Tokenized Incentives and Loyalty: Leveraging Blockchain-Based Smart Contracts for Enhancing Eco-Tourism Behavioral Engagement

Tokenized Incentives and Loyalty: Leveraging Blockchain-Based Smart Contracts for Enhancing Eco-Tourism Behavioral Engagement | 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 Tokenized Incentives and Loyalty: Leveraging Blockchain-Based Smart Contracts for Enhancing Eco-Tourism Behavioral Engagement Majid Heidari, HOSSEIN HOSSEINALIBEIKI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6939486/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 research investigates ways in which blockchain smart contracts and gamified token rewards can increase eco-tourists' engagement and loyalty to online sustainability practices. The tourism industry faces challenges when it comes to converting intentions into sustainable behaviours, despite the obvious value of environmental sustainability. Traditional loyalty frameworks do not provide much real-time feedback or emotional resonance and limit the amount of transparency the agents have. As a result, traditional loyalty frameworks lose their effectiveness over time. Therefore, we developed a simulation-based experimentation model, that was based on Agent-Based Modeling (ABM) and the Stimulus-Organism-Response (SOR) framework that consisted of 200 synthetic agents who have differing psychographic profiles and 10 decision rounds with different token incentive conditions. The simulation model tests six hypotheses from Self-Determination Theory, the Theory of Planned Behavior, and Value-Belief-Norm. The results suggest that the visibility and perceived value of the tokens mostly impact intention and behaviour. There is also a distinction between perceived behavioural control and intrinsic motivation regarding the effects of incentives. The relationship between behavioural repetition and satisfaction with token redemption produced the emergence of loyalty intentions. The proposed methodological approach shows that by controlling tokens according to users' psychological characteristics, gamified smart contracts can foster long-term eco-engagement in users. This research offers a useful plan for testing digital behaviour changes and provides practical advice for building token-based, emotionally conscious ecotourism platforms. Building on cognitive-affective theory, we integrated it with blockchain technology, which will redefine behavioural governance, gamification, and sustainability in future travel ecosystems. Blockchain Smart contract Tokenized incentives Eco-tourism behavior Agent-based modeling Sustainable loyalty engagement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Although awareness of sustainability is a significant component of engaging in eco-tourism marketing, an established intention–action gap persists. Travelers tend to advocate for principles of eco-friendliness but infrequently feel the need to intervene based on those factors, indicating that conventional pre-engagement strategies, such as leveraging environmental certification or information cues (Viglia & Acuti, 2023 ; Nieto-García et al., 2024 ), are often ineffective. These approaches might be useful in activating cognitive awareness, but almost always fail to maintain commitment or create emotional motivation for behavioral choices. Without timely prompting or a perceived meaningful reward, moral calls are abstract, distant, and easily ignored when it comes to travel decisions (Farshbafiyan Hosseininezhad et al., 2025 ). This scenario presents a challenge for tourism researchers and designers to explore different models of engagement, particularly those more in line with the behaviors of digital native travelers, which are being shaped increasingly each day due to feedback from algorithms, experiential interfaces, and urgency driven by eco-anxiety (Mosca et al., 2024 ). Accordingly, sustainability needs to pivot away from traditional models of activism, which rely on passive information dissemination, to ones that have emotional resonance, are behaviorally responsive, and are digitally mediated. The foremost challenge to providing sustainable tourism is the engagement of systems that motivate certain behaviors in travelers but instill an enduring cognitive arrangement (Ahmed & Alzoubi, 2020 ). Traditional loyalty programs are characterized by a lack of personalization, immediacy, and psychological relevance to the traveler's motivational profile. They may induce a temporary form of compliance or may even induce non-compliance; nevertheless, those systems tend not to create lasting intention, stated awareness, or maintained commitment to eco-mindsets. This becomes especially true when incentives seem generalized and delayed, a consistent shortcoming (Koo et al., 2020 ). Furthermore, legacy systems tend to be centralized and opaque, which erodes user trust (Barclay et al., 2022 ). While all of these issues may be resolved through digital means, any engagement program must now offer both psychological adaptability and technological support as an incentive for participation. Therefore, an effective engagement system should engage a user in a way that creates a reward for the user while additionally changing the cognitive and emotional feedback loops directly associated with that engagement (Rodrigues et al., 2023 ). This engagement system will also require creating incentive mechanisms that can be programmed, transparent, and responsive to behaviors and individual user profiles in real-time scenarios in the moment context; this is a change from everyday loyalty programs that are static, to something much simpler and more usable when enabled by a catalytic digital architecture that is dependent on context and users (Balasubramanian et al., 2022 ). Blockchain-enabled smart contracts offer new opportunities for addressing the limitations of traditional incentive systems (Balasubramanian et al., 2022 ) in the context of eco-tourism. Their approach provides a fully automated, rules-based mechanism for extracting verifiable and transparent tokens that utilize eco-behaviors in real-time (Barclay et al., 2022 ). Ultimately, if the contracts are executed with gamified mechanics—such as trophy badges, progression tiers, or reputation rewards—the contracts could change the framing of sustainability from a commitment to an opportunity that balances feedback and autonomy (Pasca et al., 2021 ). Alongside the immutable transaction ledger of blockchain, user-controlled identity systems provide greater trust and autonomy compared to centralized systems (Fotiou et al., 2025 ). Consequently, in real-world contexts, these platforms can facilitate dynamic, decentralized engagement representations when combined with eco-rewards personalization based on psychographic profiles (Hosseinalibeiki & Heidari, 2024 ). The synergy of programmable logic and emotionally engaging design demonstrates a shift—the concept of sustainability is absorbed into the decision journeys of tourists. Apart from transactional efficiency, accountability, and trust, the platforms provide novel, possible avenues—though we will need further exploration as part of the line of inquiry—to think about ethics, identity, and interactivity indicative of smart tourism system (Lim et al., 2025 ; Mountije et al., 2025 ). While the theory behind blockchain technology and gamified incentives is extremely interesting, the possible practical testing of ideas is limited by several factors, including development and deployment costs, ethical dilemmas, and personal differences (Chica et al., 2023 ). For this reason, simulation data now serves as a valid avenue for hypothesis testing. ABM, situated well within the S-O-R theoretical framework provides a dynamic modeling process to depict behavioral interactions with incentive stimuli and individual psychological characteristics over time (Wallinger et al., 2023 ). A simulation platform can measure how various constructs, such as motivation, perceived control, or identity in environments tokenized in an ABM, with three pieces of agent logic, Self-Determination Theory (SDT), Theory of Planned Behavior (TPB), and Value-Belief-Norm (VBN) embedded in the behavioral agent, will influence behavior (Baktash et al., 2023 ). This simulation involves 200 agents with distinct psychographic profiles to observe tokenized incentives, eco-engagement, satisfaction, and loyalty in a controlled environment. As a result, this provides a theoretically concrete architecture that provides reproducible, behaviorally valid data. In summary, this work introduces a scalable framework for future digital systems that promote sustainability, validating that programmable, psychologically informed systems can provide a bridge from intention to action in eco-tourism, as well as a framework for platform architects and policy innovators. 