A DEMATEL-Guided Agent-Based Simulation Framework for Trait-Driven Neuroadaptive Interfaces in Smart Hospitality

A DEMATEL-Guided Agent-Based Simulation Framework for Trait-Driven Neuroadaptive Interfaces in Smart Hospitality | 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 Research Article A DEMATEL-Guided Agent-Based Simulation Framework for Trait-Driven Neuroadaptive Interfaces in Smart Hospitality Majid Heidari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8108792/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study introduces a trait-driven neuroadaptive interface model for smart hospitality platforms and develops a mythological framework, grounded in the premise that effective personalization must align with user traits rather than only inferred preferences. This paper integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) causal mapping and agent-based simulation approach, drawing on Dual-Process Theory and Affective Computing, to model how emotional reactivity, cognitive load tolerance, and fairness sensitivity influence adaptive interface needs in AI-enabled tourism decision contexts. DEMATEL assesses which characteristics serve as systems drivers, providing a causal-structural basis for establishing static, personalized, and fairness-aware interfaces in the context of a simulation involving 500 simulated cognitively differentiated agents. Behavioral outcomes—trust, satisfaction, and cognitive load—were analyzed in relation to trait–interface congruence. Methodologically, this dual-stage design (causal mapping followed by simulation) offers a structured procedure for validating how latent traits shape behavior in socio-technical decision systems. The results suggest that alignment can improve the user experience, particularly when agents are emotionally reactive or fairness-sensitive and interfaces change or embed ethical transparency. Misalignment, conversely, leads to overload, confusion, or disengagement. The framework provides a replicable process for trait-based adaptation, generating implications for human–AI researchers interested in fairness-aware personalization. The proposed model advances personalization beyond preference matching by offering a computational, methodological, and ethical rationale for interfaces that adapt to neuro-cognitive variability, and the framework can be extended to other adaptive systems such as healthcare or education, supporting methodological advances in social science research on AI-mediated decision-making. Trait-Based Personalization Neuroadaptive Interfaces Agent-Based Simulation DEMATEL Methodology Fairness-Aware AI Smart Tourism Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Understanding how personalized systems align with user cognition is a growing methodological concern across decision sciences and applied behavioral research. One​‍​‌‍​‍‌ major change in the way tourism experiences are provided is the development of smart hospitality systems that move from traditional service delivery to systems that mimic the reasoning of a human decision-maker dealing with a highly complex and dynamic situation (Yang et al., 2024 ). By using AI-powered means such as recommendation systems, pricing algorithms, and booking platforms, the possibilities of travelers are altered on the one hand, and on the other hand, the emotional and cognitive ways of the travelers' experiences are also changed (Tai et al., 2021 ; Lim & Kim, ​‍​‌‍​‍‌2025). Choosing accommodation, which used to be a simple task, has now resulted in situations of cognitive load and emotionally charged moods because people perceive the system as being more or less transparent, personalized, and that it fairly represents its logic (Mohammed & Denizci Guillet, 2025 ). In this hybrid human–AI interaction setting, the psychological and affective domains are no longer external to the interface; they are important dynamically to how we, as users, perceive, accept, or resist interface adaptations using diverse user types (Lee, 2025 ). This shift calls for methodological frameworks capable of modeling latent psychological constructs within human–AI interaction systems (Plikynas et al., 2022 ). Although adaptive systems can be complicated, the current body of tourism research tends to address behavioral prediction rather than psychological understanding (Stylos, 2022 ). Personalization is practically considered in the case of a performance gain rather than an experience constructed by an internal user state that is affected psychologically (Tao et al., 2024 ). Traits such as emotional reactivity, fairness sensitivity, and cognitive load tolerance are rarely considered in system design and are not mentioned as causal elements (Saha et al., 2024 ), which opens the gap between user-centered fairness and technical adaptability (Chan, 2026 ). Although an expanding body of literature is starting to look at transparency and trust in systems, there are a few models that speak to neuro-cognitive profiles and system behaviors (Li et al., 2024 ) and very few that actually operationalize those traits in a way that considers both behavioral outcomes and psychological meaning (Yin and Hwang, 2025 ), especially under emotional arousal or complexity. This research fills this gap by establishing a trait-based framework for personalization, where personalization is viewed not as a static system output but as a dynamic unfolding of user traits in relation to the adaptive interface pathways available (Koo et al., 2025 ). Rather than assuming uniform responses to adaptive logic, the model views cognitive and emotional traits as causal forces that shape the need for adaptation. A​‍​‌‍​‍‌ causal structuring process is employed to figure out the most significant traits that influence these needs, thus representing neurocognitive variation by making system changes that are meaningful from a psychological point of ​‍​‌‍​‍‌view. The model is implemented in a simulated environment, enabling the observation of user–system interactions (El Ouadi et al., 2022 ) across static, personalized, and fairness-aware interface types. This process follows the purposes of the research: to model travelers' reactions on the basis of neurocognitive domains; to identify adaptation needs due to features using DEMATEL (Tao, et al, 2025 ); and to validate fairness-aware personalization as a psychologically responsible design factor (da Silva et al., 2021 ). This approach allows for more than behavioral considerations; it provides an interpretive architecture for personalization ethics. If trait constructs, such as fairness sensitivity and emotional reactivity, are developed into the adaptive logic of AI systems, personalization becomes responsive to user variability, rather than only generic (Kumar et al., 2025 ). Fairness is reconceptualized not as a retrospective judgment but as an inherent element of system parameters (Narayanan et al., 2024 ). Affective​‍​‌‍​‍‌ complexity is even seen as one of the design elements rather than being categorized as user noise. The model combines human interpretability with the computational response, thus making personalization not only ethically sensitive but also quite consistent throughout the user's experience (Koo et al., 2025 ). Such alignment between latent traits and interface structure responds to current methodological calls for causally grounded personalization in human-centered AI. The system's behavior as per the model goes beyond just different types and should adaptively synchronize with the user's emotional state in cases of uncertainty, overload, or affective ​‍​‌‍​‍‌conflict. The implications are multi-dimensional. Theoretically, ‍‌‍‍‌the study integrates the dual-process (Kahneman, 2011 ) and the affective computing (Picard, 2000 ) models to a single, trait-sensitive ​‍​‌‍​‍‌model. Methodologically, it offers a novel integration of DEMATEL causal modeling and simulation to examine alignment between user traits and interface behavior. In practice, it offers useful paths to tourism systems and AI interface developers to provide fair, transparent, and psychologically intelligent personalization. More widely, the framework is transferable to domains like finance, education, and healthcare, or any area where algorithmic decision aids confront neurodiverse human judgment. Ultimately, this study extends personalization research toward neuroadaptive system design by integrating empirical trait modeling with interpretability and cognitive-emotional alignment. 2 Adaptive Interfaces in Smart Hospitality 2.1 Emotion–Cognition in Tourist Decision Contexts Modeling traveler decisions in digital tourism environments requires moving beyond rational-choice models to empirically examine emotional–cognitive interactions (Moisa et al., 2025 ). Emotional reactivity, cognitive load tolerance, and fairness sensitivity (ethical concern) are now no longer topics of peripheral traits but mediators of how travelers interact with AI-driven platforms over time pressure, uncertainty, or mismatched expectations (Kwong et al., 2024 ). Utilizing contemporary models of dual-process thinking, these characteristics may be seen to specify where the balance of intuitive and deliberative options may lie, especially when the user is encountering dynamically framed options or personalized content and implicit trade-offs (Oan-Oon & Choibamroong, 2025 ). As a result, if we are to simulate tourist decision-making, it is essential that we understand the influence of these aspects on their feelings of risk, value, and usability when using technology for travel planning (Hrankai et al., 2025 ). When interfaces strive to adapt to the user, the interplay of emotion and cognition becomes highly relevant (Godovykh & Tasci, 2022 ). Although personalized recommendations are designed to decrease effort and increase customer satisfaction, the emotional state of the traveler influences whether this approach to adaptation is perceived as helpful or intrusive (Yang et al., 2024 ). For example, heightened emotional reactivity may magnify responses to system framing (Farshbafiyan Hosseininezhad et al., 2025 ), while high cognitive load may decrease the perceived value of complex personalization features (Lee, 2025 ). As a result, the interaction of these attributes should be evaluated in situations involving booking contexts that aim to replicate the real situation, whereby the interface logic is situated alongside user states that co-evolve (Becker et al., 2023 ; Yin & Hwang, 2025 ). This complexity necessitates the use of methodological tools that capture latent trait dynamics over time, such as agent-based simulation that capture emergent affective-cognitive dynamics. 2.2 Personalization, Fairness, and Trust in AI Systems Personalization is commonly one of the tech upgrades employed by tourism platforms, for example, preference filtering, destination prediction, and price personalization (Yang et al., 2024 ). However, the user's perception of fairness, control, and openness is significantly influenced by personalization that is mediated by an algorithm. If personalization is implemented without providing a reason or without accounting for trait-level sensitivities, it may distort perceived fairness and undermine user trust (Liu et al., 2025a ). It is not just the distributive outcomes alone, but procedural fairness is also critical in establishing how travelers will endorse the system's intent and legitimacy (Su et al., 2025). As recognized by frameworks of perceived personalization and transparency, adaptive behaviors must be traceable and align with user expectations when they involve traits such as fairness sensitivity and cognitive effort tolerance (Kumar et al., 2025 ). Trust in AI-powered hospitality systems, therefore, stems not just from accuracy and relevance but is a function of how well the system fits with the user's sense of psychological fit and procedural fairness (Koo et al., 2025 ). Tourists with more fairness sensitivity may see the interface as being too opaque or too opportunistic, even if they are getting optimized outcomes. In contrast, perceived transparency and user-system alignment may lead to trust development, especially when the personalization indicate inherent characteristics rather than visible preferences (Narayanan et al., 2024 ). These observations have implications about the required cognitive coherence, and ethically accountable personalization does mean that fairness-aware design should be treated as a necessary usability condition in reliable tourism AI systems (Kwong et al., 2024 ). The implementation of fairness-aware system design necessitates the causal modeling of user characteristics, as opposed to post hoc perception analysis. 2.3 Interface Logics and Design Gaps in Hospitality Platforms Building on the prior discussion of emotion, cognition, and fairness, this subsection examines how current hospitality platforms implement—or fail to implement—these adaptive logics. Tourism platforms typically use one of three interface strategies: static (the same logic for everyone), personalized (based on behavioral profiles), and adaptive fairness-aware (tailored using deeper trait structures) (Leal et al., 2025 ). Although the industry commonly employs both static and personalized strategies, it is starting to recognize the shortcomings associated with each approach. Static strategies overlook variance across users, and standard personalization often relies on the hidden logic of algorithmic design, producing a sense of being manipulated or excluded (Banerjee et al., 2023 ). Fairness-aware​‍​‌‍​‍‌ interfaces are designed to deliver the best possible performance while upholding fairness in the procedure, and their application has been limitedly investigated in tourism systems only (Su et al., ​‍​‌‍​‍‌2025). Existing studies often prioritize accuracy or UX metrics without assessing whether interface logic considers user characteristics like emotional reactivity or thresholds for fairness (Deldjoo et al., 2024 ). This design gap becomes even clearer when considering the extent to which the structure of interfaces may incorporate neurocognitive traits that can create a behavioral and attitudinal appraisal (Heidari et al., 2024 ; Li et al., 2025 ). For example, people who cannot manage a huge cognitive load might choose to simplify their things instead of hyperpersonalizing them. On the other hand, users who are very concerned about fairness might have expressed their worries and given reasons for the risk they feel (Seyfi et al., ​‍​‌‍​‍‌2025). However, discrepancies between interface logic and the processing preference of users create friction beyond the limits of personalization (Yang et al., 2024 ). Although technologies adaptive to users exist, the majority of hospitality platforms do not employ user traits as causal variables in their interface selections (Leal et al., 2025 ). The present framework models how latent psychological traits causally influence interface adaptation pathways in AI-based decision systems and presents an approach for structuring trait-logic congruence as a testable and optimizable design condition. These gaps reflect how existing research has treated emotion, cognition, fairness, and simulation largely in isolation. Table 1 highlights how the present study integrates these dimensions into a unified, trait-driven and causally modeled neuroadaptive framework. Table 1 Summary of Identified Research Gaps and Present Study Contributions. Thematic Area Studies Research Gaps Methodological Contribution of the Study Emotion–Cognition Interaction in Tourism Godovykh & Tasci ( 2022 ); Moisa et al. ( 2025 ); Lee ( 2025 ). Most studies consider emotion and cognition as two separate entities and hence do not think of their co-evolution in actual decision ​‍​‌‍​‍‌contexts. Integrates emotional reactivity and cognitive load tolerance as interacting traits in adaptive decision simulations. Personalization and Trust in AI-Driven Tourism Yang et al. ( 2024 ); Koo et al. ( 2025 ); Kumar et al. ( 2025 ). Personalization mainly assessed by accuracy or performance metrics, not user-state alignment or psychological fit. Defines personalization as neuroadaptive and evaluates trait–interface congruence affecting trust and satisfaction. Fairness and Transparency in AI Systems Deldjoo et al. ( 2024 ); Narayanan et al. ( 2024 ); Su et al. (2025). Fairness usually analyzed post-hoc rather than embedded in system logic or adaptive behavior. Presents fairness sensitivity as a socially active personality trait and ethically aligns the agent by embedding the ethical principles into the adaptive interface ​‍​‌‍​‍‌rules. Simulation and Causal Modeling in Smart Hospitality Zhang et al. ( 2022 ); Pizarro et al. ( 2023 ); Tao et al. ( 2025 ). Limited use of simulation or causal inference to link user traits with system design. Adaptivity​‍​‌‍​‍‌ employs an agent-based simulation as well as DEMATEL causal mapping to represent trait causality in adaptive ​‍​‌‍​‍‌interfaces. 3 Neuroadaptive Framework for Smart Hospitality Interfaces 3.1 Theoretical Foundations This​‍​‌‍​‍‌ framework is based on two theoretical foundations that work together to guide the modeling of latent neuro-cognitive traits and their causal influence on the interaction of users with AI-enabled hospitality ​‍​‌‍​‍‌interfaces. First, Dual-Process Theory provides an explanatory psychology framework, which illustrates human cognition as a system of fast, intuitive responses (System 1) and slower, deliberative reasoning (System 2) (Oan-Oon & Choibamroong, 2025 ). These cognitive modes aid in the construction of agent profiles—especially the emotional reactivity and cognitive load tolerance—as they affect interface perception, speed of decision-making, and stress (Seyfi et al., 2025 ). Next,​‍​‌‍​‍‌ the Affective Computing Theory explains that the treatment of emotional and cognitive characteristics serves as the input for dynamic adaptation, which is reflected or sensed by the interface (Hu et al., 2025 ). A theoretical layer that expresses a connection between personality traits and morally adaptive user interfaces (e.g., fairness-aware design patterns) to the extent that they can alter the complexity, the tone, or the depth of the personalization according to the user’s condition is supported by this strand (da Silva et al., ​‍​‌‍​‍‌2021). Altogether, the theories can create a common framework for understanding how trait-interface congruence can facilitate adaptive logic and the differences in behavior between static, personalized, tangible, and ethically calibrated adaptive system conditions. These theories serve not only as conceptual backbones but also as operational frameworks guiding the structural alignment of traits and system logic in the modeling process. 