4):941\u0026ndash;960. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/25148760\u003c/span\u003e\u003cspan address=\"10.2307/25148760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartneck C, Kulić D, Croft E, Zoghbi S (2008) Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. Int J Social Robot 1(1):71\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12369-008-0001-3\u003c/span\u003e\u003cspan address=\"10.1007/s12369-008-0001-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Shrum LJ (2002) What is Interactivity and is it Always Such a Good Thing? Implications of Definition, Person, and Situation for the Influence of Interactivity on Advertising Effectiveness. J Advertising 31(4):53\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00913367.2002.10673685\u003c/span\u003e\u003cspan address=\"10.1080/00913367.2002.10673685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerhagen T, van Nes J, Feldberg F, van Dolen W (2014) Virtual Customer Service Agents: Using Social Presence and Personalization to Shape Online Service Encounters. J Computer-Mediated Communication 19(3):529\u0026ndash;545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcc4.12066\u003c/span\u003e\u003cspan address=\"10.1111/jcc4.12066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDagger TS, Sweeney JC, Johnson LW (2007) A Hierarchical Model of Health Service Quality. J Service Res 10(2):123\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1094670507309594\u003c/span\u003e\u003cspan address=\"10.1177/1094670507309594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital Transformation, Human-centric Artificial Intelligence, AI Digital Human Advisors, Social Presence, Telepresence, Brand Trust","lastPublishedDoi":"10.21203/rs.3.rs-9084958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9084958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAI Digital Human Advisors have become increasingly prevalent as socially expressive frontline agents in digital financial platforms, yet how their design features translate into brand trust remains insufficiently understood. Drawing on the Stimulus\u0026ndash;Organism\u0026ndash;Response (S\u0026ndash;O\u0026ndash;R) framework, this study examines how form cues, behavioral cues, and interaction cues of AI Digital Human Advisors influence brand trust through presence-based experiential mechanisms. Using survey data from 487 experienced users of AI digital human advisors in financial applications and structural equation modeling, the results reveal a clear asymmetric pattern. While all three categories of design cues significantly enhance both social presence and telepresence, only social presence directly contributes to brand trust and serves as a robust mediating mechanism. Telepresence, although associated with immersive experiential engagement, does not independently foster trust in high-risk financial advisory contexts. These findings advance a human-centric perspective on digital transformation by demonstrating that trust in AI-mediated financial services is grounded more in socially responsive interaction than in immersive experiential vividness, offering implications for the design of trustworthy and sustainable AI-enabled financial platforms.\u003c/p\u003e","manuscriptTitle":"Human-Centric AI Digital Human Advisors and Brand Trust: Social Presence versus Telepresence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 13:30:45","doi":"10.21203/rs.3.rs-9084958/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-06T08:21:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T08:17:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-04T06:50:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T07:48:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-03T07:32:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d63f82f-cec9-4d70-a5fa-4e22226b02ea","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65942350,"name":"Business and commerce/Business and management"},{"id":65942351,"name":"Social science/Business and management"},{"id":65942352,"name":"Business and commerce/Information systems and information technology"},{"id":65942353,"name":"Biological sciences/Psychology"},{"id":65942354,"name":"Social science/Psychology"},{"id":65942355,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-10T13:30:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 13:30:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9084958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9084958","identity":"rs-9084958","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}