2 Behavioral and Technological Foundations of Tokenized Eco-Engagement 2.1 Smart Contracts and Blockchain Incentives in Tourism Blockchain technology has evolved from being a back-end operational efficiency solution to serving as a significant mechanism for sustainable governance in the tourism sector (Balasubramanian et al., 2022 ). Blockchain initially focused on use cases such as secure bookings and decentralized payments, then shifted to creating a method for authenticating sustainable behavior through traceable supply chains and digital certifications (Fotiou et al., 2025 ). In eco-tourism, blockchain's impact will be in its immutability in recording behavior and real-time verification of low-impact behaviours, such as carbon-neutral travel and eco-lodge stays (Rodrigues et al., 2023 ). As payment ecosystems evolve, blockchain platforms also enhance the form of value exchange, while supporting the characteristics of transparency, interoperability, and user control (Wang & Chan, 2025 ). Research on blockchain in tourist activities has affirmed that perceived utility and usability impact actual systems readiness (Corne et al., 2023 ). Blockchain can be viewed as more than a technology enabler and upgrade, but also as a governance facilitator for decentralized user-generated activity, as it grounds eco-tracking actions in a virtually immutable way (Thanasi-Boçe & Hoxha, 2025 ). In this environment, smart contracts automate the transaction process, where actions such as conservation or community service are vetted and delivered condition-based external incentives, such as the replacement of travel credits (Barclay et al., 2022 ). The merging of smart contracts with tokenized incentives represents a significant change from static incentives to dynamic behavior manipulation. In this regard, users expect rewards in the form of tokens that double as embedded nudges to shape their behavior according to predetermined logic and provide timely feedback (Lim et al., 2025 ). Encouraging sustainable behavior by having users expect rewards is more effective than traditional ex post validation and promotes sustainable behavior using psychological principles of resonance (Pasca et al., 2021 ). Smart contracts (underpinned by behavioral economics) offer personalized micro-incentives resulting from user behavioral history, autonomy, and trust (Chan, 2024 ; Hosseinalibeiki & Heidari, 2024 ). Due to the inherently customizable nature of the blockchain for identity platform-based applications, it not only improves the potential use of incentives but also enhances user engagement. Moreover, they operate in a progressive form of tourism finance, where payments and other aspects become more adaptive, traceable and ethically governed (Wang & Chan, 2025 ). As a result of the re-envisioned structure of governance plans, there are implications for sustainability policies, one being the shift of design authority away from centralized systems, and another being a shift towards decentralized rationality through agent-mediated systems (Thanasi-Boçe and Hoxha, 2025 ). 2.2 Gamified Token Systems as Behavioral Drivers in Eco-Tourism Gamification has been a strong enabler for pro-environmental behaviour in tourism, transforming intangible sustainability aspirations into interactive, emotionally engaging experiences (Pradhan et al., 2025 ). Gamification systems implement game mechanics, such as points, badges, milestones, social comparison, and instant feedback, to create cognitive and emotional engagement, leading to action-based engagement for eco-conscious behavior (Abou Kamar et al., 2024 ). It is beneficial to establish a sustained mode of behavior that can lead to eco-tourism, which is often limited and episodic, through regular behaviours that encourage participation and eco-loyalty (Sesliokuyucu & Cobanoglu, 2025 ). Similarly, token-based resources are implementing gamification in a system of practice with tangible rewards for metrics-based actions (e.g., conservation actions, sustainable transport actions), allowing the habit of sustainability to develop further into the practitioner (Choirisa et al., 2025 ). Token systems are developed through reinforcement learning systems, which use tiered reward logic and scalable value, implemented with sustainable habits in mind (Yu et al., 2024 ). When token systems are implemented on blockchain technology, along with smart contracts, the reward process becomes automated and transparent, while concurrently optimizing trust in the aforementioned platforms and reducing administrative work (Wang & Huang, 2025 ). 2.3 Psychological Drivers: Motivation, Control, and Eco-Identity Intrinsic motivation, based on Self-Determination Theory (SDT), provides a broader psychological foundation for the persistence of pro-environmental behavior in eco-tourism (Patwary et al., 2024 ). The more intrinsic beliefs tourists place on their eco-behaviour (e.g., time spent at eco-lodges, willingness to donate time and resources to conservation altruistically), the more likely they are to behave ecologically. Extrinsically constructed tools, such as gamified incentives, branded symbolic gifts, or voluntary continuous feedback loops, will not diminish intrinsic initiatives (Bhartiya et al., 2025 ). Still, these external tools can help stimulate self-actualization regarding eco-engagement. Digital systems developed with SDT principles, which emphasize autonomy and personal relevance, will leverage these benefits and ultimately promote long-term eco-engagement. In simulated environments, intrinsic motivation will act as a stable cognitive characteristic, such that the cognitive response to environmental factors is largely unaffected by the immediacy of additional or extrinsic rewards (Baktash et al., 2023 ). The cognitive responsibility is increased when systems reflect one’s value systems, thereby enhancing the emotionally inherent coherence between individual identity and sustainable behaviors (Schönherr & Pikkemaat, 2024 ). Eco-identity will likely magnify this response even further—those who internalize a sense of responsibility for the environment as part of their self-identity will respond more favorably to symbolic incentives, thus creating behavioral loyalty and increased satisfaction with eco-friendly tourism choice. Perceived behavioral control (PBC), drawn from the Theory of Planned Behavior, further strengthens this motivational foundation by influencing individuals’ belief in their capacity to act sustainably (Abou Kamar et al., 2024 ). In eco-tourism, PBC encompasses both perceived knowledge and ease of executing low-impact actions—elements that correlate strongly with behavioral intention and follow-through. Simulated agent-based models validate this: users with high PBC react more decisively to eco-stimuli and maintain behavioral coherence across repeated decisions (Bhartiya et al., 2025 ). This control is dynamic and can be actively manipulated through good design, supportive feedback, and ease of use (Chan, 2024 ). When gamified systems engage users across all three psychological processes (motivation, identity, and control), a cyclical engagement and affective loyalty begins. Users feel supported by a psychologically consistent system that helps them to feel in control, be in alignment, and be affectively committed to sustainable choices. These psychological drivers need to be considered as interrelated systems for tourism systems that encourage long-term behavior. These processes work dynamically and can influence the parameters of meaningful, sustainable ecological engagement (Schönherr & Pikkemaat, 2024 ). 3 Simulating Eco-Behavioral Dynamics through Tokenized Incentives 3.1 Theoretical Basis for Gamified Eco-Design The conceptual framework for this research comprises four complementary theories that demonstrate how blockchain-based token incentives interact with ecotourist behaviors. Self-Determination Theory (SDT), which provides a framework for explaining how tokenized rewards can either positively influence environmentally friendly choices by creating a sense of autonomy or negatively, by interfering with autonomy if the token incentive is perceived as controlling (Patwary et al., 2024). The Theory of Planned Behaviour (TPB) offers an additional rational dimension, with the roles of attitude, subjective norms, and perceived behavioural control as predictors of eco-intention, which are amenable to subtle influence by programmable incentives through automation and feedback (Nguyen et al., 2023). Gamification Theory enhances this perspective by demonstrating how digital game effects – including badges, points, or incentivization tiers – can enhance engagement through the enactment of structured, psychologically salient experiences (Sesliokuyucu & Cobanoglu, 2025). Additionally, the VBN Theory provides a framework for the ethical assumptions underlying eco-behaviour. When token systems have stable underpinning environmental values, they can foster and reinforce norm-consistent behaviours and promote long-lasting loyalty (Landon et al., 2018; Park et al., 2022). All four theories yield a stacked paradigm in examining cognition, affect, and ethics within a tourism ecosystem structured by smart contracts, which can also be enhanced by transitioning to ABM to facilitate the modelling of behavioural changes through a dynamic multi-agent system (Wallinger et al., 2023). 3.2 Stimulus–Organism–Response (SOR) Framework S-O-R framework acts as a critical translational vehicle within the research, bridging theory to simulation modeling to the logic of hypothesis. In this setting, stimuli are tokenized incentives, delivered as blockchain smart contracts, with differences in visibility, value, and gamification structure. These act as the initial input, triggering internal organismic states, including motivation activation, perceived behavioral control, and eco-affective appraisal (Qiu et al., 2023). These organism-level reactions are modeled in agents’ cognitive-affective parameters and are empirically mirrored in moderating and mediating variables such as intrinsic motivation and action frequency. The final response phase encompasses the behavioural outcomes: intention, eco-action frequency, satisfaction with token redemption, and loyalty following long-term engagement. This three-pronged approach aligns with the six hypotheses, the input-output logic of the simulation, and a consistent interpretation of behavioral clustering across the results. Furthermore, the S-O-R framework provides a structure to the agent-based model; each agent receives inputs, processes these through its states, and produces observable behaviors (Baktash et al., 2023). In the analysis phase, S-O-R also aids with interpreting differences in behavioural engagement across profiles—an essential analytic pillar. 