3.2 Model Structure and Hypotheses Development The proposed model integrates the latent neuro-cognitive traits, adaptive interface logic, and simulation-based decision contexts to experimentally measure the change in user experience in hospitality booking due to AI-powered personalized systems. ​‍​‌Three neuro-cognitive traits—emotional reactivity, cognitive load tolerability, and fairness sensitivities—are situationally relevant to distinguish user profiles. These traits are mapped to interface response demands using a DEMATEL-based causal structure, which defines how specific traits influence the need for adaptation or fairness calibration. The interface logic incorporates three system types: static (non-adaptive), personalized (trait-responsive), and fairness-aware (ethically optimized). To simulate real-world booking dynamics, agents are exposed to decision scenarios featuring overchoice, conflicting options, or framing manipulations (Fig. 1 ). This structured integration establishes the model’s internal logic and provides the basis for hypothesis formulation grounded in causal inference and simulation dynamics. Fairness​‍​‌‍​‍‌ perception of different personalization levels is also dependent on the system communication of its decisions in a transparent and ethical ​‍​‌‍​‍‌way. If the context is emotionally or cognitively charged, then fairness-aware designs will most likely reduce the negative impact and increase acceptance by matching users' expectations of procedural justice through algorithmic behavior (Narayanan et al., 2024 ). H1 Fairness-aware interfaces enhance perceived fairness and trust more than purely personalized or static interfaces. Not all user traits have the same influences on interface needs. The DEMATEL modeling shows which traits influence adaptation needs the most, e.g., sensitivity to fairness or cognitive load. The shared traits are most likely associated with the interface logic when system calibration is satisfactory, leading to congruent interactions. H2a Traits identified as dominant causal drivers in the trait–interface influence structure show stronger structural alignment with adaptive interface logic. Nevertheless, the concept is not limited to the alignment of traits and interface responses. This alignment is proposed to produce observable behavioral benefits, most importantly improved decision efficiency. The user is more likely to make a quick decision with confidence when they feel the interface gets how they need to process information (Liu et al., 2025b ). H2b Higher alignment between agent traits and adaptive interface logic leads to greater decision efficiency. Among core neurocognitive traits, emotional reactivity is particularly relevant to the satisfaction afforded by adaptive systems. Users that engage in decisions effectively may respond positively to an interface that inserts complexity, tone, or message framing into adaptation so as not to contribute to feelings of stress or overwhelm (Fominska et al., 2024 ). H3 Agents with high emotional reactivity reports greater satisfaction when interacting with adaptive interfaces. ​‍​‌‍​‍‌ Apart from satisfaction, it is also assumed that the structural weight of certain traits will influence the perceived trustworthiness of the system. Those traits that play a more central causal role in the DEMATEL model are likely to have a significantly stronger impact on user trust, especially when the system design clearly demonstrates their presence. H4 Traits with greater influence on interface design are positively associated with trust in adaptive interface outcomes. Cognitive load is expected to have a negative effect on trust when it is high or out of alignment. If the design is unable to strategically utilize cognitive load to customize or increase fairness awareness in the structural design, users may disengage or even develop counterfactual beliefs regarding the system's objectives (da Silva et al., 2021 ; Yin & Hwang, 2025 ). Cognitive pressure is thus relative to both and must be mitigated in order for the system to be credible. H5 High cognitive load reduces trust unless mitigated by personalized or fairness-aware interface features. 4 Methodology 4.1 Simulation Design The decision to opt for a simulation design in this research, rather than an experimental design, helped to vary user traits, interface types, and decision contexts considerably (Pizarro et al., 2023 ).‍ The environment contains 500 simulated cognitively differentiated agents, each with a unique profile containing three validated constructs (emotional reactivity, cognitive load tolerance, and fairness sensitivity). Each construct was represented as a continuous variable defined on the continuous [0, 1] interval, where each trait is randomized by sampling from beta-like distributions to represent psychological variability. Agents engaged with booking tasks that included overchoice, tradeoffs, and emotional framing. This design enables a high degree of control over interaction complexity, allowing examination of trait-specific responses under varying decision loads and interface framings. Different​‍​‌‍​‍‌ interfaces were randomly assigned to be static (non-adaptive), personalized (trait-responsive), or fairness-aware (ethically calibrated). Randomly‍‌‍‍‌ assigning interfaces allowed for equal exposure between groups and ensured that the comparisons made were valid. The simulation captured three measurable behavioral indicators—trust, satisfaction, and cognitive load (measured through decision latency and reversal frequency)—to provide a deep dive into the effects of the different trait-interface configurations on the results. The simulation framework provides a systematic, theory-driven approach to testing personalization and fairness-driven changes, offering significant support for causal inference when adaptive interface design is being assessed.​‍​‌‍​‍‌ 4.2 DEMATEL Logic and Analysis Procedure To elucidate the causal links between neuro-cognitive traits and the demands of interface adaptation, DEMATEL was chosen because it can organize the influence of inter-variables within complex, non-linear systems, thereby providing directional clarity, which is appropriate for multi-criteria decision modeling (Tao et al., 2025 ). This research modeled six constructs, including three user traits (emotional reactivity, cognitive load tolerance, and fairness sensitivity) and three adaptive interface demands (simplicity, ethical alignment, and information control). ​‍​‌‍​‍‌Pairwise influence ratings were based on literature-guided structured elicitation from domain experts, producing a direct influence matrix that captures the strength of influence each construct has on the others. Its ability to disentangle layered causal effects has been proven in root cause analyses beyond interface design, including infrastructure systems (Shooshtarian et al., 2024 ). Seven experts with backgrounds for tourism technology, human–AI interaction, and user experience design contributed pairwise ratings using a structured DEMATEL survey. This matrix was normalized to compute Prominence (D + R) and Net Causality (D – R) scores for each construct. Constructs with high prominence and with positive net causality were identified as drivers in the adaptive interface architecture. Those​‍​‌‍​‍‌ traits that were singled out as the main causal drivers (positive D-R values) of the adaptive behavior logic have become the leading parameters by which the behavior of the adaptive interface is determined. The DEMATEL origin rules have been implemented in the simulation environment; thus, they govern the interaction of agents with interface situations according to their neuro-cognitive ​‍​‌‍​‍‌profiles. Behavioral outcomes were defined as follows: trust reflects the degree of alignment between agent decisions and system suggestions; satisfaction was recorded based on post-decision evaluations; and cognitive load was estimated through decision latency and the number of reversals. 5 Linking DEMATEL Causality to the Simulation Framework Table‍‌‍‍‌ 2 serves as an explicit conceptual link between the causal modeling based on DEMATEL and the simulation analysis by showing in detail how the influence structure derived from DEMATEL guided the simulation design. The table indicates the traits that acted as system drivers, the manner in which these influences were incorporated into the adaptive logic, and the behavioral outcomes chosen for model validation. Thus, this connection enables the causal drivers identified structurally to be examined behaviorally (Tao et al., 2025 ) in terms of their impact on trust, satisfaction, and cognitive efficiency within the simulated environment. Table 2 Integration of DEMATEL Results with Simulation Framework. Construct / Trait DEMATEL Role (Prominence & Causality) Simulation Function Outcomes Emotional Reactivity High prominence (+ 0.33); primary causal driver of interface tone adaptation. Controls interface affective tone (calm ↔ stimulating). ↑ Trust ↑ Satisfaction ↓ Cognitive Load Cognitive Load Tolerance Moderate prominence (–0.19); reactive variable influenced by emotional drivers. Modulates task complexity and decision latency. ↑ Decision Efficiency ↓ Cognitive Load Fairness Sensitivity Moderate prominence (–0.10); ethical driver influencing transparency mechanisms. Activates fairness-aware interface logic. ↑ Perceived Fairness ↑ Satisfaction Interface Type Rules Derived from causal structure among traits and interface demands. Define adaptive logic for Static / Personalized / Fairness-Aware conditions. All behavioral outcomes combined 5.1 DEMATEL Causal Mapping of Trait–Interface Influence This section identifies which neurocognitive traits most strongly influence adaptive requirements in smart hospitality systems. In the context of this study, six key constructs are evaluated: three user traits—emotional reactivity, cognitive load tolerance, and fairness sensitivity—and three adaptive interface demands—simplicity, ethical alignment, and information control. These constructs are all based on the study's theoretical framework that synthesizes affective computing and dual-process decision theory (Hu et al., 2025 ; Oan-Oon & Choibamroong, 2025 ). As interface personalization must reflect significant psychological differences, understanding the causal hierarchy of these constructs is therefore essential (Tao et al., 2025 ). To establish which user traits most strongly influence the requirements for interface adaptation, a DEMATEL-based causal analysis was conducted using expert-derived pairwise ratings. To identify the directional influence among user traits and interface demands, seven experts were recruited, based on their knowledge and experience in tourism informatics, AI-driven interfaces, and user experience design. Each expert then completed a structured DEMATEL matrix, indicating the influence of each construct on every other construct by rating it using a 0–4 scale (no influence to high influence). The six constructs were compared in all pairwise combinations, with diagonal self-influence entries excluded. The raw pairwise ratings were aggregated to form the Direct Influence Matrix presented. As shown in Table 3 , emotional reactivity exerts substantial influence on simplicity, ethical alignment, and information control. Fairness sensitivity shows dominating effects on ethical alignment, while cognitive load tolerance sustains effects around simplicity and information control demands. The initial results point to a directional reasoning that supports the research's trait-driven adaptation framework: user traits are not only significant but also the main components structurally that lead to the generation of adaptive interface requirements (Deldjoo et al., 2024 ; Narayanan et al., 2024 ).‍‌‍‍‌‍‌‍‍‍‌‍‍‌ Table 3 Direct Influence Matrix. Construct Emotional Reactivity Cognitive Load Tolerance Fairness Sensitivity Simplicity Ethical Alignment Information Control Emotional Reactivity 0.0 2.43 2.0 2.29 2.29 2.14 Cognitive Load Tolerance 2.0 0.0 2.0 2.29 1.29 2.29 Fairness Sensitivity 1.14 2.43 0.0 1.86 2.43 2.14 Simplicity 2.14 2.29 2.0 0.0 1.71 1.71 Ethical Alignment 3.0 1.29 2.29 2.0 0.0 2.29 Information Control 1.86 2.0 2.0 1.71 1.29 0.0 Following the structural overview in the direct matrix, Table 4 provides a more detailed analysis by revealing the causal architecture underlying trait–demand processes. With expert input and appropriate max-normalization, the matrix provides a way to quantify the two key indicators: Prominence (D + R) and Net Causality (D–R). Prominence identifies which constructs are fixed in the system, while net causality separates the active drivers from the passive receivers, the key purpose of this causal modeling. The analysis suggests that Ethical Alignment (+ 0.62) and Emotional Reactivity (+ 0.33) functioned strongly as defining influences on the behavior of the system. In contrast, Information Control (–0.57) and Cognitive Load Tolerance (–0.19) are found to be structurally passive, being influenced rather than influencing. Emotional Reactivity is uniquely identified as having the greatest prominence within the system (7.1), which further highlights its importance in overall adaptive results. These trends confirm that adaptive interface design must recognize user behavior; it must also specify distinct neurocognitive characteristics. The emergent causal configuration informs the simulation logic that follows, ensuring arising agent responses properly embody a theoretically based, structurally validated influence system. This causal configuration also reinforces that fairness-sensitive and emotionally reactive traits jointly anchor the adaptive logic, confirming the dual cognitive-affective foundation of the framework. Therefore,‍‌‍‍‌ traits with greater net causality should show a greater influence in subsequent behavioral metrics, especially trust and satisfaction, when they are integrated into adaptive ‍‌‍‍‌rules. Table 4 Total Influence Matrix with Prominence and Net Causality. Construct Emotional Reactivity Cognitive Load Tolerance Fairness Sensitivity Simplicity Ethical Alignment Information Control D + R (Prominence) D–R (Net Causality) Emotional Reactivity 0.0 0.81 0.67 0.76 0.76 0.71 7.1 0.33 Cognitive Load Tolerance 0.67 0.0 0.67 0.76 0.43 0.76 6.76 -0.19 Fairness Sensitivity 0.38 0.81 0.0 0.62 0.81 0.71 6.76 -0.1 Simplicity 0.71 0.76 0.67 0.0 0.57 0.57 6.67 -0.1 Ethical Alignment 1.0 0.43 0.76 0.67 0.0 0.76 6.62 0.62 Information Control 0.62 0.67 0.67 0.57 0.43 0.0 6.48 -0.57 Note: Prominence (D + R) represents total influence strength; Net Causality (D – R) represents directional dominance (positive = driver, negative = receiver). Following the matrix analysis, the causal map in Fig. 2 translates the numerical structure into a visual configuration that clarifies directional dominance within the system. The circular arrangement distinguishes neuro-cognitive traits from interface demands and highlights the asymmetry of influence flow. Emotional Reactivity and Ethical Alignment emerge as highly influential sources of systemic influence, demonstrated by having many outbound connections to other constructs. This visual clustering demonstrated their positioning as proactive anchors regarding the proximal influence of adaptive requirements, consistent with the earlier analysis that traits serve not just as passive moderators but active design variables. Contrasting with this, Information Control and Cognitive Load Tolerance perform engagement within the adaptive interface model as reactive, context-driven influences and were observed as influence receivers. The dominant influence structure demonstrates cognitive-affective architecture whereby emotional contagion and ethical framing shape interface expectations and influence system level adaptation logic. These attributes are not solely inputs, but instead directional levers that decide whether the interface privileges simplicity, transparency, and user control. Their causal weight is more than influence and describes structural centrality as dominant anchors of interpretation in variation, or adaptive personalization. This understanding serves as the foundation for the simulation phase, where agent behavior is simulated according to these dominant traits. By mapping these causal relationships into decision rules, the next section operationalizes trait-driven interface responsiveness and evaluates how it manifests in trust, satisfaction, and cognitive processing. 5.2 Behavioral Effects of Interface Types This‍‌‍‍‌ part goes on to analyze the interface types that serve as a link between the user's behavior and the simulated environment after the identification of trait-interface influence structures. These interfaces were designed to reflect their causal priorities as per the DEMATEL results: personalized systems focused on the most straightforward solution responsive to the user's traits, whereas fairness-aware interfaces merged the ethical aspects with the features such as transparency and control (Leal et al., ‍‌‍‍‌2025). Each of the agents interacted with one of three interface conditions, i.e., static, personalized, or fairness-aware, whose logic stems from this causal mapping (Yang et al., 2024 ). The‍‌‍‍‌ main idea is to find out if the changes in the adaptive designs cause any differences in the level of trust, satisfaction, and cognitive load (Narayanan et al., 2024 ). ‍‌‍‍‌‍‌‍‍‌A group-based statistical comparison across interface types was used to measure these impacts. All‍‌‍‍‌ findings in this section are from the agent-based simulation of 500 agents with cognitive ‍‌‍‍‌differentiation. Building on the causal characteristic mapping, the descriptive data in Table 5 reveal clear differences in performance by interface type. ‍‌‍‍‌The observed ordering of the different interface types from an ethical and affective perspective corresponds to the priorities implied by the causal structure. Embedding‍‌‍‍‌ fairness-aware and trait-responsive rules seems to lead to increased trust and satisfaction, as well as decreased cognitive load (Godovykh & Tasci, 2022 ; Fominska et al., 2024 ). ‍‌‍‍‌Fairness-aware interfaces produced the highest levels of trust and satisfaction and the lowest cognitive load (Trust 4.2; Satisfaction 4.1; Load 2.7), followed by personalized (3.5; 3.7; 3.2) and then static (2.8; 2.9; 3.8) ones. Furthermore, personalized interfaces were better than static designs in all three ‍‌‍‍‌outcomes. The inferior performance with static interfaces reinforces the limitation of any non-adaptive design. These patterns suggest preliminary evidence that interfaces that responded to affective and fairness sensitivities can enhance experiential quality across the emotional and cognitive dimensions (Su & Ha, 2025 ). Table 5 Descriptive Statistics of Behavioral Outcomes by Interface Type. Interface Type Trust (M ± SD) Satisfaction (M ± SD) Cognitive Load (M ± SD) Static 2.8 ± 0.9 2.9 ± 1.0 3.8 ± 1.1 Personalized 3.5 ± 0.8 3.7 ± 0.9 3.2 ± 0.8 Fairness-Aware 4.2 ± 0.6 4.1 ± 0.7 2.7 ± 0.7 In addition to the descriptive trends, inferential tests were used to determine if observed differences in outcomes were statistically significant. Table 6 shows that all three behavioral variables are statistically significantly different between interface types. Trust showed the largest variance, suggesting that design logic heavily impacts system credibility. One of the significant factors contributing to user satisfaction was the ability to design the interface flexibly. At the same time, the condition that was ethically adjusted reduced the user's cognitive load. These outcomes serve as proof that such interfaces that adapt to users' needs and take into account users' emotions and fairness-related characteristics can lead to an increase in users' confidence, positive affective experience, and relief of cognitive load (supports H1, H5) (Fominska et al., 2024 ; Yang et al., 2024 ). ‍‌‍‍‌The size and consistency of these effects provide behavioral legitimacy to the previous causal trait mapping. These aggregate differences set the stage for the next analysis, which examines how alignment between agent traits and interface logic drives these behavioral effects. Table 6 ANOVA Results for Behavioral Outcomes by Interface Type. Outcome Test Statistic p-value Effect Size (η²) Trust F(2, 497) = 42.3 < .001 0.15 Satisfaction F(2, 497) = 31.8 < .001 0.11 Cognitive Load F(2, 497) = 28.6 < .001 0.1 Note: Comparison across: Static, Personalized, Fairness-Aware. To further illustrate patterns of outcomes, Fig. 3 displays the distribution of agent responses across interface types. The adaptive interfaces outperformed static systems; dispersion narrowed for trust and satisfaction under fairness-aware systems, while cognitive-load dispersion remained wider under static systems. The‍‌‍‍‌ static interfaces had scores that varied significantly in terms of trust and satisfaction; nevertheless, cognitive load showed the highest variation under non-adaptive conditions. ‍‌‍‌Such visual signals align with earlier statistical results. Hence, they clearly convey that morally aware and emotionally sensitive interfaces lead, on average, to higher as well as more stable and psychologically efficient user experiences (Narayanan et al., 2024 ; Koo et al., 2025 ).