3.3 Translating Cognitive Models into Smart Contract Simulation To operationalize the multidimensional theoretical integration and S-O-R framework, this study designed a simulation-ready behavioral architecture that integrates psychological modeling with programmable smart contract logic. Incentive visibility was a strong moderator where smart contracts accounted for social-visible gamification (Figure 1). When tourists recognize the relevant token-linked behavior, eco-intentionality will continue to grow based on expectancy disconfirmation or peer signaling (Choirisa et al, 2025). Transparency in token design also promotes behavioral salience and can motivate a participant to adopt sustainable behaviors even before they receive any direct benefit. H1: Token visibility significantly increases tourists' intentions to perform eco-friendly behaviors. Beyond mere awareness, the perceived value of the token itself can influence actual behavior. If tourists believed that the tokens earned offer a form of utility in their own right, whether accounting for benefits that can be redeemed, reputation, or social status, it's more likely that they will be incentivized to engage in eco-tourism behaviour. Perceived value triggers both extrinsic motivation and evaluation of cognitive cost-benefit, ultimately increasing the intention-action link (Choirisa et al., 2025). In our simulation, we parameterized the perceived token value through smart contract logic and examined how agent behavior changes under different incentive values. H2: Higher perceived token value positively influences tourists' actual engagement in eco-tourism activities. The impact of incentives is not homogeneous, as it varies based on each person's sense of controllability. Perceived behavioral control—a central concept within the Theory of Planned Behavior—can increase the motivational impact of tokens; if a person is confident about their ability to act sustainably, e.g., they possess time, access, and knowledge, then tokens can reinforce motivation, as opposed to simply bringing attention to a prompt (Akter & Hasan, 2023). This moderation is modeled in the agents with internal PBC weights, which determine how the agent responds to decision points created by incentives. H3: Perceived behavioral control strengthens the relationship between token incentives and eco-behavioral intentions. Along with perceived control, the quality of the motivation is also relevant. According to SDT, individuals with a high level of intrinsic motivation towards caring for the environment may be more reliably engaged in sustainable behaviors, particularly if tokenized incentives align with their values. Rather than undermining tourists’ autonomy, this alignment may reinforce continued engagement through their sense of internal satisfaction and in realizing mutual goals (Patwary et al., 2024). H4: Intrinsic motivation moderates the relationship between token incentives and sustained eco-tourism engagement. Behavioural change is not an event but a process of repetition and habitual behaviour. In ecotourism, the frequency of tourist behavior mediates the course from a one-time experience to loyalty. Tourists who engage in high-frequency interactions are more likely to form habitual behaviors, which develop familiarity (Habits and Routines) and become additional behavioral contexts for positive brand experiences and intentions. For developmental continuity in a behavioural context, action frequency serves as the behavioural bridge between initial tourism incentives for visitation and long-term destination commitment (Frías-Jamilena et al., 2022). H5: Frequency of performed eco-tourism actions mediates the relationship between token-based incentives and tourists' loyalty intentions. Loyalty is dependent not only on frequency of behaviour but also on affective satisfaction and, in particular, affective satisfaction with the experience of token redemption. Should the processes of the claims and subsequent use of tokens be effortless, transparent, and rewarding, tourists are more likely to form intentions to return to the destination and recommend it to others. Satisfaction can be considered as a reward cycle, and this feeling of attachment is important in a gamified approach (Choirisa et al., 2025). H6: Satisfaction with the token redemption process significantly predicts tourists’ future loyalty and revisit behaviors. 4 Methodology 4.1 Simulation Architecture and Token Stimulus Logic This study employed a simulation-based experimental design, an application of ABM, to investigate whether and how tokenized smart contract incentives influence eco-tourist involvement within evolving psychographic and contextual contexts. The study generated 200 synthetic agents (N = 200), designed to represent a hypothetical eco-tourist, and created psychological profiles of each agent that consisted of three primary psychological characteristics: intrinsic motivation levels (low, medium, high), eco-identity orientation (present or absent), and perceived behavioral control (a continuous scale from 0.0 to 1.0). The psychological profiles were based on SDT, TPB, Gamification Theory, and the VBN Theory as the underlying systems of the S-O-R framework. In the simulation, agents completed eco-tourism decision-making tasks across 10 sequential rounds that included exposure to different incentive stimuli: token visibility (low to high), token value (low to high), and gamification format (fixed vs. tiered rewards based on cumulative eco-action frequency). Motivational triggers, which are grounded in the recognition of and consideration for environmental stimuli by the agents, were equivalent to simulating world decision-making. While we did not acquire in-situ behavior data from real human participants, the simulation produced behavior data outputs with a predefined level of resolution, due to the modeling parameters: fixed token thresholds, eligibility to redeem with limits, and a constant random seed to ensure reproducibility. 4.2 Agent Profiling, Variable Mapping, and Data Structuring The simulation generated a multi-structured dataset that recorded agent behavior across all profiles, incentive conditions, and rounds of decision-making. These data were organized using the S-O-R framework, which extracted stimulus-level data (token visibility, perceived value, and gamification format), organism-level data (internal intrinsic motivation, eco-identity, and perceived behavioral control), and response-level data (eco-action frequency, token redemption, satisfaction, and loyalty intention). While the data were synthetic, they were grounded in a transparent rule-based logic that modeled realistic behavior through time-series logging at both the agent and round levels. This enabled profile-specific tracking and aggregation while ensuring reproducibility. The analysis was performed in a four-step process: (1) descriptive statistics to provide summary statistics of agent behavior and imitation of the realistic aspects of simulation; (2) regression and path modeling to evaluate main effects (H1-H2) and mediation and moderation theories (H3-H6); (3) psychographic clustering to demonstrate patterns of behavior brought on by motivational identity or contact with motivational forces; and (4) sensitivity analysis by changing simulation input parameters (incentive thresholds, motivation weight) to assess robustness of the simulation. All of these methods together contribute to a precise examination of the impact of token-based micro-incentives on the behavior of simulated eco-tourists. Although based on simulation, this study's behavioral architecture is well-established by empirically grounded psychological theories that lend cognitive and affective verisimilitude to the agent-based logic, ensuring that the simulated behaviors follow pathways typically observed in sustainable tourism environments. The model predictions reflected the empirical findings, which showed that gamified stimuli impacted eco-actions, consistent with the cue-based behavior of Frías-Jamilena et al. ( 2022 ). Similarly, the moderators of intrinsic motivation and perceived behavioural control identified by Patwary et al. ( 2024 ) can be found in the study's agent clusters and interaction models. The loyalty effect also aligned with Boukis's (2024) findings regarding reward satisfaction and backs the attitudinal outputs of the simulations. While the model was not developed to mirror a single empirical context, it was designed for theoretical consistency and behavioural plausibility. Figure 2 diagrammatically models the simulation-empirical alignment and proposed validation loop. 5 Data analysis and results 5.1 Simulation Output and Behavioral Overview To develop a controlled simulation that is valid and replicable, 200 agents (N = 200) were instantiated in an agent-based model to demonstrate decision making in an eco-tourism context. The agents made decisions in 10 decision rounds that were exposed to tokenized incentive conditions structured by programmable smart contract logic. Incentive conditions were presented under various conditions, including three levels of token visibility (low, medium, high), three levels of perceived token value (low, medium, high), and two competing versions of gamification logic (fixed reward vs. tiered incentive). Agent profiles were algorithmically categorized based on intrinsic motivation (low, medium, high), presence of eco-identity (positive or negative), and perceived behavioral control (indexed between 0.0 and 1.0). All the various offers of incentive triggers, behavioral thresholds, and environmental conditions were consistently, validly, and reliably presented through a pre-arranged logic of parametric control, ensuring internal consistency and replicability. As shown in Table 1 , the simulation constructs embody a factorial approach that aims to represent combinations of psychological and structural features with respect to the variables and conditions of the S-O-R framework and the theoretical model. In this way, the study examined six structured hypotheses regarding the combinations of internal cognitive–affective profiles and external intensities of incentives, which logically resembled behavioral heterogeneity and analytical generalizability in the synthetic data set used in the study. Table 1 Simulation Parameters and Configuration Settings. Simulation Parameter Configuration Total Number of Agents 200 agents Simulation Rounds per Agent 10 rounds Token Visibility Levels Low, Medium, High Perceived Token Value Levels Low, Medium, High Gamification Formats Fixed Rewards, Tiered Rewards Intrinsic Motivation Levels Low, Medium, High Eco-Identity Orientation Types Present, Absent Perceived Behavioral Control Index Range 0.0–1.0 (continuous scale) Redemption Threshold (Eco-Actions) 60% cumulative eco-action compliance Random Seed for Reproducibility Set for consistency across runs Behavioral outcomes resulting from the token conditions present an increasingly clear effect of incentive structure on participation and loyalty-related variables (Table 2 ). The mean number of eco-actions per round was higher for agents in the high-value token incentive (7.2) compared to the medium (5.9) and low (3.5) incentives. This increased participation led to a more frequent uptake of tokens and improved redemption rates. 79% of agents in the high-token group qualified for the specified cash redemption, compared to only 28% of agents in the low-token group. The mean satisfaction with the redemption experience also increased with the value of token redemption from 2.7 (low) to 4.5 (high) on a 5-point Likert scale, which also represented a greater intent to return related to satisfaction, in this case, mean loyalty intentions rated highest at 4.6 in the high tokens group. Table 2 Descriptive Statistics of Agent Behavior by Token Condition. Token Condition Avg. Token Uptake Rate Avg. Eco-Actions per Agent Redemption Success Rate (%) Avg. Satisfaction Score (1–5) Avg. Loyalty Intention (1–5) Low 0.42 3.5 28 2.7 2.8 Medium 0.68 5.9 56 3.9 4.0 High 0.83 7.2 79 4.5 4.6 Figure 3 illustrates these trends by demonstrating a clear upward trend in the average number of eco-actions per agent as both visibility and the value of the tokens increased. These findings provide preliminary confirmation of the directional assumptions listed in H1 and H2, providing a descriptive framework for the eventual testing of structural and moderated hypotheses. The tendency of agents to engage in more eco-actions with league tokens merely confirms the decision logic of the SOR-driven simulation. Examining the behavior dynamics within the S-O-R framework shows a logical flow from incentive exposure to loyalty-aligned outcomes. The simulation, as demonstrated in Fig. 4 , was justified to model this behavior stream organized to show that tokenized incentives/gamification logic (stimuli) initiated internal states (organism) like intrinsic motivation, perceived behavioral control, ecological identity salience, that led to some eco-tourism action, satisfaction with redeemed tokens, and stated loyalty intentions (response). The converging behavior trajectories suggest that agents behaved according to theoretically expected patterns, with the higher visibility and value of the tokens acting to influence active motivation and decision-making confidence. This flow reinforces the internal validity of the simulation as well as the theoretical strength of S-O-R for modeling decision outcomes. Additionally, this framework provides a conceptual basis for structural hypothesis testing as discussed in section 5.2 , where causal relationships are considered. As such, the simulation supported behavioral outcomes aligned with the S-O-R framework and offered a logical psychological flow from incentive design to sustainable eco-tourist behavior. 5.2 Token Effects and Psychological Modulation of Eco-Intentions The first stage of the causal analysis explored the direct predictive effects of the tokenized incentive elements, specifically token visibility (H1) and perceived value of the token (H2), on the outcome variables of eco-tourism engagement (Table 3 ). The findings for both predictor variables was statistically significant in relation to their dependent variables. token visibility produced a standardized beta coefficient β = 0.42 (p < .001) and was an important predictor of eco-intention, indicating prior exposure to the incentive mechanisms positively influences cognitive accessibility and the motivational salience of pro-environmental intentions. The token value produced a strong positive effect on eco-behavior (β = 0.57, p < .001), confirming the perceived functional usefulness of the token reward is a salient driver of behavioral activation. The R-squared values for both models were moderate (0.34 for intention and 0.41 for behavior), suggesting that tokenized incentive measures explain a fair amount of the variance in simulated decision-making outcomes. These findings provide empirical evidence for the theoretical rationale of the simulation that perceptual salience (H1) and value expectancy (H2) play an ontological role in converting incentive stimuli into sustainable behavioral responses. This stage confirms the stimulus–response processes in S-O-Rand serves as a preamble to investigating how internal, psychological traits may shape or mediate these relationships. Table 3 Regression results for H1 and H2. Predictor Variable Outcome Variable Beta Coefficient Standard Error p-Value R² Token Visibility (H1) Eco-Intention 0.42 0.06 < .001 0.34 Token Value (H2) Eco-Behavior 0.57 0.05 < .001 0.41 Phase two of the analysis explored how internal psychological characteristics could help moderate the association between tokenized incentives and eco-tourism engagement outcomes (Table 4 ). The allocation of PBC substantially moderated the association between incentive stimulation and eco-intention (β = 0.21, p < .01), whereby individuals with higher levels of volitional confidence demonstrated greater sensitivity to the incentives; highlighting the fact that when someone believes they can act sustainably, they are more likely to turn stimuli into a behavioral intention. Additionally, intrinsic motivation moderated the token incentive on sustained behavior, with a small, positive interaction (β = 0.26, p < .01), suggesting that active individuals motivated by intrinsic environmental values also exhibited eco-actions that demonstrated sustained behavior and affective reinforcement. A further mediation analysis identified that the frequency of eco-action played a central role in delineating the link between token exposure and loyalty intention. The statistically significant indirect effect of eco-action was 0.31 (p < .001), indicating that while this path is important, performing the behaviour repeatedly is crucial for sustaining longitudinal commitments. This analysis validates the "Organism-Response" level of the S-O-R framework, confirming that the intersectional practices of the agents' responses to incentives depend on the internal cognitive-affective state of the agent. Table 4 Moderation and Mediation results for H3-H5. Effect Type Predictor Moderator / Mediator Outcome Variable Interaction / Indirect Effect (β) p-Value Moderation (H3) Token Incentive × PBC Perceived Behavioral Control Eco-Intention 0.21 < .01 Moderation (H4) Token Incentive × Motivation Intrinsic Motivation Eco-Behavior 0.26 < .01 Mediation (H5) Token Incentive Eco-Action Frequency Loyalty Intention 0.31 < .001 To assess the final stage of the behavioral response cycle, a direct effect analysis was conducted to test whether satisfaction with the token redemption process predicts tourists’ loyalty intentions. As conceptualized in H6, this path represents an affective–behavioral linkage within the Response layer of the S-O-R framework. Table 5 outlines the outcome of the regression computations and shows that the regression model yielded a statistically significant direct effect (β = 0.52, p < 0.001); indicating that the more tourists perceive the token redemption process as being clear, rewarding, and enjoyable, the more likely they are to form intentions to revisit or remain loyal to a sustainable tourism platform. Therefore, this result further highlights the significance of emotional resolution and post-engagement evaluation, considering the endurance required for eco-tourism. The data empowers the emerging structural logic of the simulation by showing what constitutes meaningful affectual experiences, rather than simply symbolic exposure or the number of actions undertaken, as influential factors affecting loyalty-related outcomes in sustainability systems where the mediating environment is digital. Table 5 Direct Effect Analysis: Satisfaction as a Predictor of Loyalty Intention (H6). Effect Type Predictor Moderator / Mediator Outcome Variable Effect Size (β) p-Value Direct Effect (H6) Token Redemption Satisfaction – Loyalty Intention 0.52 < .001 5.3 Psychographic Clustering and Segment-Level Analysis Psychographic clustering was employed to assess