‍‌‍‍‌ The outcomes across interface types suggest a functional value in trait-informed interface design. Increased trust under fairness-aware conditions demonstrates the successful integration of ethical responsiveness into system logic, with users attuned to fairness and transparency. Increases in satisfaction associated with adaptive formats—especially among agents with heightened emotional reactivity—also suggest the presence of personalization mechanisms that were affectively attuned. The diminished cognitive load on fairness-aware interfaces also suggests that system logic matching user tolerance thresholds may help reduce cognitive effort. These findings extend the causal structures identified earlier into the behavioral domain, setting the stage for the next section, which tests how trait–interface alignment predicts performance outcomes. In particular, the trust gains under fairness-aware conditions are consistent with ethically calibrated adaptation mitigating skepticism that otherwise accompanies higher load or opacity. 5.3 Trait–Interface Alignment and Performance Outcomes This section explores how the behavioral impact of an interface design depends not merely on the type but also on the degree of psychological alignment between user traits and system logic. The simulation design embedded trait-driven responsiveness into interface behavior, guided by a previously established structure of cognitive–affective influence. Interfaces varied in their sensitivity to user needs—some optimizing for emotional simplicity, others for ethical coherence—creating natural differences in how well they matched agent dispositions. To measure this, a trait-interface alignment score was calculated for each agent, indicating how well the agent’s trait profile matched the adaptive strategy of the interface. ‍‌‍The analysis examines trait–outcome correlations and interaction effects with interface types. To ground the alignment analysis in differentiated user psychology, we first examine how each neuro-cognitive trait independently associates with core behavioral outcomes. Table‍‌‍‍‌ 7 shows the connections that reveal different layers of influence. Among the four trust predictors, fairness sensitivity was the strongest, which thus corroborates the idea that people who are sensitized to perceive justice in the procedures of a system's interaction consider that system more trustworthy (Narayanan et al., 2024 ; Su & Ha, 2025 ). Additionally, emotional reactivity was both highly and positively associated with satisfaction, which indicates agents that are more emotionally invested receive a higher experiential benefit from their interaction with the system (Lee, 2025 ). Cognitive‍‌‍‍‌ load tolerance, in the end, was the factor that significantly negatively correlated with perceived load, thus indicating the reduction of the perception of the effort made. The findings support the idea that each characteristic influences the person’s behavior through a different psychological mechanism and, thus, offer an opportunity to examine whether that interaction with the interface logic strengthens or disrupts these patterns (Yin & Hwang, ‍‌‍‍‌2025). The pattern that fairness sensitivity is most associated with trust, emotional reactivity with satisfaction, and cognitive-load tolerance with lower perceived load accords with the structural roles identified earlier, suggesting that causal centrality carries forward into behavioral expression once rules are embedded (consistent with H2a, H3, and H4). Table 7 Trait-Outcome Correlation Matrix. Trait \ Outcome Trust Satisfaction Cognitive Load Emotional Reactivity -0.02 0.51 0.01 Cognitive Load Tolerance -0.02 -0.15 -0.62 Fairness Sensitivity 0.56 -0.03 0.06 Note: Trait–outcome associations are directionally consistent with DEMATEL-identified causal roles. This phase assessed if the congruence between individual traits and interface conditions affects behavioral outcomes, building on the previous structure of relationships. The individual effects of user traits and adaptive interfaces stand as statistically significant; sensitivity to fairness predicts trustworthy behavior, emotional reactivity predicts satisfaction, and cognitive load tolerance predicts cognitive load. Those patterns are visually confirmed in Fig. 4 , where trait–outcome relationships demonstrate variation in slope for each type of interface. None of the interaction terms are statistically significant (Table 8 ), indicating while the traits and the type of interface may have separate effects on behavior, their mutually aligned effects do not produce additional additive effects (H2b not statistically supported). Adaptive interfaces, particularly fairness-aware and personalized designs, still exceed static interfaces across all outcome measures. The findings illustrate the value of trait-informed personalization, even though trait–interface alignment (interaction terms) did not reach statistical significance. These results support an approach for interfaces, in a neuroadaptive sense, that would promote trust, satisfaction, and cognitive efficiency for participants. Similar‍‌‍‍‌ findings were reported in research on cultural and trait-based variations in agent-driven social cohesion models (Plikynas et al., 2022 ), which, in turn, support the idea that personality traits significantly influence complex adaptive ‍‌‍‍‌behavior. Table 8 Trait–Interface Alignment Models for Behavioral Outcomes. Outcome Predictor Coefficient p-values Trust Intercept (Static Baseline) 2.00 < .001 Trust Fairness Sensitivity (Trait) 1.05 < .001 Trust Fairness-Aware Interface (vs. Static) 0.36 < .001 Trust Fairness Sensitivity × Fairness-Aware Interface 0.03 0.793 Satisfaction Intercept (Static Baseline) 2.18 < .001 Satisfaction Emotional Reactivity (Trait) 0.92 < .001 Satisfaction Personalized Interface (vs. Static) 0.39 < .001 Satisfaction Emotional Reactivity × Personalized Interface 0.05 0.665 Cognitive Load Intercept (Static Baseline) 3.42 < .001 Cognitive Load Cognitive Load Tolerance (Trait) -1.11 < .001 Cognitive Load Personalized Interface (vs. Static) -0.23 < .001 Cognitive Load Cognitive Load Tolerance × Personalized Interface 0.11 0.340 Note: “Intercept” indicates the predicted outcome score when both trait and interface values are zero. Interaction terms test whether the effect of each trait changes depending on the assigned interface type, predictors with p-values < 0.05 were statistically significant. These findings indirectly reinforce the broader logic of trait-informed personalization. Trust, satisfaction, and cognitive load measures showed that interfaces that adapted to users' core traits were generally superior to their static counterparts. Traits with greater causal influence in the DEMATEL model—such as fairness, sensitivity, and emotional reactivity—also emerged as significant behavioral predictors, validating their structural relevance. While‍‌‍‍‌ interaction terms failed to reach statistical significance, their directions are still in line with what was expected: adaptive interfaces increase trust and satisfaction and decrease perceived load, thus showing that efficiency benefits can be obtained even without statistically significant trait–interface amplification. These‍‌‍‍‌ findings are in line with the idea that personalization based on the causal trait structure can enhance the user experience, which is also valid in a situation where no explicit psychological alignment is present (Kumar et al., ‍‌‍‍‌2025). 5.4 Integrated Interpretation of Simulation Findings This final interpretive analysis integrates the structural logic of trait-based interface requirements, the behaviors that result from design variance, as well as the performance-impacting psychological alignment. In seeing these components tightly integrated and not in isolation, the synthesis explores how underlying neuro-cognitive dispositions, including emotional reactivity, fairness considerations, and cognitive load tolerance, influence not only what users require from the adaptive system but also how users behave when these requirements are met or misaligned. The‍‌‍‍‌ integration provides understanding of the processes through which personalized and fairness-aware interfaces influence, unveiling deeper patterns of trust formation, emotional experience, and cognitive relief. In sum, these outcomes are supportive of the main hypothesis (H1–H5) that fairness-aware and trait-responsive designs increase trust, satisfaction, and the feeling of being in control by matching the main causal ‍‌‍‍‌traits. Based on the causal architecture and behavioral findings, the following synthesis explains which adaptive interface types are most effective for each neurocognitive trait and why. Rather than simply summarizing outcomes, this integrative framework links causal dominance (as identified through DEMATEL), theoretical grounding, and behavioral response patterns to guide interface optimization. Every trait is paired with an interface logic that relates most closely to its fundamental cognitive or emotional processing style via affect-driven adaptation, simplification, or fairness evaluation. The analysis examines the implications of misalignment and reveals a design-critical perspective on what can go wrong when personalization neglects user dispositions. In placing these relationships within the theoretical framework of the study, Table 9 acts as a system-level map to convert trait-based differences into coherent design logic. The interpretive synthesis aligns with the empirical results, leading to practical and ethical implications. Table 9 Trait–Interface Design Logic and Simulation-Based Impact. Trait Causal Role (D–R) Theoretical Lens Best Interface Type Design Logic and Trait Alignment Outcomes and Misalignment Risk Emotional Reactivity + 0.33 (Driver) Affective Computing Theory, Dual-Process Theory (System 1). Personalized Responsive interfaces, for instance, adjust their tone and message framing according to the user's emotional sensitivity; thereby, they help lower the user's stress and increase their engagement level ↑ Satisfaction, ↑ Decision Confidence; if mismatched: emotional overload, frustration, disengagement. Cognitive Load Tolerance –0.19 (Receiver) Dual-Process Theory (System 2). Personalized Simplified information structure means less mental effort for users of low tolerance, providing more clarity and speed in choice. ↓ Cognitive Load, ↑ Efficiency; if mismatched: delay, confusion, decision reversal. Fairness Sensitivity –0.10 (Receiver) Ethical Adaptation Principles (Affective Computing & System Transparency). Fairness-Aware Transparent‍‌‍‍‌ and ethically calibrated design is in line with the justice concerns, thereby, it increases trust and the perception of procedural ‍‌‍‍‌legitimacy. ↑ Trust, ↑ System Acceptance; if mismatched: skepticism, resistance, perceived bias. These findings indicate that personalization is most effective when it appears to operate within the structural logic of user traits rather than through surface-level adaptation. ‍‌‍‍‌ The main features that control and have a significant impact on the system at a causal level, for example, emotional reactivity and fairness sensitivity, not only set the stage for the interaction but also serve as a kind of map for the behavioral outcomes in situations where the interaction is sensitive to them. The linkage of causal structure with behavioral data, as shown, is a very strong argument in favor of using neuroadaptive reasoning to instruct the architecture of interfaces and to make sure that adaptive systems are not just responding to users but, actually, by the use of trait-driven personalization as a principle, they are giving a socially responsible form of empowerment to the users.‍‌‍‍‌ 6 Discussion and Conclusions 6.1 Neuro-Cognitive Alignment in Adaptive Interfaces This research views the adaptation of the interface as dependent on neurocognitive processes, where user characteristics are not merely residual modifier states of the interface (Heidari et al., 2024 )—in fact, they define the fundamental reasoning of the system. Emotional reactivity, fairness sensitivity, and cognitive load tolerance each exerted distinct causal influence on interface demands, and when these demands were met—through personalization or fairness-aware design—users exhibited marked improvements in trust, satisfaction, and decision efficiency. Importantly, the same attributes that determined the system's architecture through DEMATEL also predicted behavioral outcomes in the simulation, thus demonstrating dual validity: structural influence in system logic and experiential impact in user behavior. These results extend beyond surface-level personalization by offering a model in which adaptive systems must operate using an internal logic congruous with user psychological architectures (Kwong et al., 2024 ). This dual convergence—of structural prominence and behavioral predictiveness—confirms that neuro-cognitive alignment is not an optional optimization but a foundational design principle. The results indicate alignment between users' dispositions and interface design is more than advantageous; it is necessary. Instances of misalignment constantly offered up behavioral friction: emotionally reactive agents expressed lower satisfaction if interfaces did not moderate the tone; fairness-sensitive users disengaged when transparency was absent; and cognitively sensitive users slowed or reverted decision-making when overloaded with complexity. These dynamics indicate that personalization is not ethically neutral; the success of the designs or task is liable to either reinforce or disrupt cognitive trust and affective rapport (Koo et al., 2025 ). Accordingly, neuroadaptive design should be framed not only as a technical improvement but also as a psychological accountability: the system should know enough to support the user, beyond serving content. 6.2 Theoretical and Practical Implications The study offers new theoretical integration by embedding Dual-Process Theory and Affective Computing in a single simulation model. Compared​‍​‌‍​‍‌ to previous studies, which consider traits as statistical covariates, the current framework views them as causal factors that are integrated into the interface ​‍​‌‍​‍‌logic. ​‍​‌‍​‍‌This design choice operationalizes the structural roles identified in the DEMATEL model, allowing constructs like emotional reactivity or fairness sensitivity to function not only as explanatory variables but as behavioral determinants across H1–H5. This integrated method allows both trait causality (via DEMATEL) and behavioral alignment (via simulation outcomes) to be examined within a replicable system framework. The​‍​‌‍​‍‌ framework acts as a tool for developers and designers to incorporate the ethics of personalization and transparency in digital tourism systems. Systems can be contextually intelligent; thus, they can match the interaction tone, information complexity, and fairness logic to the changing user profiles (Narayanan et al., 2024 ). The design principles derived here are of a universal nature: fairness-aware interfaces can enhance procedural legitimacy in healthcare platforms, cognitive load reduction can be used for fintech onboarding, and emotion-sensitive adaptation can be used for AI tutors. In fact, this model redefines personalization as a performance strategy with a responsiveness ethic, whereby adaptive systems are not only required to predict outcomes but also to understand the people behind ​‍​‌‍​‍‌them. 6.3 Limitations and Future Directions ​‍​‌‍​‍‌ The simulation setup provides a controlled environment to evaluate trait-driven adaptation (Pizarro et al., 2023 ); however, it simplifies and removes the vast majority of the natural complexity of user interactions that occur in the real world. Several important contextual factors, for example, latency, interface delays, emotional framing, or multitasking pressure, were kept constant, thereby limiting the ecological validity of the ​‍​‌‍​‍‌findings. Additionally, traits were treated as dispositional, or stable, rather than dynamic states, thus preventing their instantaneous co-adaptation. Future research could pursue empirical validation through longitudinal studies or physiological measurements to explain how trait-interface alignment is established under true decision pressures and environmental uncertainty, and especially when including cultural contexts with differences in fairness norms or bases for cognitive load. In addition to ecological realism, the current model is constrained to three high-salience traits, which may overlook other psychologically relevant dimensions, such as locus of control, impulsivity, or affective instability. Broadening the set of trait dimensions may allow for observations of multi-trait interactive effects or conditional adaptation effects under compound cognitive–emotional profiles. Expanding the trait set would also allow the testing of nonlinear or compensatory dynamics—where high emotional reactivity might counterbalance low cognitive load tolerance, or vice versa—revealing richer adaptive strategies. Methodologically, future research could improve the DEMATEL phase using fuzzy-set logic or adaptive weighting methods or incorporate iterative expert calibrations to reduce domain surface biases and account for temporal variation in trait expression. This would enable a paradigm shift from reactive personalization to completely neuroadaptive ecosystems that actively monitor, predict, and ethically respond to the changing psychological needs of digitally mediated users over time. Declarations Author Contribution **Author contribution declaration: MH** wrote the main manuscript text and data analysis, responsible for data management, original draft preparation, visualization, and investigation. As well as the review and editing. Compliance with Ethical Standards: The author declares no financial or personal conflicts of interest that could have influenced the research presented. This research did not involve any studies with human participants or animals. All data used in the simulation were synthetically generated and do not pertain to any real individuals or living subjects. Informed consent was not applicable, as the study did not involve any human participants (only experts) or the collection of personal data. Funding Declaration: The author declares that no funding was received for the development, execution, or publication of this research. References Banerjee, A., Banik, P., & Wörndl, W. (2023). A review on individual and multistakeholder fairness in tourism recommender systems. Frontiers in big Data, 6, 1168692. https://doi.org/10.3389/fdata.2023.1168692 Becker, M., Mahr, D., & Odekerken-Schröder, G. (2023). Customer comfort during service robot interactions. Service Business , 17 (1), 137-165. https://doi.org/10.1007/s11628-022-00499-4 Chan, J. (2026). Compensation request to customer: Comparing the perceived fairness of AI vs. human representatives and the influence of solitude and companionship. International Journal of Hospitality Management, 132, 104389. https://doi.org/10.1016/j.ijhm.2025.104389 da Silva, D. C., Manzato, M. G., & Durão, F. A. (2021). Exploiting personalized calibration and metrics for fairness recommendation. Expert Systems with Applications, 181, 115112. https://doi.org/10.1016/j.eswa.2021.115112 Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., & Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59-108. https://doi.org/10.1007/s11257-023-09364-z