whether distinct engagement profiles among simulated eco-tourists could be determined based on three organization-level traits: intrinsic motivation, eco-identity presence, and perceived behavioral control (PBC). Four clusters were retained based on the stability of convergence and usability of the profile (Table 6 ); Cluster 1 (High Resonance Seekers) had the highest motivation (0.89), strong PBC (0.83), and the near-universal presence of eco-identity (94%), suggesting the presence of a profile of intrinsically aligned and self-determining agents. Cluster 2 (Nostalgic Resonators) had similar high eco-identity (87%) as well as moderately high motivation (0.78), indicating reflective dealing due to some identity resonance as opposed to pure behavioral autonomy. Cluster 3 (Emotionally Neutral Responders) had middle-ranking scores on the three engagement positions and represents a cohort of simulated ecotourists with lesser psychological alignment to sustainability ambitions. Cluster 4 (Low Identity–Affect Alignment) had the lowest motivation score (0.43) and the lowest eco-identity presence (33%), suggesting limited affective or cognitive resonance with eco-tourism incentives. The three profiles outline how the underlying psychological configurations of simulated agents indicate the level to which agents are stratified prior to behavioral exposure, laying the groundwork for differential responses explored in the following paragraphs. Table 6 Cluster Profiles and Descriptive Characteristics. Cluster ID Cluster Label Motivation Level (0–1) Eco-Identity Presence (%) PBC Score (0–1) C1 High Resonance Seekers 0.89 94 0.83 C2 Nostalgic Resonators 0.78 87 0.74 C3 Emotionally Neutral Responders 0.51 55 0.61 C4 Low Identity–Affect Alignment 0.43 33 0.49 To determine how psychological profiles manifested in behavioral engagement, each cluster was analyzed based on three measures: frequency of eco-actions; token uptake rate, and redemption success (Fig. 5 ); clusters 1 and 2 had higher levels of engagement on all measures. High Resonance Seekers completed the highest number of eco-actions (14.2) and had the best overall token uptake (92%) and redemption rate (88%), in line with their high motivation and consistency of identity. Nostalgic Resonators were also engaged (13.1 eco-actions, 87% uptake), although their engagement was probably based more on emotional resonance than actional assertiveness. Cluster 3 was in the middle (with 9.8 eco-actions) and only slightly lower in uptakes, indicating a more detached relationship between their orientation to the stimuli. Cluster 4 exhibited the least engagement (7.4 eco-actions, 51% uptake, 47% success) arguably demonstrating behavior costs associated with low cognitive and affective engagement. These differences indicate that internal psychographic architecture not only influenced intention formation, but also influenced downstream implications with respect to incentive systems, thereby highlighting one potential strategic advantage of segment-specific personalization. In addition to the difference in behaviour engagement, the clusters also differed considerably in their affective engagement and loyalty formation (Fig. 6 ); C1 had the highest mean evaluation of satisfaction with the token redemption (4.6) and loyalty intention scores (4.5), suggesting a strong congruence of pre-existing psychological dispositions and incentive responses. Nostalgic Resonators had slightly lower satisfaction and loyalty rate scores of 4.3 and 4.2, respectively, indicating that, although they had somewhat lower autonomy relative to C1, perhaps emotional resonance will be sufficient to sustain future behavioural engagement commitment. C3 had a moderate amount of loyalty (3.5) and satisfaction (3.7), consistent with their relatively disengaged style of behaviour. In contrast, cluster 4 demonstrated the lowest satisfaction (3.1) and loyalty (2.9), indicating that while they may be affected, motivational resonance and identity transformation are critical for achieving positive affective and long-term engagement outcomes. Results highlight the utility of psychographic segmentation in predicting not only behaviour but also future attitudinal loyalty post-intervention. Strategic implications suggest creating future tokenized offerings not just to incite actions, but rather to layer meaning, emotional fortification, and value congruence in the eco-tourism journey. 5.4 Sensitivity Analysis and Simulation Robustness To test the robustness and generalizability of the simulation results, a sensitivity analysis was conducted in which five parameters were independently varied: token values, token visibility, intrinsic motivation weight, redemption threshold, and agent decision rounds. For each parameter, we varied one parameter at a time while holding the others at a constant value to examine the marginal effect of the parameter on the variables of eco-action frequency, token redemption satisfaction, and loyalty intention. The results of the sensitivity analysis are presented in Table 7 . The effects of token value and motivation weight denote the largest effects (26% and 22% increases, respectively). The changes in token visibility also produced substantial increases in behavior (+ 18%). However, moving the redemption threshold from 50–80% resulted in decreases in satisfaction (− 0.3) and loyalty (− 0.4) behavior, indicating the behavioral cost of having very restrictive reward standards. The sensitivity testing reinforced the point that observed relationships would not result solely from fixed input conditions, providing us with good evidence of patterns of non-arbitrary responses for reasonable incentive configurations. Table 7 Sensitivity Test Results by Parameter Set. Parameter Varied Change in Eco-Actions (%) Change in Satisfaction Score Change in Loyalty Intention Token Value (Low → High) + 26% + 0.7 + 0.6 Token Visibility (20% → 80%) + 18% + 0.4 + 0.5 Motivation Weight (0.3 → 0.8) + 22% + 0.6 + 0.5 Redemption Threshold (50% → 80%) −11% −0.3 −0.4 Rounds per Agent (5 → 15) + 9% + 0.2 + 0.3 The sensitivity analysis findings confirmed that the simulation exhibited both structural stability and directional consistency in a broad range of input settings, while providing strategic insights for parameter tuning. The segmented view provided in Fig. 7 separates the behavioral from the attitudinal responses. The top panel displays the percentage change in frequency of eco-actions, and the bottom panel shows the changes in satisfaction and loyalty intention scores. The token value had the largest impact, increasing eco-actions by 26% and satisfaction and loyalty by 0.7 and 0.6 points, respectively. The next most impactful parameter was motivation weighting, confirming the behavioral and affective acknowledgement that incentive motivation salience must align with intrinsic motivation tendency. Token visibility had a fairly ambivalent influence, but helped boost everything, acting as a consistent but modest lift across all metrics. However, the imposition of stricter redemption thresholds notably decreased both behavioral (− 11%) and attitudinal behavior (− 0.3 to − 0.4) due to over-reward friction. The segmented visualization suggests that purposeful change in a small number of attributes known to have high impacts—the salience of value and motivation—can have meaningful and impactful effects on eco-tourism in action, both for behavioral types and for loyalty-type actions. 6 Discussion and Implications 6.1 Cognitive–Affective Pathways in Eco-Engagement and Loyalty Formation The simulations provide strong support for the main premises of S.O.R framework, especially the use of external characteristics that influence an individual’s behavioural engagement. The visual appearance of tokens (H1) significantly increased eco-intention. The evidence suggests that behaviours perceived saliently (in this case, visual) help to activate sustainability-based decision-making. Visibility, transparency, and consubstantial token awards directed cognitive access and motivational relevance towards pre-behaviour planning. Similarly, token value (H2) was a significant factor in behavioural execution; higher-scaled incentives supported behavioural involvement and enhanced redemption activation. This aligns with the ecotourism literature on salience and perceived utility in decision-making (Farshbafiyan Hosseininezhad et al., 2025 ). This effect was highlighted in the simulations, as there were consistently higher action frequencies and loyalty scores as token tiers fluctuated, suggesting that incentives were intentionally designed to be both psychologically meaningful and that excessive salience can help convert users from normative behaviours towards the environment to engaged participants (Qiu et al., 2023 ). It can also be considered that the simulation supports the first stage of the S-O-R framework, as stimulus layer variables that are programmatically structured and intentional provide helpful behavioural change interventions within the context of gamified tourism studies (Boukis, 2024 ). In addition to the above direct effects, the model identified more complex organismic processes that mediated and moderated the effects of tokenized incentives. People who felt they controlled their actions (H3) were better at turning exposure to tokens into intentions, and those who felt more in control were more motivated, which helped boost their confidence in engaging with digital environments. Intrinsic motivation (H4) resulted in sustained behaviour, indicating that users who had internalized their values in the environment showed more consistent responses after rounds of exposure to pro-environmental behaviours. This suggests that effective tokenization is more interpersonal concerning social-psychosocial fit rather than structural design (Chan, 2024 ). The research additionally indicated that the frequency with which individuals act and re-engage in pro-environmental behaviours is an associative variable (H5), which supports our thesis on frequency equating normality over time leading to a habit of learnt exposure to a stimulus and loyalty intention (Frías-Jamilena et al., 2022 ). In addition, satisfaction with the redemption process (H6) was a strong predictor of future loyalty in terms of affect, with high satisfaction with the redemption process being positively linked to the intention to recommend/advice and intention to revisit (Boukis, 2024 ). This research confirms that sustainable behaviour in ecotourism is not only a matter of getting rewards; there are more intangible factors in the form of (1) cognitive, (2) affective, and (3) behavioural factors, each of which is reflected in the complex model that has six main hypotheses. 6.2 Theoretical, Methodological, and Practical Contributions This research introduces a new lens to view engagement behaviour in ecotourism by utilizing the framework of SDT, TPB and VBN within the S-O-R framework. SDT suggests that intrinsic motivation is more fundamental to understanding commitment than extrinsically driven motivations. TPB outlines the cognitive mechanisms for the development of behavioural intentions through perceived control, as well as attitudinal endorsement (Bhartiya et al., 2025 ). VBN provides an ethical and identity-based perspective. This perspective highlights the importance of tokenized incentives that foster alignment with ecological values. Tokenized incentives presented within a VBN framework foster long-term attitudinal loyalty (Landon et al., 2018 ). We do not view these dimensions in isolation; rather, they are operationalized into programmable agent logic that allows behaviours to emerge using realistic cognitive-affective traits. This simulation-ready configuration fits with the present trajectories within tourism scholarship that view the potential for blockchain technology to serve as a psychological infrastructure, allowing socio-psychological, value-coherent and ethically constituted digital interventions (Mountije et al., 2025 ). The conceptual contribution is reconceptualizing intention and loyalty as patterns, not states, altogether still mutable through iterations of feedback-driven engagement with gamified smart contracts (Yu et al., 2024 ). Methodologically, this research uses ABM to analyze how tokenized incentives behave across various psychographic characteristics in digitally mediated tourism systems. Smart contracts were configured as programmable logic cages, framing the reward value, the reward visibility, and the parameters of reward engagement to mimic the habitation structures of "real world" eco-incentives. This method allowed for the testing of behavioural pathways in a controlled, repeatable way without the ethical and logistical management issues that plague early-stage field implementation studies. In the context of the simulation architecture, the study could engage how an environmental characteristic operates, what user characteristics are and how much participation there is within the participation tier paradigm. The aforementioned methods intend to maximize analytical generalizability and maintain psychological realism. Furthermore, this is of interest to tourism developers and policy designers looking to incorporate behavioural reasoning into blockchain-based sustainability into sustainability modeling. The observable usefulness of these design patterns of tiered rewards, real-time redemption, and psycho-behavioural language provides scalable design patterns for creating loyalty and behavioural compliance, particularly as advancements continue in real-time payments whereby triggering incentives via blockchain transaction execution will be verifiable (Wang & Chan, 2025 ). 6.3 Limitations, Simulation Scope, and Future Research Directions Despite having a strong simulation-based platform for examining tokenized ecotourism behaviour, the research has several limitations related to its virtual experimental conditions. As agents’ behaviours were modelled based on predetermined parameters and assigned psychological weights that were theoretically grounded, it is acknowledged that human behaviour is often unpredictable, and this research does not intend to model actual tourist behaviour. The research has also used rationality as an abstraction for reward logic but did not include behaviours such as resistance to gamification, token fatigue, or ethical resistance to transactional participation. While we can ensure internal validity by profiling agents and applying the same rules, accepting the limitations of external validity is more challenging. The research allows for limited generalization and thus demonstrates the need for hybrid methodological frameworks that involve the precision of simulations with the richness of real-world behaviours. Future digital sustainability approaches must also incorporate systemic methods that align technology with user values and ecological outcomes (Rodrigues et al., 2023 ). Future research may expand on the simulation’s conceptual mapping by utilizing hybrid approaches consisting of empirical user studies, A/B testing on ecotourism platforms and participatory design with actual travelers. One promising avenue is the concept of ‘digital twins,’ which combine real-usage data with simulated environments to provide a richer understanding of behavioural variability and dynamic feedback. Alternatively, one option would be to enhance the agent's architecture further by integrating social identity factors, peer influence structures and cultural significance of incentives and be able to capture the extent to which social norms and local values condition responsiveness to tokenized incentives. Personalization algorithms can also be integrated into the smart contract logic to allow both adaptive gamification and dynamic distributions of tokens. In the future, researchers should also consider adding biometric and affective feedback (i.e., using wearables) to the models to increase their knowledge about emotional gratification and engagement. The future direction should be to combine the accuracy of simulation outputs with the complexity of behaviour—to produce decision models that are both computationally robust and contextually empathetic. This empirical calibration approach has the added benefit of building upon theoretical alignment and providing legitimacy for the transition from simulation rigor to real-life studies. Declarations Author Contribution M.H. and H.H. contributed equally to the writing of this manuscript. M.H. formulated the theoretical framework and wrote the main body of the manuscript (Section 3: Conceptual Framework, 4: Methodology, and 5: Data Analysis and Results). H.H. wrote the blockchain rules and simulation model architecture based on the smart contracts. The authors wrote the simulation logic, Introduction and Literature Review sections together, collaborated on the results analysis and wrote Section 6 (Discussion and Implications). All authors approved the final draft of the manuscript. 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Lecture Notes in Computer Science, vol 15768. Springer, Cham. https://doi.org/10.1007/978-3-031-93845-0_6 Yu, J. J., Hu, J. J., Jiang, W., & Walters, G. (2024). Not just a game: Understanding eco-gamification in sustainable destination development. Journal of Hospitality and Tourism Management, 60, 10-21. https://doi.org/10.1016/j.jhtm.2024.06.005 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. 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13:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6939486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6939486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85844287,"identity":"97c0dd8a-cbdb-4664-9a6c-b37240262577","added_by":"auto","created_at":"2025-07-02 09:27:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190209,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation model of tokenized smart contracts driving eco-tourism engagement and loyalty outcomes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/081912825bcddb7f816c733a.png"},{"id":85845073,"identity":"886991a0-a47d-4faa-8d08-3efb1124542b","added_by":"auto","created_at":"2025-07-02 09:35:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137472,"visible":true,"origin":"","legend":"\u003cp\u003eValidation loop linking simulation outputs to empirical eco-tourism behavior patterns.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/bae355d30ccf47f24531d2e8.png"},{"id":85845072,"identity":"18a35d93-2021-404c-87e4-de8235954c3d","added_by":"auto","created_at":"2025-07-02 09:35:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17994,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Eco-Actions per Agent by Token Condition.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/e2c8b6637236434e65de0eaa.png"},{"id":85844290,"identity":"853abc6c-9a34-4d42-8dfb-2aa979b140d5","added_by":"auto","created_at":"2025-07-02 09:27:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11207,"visible":true,"origin":"","legend":"\u003cp\u003eSOR-Based Behavioral Flow.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/d396c6e7dd093ed4b0f991f5.png"},{"id":85844292,"identity":"66d9b194-7f9c-4ae7-8a9e-d1e94f66f483","added_by":"auto","created_at":"2025-07-02 09:27:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105987,"visible":true,"origin":"","legend":"\u003cp\u003eBehavioral Engagement Metrics by Cluster.