El Ouadi, J., Errousso, H., Malhene, N., & Benhadou, S. (2022). On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city. Quality & Quantity , 56(6), 3893-3932. https://doi.org/10.1007/s11135-021-01282-9 Farshbafiyan Hosseininezhad, M., Heidari, M., & Letizia Guerra, M. (2025). The framing effect and sustainable hotel booking behaviour: A nudge marketing study. European Journal of Tourism Research, 39, 1-14. https://doi.org/10.54055/ejtr.v39i.3760 Fominska, I., Di Tore, S., Nappi, M., Iovane, G., Sibilio, M., & Gelo, A. (2024). Approaches to identifying emotions and affections during the museum learning experience in the context of the future internet. Future Internet, 16(11), 417. https://doi.org/10.3390/fi16110417 Godovykh, M., & Tasci, A. D. (2022). Emotions, feelings, and moods in tourism and hospitality research: Conceptual and methodological differences. Tourism and Hospitality Research, 22(2), 247-253. https://doi.org/10.1177/14673584211039867 Heidari, M., Verderese, D., & Saviano, M. (2024). Exploring the contribution of neuroscientific approach to sustainable smart tourism. In ITM Web of Conferences (Vol. 62, p. 03001). EDP Sciences. https://doi.org/10.1051/itmconf/20246203001 Hrankai, R., Chan, I. C. C., & Senbeto, D. L. (2025). Understanding the Role of Response Latency on Position Bias in Tourists’ Online Travel Planning. Journal of Hospitality & Tourism Research, 10963480251324553. https://doi.org/10.1177/10963480251324553 Hu, H., Wan, Y., Tang, K. Y., Li, Q., & Wang, X. (2025). Affective-Computing-Driven Personalized Display of Cultural Information for Commercial Heritage Architecture. Applied Sciences (2076-3417), 15(7). https://doi.org/10.3390/app15073459 Kahneman, D. (2011). Thinking, fast and slow. macmillan. Koo, I., Zaman, U., Ha, H., & Nawaz, S. (2025). Assessing the interplay of trust dynamics, personalization, ethical AI practices, and tourist behavior in the adoption of AI-driven smart tourism technologies. Journal of Open Innovation: Technology, Market, and Complexity , 11 (1), 100455. https://doi.org/10.1016/j.joitmc.2024.100455 Kumar, A. S., Mosikan, M., Dutta, A., Musirin, I. B., Akula, V. S. G., Khalid, S., ... & S. K., T. (2025). AI-driven personalization in tourism: Balancing innovation with ethical challenges. In AI Technologies for Personalized and Sustainable Tourism (pp. 31-58). IGI Global. https://doi.org/10.4018/979-8-3693-5678-4.ch002 Kwong, A. T. M., Omar, S. I., & Aliah, M. N. (2024). The ethical implications of AI-Powered personalization in hospitality and tourism. In Impact of AI and tech-driven solutions in hospitality and tourism (pp. 103-122). IGI Global. https://doi.org/10.4018/979-8-3693-6755-1.ch006 Leal, F., Veloso, B., Malheiro, B., & Burguillo, J. C. (2025). Towards adaptive and transparent tourism recommendations: A survey. Expert Systems, 42(1), e13400. https://doi.org/10.1111/exsy.13400 Lee, N. (2025). Rational or Emotional? The Effectiveness of Tourist-Generated Content on Travel Intention: A Moderated Mediation Analysis. Journal of Smart Tourism, 27652157251344346. https://doi.org/10.1177/27652157251344346 Li, J., Ji, X., & Chen, F. (2025). Does information overload attract or Repel self-driving tourists?. Journal of Hospitality and Tourism Management, 63, 343-365. https://doi.org/10.1016/j.jhtm.2025.05.008 Li, P., Lv, Y., Wang, R., Chen, T., Gao, J., & Huang, Z. (2024). How do illegitimate tasks affect hospitality employees’ adaptive performance? An explanation from the perspective of cognitive-affective system theory of personality. International Journal of Contemporary Hospitality Management, 36(9), 3032-3051. https://doi.org/10.1108/IJCHM-04-2023-0538 Lim, S. E., & Kim, M. (2025). AI-powered personalized recommendations and pricing: Moderating effects of ethical AI and consumer empowerment. International Journal of Hospitality Management, 130, 104259. https://doi.org/10.1016/j.ijhm.2025.104259 Liu, M., Liu, X., Li, L., Zhou, J., & Liu, S. (2025a). Approach and avoidance mechanisms in the effects of algorithm transparency on the service performance of food-delivery riders. Current Issues in Tourism, 28(14), 2366-2380. https://doi.org/10.1080/13683500.2024.2376208 Liu, X., Wang, D., & Li, X. (2025b). Phygital Heuristics in Tourists’ Decision-Making in Motion. Journal of Hospitality & Tourism Research, 10963480251319979. https://doi.org/10.1177/10963480251319979 Mohammed, I., & Denizci Guillet, B. (2025). Heuristics and biases in human–algorithm interaction and hotel revenue management override decision-making. International Journal of Contemporary Hospitality Management, 37(2), 358-379. https://doi.org/10.1108/IJCHM-02-2024-0288 Moisa, D. G., Parapanos, D., & Heap, T. (2025). The role of digital footprints for destination competitiveness and engagement: utilizing mobile technology for tourist segmentation integrating personality traits. International Journal of Tourism Research, 27(1), e70006. https://doi.org/10.1002/jtr.70006 Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & De Cremer, D. (2024). Fairness perceptions of artificial intelligence: A review and path forward. International Journal of Human–Computer Interaction, 40(1), 4-23. https://doi.org/10.1080/10447318.2023.2210890 Oan-Oon, R., & Choibamroong, T. (2025). Multidimensional bandwagon effect and dual-process decision making: An integrated model of the theory of planned behavior from the perspective of American millennials’ travel intention. Heliyon, 11(1). https://doi.org/10.1016/j.heliyon.2024.e41375 Picard, R. W. (2000). Affective computing. MIT press. Pizarro, V., Leger, P., Hidalgo-Alcázar, C., & Figueroa, I. (2023). ABM RoutePlanner: An agent-based model simulation for suggesting preference-based routes in Spain. Journal of Simulation, 17(4), 444-461. https://doi.org/10.1080/17477778.2022.2027826 Plikynas, D., Miliauskas, A., Laužikas, R., Dulskis, V., & Sakalauskas, L. (2022). The cultural impact on social cohesion: an agent-based modeling approach. Quality & Quantity , 56(6), 4161-4192. https://doi.org/10.1007/s11135-021-01293-6 Saha, P., Sengupta, A., & Gupta, P. (2024). Influence of personality traits on generation Z consumers' click-through intentions towards personalized advertisements: A mixed-methods study. Heliyon, 10(15). https://doi.org/10.1016/j.heliyon.2024.e34559 Seyfi, S., Kim, M. J., Nazifi, A., Murdy, S., & Vo-Thanh, T. (2025). Understanding tourist barriers and personality influences in embracing generative AI for travel planning and decision-making. International Journal of Hospitality Management, 126, 104105. https://doi.org/10.1016/j.ijhm.2025.104105 Shooshtarian, S., Gurmu, A. T., & Mahmood, M. N. (2024). Application of the DEMATEL approach to analyze the root causes of building defects. Quality & Quantity , 58 (5), 4641-4660. https://doi.org/10.1007/s11135-024-01872-3 Stylos, N. (2022). An integrated duality theory framework (IDTF): Marking pathways for consumer decision-making researchers in the hospitality and tourism industry. International Journal of Contemporary Hospitality Management, 34(7), 2597-2619. https://doi.org/10.1108/IJCHM-10-2021-1256 Su, Z., & Ha, H. Y. (2025). Longitudinal impact of perceived fairness after service failures: Evidence from online travel agencies. International Journal of Hospitality Management, 128, 104177. https://doi.org/10.1016/j.ijhm.2025.104177 Tai, Y. F., Wang, Y. C., & Luo, C. C. (2021). Technology-or human-related service innovation? Enhancing customer satisfaction, delight, and loyalty in the hospitality industry. Service Business, 15(4), 667-694. https://doi.org/10.1007/s11628-021-00461-w Tao, H., Peiying, S., Huihui, W., & Jiamin, F. (2024). How do affective and cognitive involvement affect tourism intentions: A perspective on blogger impact. Current Issues in Tourism, 1-20. https://doi.org/10.1080/13683500.2024.2392763 Tao, X., Nor Akmar, A. A., Syamsul Herman, M. A., & Suziana, H. (2025). Decision-Making Analysis for Sustainable Tourism Development: A DEMATEL and SEM Approach to Infrastructure and Policy Optimization in Ethnic Village Tourism. Decision Making: Applications in Management and Engineering, 419-435. https://doi.org/10.31181/dmame8120251382 Yang, X., Zhang, L., & Feng, Z. (2024). Personalized tourism recommendations and the E-tourism user experience. Journal of Travel Research, 63(5), 1183-1200. https://doi.org/10.1177/00472875231187332 Yin, J., & Hwang, Y. H. (2025). Information overload and tourists’ booking discontinuance intention: an application of transactional theory of stress and coping. Current Issues in Tourism, 1-17. https://doi.org/10.1080/13683500.2025.2531218 Zhang, S., Zhen, F., Wang, B., Li, Z., & Qin, X. (2022). Coupling social media and agent-based modelling: A novel approach for supporting smart tourism planning. Journal of Urban Technology, 29(2), 79-97. https://doi.org/10.1080/10630732.2020.1847987 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8108792","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545518630,"identity":"28a54152-ace4-4e36-ab2e-f334b7fe41fc","order_by":0,"name":"Majid Heidari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACCQaGAzwMDDz8QPYBIOYhXotkA1QLYT0SUJMNDkAFCGrRnd188MCbmnsyxjdyDx78UcMgY09Ii9mdYwkH5xwr5jG7kZdwmOcYEQ4zu5FjcJiHLYEHzGBgI0pL/ofDPP8SeIxn5Bgc/PGPOFsYDvO2JfAYSOQYHOBtI0pLmsHBuX0JPBJn3hgc5u2T4OE5QFBL8uMPb74l2PO35xh//PHNxp69gZA1aECCRPWjYBSMglEwCrACAAN/PsFTynYRAAAAAElFTkSuQmCC","orcid":"","institution":"University of Salerno","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Heidari","suffix":""}],"badges":[],"createdAt":"2025-11-13 20:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8108792/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8108792/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96051967,"identity":"065a0c2d-9e9b-4d2d-b9f7-823388533450","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7663029,"visible":true,"origin":"","legend":"","description":"","filename":"AnonymisedManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/8bff2fc4a6ea802b7debc611.docx"},{"id":96051962,"identity":"85ab86b9-6882-45a2-a0a1-b59b1d7ed985","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3957,"visible":true,"origin":"","legend":"","description":"","filename":"f44fb00162484f9f83e8299d17fc6a91.json","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/00d2dfe3d9711a0a3328a0a5.json"},{"id":96051963,"identity":"008ff831-7e24-4607-9155-a4d5fb1ea92e","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158525,"visible":true,"origin":"","legend":"","description":"","filename":"f44fb00162484f9f83e8299d17fc6a911enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/e08c765693a4680326c0df06.xml"},{"id":96051960,"identity":"04dc78b0-daa9-4dac-b8fe-93b35411cb48","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67202,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/eb8b4c2696a3941803080c5d.png"},{"id":96051965,"identity":"8adadf9d-b5d1-4bdd-83bc-15164036cd4a","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":590246,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/6bd93ae6c146905e7d0bfc88.png"},{"id":96051968,"identity":"9b0cb47c-b7eb-4b15-bdb8-694b730da5b6","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":975989,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/029cb8836cb11573397dab2c.png"},{"id":96051966,"identity":"62a6c425-ee2b-4087-af17-2d133c1e225e","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4262141,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/87ff0f3793582a02e018eab7.png"},{"id":96246185,"identity":"a9315692-30ca-4f9c-b394-12521f5463c9","added_by":"auto","created_at":"2025-11-19 07:25:00","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32372,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/fb49feeb40b83a5ebd03f94e.png"},{"id":96247452,"identity":"f6abd96b-db85-4c97-9e7c-07a7ae99ae77","added_by":"auto","created_at":"2025-11-19 07:27:29","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99380,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/a769436cd4d38a4c3118e868.png"},{"id":96248129,"identity":"b4816398-e345-421a-bb0a-e1eff9253483","added_by":"auto","created_at":"2025-11-19 07:28:05","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193326,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/079dd8acf85e3d33340238f2.png"},{"id":96051970,"identity":"71466af1-01aa-4a01-99eb-203c532eae93","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":503513,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/20f92a8c58dd7412257abf83.png"},{"id":96051971,"identity":"7ead80a7-3320-45f4-bbec-6e4b928487ce","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156910,"visible":true,"origin":"","legend":"","description":"","filename":"f44fb00162484f9f83e8299d17fc6a911structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/73aa763ea9f47763cef88cc8.xml"},{"id":96051972,"identity":"cb31415a-75b7-468c-9f2d-0024f43a466a","added_by":"auto","created_at":"2025-11-17 06:46:20","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161499,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/bea9ea2dc6b380b074d1d02a.html"},{"id":96248097,"identity":"9a5a84b0-16b3-49ad-9a40-b9e7279a6d78","added_by":"auto","created_at":"2025-11-19 07:28:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122546,"visible":true,"origin":"","legend":"\u003cp\u003eNeuroadaptive Interface Personalization Model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/9e1adb208f41d3e6db55775c.png"},{"id":96051957,"identity":"6665ba5c-fb8a-42f5-a69e-2304eca66b10","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40169,"visible":true,"origin":"","legend":"\u003cp\u003eDEMATEL Causal Map of Neuro-Cognitive Traits and Interface Demands.\u003c/p\u003e\n\u003cp\u003eNote: Blue nodes = driver constructs (positive D–R); Red nodes = receiver constructs (negative D–R). Edge weights denote normalized influence strength.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/94edb0e5693467ff9ec65096.png"},{"id":96051958,"identity":"c7fa253d-77b6-4014-a8a1-bde8de5f128b","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62963,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Behavioral Outcomes by Interface Type.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/4521f40b19ed75d845e35861.png"},{"id":96051961,"identity":"e1845928-b2b0-401c-bcf7-16ddc055f7c8","added_by":"auto","created_at":"2025-11-17 06:46:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193647,"visible":true,"origin":"","legend":"\u003cp\u003eTrait-Interface Interaction Effects on Behavioral Outcomes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/04bd4fcc3b1a65065fca88e1.png"},{"id":96604685,"identity":"85a50bf4-5a10-47d1-bce3-2e8ff2669746","added_by":"auto","created_at":"2025-11-24 09:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1613662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8108792/v1/19b973b8-1031-4a19-9a9c-aae6c628b2ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A DEMATEL-Guided Agent-Based Simulation Framework for Trait-Driven Neuroadaptive Interfaces in Smart Hospitality","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eUnderstanding how personalized systems align with user cognition is a growing methodological concern across decision sciences and applied behavioral research. One​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; major change in the way tourism experiences are provided is the development of smart hospitality systems that move from traditional service delivery to systems that mimic the reasoning of a human decision-maker dealing with a highly complex and dynamic situation (Yang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By using AI-powered means such as recommendation systems, pricing algorithms, and booking platforms, the possibilities of travelers are altered on the one hand, and on the other hand, the emotional and cognitive ways of the travelers' experiences are also changed (Tai et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lim \u0026amp; Kim, ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;2025). Choosing accommodation, which used to be a simple task, has now resulted in situations of cognitive load and emotionally charged moods because people perceive the system as being more or less transparent, personalized, and that it fairly represents its logic (Mohammed \u0026amp; Denizci Guillet, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this hybrid human\u0026ndash;AI interaction setting, the psychological and affective domains are no longer external to the interface; they are important dynamically to how we, as users, perceive, accept, or resist interface adaptations using diverse user types (Lee, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This shift calls for methodological frameworks capable of modeling latent psychological constructs within human\u0026ndash;AI interaction systems (Plikynas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough adaptive systems can be complicated, the current body of tourism research tends to address behavioral prediction rather than psychological understanding (Stylos, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Personalization is practically considered in the case of a performance gain rather than an experience constructed by an internal user state that is affected psychologically (Tao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Traits such as emotional reactivity, fairness sensitivity, and cognitive load tolerance are rarely considered in system design and are not mentioned as causal elements (Saha et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which opens the gap between user-centered fairness and technical adaptability (Chan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Although an expanding body of literature is starting to look at transparency and trust in systems, there are a few models that speak to neuro-cognitive profiles and system behaviors (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and very few that actually operationalize those traits in a way that considers both behavioral outcomes and psychological meaning (Yin and Hwang, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), especially under emotional arousal or complexity.\u003c/p\u003e\u003cp\u003eThis research fills this gap by establishing a trait-based framework for personalization, where personalization is viewed not as a static system output but as a dynamic unfolding of user traits in relation to the adaptive interface pathways available (Koo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Rather than assuming uniform responses to adaptive logic, the model views cognitive and emotional traits as causal forces that shape the need for adaptation. A​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; causal structuring process is employed to figure out the most significant traits that influence these needs, thus representing neurocognitive variation by making system changes that are meaningful from a psychological point of ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;view. The model is implemented in a simulated environment, enabling the observation of user\u0026ndash;system interactions (El Ouadi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) across static, personalized, and fairness-aware interface types. This process follows the purposes of the research: to model travelers' reactions on the basis of neurocognitive domains; to identify adaptation needs due to features using DEMATEL (Tao, et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); and to validate fairness-aware personalization as a psychologically responsible design factor (da Silva et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis approach allows for more than behavioral considerations; it provides an interpretive architecture for personalization ethics. If trait constructs, such as fairness sensitivity and emotional reactivity, are developed into the adaptive logic of AI systems, personalization becomes responsive to user variability, rather than only generic (Kumar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Fairness is reconceptualized not as a retrospective judgment but as an inherent element of system parameters (Narayanan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Affective​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; complexity is even seen as one of the design elements rather than being categorized as user noise. The model combines human interpretability with the computational response, thus making personalization not only ethically sensitive but also quite consistent throughout the user's experience (Koo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such alignment between latent traits and interface structure responds to current methodological calls for causally grounded personalization in human-centered AI. The system's behavior as per the model goes beyond just different types and should adaptively synchronize with the user's emotional state in cases of uncertainty, overload, or affective ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;conflict.