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/597e61d1ee00e7ba1cb95945.png"},{"id":85845074,"identity":"4d14a14d-c2cb-4451-915c-efeea1a14716","added_by":"auto","created_at":"2025-07-02 09:35:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99322,"visible":true,"origin":"","legend":"\u003cp\u003eLoyalty ad Satisfaction by Cluster.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/ec968a93a58b01bf5e54ff09.png"},{"id":85844293,"identity":"fcb4ad8e-59bf-48fc-8436-fa2364bf4588","added_by":"auto","created_at":"2025-07-02 09:27:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":146172,"visible":true,"origin":"","legend":"\u003cp\u003eOutcome Variability by Simulation Parameter.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/49b6ab85b229376d1c719a8e.png"},{"id":92381631,"identity":"7ff5a4af-3fce-4b18-b652-705982ecc79f","added_by":"auto","created_at":"2025-09-29 06:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1845885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6939486/v1/dc107cde-57a6-44c9-936d-7c07de75d1c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tokenized Incentives and Loyalty: Leveraging Blockchain-Based Smart Contracts for Enhancing Eco-Tourism Behavioral Engagement","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAlthough awareness of sustainability is a significant component of engaging in eco-tourism marketing, an established intention\u0026ndash;action gap persists. Travelers tend to advocate for principles of eco-friendliness but infrequently feel the need to intervene based on those factors, indicating that conventional pre-engagement strategies, such as leveraging environmental certification or information cues (Viglia \u0026amp; Acuti, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nieto-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), are often ineffective. These approaches might be useful in activating cognitive awareness, but almost always fail to maintain commitment or create emotional motivation for behavioral choices. Without timely prompting or a perceived meaningful reward, moral calls are abstract, distant, and easily ignored when it comes to travel decisions (Farshbafiyan Hosseininezhad et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This scenario presents a challenge for tourism researchers and designers to explore different models of engagement, particularly those more in line with the behaviors of digital native travelers, which are being shaped increasingly each day due to feedback from algorithms, experiential interfaces, and urgency driven by eco-anxiety (Mosca et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, sustainability needs to pivot away from traditional models of activism, which rely on passive information dissemination, to ones that have emotional resonance, are behaviorally responsive, and are digitally mediated.\u003c/p\u003e \u003cp\u003eThe foremost challenge to providing sustainable tourism is the engagement of systems that motivate certain behaviors in travelers but instill an enduring cognitive arrangement (Ahmed \u0026amp; Alzoubi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Traditional loyalty programs are characterized by a lack of personalization, immediacy, and psychological relevance to the traveler's motivational profile. They may induce a temporary form of compliance or may even induce non-compliance; nevertheless, those systems tend not to create lasting intention, stated awareness, or maintained commitment to eco-mindsets. This becomes especially true when incentives seem generalized and delayed, a consistent shortcoming (Koo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, legacy systems tend to be centralized and opaque, which erodes user trust (Barclay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While all of these issues may be resolved through digital means, any engagement program must now offer both psychological adaptability and technological support as an incentive for participation. Therefore, an effective engagement system should engage a user in a way that creates a reward for the user while additionally changing the cognitive and emotional feedback loops directly associated with that engagement (Rodrigues et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This engagement system will also require creating incentive mechanisms that can be programmed, transparent, and responsive to behaviors and individual user profiles in real-time scenarios in the moment context; this is a change from everyday loyalty programs that are static, to something much simpler and more usable when enabled by a catalytic digital architecture that is dependent on context and users (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlockchain-enabled smart contracts offer new opportunities for addressing the limitations of traditional incentive systems (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the context of eco-tourism. Their approach provides a fully automated, rules-based mechanism for extracting verifiable and transparent tokens that utilize eco-behaviors in real-time (Barclay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ultimately, if the contracts are executed with gamified mechanics\u0026mdash;such as trophy badges, progression tiers, or reputation rewards\u0026mdash;the contracts could change the framing of sustainability from a commitment to an opportunity that balances feedback and autonomy (Pasca et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alongside the immutable transaction ledger of blockchain, user-controlled identity systems provide greater trust and autonomy compared to centralized systems (Fotiou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, in real-world contexts, these platforms can facilitate dynamic, decentralized engagement representations when combined with eco-rewards personalization based on psychographic profiles (Hosseinalibeiki \u0026amp; Heidari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The synergy of programmable logic and emotionally engaging design demonstrates a shift\u0026mdash;the concept of sustainability is absorbed into the decision journeys of tourists. Apart from transactional efficiency, accountability, and trust, the platforms provide novel, possible avenues\u0026mdash;though we will need further exploration as part of the line of inquiry\u0026mdash;to think about ethics, identity, and interactivity indicative of smart tourism system (Lim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mountije et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the theory behind blockchain technology and gamified incentives is extremely interesting, the possible practical testing of ideas is limited by several factors, including development and deployment costs, ethical dilemmas, and personal differences (Chica et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For this reason, simulation data now serves as a valid avenue for hypothesis testing. ABM, situated well within the S-O-R theoretical framework provides a dynamic modeling process to depict behavioral interactions with incentive stimuli and individual psychological characteristics over time (Wallinger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A simulation platform can measure how various constructs, such as motivation, perceived control, or identity in environments tokenized in an ABM, with three pieces of agent logic, Self-Determination Theory (SDT), Theory of Planned Behavior (TPB), and Value-Belief-Norm (VBN) embedded in the behavioral agent, will influence behavior (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This simulation involves 200 agents with distinct psychographic profiles to observe tokenized incentives, eco-engagement, satisfaction, and loyalty in a controlled environment. As a result, this provides a theoretically concrete architecture that provides reproducible, behaviorally valid data. In summary, this work introduces a scalable framework for future digital systems that promote sustainability, validating that programmable, psychologically informed systems can provide a bridge from intention to action in eco-tourism, as well as a framework for platform architects and policy innovators.