\u003c/p\u003e\u003cp\u003eThe implications are multi-dimensional. Theoretically, \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;the study integrates the dual-process (Kahneman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and the affective computing (Picard, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) models to a single, trait-sensitive ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;model. Methodologically, it offers a novel integration of DEMATEL causal modeling and simulation to examine alignment between user traits and interface behavior. In practice, it offers useful paths to tourism systems and AI interface developers to provide fair, transparent, and psychologically intelligent personalization. More widely, the framework is transferable to domains like finance, education, and healthcare, or any area where algorithmic decision aids confront neurodiverse human judgment. Ultimately, this study extends personalization research toward neuroadaptive system design by integrating empirical trait modeling with interpretability and cognitive-emotional alignment.\u003c/p\u003e"},{"header":"2 Adaptive Interfaces in Smart Hospitality","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Emotion\u0026ndash;Cognition in Tourist Decision Contexts\u003c/h2\u003e\u003cp\u003eModeling traveler decisions in digital tourism environments requires moving beyond rational-choice models to empirically examine emotional\u0026ndash;cognitive interactions (Moisa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Emotional reactivity, cognitive load tolerance, and fairness sensitivity (ethical concern) are now no longer topics of peripheral traits but mediators of how travelers interact with AI-driven platforms over time pressure, uncertainty, or mismatched expectations (Kwong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Utilizing contemporary models of dual-process thinking, these characteristics may be seen to specify where the balance of intuitive and deliberative options may lie, especially when the user is encountering dynamically framed options or personalized content and implicit trade-offs (Oan-Oon \u0026amp; Choibamroong, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, if we are to simulate tourist decision-making, it is essential that we understand the influence of these aspects on their feelings of risk, value, and usability when using technology for travel planning (Hrankai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen interfaces strive to adapt to the user, the interplay of emotion and cognition becomes highly relevant (Godovykh \u0026amp; Tasci, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although personalized recommendations are designed to decrease effort and increase customer satisfaction, the emotional state of the traveler influences whether this approach to adaptation is perceived as helpful or intrusive (Yang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, heightened emotional reactivity may magnify responses to system framing (Farshbafiyan Hosseininezhad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while high cognitive load may decrease the perceived value of complex personalization features (Lee, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, the interaction of these attributes should be evaluated in situations involving booking contexts that aim to replicate the real situation, whereby the interface logic is situated alongside user states that co-evolve (Becker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yin \u0026amp; Hwang, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This complexity necessitates the use of methodological tools that capture latent trait dynamics over time, such as agent-based simulation that capture emergent affective-cognitive dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Personalization, Fairness, and Trust in AI Systems\u003c/h2\u003e\u003cp\u003ePersonalization is commonly one of the tech upgrades employed by tourism platforms, for example, preference filtering, destination prediction, and price personalization (Yang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the user's perception of fairness, control, and openness is significantly influenced by personalization that is mediated by an algorithm. If personalization is implemented without providing a reason or without accounting for trait-level sensitivities, it may distort perceived fairness and undermine user trust (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). It is not just the distributive outcomes alone, but procedural fairness is also critical in establishing how travelers will endorse the system's intent and legitimacy (Su et al., 2025). As recognized by frameworks of perceived personalization and transparency, adaptive behaviors must be traceable and align with user expectations when they involve traits such as fairness sensitivity and cognitive effort tolerance (Kumar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTrust in AI-powered hospitality systems, therefore, stems not just from accuracy and relevance but is a function of how well the system fits with the user's sense of psychological fit and procedural fairness (Koo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Tourists with more fairness sensitivity may see the interface as being too opaque or too opportunistic, even if they are getting optimized outcomes. In contrast, perceived transparency and user-system alignment may lead to trust development, especially when the personalization indicate inherent characteristics rather than visible preferences (Narayanan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These observations have implications about the required cognitive coherence, and ethically accountable personalization does mean that fairness-aware design should be treated as a necessary usability condition in reliable tourism AI systems (Kwong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The implementation of fairness-aware system design necessitates the causal modeling of user characteristics, as opposed to post hoc perception analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Interface Logics and Design Gaps in Hospitality Platforms\u003c/h2\u003e\u003cp\u003eBuilding on the prior discussion of emotion, cognition, and fairness, this subsection examines how current hospitality platforms implement\u0026mdash;or fail to implement\u0026mdash;these adaptive logics. Tourism platforms typically use one of three interface strategies: static (the same logic for everyone), personalized (based on behavioral profiles), and adaptive fairness-aware (tailored using deeper trait structures) (Leal et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although the industry commonly employs both static and personalized strategies, it is starting to recognize the shortcomings associated with each approach. Static strategies overlook variance across users, and standard personalization often relies on the hidden logic of algorithmic design, producing a sense of being manipulated or excluded (Banerjee et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fairness-aware​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; interfaces are designed to deliver the best possible performance while upholding fairness in the procedure, and their application has been limitedly investigated in tourism systems only (Su et al., ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;2025). Existing studies often prioritize accuracy or UX metrics without assessing whether interface logic considers user characteristics like emotional reactivity or thresholds for fairness (Deldjoo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis design gap becomes even clearer when considering the extent to which the structure of interfaces may incorporate neurocognitive traits that can create a behavioral and attitudinal appraisal (Heidari et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, people who cannot manage a huge cognitive load might choose to simplify their things instead of hyperpersonalizing them. On the other hand, users who are very concerned about fairness might have expressed their worries and given reasons for the risk they feel (Seyfi et al., ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;2025). However, discrepancies between interface logic and the processing preference of users create friction beyond the limits of personalization (Yang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although technologies adaptive to users exist, the majority of hospitality platforms do not employ user traits as causal variables in their interface selections (Leal et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The present framework models how latent psychological traits causally influence interface adaptation pathways in AI-based decision systems and presents an approach for structuring trait-logic congruence as a testable and optimizable design condition.\u003c/p\u003e\u003cp\u003eThese gaps reflect how existing research has treated emotion, cognition, fairness, and simulation largely in isolation. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights how the present study integrates these dimensions into a unified, trait-driven and causally modeled neuroadaptive framework.\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\u003eSummary of Identified Research Gaps and Present Study Contributions.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThematic Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResearch Gaps\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethodological Contribution of the Study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotion\u0026ndash;Cognition Interaction in Tourism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGodovykh \u0026amp; Tasci (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Moisa et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Lee (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMost studies consider emotion and cognition as two separate entities and hence do not think of their co-evolution in actual decision ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;contexts.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrates emotional reactivity and cognitive load tolerance as interacting traits in adaptive decision simulations.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonalization and Trust in AI-Driven Tourism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Koo et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Kumar et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePersonalization mainly assessed by accuracy or performance metrics, not user-state alignment or psychological fit.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDefines personalization as neuroadaptive and evaluates trait\u0026ndash;interface congruence affecting trust and satisfaction.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFairness and Transparency in AI Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeldjoo et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Narayanan et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Su et al. (2025).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFairness usually analyzed post-hoc rather than embedded in system logic or adaptive behavior.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresents fairness sensitivity as a socially active personality trait and ethically aligns the agent by embedding the ethical principles into the adaptive interface ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;rules.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimulation and Causal Modeling in Smart Hospitality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZhang et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Pizarro et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Tao et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLimited use of simulation or causal inference to link user traits with system design.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdaptivity​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; employs an agent-based simulation as well as DEMATEL causal mapping to represent trait causality in adaptive ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;interfaces.\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"},{"header":"3 Neuroadaptive Framework for Smart Hospitality Interfaces","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Theoretical Foundations\u003c/h2\u003e\u003cp\u003eThis​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; framework is based on two theoretical foundations that work together to guide the modeling of latent neuro-cognitive traits and their causal influence on the interaction of users with AI-enabled hospitality ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;interfaces. First, Dual-Process Theory provides an explanatory psychology framework, which illustrates human cognition as a system of fast, intuitive responses (System 1) and slower, deliberative reasoning (System 2) (Oan-Oon \u0026amp; Choibamroong, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These cognitive modes aid in the construction of agent profiles\u0026mdash;especially the emotional reactivity and cognitive load tolerance\u0026mdash;as they affect interface perception, speed of decision-making, and stress (Seyfi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Next,​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; the Affective Computing Theory explains that the treatment of emotional and cognitive characteristics serves as the input for dynamic adaptation, which is reflected or sensed by the interface (Hu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA theoretical layer that expresses a connection between personality traits and morally adaptive user interfaces (e.g., fairness-aware design patterns) to the extent that they can alter the complexity, the tone, or the depth of the personalization according to the user\u0026rsquo;s condition is supported by this strand (da Silva et al., ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;2021). Altogether, the theories can create a common framework for understanding how trait-interface congruence can facilitate adaptive logic and the differences in behavior between static, personalized, tangible, and ethically calibrated adaptive system conditions. These theories serve not only as conceptual backbones but also as operational frameworks guiding the structural alignment of traits and system logic in the modeling process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Model Structure and Hypotheses Development\u003c/h2\u003e\u003cp\u003eThe proposed model integrates the latent neuro-cognitive traits, adaptive interface logic, and simulation-based decision contexts to experimentally measure the change in user experience in hospitality booking due to AI-powered personalized systems. ​\u0026zwj;​\u0026zwnj;Three neuro-cognitive traits\u0026mdash;emotional reactivity, cognitive load tolerability, and fairness sensitivities\u0026mdash;are situationally relevant to distinguish user profiles. These traits are mapped to interface response demands using a DEMATEL-based causal structure, which defines how specific traits influence the need for adaptation or fairness calibration. The interface logic incorporates three system types: static (non-adaptive), personalized (trait-responsive), and fairness-aware (ethically optimized). To simulate real-world booking dynamics, agents are exposed to decision scenarios featuring overchoice, conflicting options, or framing manipulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This structured integration establishes the model\u0026rsquo;s internal logic and provides the basis for hypothesis formulation grounded in causal inference and simulation dynamics.\u003c/p\u003e\u003cp\u003eFairness​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; perception of different personalization levels is also dependent on the system communication of its decisions in a transparent and ethical ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;way. If the context is emotionally or cognitively charged, then fairness-aware designs will most likely reduce the negative impact and increase acceptance by matching users' expectations of procedural justice through algorithmic behavior (Narayanan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eFairness-aware interfaces enhance perceived fairness and trust more than purely personalized or static interfaces.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eNot all user traits have the same influences on interface needs. The DEMATEL modeling shows which traits influence adaptation needs the most, e.g., sensitivity to fairness or cognitive load. The shared traits are most likely associated with the interface logic when system calibration is satisfactory, leading to congruent interactions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2a\u003c/strong\u003e\u003cp\u003e\u003cem\u003eTraits identified as dominant causal drivers in the trait\u0026ndash;interface influence structure show stronger structural alignment with adaptive interface logic.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eNevertheless, the concept is not limited to the alignment of traits and interface responses. This alignment is proposed to produce observable behavioral benefits, most importantly improved decision efficiency. The user is more likely to make a quick decision with confidence when they feel the interface gets how they need to process information (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2b\u003c/strong\u003e\u003cp\u003e\u003cem\u003eHigher alignment between agent traits and adaptive interface logic leads to greater decision efficiency.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAmong core neurocognitive traits, emotional reactivity is particularly relevant to the satisfaction afforded by adaptive systems. Users that engage in decisions effectively may respond positively to an interface that inserts complexity, tone, or message framing into adaptation so as not to contribute to feelings of stress or overwhelm (Fominska et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAgents with high emotional reactivity reports greater satisfaction when interacting with adaptive interfaces.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; Apart from satisfaction, it is also assumed that the structural weight of certain traits will influence the perceived trustworthiness of the system. Those traits that play a more central causal role in the DEMATEL model are likely to have a significantly stronger impact on user trust, especially when the system design clearly demonstrates their presence.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003e\u003cem\u003eTraits with greater influence on interface design are positively associated with trust in adaptive interface outcomes.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eCognitive load is expected to have a negative effect on trust when it is high or out of alignment. If the design is unable to strategically utilize cognitive load to customize or increase fairness awareness in the structural design, users may disengage or even develop counterfactual beliefs regarding the system's objectives (da Silva et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin \u0026amp; Hwang, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Cognitive pressure is thus relative to both and must be mitigated in order for the system to be credible.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003cp\u003e\u003cem\u003eHigh cognitive load reduces trust unless mitigated by personalized or fairness-aware interface features.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Simulation Design\u003c/h2\u003e\u003cp\u003eThe decision to opt for a simulation design in this research, rather than an experimental design, helped to vary user traits, interface types, and decision contexts considerably (Pizarro et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u0026zwj; The environment contains 500 simulated cognitively differentiated agents, each with a unique profile containing three validated constructs (emotional reactivity, cognitive load tolerance, and fairness sensitivity). Each construct was represented as a continuous variable defined on the continuous [0, 1] interval, where each trait is randomized by sampling from beta-like distributions to represent psychological variability. Agents engaged with booking tasks that included overchoice, tradeoffs, and emotional framing. This design enables a high degree of control over interaction complexity, allowing examination of trait-specific responses under varying decision loads and interface framings.\u003c/p\u003e\u003cp\u003eDifferent​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; interfaces were randomly assigned to be static (non-adaptive), personalized (trait-responsive), or fairness-aware (ethically calibrated). Randomly\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; assigning interfaces allowed for equal exposure between groups and ensured that the comparisons made were valid. The simulation captured three measurable behavioral indicators\u0026mdash;trust, satisfaction, and cognitive load (measured through decision latency and reversal frequency)\u0026mdash;to provide a deep dive into the effects of the different trait-interface configurations on the results. The simulation framework provides a systematic, theory-driven approach to testing personalization and fairness-driven changes, offering significant support for causal inference when adaptive interface design is being assessed.​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 DEMATEL Logic and Analysis Procedure\u003c/h2\u003e\u003cp\u003eTo elucidate the causal links between neuro-cognitive traits and the demands of interface adaptation, DEMATEL was chosen because it can organize the influence of inter-variables within complex, non-linear systems, thereby providing directional clarity, which is appropriate for multi-criteria decision modeling (Tao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This research modeled six constructs, including three user traits (emotional reactivity, cognitive load tolerance, and fairness sensitivity) and three adaptive interface demands (simplicity, ethical alignment, and information control). ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;Pairwise influence ratings were based on literature-guided structured elicitation from domain experts, producing a direct influence matrix that captures the strength of influence each construct has on the others. Its ability to disentangle layered causal effects has been proven in root cause analyses beyond interface design, including infrastructure systems (Shooshtarian et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Seven experts with backgrounds for tourism technology, human\u0026ndash;AI interaction, and user experience design contributed pairwise ratings using a structured DEMATEL survey. This matrix was normalized to compute Prominence (D\u0026thinsp;+\u0026thinsp;R) and Net Causality (D \u0026ndash; R) scores for each construct. Constructs with high prominence and with positive net causality were identified as drivers in the adaptive interface architecture.\u003c/p\u003e\u003cp\u003eThose​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; traits that were singled out as the main causal drivers (positive D-R values) of the adaptive behavior logic have become the leading parameters by which the behavior of the adaptive interface is determined. The DEMATEL origin rules have been implemented in the simulation environment; thus, they govern the interaction of agents with interface situations according to their neuro-cognitive ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;profiles. Behavioral outcomes were defined as follows: trust reflects the degree of alignment between agent decisions and system suggestions; satisfaction was recorded based on post-decision evaluations; and cognitive load was estimated through decision latency and the number of reversals.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Linking DEMATEL Causality to the Simulation Framework","content":"\u003cp\u003eTable\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; 2 serves as an explicit conceptual link between the causal modeling based on DEMATEL and the simulation analysis by showing in detail how the influence structure derived from DEMATEL guided the simulation design. The table indicates the traits that acted as system drivers, the manner in which these influences were incorporated into the adaptive logic, and the behavioral outcomes chosen for model validation. Thus, this connection enables the causal drivers identified structurally to be examined behaviorally (Tao et al.,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) in terms of their impact on trust, satisfaction, and cognitive efficiency within the simulated environment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntegration of DEMATEL Results with Simulation Framework.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct / Trait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDEMATEL Role (Prominence \u0026amp; Causality)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimulation Function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh prominence (+\u0026thinsp;0.33); primary causal driver of interface tone adaptation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControls interface affective tone (calm \u0026harr; stimulating).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Trust \u0026uarr; Satisfaction \u0026darr; Cognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate prominence (\u0026ndash;0.19); reactive variable influenced by emotional drivers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModulates task complexity and decision latency.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Decision Efficiency \u0026darr; Cognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate prominence (\u0026ndash;0.10); ethical driver influencing transparency mechanisms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActivates fairness-aware interface logic.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Perceived Fairness \u0026uarr; Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterface Type Rules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerived from causal structure among traits and interface demands.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefine adaptive logic for Static / Personalized / Fairness-Aware conditions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll behavioral outcomes combined\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 DEMATEL Causal Mapping of Trait\u0026ndash;Interface Influence\u003c/h2\u003e\n \u003cp\u003eThis section identifies which neurocognitive traits most strongly influence adaptive requirements in smart hospitality systems. In the context of this study, six key constructs are evaluated: three user traits\u0026mdash;emotional reactivity, cognitive load tolerance, and fairness sensitivity\u0026mdash;and three adaptive interface demands\u0026mdash;simplicity, ethical alignment, and information control. These constructs are all based on the study\u0026apos;s theoretical framework that synthesizes affective computing and dual-process decision theory (Hu et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Oan-Oon \u0026amp; Choibamroong, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). As interface personalization must reflect significant psychological differences, understanding the causal hierarchy of these constructs is therefore essential (Tao et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). To establish which user traits most strongly influence the requirements for interface adaptation, a DEMATEL-based causal analysis was conducted using expert-derived pairwise ratings.\u003c/p\u003e\n \u003cp\u003eTo identify the directional influence among user traits and interface demands, seven experts were recruited, based on their knowledge and experience in tourism informatics, AI-driven interfaces, and user experience design. Each expert then completed a structured DEMATEL matrix, indicating the influence of each construct on every other construct by rating it using a 0\u0026ndash;4 scale (no influence to high influence). The six constructs were compared in all pairwise combinations, with diagonal self-influence entries excluded. The raw pairwise ratings were aggregated to form the Direct Influence Matrix presented. As shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, emotional reactivity exerts substantial influence on simplicity, ethical alignment, and information control. Fairness sensitivity shows dominating effects on ethical alignment, while cognitive load tolerance sustains effects around simplicity and information control demands. The initial results point to a directional reasoning that supports the research\u0026apos;s trait-driven adaptation framework: user traits are not only significant but also the main components structurally that lead to the generation of adaptive interface requirements (Deldjoo et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Narayanan et al.,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDirect Influence Matrix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimplicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEthical Alignment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInformation Control\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimplicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Alignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFollowing the structural overview in the direct matrix, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e provides a more detailed analysis by revealing the causal architecture underlying trait\u0026ndash;demand processes. With expert input and appropriate max-normalization, the matrix provides a way to quantify the two key indicators: Prominence (D\u0026thinsp;+\u0026thinsp;R) and Net Causality (D\u0026ndash;R). Prominence identifies which constructs are fixed in the system, while net causality separates the active drivers from the passive receivers, the key purpose of this causal modeling. The analysis suggests that Ethical Alignment (+\u0026thinsp;0.62) and Emotional Reactivity (+\u0026thinsp;0.33) functioned strongly as defining influences on the behavior of the system. In contrast, Information Control (\u0026ndash;0.57) and Cognitive Load Tolerance (\u0026ndash;0.19) are found to be structurally passive, being influenced rather than influencing. Emotional Reactivity is uniquely identified as having the greatest prominence within the system (7.1), which further highlights its importance in overall adaptive results. These trends confirm that adaptive interface design must recognize user behavior; it must also specify distinct neurocognitive characteristics. The emergent causal configuration informs the simulation logic that follows, ensuring arising agent responses properly embody a theoretically based, structurally validated influence system. This causal configuration also reinforces that fairness-sensitive and emotionally reactive traits jointly anchor the adaptive logic, confirming the dual cognitive-affective foundation of the framework. Therefore,\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; traits with greater net causality should show a greater influence in subsequent behavioral metrics, especially trust and satisfaction, when they are integrated into adaptive \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;rules.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTotal Influence Matrix with Prominence and Net Causality.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimplicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEthical Alignment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInformation Control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u0026thinsp;+\u0026thinsp;R (Prominence)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u0026ndash;R (Net Causality)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimplicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Alignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: Prominence (D\u0026thinsp;+\u0026thinsp;R) represents total influence strength; Net Causality (D \u0026ndash; R) represents directional dominance (positive\u0026thinsp;=\u0026thinsp;driver, negative\u0026thinsp;=\u0026thinsp;receiver).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFollowing the matrix analysis, the causal map in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e translates the numerical structure into a visual configuration that clarifies directional dominance within the system. The circular arrangement distinguishes neuro-cognitive traits from interface demands and highlights the asymmetry of influence flow. Emotional Reactivity and Ethical Alignment emerge as highly influential sources of systemic influence, demonstrated by having many outbound connections to other constructs. This visual clustering demonstrated their positioning as proactive anchors regarding the proximal influence of adaptive requirements, consistent with the earlier analysis that traits serve not just as passive moderators but active design variables. Contrasting with this, Information Control and Cognitive Load Tolerance perform engagement within the adaptive interface model as reactive, context-driven influences and were observed as influence receivers.\u003c/p\u003e\n \u003cp\u003eThe dominant influence structure demonstrates cognitive-affective architecture whereby emotional contagion and ethical framing shape interface expectations and influence system level adaptation logic. These attributes are not solely inputs, but instead directional levers that decide whether the interface privileges simplicity, transparency, and user control. Their causal weight is more than influence and describes structural centrality as dominant anchors of interpretation in variation, or adaptive personalization. This understanding serves as the foundation for the simulation phase, where agent behavior is simulated according to these dominant traits. By mapping these causal relationships into decision rules, the next section operationalizes trait-driven interface responsiveness and evaluates how it manifests in trust, satisfaction, and cognitive processing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Behavioral Effects of Interface Types\u003c/h2\u003e\n \u003cp\u003eThis\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; part goes on to analyze the interface types that serve as a link between the user\u0026apos;s behavior and the simulated environment after the identification of trait-interface influence structures. These interfaces were designed to reflect their causal priorities as per the DEMATEL results: personalized systems focused on the most straightforward solution responsive to the user\u0026apos;s traits, whereas fairness-aware interfaces merged the ethical aspects with the features such as transparency and control (Leal et al., \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;2025). Each of the agents interacted with one of three interface conditions, i.e., static, personalized, or fairness-aware, whose logic stems from this causal mapping (Yang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; main idea is to find out if the changes in the adaptive designs cause any differences in the level of trust, satisfaction, and cognitive load (Narayanan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;A group-based statistical comparison across interface types was used to measure these impacts. All\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; findings in this section are from the agent-based simulation of 500 agents with cognitive \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;differentiation.\u003c/p\u003e\n \u003cp\u003eBuilding on the causal characteristic mapping, the descriptive data in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reveal clear differences in performance by interface type. \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;The observed ordering of the different interface types from an ethical and affective perspective corresponds to the priorities implied by the causal structure. Embedding\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; fairness-aware and trait-responsive rules seems to lead to increased trust and satisfaction, as well as decreased cognitive load (Godovykh \u0026amp; Tasci, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fominska et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;Fairness-aware interfaces produced the highest levels of trust and satisfaction and the lowest cognitive load (Trust 4.2; Satisfaction 4.1; Load 2.7), followed by personalized (3.5; 3.7; 3.2) and then static (2.8; 2.9; 3.8) ones. Furthermore, personalized interfaces were better than static designs in all three \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;outcomes. The inferior performance with static interfaces reinforces the limitation of any non-adaptive design. These patterns suggest preliminary evidence that interfaces that responded to affective and fairness sensitivities can enhance experiential quality across the emotional and cognitive dimensions (Su \u0026amp; Ha, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of Behavioral Outcomes by Interface Type.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterface Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrust (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatisfaction (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive Load (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness-Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn addition to the descriptive trends, inferential tests were used to determine if observed differences in outcomes were statistically significant. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows that all three behavioral variables are statistically significantly different between interface types. Trust showed the largest variance, suggesting that design logic heavily impacts system credibility. One of the significant factors contributing to user satisfaction was the ability to design the interface flexibly. At the same time, the condition that was ethically adjusted reduced the user\u0026apos;s cognitive load. These outcomes serve as proof that such interfaces that adapt to users\u0026apos; needs and take into account users\u0026apos; emotions and fairness-related characteristics can lead to an increase in users\u0026apos; confidence, positive affective experience, and relief of cognitive load (supports H1, H5) (Fominska et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;The size and consistency of these effects provide behavioral legitimacy to the previous causal trait mapping. These aggregate differences set the stage for the next analysis, which examines how alignment between agent traits and interface logic drives these behavioral effects.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eANOVA Results for Behavioral Outcomes by Interface Type.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect Size (\u0026eta;\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(2, 497)\u0026thinsp;=\u0026thinsp;42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(2, 497)\u0026thinsp;=\u0026thinsp;31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(2, 497)\u0026thinsp;=\u0026thinsp;28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: Comparison across: Static, Personalized, Fairness-Aware.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo further illustrate patterns of outcomes, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the distribution of agent responses across interface types. The adaptive interfaces outperformed static systems; dispersion narrowed for trust and satisfaction under fairness-aware systems, while cognitive-load dispersion remained wider under static systems. The\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; static interfaces had scores that varied significantly in terms of trust and satisfaction; nevertheless, cognitive load showed the highest variation under non-adaptive conditions. \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwnj;Such visual signals align with earlier statistical results. Hence, they clearly convey that morally aware and emotionally sensitive interfaces lead, on average, to higher as well as more stable and psychologically efficient user experiences (Narayanan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Koo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;\u003c/p\u003e\n \u003cp\u003eThe outcomes across interface types suggest a functional value in trait-informed interface design. Increased trust under fairness-aware conditions demonstrates the successful integration of ethical responsiveness into system logic, with users attuned to fairness and transparency. Increases in satisfaction associated with adaptive formats\u0026mdash;especially among agents with heightened emotional reactivity\u0026mdash;also suggest the presence of personalization mechanisms that were affectively attuned. The diminished cognitive load on fairness-aware interfaces also suggests that system logic matching user tolerance thresholds may help reduce cognitive effort. These findings extend the causal structures identified earlier into the behavioral domain, setting the stage for the next section, which tests how trait\u0026ndash;interface alignment predicts performance outcomes. In particular, the trust gains under fairness-aware conditions are consistent with ethically calibrated adaptation mitigating skepticism that otherwise accompanies higher load or opacity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Trait\u0026ndash;Interface Alignment and Performance Outcomes\u003c/h2\u003e\n \u003cp\u003eThis section explores how the behavioral impact of an interface design depends not merely on the type but also on the degree of psychological alignment between user traits and system logic. The simulation design embedded trait-driven responsiveness into interface behavior, guided by a previously established structure of cognitive\u0026ndash;affective influence. Interfaces varied in their sensitivity to user needs\u0026mdash;some optimizing for emotional simplicity, others for ethical coherence\u0026mdash;creating natural differences in how well they matched agent dispositions. To measure this, a trait-interface alignment score was calculated for each agent, indicating how well the agent\u0026rsquo;s trait profile matched the adaptive strategy of the interface. \u0026zwj;\u0026zwnj;\u0026zwj;The analysis examines trait\u0026ndash;outcome correlations and interaction effects with interface types.\u003c/p\u003e\n \u003cp\u003eTo ground the alignment analysis in differentiated user psychology, we first examine how each neuro-cognitive trait independently associates with core behavioral outcomes. Table\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; 7 shows the connections that reveal different layers of influence. Among the four trust predictors, fairness sensitivity was the strongest, which thus corroborates the idea that people who are sensitized to perceive justice in the procedures of a system\u0026apos;s interaction consider that system more trustworthy (Narayanan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Su \u0026amp; Ha, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, emotional reactivity was both highly and positively associated with satisfaction, which indicates agents that are more emotionally invested receive a higher experiential benefit from their interaction with the system (Lee, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Cognitive\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; load tolerance, in the end, was the factor that significantly negatively correlated with perceived load, thus indicating the reduction of the perception of the effort made. The findings support the idea that each characteristic influences the person\u0026rsquo;s behavior through a different psychological mechanism and, thus, offer an opportunity to examine whether that interaction with the interface logic strengthens or disrupts these patterns (Yin \u0026amp; Hwang, \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;2025). The pattern that fairness sensitivity is most associated with trust, emotional reactivity with satisfaction, and cognitive-load tolerance with lower perceived load accords with the structural roles identified earlier, suggesting that causal centrality carries forward into behavioral expression once rules are embedded (consistent with H2a, H3, and H4).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrait-Outcome Correlation Matrix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait \\ Outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: Trait\u0026ndash;outcome associations are directionally consistent with DEMATEL-identified causal roles.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis phase assessed if the congruence between individual traits and interface conditions affects behavioral outcomes, building on the previous structure of relationships. The individual effects of user traits and adaptive interfaces stand as statistically significant; sensitivity to fairness predicts trustworthy behavior, emotional reactivity predicts satisfaction, and cognitive load tolerance predicts cognitive load. Those patterns are visually confirmed in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, where trait\u0026ndash;outcome relationships demonstrate variation in slope for each type of interface. None of the interaction terms are statistically significant (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating while the traits and the type of interface may have separate effects on behavior, their mutually aligned effects do not produce additional additive effects (H2b not statistically supported). Adaptive interfaces, particularly fairness-aware and personalized designs, still exceed static interfaces across all outcome measures. The findings illustrate the value of trait-informed personalization, even though trait\u0026ndash;interface alignment (interaction terms) did not reach statistical significance. These results support an approach for interfaces, in a neuroadaptive sense, that would promote trust, satisfaction, and cognitive efficiency for participants. Similar\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; findings were reported in research on cultural and trait-based variations in agent-driven social cohesion models (Plikynas et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which, in turn, support the idea that personality traits significantly influence complex adaptive \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;behavior.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrait\u0026ndash;Interface Alignment Models for Behavioral Outcomes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept (Static Baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity (Trait)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness-Aware Interface (vs. Static)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity \u0026times; Fairness-Aware Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept (Static Baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity (Trait)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized Interface (vs. Static)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity \u0026times; Personalized Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept (Static Baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance (Trait)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized Interface (vs. Static)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance \u0026times; Personalized Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: \u0026ldquo;Intercept\u0026rdquo; indicates the predicted outcome score when both trait and interface values are zero. Interaction terms test whether the effect of each trait changes depending on the assigned interface type, predictors with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were statistically significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThese findings indirectly reinforce the broader logic of trait-informed personalization. Trust, satisfaction, and cognitive load measures showed that interfaces that adapted to users\u0026apos; core traits were generally superior to their static counterparts. Traits with greater causal influence in the DEMATEL model\u0026mdash;such as fairness, sensitivity, and emotional reactivity\u0026mdash;also emerged as significant behavioral predictors, validating their structural relevance. While\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; interaction terms failed to reach statistical significance, their directions are still in line with what was expected: adaptive interfaces increase trust and satisfaction and decrease perceived load, thus showing that efficiency benefits can be obtained even without statistically significant trait\u0026ndash;interface amplification. These\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; findings are in line with the idea that personalization based on the causal trait structure can enhance the user experience, which is also valid in a situation where no explicit psychological alignment is present (Kumar et al., \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;2025).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Integrated Interpretation of Simulation Findings\u003c/h2\u003e\n \u003cp\u003eThis final interpretive analysis integrates the structural logic of trait-based interface requirements, the behaviors that result from design variance, as well as the performance-impacting psychological alignment. In seeing these components tightly integrated and not in isolation, the synthesis explores how underlying neuro-cognitive dispositions, including emotional reactivity, fairness considerations, and cognitive load tolerance, influence not only what users require from the adaptive system but also how users behave when these requirements are met or misaligned. The\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; integration provides understanding of the processes through which personalized and fairness-aware interfaces influence, unveiling deeper patterns of trust formation, emotional experience, and cognitive relief. In sum, these outcomes are supportive of the main hypothesis (H1\u0026ndash;H5) that fairness-aware and trait-responsive designs increase trust, satisfaction, and the feeling of being in control by matching the main causal \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;traits.\u003c/p\u003e\n \u003cp\u003eBased on the causal architecture and behavioral findings, the following synthesis explains which adaptive interface types are most effective for each neurocognitive trait and why. Rather than simply summarizing outcomes, this integrative framework links causal dominance (as identified through DEMATEL), theoretical grounding, and behavioral response patterns to guide interface optimization. Every trait is paired with an interface logic that relates most closely to its fundamental cognitive or emotional processing style via affect-driven adaptation, simplification, or fairness evaluation. The analysis examines the implications of misalignment and reveals a design-critical perspective on what can go wrong when personalization neglects user dispositions. In placing these relationships within the theoretical framework of the study, Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e acts as a system-level map to convert trait-based differences into coherent design logic. The interpretive synthesis aligns with the empirical results, leading to practical and ethical implications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrait\u0026ndash;Interface Design Logic and Simulation-Based Impact.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCausal Role\u003c/p\u003e\n \u003cp\u003e(D\u0026ndash;R)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTheoretical Lens\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBest Interface Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDesign Logic and Trait Alignment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcomes and Misalignment Risk\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.33 (Driver)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective Computing Theory, Dual-Process Theory (System 1).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponsive interfaces, for instance, adjust their tone and message framing according to the user\u0026apos;s emotional sensitivity; thereby, they help lower the user\u0026apos;s stress and increase their engagement level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Satisfaction, \u0026uarr; Decision Confidence; if mismatched: emotional overload, frustration, disengagement.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Load Tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.19 (Receiver)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDual-Process Theory (System 2).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimplified information structure means less mental effort for users of low tolerance, providing more clarity and speed in choice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026darr; Cognitive Load, \u0026uarr; Efficiency; if mismatched: delay, confusion, decision reversal.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.10 (Receiver)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Adaptation Principles (Affective Computing \u0026amp; System Transparency).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness-Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransparent\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; and ethically calibrated design is in line with the justice concerns, thereby, it increases trust and the perception of procedural \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;legitimacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026uarr; Trust, \u0026uarr; System Acceptance; if mismatched: skepticism, resistance, perceived bias.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThese findings indicate that personalization is most effective when it appears to operate within the structural logic of user traits rather than through surface-level adaptation. \u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj; The main features that control and have a significant impact on the system at a causal level, for example, emotional reactivity and fairness sensitivity, not only set the stage for the interaction but also serve as a kind of map for the behavioral outcomes in situations where the interaction is sensitive to them. The linkage of causal structure with behavioral data, as shown, is a very strong argument in favor of using neuroadaptive reasoning to instruct the architecture of interfaces and to make sure that adaptive systems are not just responding to users but, actually, by the use of trait-driven personalization as a principle, they are giving a socially responsible form of empowerment to the users.\u0026zwj;\u0026zwnj;\u0026zwj;\u0026zwj;\u0026zwnj;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6 Discussion and Conclusions","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Neuro-Cognitive Alignment in Adaptive Interfaces\u003c/h2\u003e\u003cp\u003eThis research views the adaptation of the interface as dependent on neurocognitive processes, where user characteristics are not merely residual modifier states of the interface (Heidari et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026mdash;in fact, they define the fundamental reasoning of the system. Emotional reactivity, fairness sensitivity, and cognitive load tolerance each exerted distinct causal influence on interface demands, and when these demands were met\u0026mdash;through personalization or fairness-aware design\u0026mdash;users exhibited marked improvements in trust, satisfaction, and decision efficiency. Importantly, the same attributes that determined the system's architecture through DEMATEL also predicted behavioral outcomes in the simulation, thus demonstrating dual validity: structural influence in system logic and experiential impact in user behavior. These results extend beyond surface-level personalization by offering a model in which adaptive systems must operate using an internal logic congruous with user psychological architectures (Kwong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This dual convergence\u0026mdash;of structural prominence and behavioral predictiveness\u0026mdash;confirms that neuro-cognitive alignment is not an optional optimization but a foundational design principle.\u003c/p\u003e\u003cp\u003eThe results indicate alignment between users' dispositions and interface design is more than advantageous; it is necessary. Instances of misalignment constantly offered up behavioral friction: emotionally reactive agents expressed lower satisfaction if interfaces did not moderate the tone; fairness-sensitive users disengaged when transparency was absent; and cognitively sensitive users slowed or reverted decision-making when overloaded with complexity. These dynamics indicate that personalization is not ethically neutral; the success of the designs or task is liable to either reinforce or disrupt cognitive trust and affective rapport (Koo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, neuroadaptive design should be framed not only as a technical improvement but also as a psychological accountability: the system should know enough to support the user, beyond serving content.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Theoretical and Practical Implications\u003c/h2\u003e\u003cp\u003eThe study offers new theoretical integration by embedding Dual-Process Theory and Affective Computing in a single simulation model. Compared​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; to previous studies, which consider traits as statistical covariates, the current framework views them as causal factors that are integrated into the interface ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;logic. ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;This design choice operationalizes the structural roles identified in the DEMATEL model, allowing constructs like emotional reactivity or fairness sensitivity to function not only as explanatory variables but as behavioral determinants across H1\u0026ndash;H5. This integrated method allows both trait causality (via DEMATEL) and behavioral alignment (via simulation outcomes) to be examined within a replicable system framework.\u003c/p\u003e\u003cp\u003eThe​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; framework acts as a tool for developers and designers to incorporate the ethics of personalization and transparency in digital tourism systems. Systems can be contextually intelligent; thus, they can match the interaction tone, information complexity, and fairness logic to the changing user profiles (Narayanan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The design principles derived here are of a universal nature: fairness-aware interfaces can enhance procedural legitimacy in healthcare platforms, cognitive load reduction can be used for fintech onboarding, and emotion-sensitive adaptation can be used for AI tutors. In fact, this model redefines personalization as a performance strategy with a responsiveness ethic, whereby adaptive systems are not only required to predict outcomes but also to understand the people behind ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;them.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Limitations and Future Directions\u003c/h2\u003e\u003cp\u003e​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj; The simulation setup provides a controlled environment to evaluate trait-driven adaptation (Pizarro et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); however, it simplifies and removes the vast majority of the natural complexity of user interactions that occur in the real world. Several important contextual factors, for example, latency, interface delays, emotional framing, or multitasking pressure, were kept constant, thereby limiting the ecological validity of the ​\u0026zwj;​\u0026zwnj;\u0026zwj;​\u0026zwj;\u0026zwnj;findings. Additionally, traits were treated as dispositional, or stable, rather than dynamic states, thus preventing their instantaneous co-adaptation. Future research could pursue empirical validation through longitudinal studies or physiological measurements to explain how trait-interface alignment is established under true decision pressures and environmental uncertainty, and especially when including cultural contexts with differences in fairness norms or bases for cognitive load.\u003c/p\u003e\u003cp\u003eIn addition to ecological realism, the current model is constrained to three high-salience traits, which may overlook other psychologically relevant dimensions, such as locus of control, impulsivity, or affective instability. Broadening the set of trait dimensions may allow for observations of multi-trait interactive effects or conditional adaptation effects under compound cognitive\u0026ndash;emotional profiles. Expanding the trait set would also allow the testing of nonlinear or compensatory dynamics\u0026mdash;where high emotional reactivity might counterbalance low cognitive load tolerance, or vice versa\u0026mdash;revealing richer adaptive strategies. Methodologically, future research could improve the DEMATEL phase using fuzzy-set logic or adaptive weighting methods or incorporate iterative expert calibrations to reduce domain surface biases and account for temporal variation in trait expression. This would enable a paradigm shift from reactive personalization to completely neuroadaptive ecosystems that actively monitor, predict, and ethically respond to the changing psychological needs of digitally mediated users over time.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e**Author contribution declaration: MH** wrote the main manuscript text and data analysis, responsible for data management, original draft preparation, visualization, and investigation. As well as the review and editing.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cem\u003eThe author declares no financial or personal conflicts of interest that could have influenced the research presented.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eThis research did not involve any studies with human participants or animals. All data used in the simulation were synthetically generated and do not pertain to any real individuals or living subjects.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eInformed consent was not applicable, as the study did not involve any human participants (only experts) or the collection of personal data.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding Declaration:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;The author declares that no funding was received for the development, execution, or publication of this research.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanerjee, A., Banik, P., \u0026amp; W\u0026ouml;rndl, W. (2023). A review on individual and multistakeholder fairness in tourism recommender systems. Frontiers in big Data, 6, 1168692. https://doi.org/10.3389/fdata.2023.1168692 \u003c/li\u003e\n\u003cli\u003eBecker, M., Mahr, D., \u0026amp; Odekerken-Schr\u0026ouml;der, G. (2023). Customer comfort during service robot interactions. Service Business , 17 (1), 137-165. https://doi.org/10.1007/s11628-022-00499-4 \u003c/li\u003e\n\u003cli\u003eChan, J. (2026). Compensation request to customer: Comparing the perceived fairness of AI vs. human representatives and the influence of solitude and companionship. International Journal of Hospitality Management, 132, 104389. https://doi.org/10.1016/j.ijhm.2025.104389 \u003c/li\u003e\n\u003cli\u003eda Silva, D. C., Manzato, M. G., \u0026amp; Dur\u0026atilde;o, F. A. (2021). Exploiting personalized calibration and metrics for fairness recommendation. Expert Systems with Applications, 181, 115112. https://doi.org/10.1016/j.eswa.2021.115112 \u003c/li\u003e\n\u003cli\u003eDeldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., \u0026amp; Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59-108. https://doi.org/10.1007/s11257-023-09364-z \u003c/li\u003e\n\u003cli\u003eEl Ouadi, J., Errousso, H., Malhene, N., \u0026amp; Benhadou, S. (2022). On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city. \u003cem\u003eQuality \u0026amp; Quantity\u003c/em\u003e, 56(6), 3893-3932. https://doi.org/10.1007/s11135-021-01282-9 \u003c/li\u003e\n\u003cli\u003eFarshbafiyan Hosseininezhad, M., Heidari, M., \u0026amp; Letizia Guerra, M. (2025). The framing effect and sustainable hotel booking behaviour: A nudge marketing study. European Journal of Tourism Research, 39, 1-14. https://doi.org/10.54055/ejtr.v39i.3760 \u003c/li\u003e\n\u003cli\u003eFominska, I., Di Tore, S., Nappi, M., Iovane, G., Sibilio, M., \u0026amp; Gelo, A. (2024). Approaches to identifying emotions and affections during the museum learning experience in the context of the future internet. Future Internet, 16(11), 417. https://doi.org/10.3390/fi16110417 \u003c/li\u003e\n\u003cli\u003eGodovykh, M., \u0026amp; Tasci, A. D. (2022). Emotions, feelings, and moods in tourism and hospitality research: Conceptual and methodological differences. Tourism and Hospitality Research, 22(2), 247-253. https://doi.org/10.1177/14673584211039867 \u003c/li\u003e\n\u003cli\u003eHeidari, M., Verderese, D., \u0026amp; Saviano, M. (2024). Exploring the contribution of neuroscientific approach to sustainable smart tourism. In ITM Web of Conferences (Vol. 62, p. 03001). EDP Sciences. https://doi.org/10.1051/itmconf/20246203001 \u003c/li\u003e\n\u003cli\u003eHrankai, R., Chan, I. C. C., \u0026amp; Senbeto, D. L. (2025). Understanding the Role of Response Latency on Position Bias in Tourists\u0026rsquo; Online Travel Planning. Journal of Hospitality \u0026amp; Tourism Research, 10963480251324553. https://doi.org/10.1177/10963480251324553 \u003c/li\u003e\n\u003cli\u003eHu, H., Wan, Y., Tang, K. Y., Li, Q., \u0026amp; Wang, X. (2025). Affective-Computing-Driven Personalized Display of Cultural Information for Commercial Heritage Architecture. Applied Sciences (2076-3417), 15(7). https://doi.org/10.3390/app15073459 \u003c/li\u003e\n\u003cli\u003eKahneman, D. (2011). Thinking, fast and slow. macmillan.\u003c/li\u003e\n\u003cli\u003eKoo, I., Zaman, U., Ha, H., \u0026amp; Nawaz, S. (2025). Assessing the interplay of trust dynamics, personalization, ethical AI practices, and tourist behavior in the adoption of AI-driven smart tourism technologies. Journal of Open Innovation: Technology, Market, and Complexity , 11 (1), 100455. https://doi.org/10.1016/j.joitmc.2024.100455 \u003c/li\u003e\n\u003cli\u003eKumar, A. S., Mosikan, M., Dutta, A., Musirin, I. B., Akula, V. S. G., Khalid, S., ... \u0026amp; S. K., T. (2025). AI-driven personalization in tourism: Balancing innovation with ethical challenges. In AI Technologies for Personalized and Sustainable Tourism (pp. 31-58). IGI Global. https://doi.org/10.4018/979-8-3693-5678-4.ch002 \u003c/li\u003e\n\u003cli\u003eKwong, A. T. M., Omar, S. I., \u0026amp; Aliah, M. N. (2024). The ethical implications of AI-Powered personalization in hospitality and tourism. In Impact of AI and tech-driven solutions in hospitality and tourism (pp. 103-122). IGI Global. https://doi.org/10.4018/979-8-3693-6755-1.ch006 \u003c/li\u003e\n\u003cli\u003eLeal, F., Veloso, B., Malheiro, B., \u0026amp; Burguillo, J. C. (2025). Towards adaptive and transparent tourism recommendations: A survey. Expert Systems, 42(1), e13400. https://doi.org/10.1111/exsy.13400 \u003c/li\u003e\n\u003cli\u003eLee, N. (2025). Rational or Emotional? The Effectiveness of Tourist-Generated Content on Travel Intention: A Moderated Mediation Analysis. Journal of Smart Tourism, 27652157251344346. https://doi.org/10.1177/27652157251344346 \u003c/li\u003e\n\u003cli\u003eLi, J., Ji, X., \u0026amp; Chen, F. (2025). Does information overload attract or Repel self-driving tourists?. Journal of Hospitality and Tourism Management, 63, 343-365. https://doi.org/10.1016/j.jhtm.2025.05.008 \u003c/li\u003e\n\u003cli\u003eLi, P., Lv, Y., Wang, R., Chen, T., Gao, J., \u0026amp; Huang, Z. (2024). How do illegitimate tasks affect hospitality employees\u0026rsquo; adaptive performance? An explanation from the perspective of cognitive-affective system theory of personality. International Journal of Contemporary Hospitality Management, 36(9), 3032-3051. https://doi.org/10.1108/IJCHM-04-2023-0538 \u003c/li\u003e\n\u003cli\u003eLim, S. E., \u0026amp; Kim, M. (2025). AI-powered personalized recommendations and pricing: Moderating effects of ethical AI and consumer empowerment. International Journal of Hospitality Management, 130, 104259. https://doi.org/10.1016/j.ijhm.2025.104259 \u003c/li\u003e\n\u003cli\u003eLiu, M., Liu, X., Li, L., Zhou, J., \u0026amp; Liu, S. (2025a). Approach and avoidance mechanisms in the effects of algorithm transparency on the service performance of food-delivery riders. Current Issues in Tourism, 28(14), 2366-2380. https://doi.org/10.1080/13683500.2024.2376208 \u003c/li\u003e\n\u003cli\u003eLiu, X., Wang, D., \u0026amp; Li, X. (2025b). Phygital Heuristics in Tourists\u0026rsquo; Decision-Making in Motion. Journal of Hospitality \u0026amp; Tourism Research, 10963480251319979. https://doi.org/10.1177/10963480251319979 \u003c/li\u003e\n\u003cli\u003eMohammed, I., \u0026amp; Denizci Guillet, B. (2025). Heuristics and biases in human\u0026ndash;algorithm interaction and hotel revenue management override decision-making. International Journal of Contemporary Hospitality Management, 37(2), 358-379. https://doi.org/10.1108/IJCHM-02-2024-0288 \u003c/li\u003e\n\u003cli\u003eMoisa, D. G., Parapanos, D., \u0026amp; Heap, T. (2025). The role of digital footprints for destination competitiveness and engagement: utilizing mobile technology for tourist segmentation integrating personality traits. International Journal of Tourism Research, 27(1), e70006. https://doi.org/10.1002/jtr.70006 \u003c/li\u003e\n\u003cli\u003eNarayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., \u0026amp; De Cremer, D. (2024). Fairness perceptions of artificial intelligence: A review and path forward. International Journal of Human\u0026ndash;Computer Interaction, 40(1), 4-23. https://doi.org/10.1080/10447318.2023.2210890 \u003c/li\u003e\n\u003cli\u003eOan-Oon, R., \u0026amp; Choibamroong, T. (2025). Multidimensional bandwagon effect and dual-process decision making: An integrated model of the theory of planned behavior from the perspective of American millennials\u0026rsquo; travel intention. Heliyon, 11(1). https://doi.org/10.1016/j.heliyon.2024.e41375 \u003c/li\u003e\n\u003cli\u003ePicard, R. W. (2000). Affective computing. MIT press.\u003c/li\u003e\n\u003cli\u003ePizarro, V., Leger, P., Hidalgo-Alc\u0026aacute;zar, C., \u0026amp; Figueroa, I. (2023). ABM RoutePlanner: An agent-based model simulation for suggesting preference-based routes in Spain. Journal of Simulation, 17(4), 444-461. https://doi.org/10.1080/17477778.2022.2027826 \u003c/li\u003e\n\u003cli\u003ePlikynas, D., Miliauskas, A., Laužikas, R., Dulskis, V., \u0026amp; Sakalauskas, L. (2022). The cultural impact on social cohesion: an agent-based modeling approach. \u003cem\u003eQuality \u0026amp; Quantity\u003c/em\u003e, 56(6), 4161-4192. https://doi.org/10.1007/s11135-021-01293-6 \u003c/li\u003e\n\u003cli\u003eSaha, P., Sengupta, A., \u0026amp; Gupta, P. (2024). Influence of personality traits on generation Z consumers\u0026apos; click-through intentions towards personalized advertisements: A mixed-methods study. Heliyon, 10(15). https://doi.org/10.1016/j.heliyon.2024.e34559 \u003c/li\u003e\n\u003cli\u003eSeyfi, S., Kim, M. J., Nazifi, A., Murdy, S., \u0026amp; Vo-Thanh, T. (2025). Understanding tourist barriers and personality influences in embracing generative AI for travel planning and decision-making. International Journal of Hospitality Management, 126, 104105. https://doi.org/10.1016/j.ijhm.2025.104105 \u003c/li\u003e\n\u003cli\u003eShooshtarian, S., Gurmu, A. T., \u0026amp; Mahmood, M. N. (2024). Application of the DEMATEL approach to analyze the root causes of building defects. \u003cem\u003eQuality \u0026amp; Quantity\u003c/em\u003e, 58 (5), 4641-4660. https://doi.org/10.1007/s11135-024-01872-3 \u003c/li\u003e\n\u003cli\u003eStylos, N. (2022). An integrated duality theory framework (IDTF): Marking pathways for consumer decision-making researchers in the hospitality and tourism industry. International Journal of Contemporary Hospitality Management, 34(7), 2597-2619. https://doi.org/10.1108/IJCHM-10-2021-1256 \u003c/li\u003e\n\u003cli\u003eSu, Z., \u0026amp; Ha, H. Y. (2025). Longitudinal impact of perceived fairness after service failures: Evidence from online travel agencies. International Journal of Hospitality Management, 128, 104177. https://doi.org/10.1016/j.ijhm.2025.104177 \u003c/li\u003e\n\u003cli\u003eTai, Y. F., Wang, Y. C., \u0026amp; Luo, C. C. (2021). Technology-or human-related service innovation? Enhancing customer satisfaction, delight, and loyalty in the hospitality industry. Service Business, 15(4), 667-694. https://doi.org/10.1007/s11628-021-00461-w \u003c/li\u003e\n\u003cli\u003eTao, H., Peiying, S., Huihui, W., \u0026amp; Jiamin, F. (2024). How do affective and cognitive involvement affect tourism intentions: A perspective on blogger impact. Current Issues in Tourism, 1-20. https://doi.org/10.1080/13683500.2024.2392763 \u003c/li\u003e\n\u003cli\u003eTao, X., Nor Akmar, A. A., Syamsul Herman, M. A., \u0026amp; Suziana, H. (2025). Decision-Making Analysis for Sustainable Tourism Development: A DEMATEL and SEM Approach to Infrastructure and Policy Optimization in Ethnic Village Tourism. Decision Making: Applications in Management and Engineering, 419-435. https://doi.org/10.31181/dmame8120251382 \u003c/li\u003e\n\u003cli\u003eYang, X., Zhang, L., \u0026amp; Feng, Z. (2024). Personalized tourism recommendations and the E-tourism user experience. Journal of Travel Research, 63(5), 1183-1200. https://doi.org/10.1177/00472875231187332 \u003c/li\u003e\n\u003cli\u003eYin, J., \u0026amp; Hwang, Y. H. (2025). Information overload and tourists\u0026rsquo; booking discontinuance intention: an application of transactional theory of stress and coping. Current Issues in Tourism, 1-17. https://doi.org/10.1080/13683500.2025.2531218 \u003c/li\u003e\n\u003cli\u003eZhang, S., Zhen, F., Wang, B., Li, Z., \u0026amp; Qin, X. (2022). Coupling social media and agent-based modelling: A novel approach for supporting smart tourism planning. Journal of Urban Technology, 29(2), 79-97. https://doi.org/10.1080/10630732.2020.1847987 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trait-Based Personalization, Neuroadaptive Interfaces, Agent-Based Simulation, DEMATEL Methodology, Fairness-Aware AI, Smart Tourism Systems","lastPublishedDoi":"10.21203/rs.3.rs-8108792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8108792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a trait-driven neuroadaptive interface model for smart hospitality platforms and develops a mythological framework, grounded in the premise that effective personalization must align with user traits rather than only inferred preferences. This paper integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) causal mapping and agent-based simulation approach, drawing on Dual-Process Theory and Affective Computing, to model how emotional reactivity, cognitive load tolerance, and fairness sensitivity influence adaptive interface needs in AI-enabled tourism decision contexts. DEMATEL assesses which characteristics serve as systems drivers, providing a causal-structural basis for establishing static, personalized, and fairness-aware interfaces in the context of a simulation involving 500 simulated cognitively differentiated agents. Behavioral outcomes\u0026mdash;trust, satisfaction, and cognitive load\u0026mdash;were analyzed in relation to trait\u0026ndash;interface congruence. Methodologically, this dual-stage design (causal mapping followed by simulation) offers a structured procedure for validating how latent traits shape behavior in socio-technical decision systems. The results suggest that alignment can improve the user experience, particularly when agents are emotionally reactive or fairness-sensitive and interfaces change or embed ethical transparency. Misalignment, conversely, leads to overload, confusion, or disengagement. The framework provides a replicable process for trait-based adaptation, generating implications for human\u0026ndash;AI researchers interested in fairness-aware personalization. The proposed model advances personalization beyond preference matching by offering a computational, methodological, and ethical rationale for interfaces that adapt to neuro-cognitive variability, and the framework can be extended to other adaptive systems such as healthcare or education, supporting methodological advances in social science research on AI-mediated decision-making.\u003c/p\u003e","manuscriptTitle":"A DEMATEL-Guided Agent-Based Simulation Framework for Trait-Driven Neuroadaptive Interfaces in Smart Hospitality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:46:15","doi":"10.21203/rs.3.rs-8108792/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e06fc7fa-31a3-4782-b9fb-5ae2f05c4ad7","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T10:02:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:46:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8108792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8108792","identity":"rs-8108792","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}