\u003c/p\u003e"},{"header":"2 Behavioral and Technological Foundations of Tokenized Eco-Engagement","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Smart Contracts and Blockchain Incentives in Tourism\u003c/h2\u003e \u003cp\u003eBlockchain technology has evolved from being a back-end operational efficiency solution to serving as a significant mechanism for sustainable governance in the tourism sector (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Blockchain initially focused on use cases such as secure bookings and decentralized payments, then shifted to creating a method for authenticating sustainable behavior through traceable supply chains and digital certifications (Fotiou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In eco-tourism, blockchain's impact will be in its immutability in recording behavior and real-time verification of low-impact behaviours, such as carbon-neutral travel and eco-lodge stays (Rodrigues et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As payment ecosystems evolve, blockchain platforms also enhance the form of value exchange, while supporting the characteristics of transparency, interoperability, and user control (Wang \u0026amp; Chan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research on blockchain in tourist activities has affirmed that perceived utility and usability impact actual systems readiness (Corne et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Blockchain can be viewed as more than a technology enabler and upgrade, but also as a governance facilitator for decentralized user-generated activity, as it grounds eco-tracking actions in a virtually immutable way (Thanasi-Bo\u0026ccedil;e \u0026amp; Hoxha, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this environment, smart contracts automate the transaction process, where actions such as conservation or community service are vetted and delivered condition-based external incentives, such as the replacement of travel credits (Barclay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe merging of smart contracts with tokenized incentives represents a significant change from static incentives to dynamic behavior manipulation. In this regard, users expect rewards in the form of tokens that double as embedded nudges to shape their behavior according to predetermined logic and provide timely feedback (Lim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Encouraging sustainable behavior by having users expect rewards is more effective than traditional ex post validation and promotes sustainable behavior using psychological principles of resonance (Pasca et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Smart contracts (underpinned by behavioral economics) offer personalized micro-incentives resulting from user behavioral history, autonomy, and trust (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hosseinalibeiki \u0026amp; Heidari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to the inherently customizable nature of the blockchain for identity platform-based applications, it not only improves the potential use of incentives but also enhances user engagement. Moreover, they operate in a progressive form of tourism finance, where payments and other aspects become more adaptive, traceable and ethically governed (Wang \u0026amp; Chan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result of the re-envisioned structure of governance plans, there are implications for sustainability policies, one being the shift of design authority away from centralized systems, and another being a shift towards decentralized rationality through agent-mediated systems (Thanasi-Bo\u0026ccedil;e and Hoxha, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Gamified Token Systems as Behavioral Drivers in Eco-Tourism\u003c/h2\u003e \u003cp\u003eGamification has been a strong enabler for pro-environmental behaviour in tourism, transforming intangible sustainability aspirations into interactive, emotionally engaging experiences (Pradhan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Gamification systems implement game mechanics, such as points, badges, milestones, social comparison, and instant feedback, to create cognitive and emotional engagement, leading to action-based engagement for eco-conscious behavior (Abou Kamar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is beneficial to establish a sustained mode of behavior that can lead to eco-tourism, which is often limited and episodic, through regular behaviours that encourage participation and eco-loyalty (Sesliokuyucu \u0026amp; Cobanoglu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, token-based resources are implementing gamification in a system of practice with tangible rewards for metrics-based actions (e.g., conservation actions, sustainable transport actions), allowing the habit of sustainability to develop further into the practitioner (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Token systems are developed through reinforcement learning systems, which use tiered reward logic and scalable value, implemented with sustainable habits in mind (Yu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When token systems are implemented on blockchain technology, along with smart contracts, the reward process becomes automated and transparent, while concurrently optimizing trust in the aforementioned platforms and reducing administrative work (Wang \u0026amp; Huang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Psychological Drivers: Motivation, Control, and Eco-Identity\u003c/h2\u003e \u003cp\u003eIntrinsic motivation, based on Self-Determination Theory (SDT), provides a broader psychological foundation for the persistence of pro-environmental behavior in eco-tourism (Patwary et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The more intrinsic beliefs tourists place on their eco-behaviour (e.g., time spent at eco-lodges, willingness to donate time and resources to conservation altruistically), the more likely they are to behave ecologically. Extrinsically constructed tools, such as gamified incentives, branded symbolic gifts, or voluntary continuous feedback loops, will not diminish intrinsic initiatives (Bhartiya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Still, these external tools can help stimulate self-actualization regarding eco-engagement. Digital systems developed with SDT principles, which emphasize autonomy and personal relevance, will leverage these benefits and ultimately promote long-term eco-engagement. In simulated environments, intrinsic motivation will act as a stable cognitive characteristic, such that the cognitive response to environmental factors is largely unaffected by the immediacy of additional or extrinsic rewards (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The cognitive responsibility is increased when systems reflect one\u0026rsquo;s value systems, thereby enhancing the emotionally inherent coherence between individual identity and sustainable behaviors (Sch\u0026ouml;nherr \u0026amp; Pikkemaat, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eco-identity will likely magnify this response even further\u0026mdash;those who internalize a sense of responsibility for the environment as part of their self-identity will respond more favorably to symbolic incentives, thus creating behavioral loyalty and increased satisfaction with eco-friendly tourism choice.\u003c/p\u003e \u003cp\u003ePerceived behavioral control (PBC), drawn from the Theory of Planned Behavior, further strengthens this motivational foundation by influencing individuals\u0026rsquo; belief in their capacity to act sustainably (Abou Kamar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In eco-tourism, PBC encompasses both perceived knowledge and ease of executing low-impact actions\u0026mdash;elements that correlate strongly with behavioral intention and follow-through. Simulated agent-based models validate this: users with high PBC react more decisively to eco-stimuli and maintain behavioral coherence across repeated decisions (Bhartiya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This control is dynamic and can be actively manipulated through good design, supportive feedback, and ease of use (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When gamified systems engage users across all three psychological processes (motivation, identity, and control), a cyclical engagement and affective loyalty begins. Users feel supported by a psychologically consistent system that helps them to feel in control, be in alignment, and be affectively committed to sustainable choices. These psychological drivers need to be considered as interrelated systems for tourism systems that encourage long-term behavior. These processes work dynamically and can influence the parameters of meaningful, sustainable ecological engagement (Sch\u0026ouml;nherr \u0026amp; Pikkemaat, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Simulating Eco-Behavioral Dynamics through Tokenized Incentives","content":"\u003cp\u003e\u003cstrong\u003e3.1 Theoretical Basis for Gamified Eco-Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptual framework for this research comprises four complementary theories that demonstrate how blockchain-based token incentives interact with ecotourist behaviors. Self-Determination Theory (SDT), which provides a framework for explaining how tokenized rewards can either positively influence environmentally friendly choices by creating a sense of autonomy or negatively, by interfering with autonomy if the token incentive is perceived as controlling (Patwary et al., 2024). The Theory of Planned Behaviour (TPB) offers an additional rational dimension, with the roles of attitude, subjective norms, and perceived behavioural control as predictors of eco-intention, which are amenable to subtle influence by programmable incentives through automation and feedback (Nguyen et al., 2023). Gamification Theory enhances this perspective by demonstrating how digital game effects \u0026ndash; including badges, points, or incentivization tiers \u0026ndash; can enhance engagement through the enactment of structured, psychologically salient experiences (Sesliokuyucu \u0026amp; Cobanoglu, 2025). Additionally, the VBN Theory provides a framework for the ethical assumptions underlying eco-behaviour. When token systems have stable underpinning environmental values, they can foster and reinforce norm-consistent behaviours and promote long-lasting loyalty (Landon et al., 2018; Park et al., 2022). All four theories yield a stacked paradigm in examining cognition, affect, and ethics within a tourism ecosystem structured by smart contracts, which can also be enhanced by transitioning to ABM to facilitate the modelling of behavioural changes through a dynamic multi-agent system (Wallinger et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Stimulus\u0026ndash;Organism\u0026ndash;Response (SOR) Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS-O-R framework acts as a critical translational vehicle within the research, bridging theory to simulation modeling to the logic of hypothesis. In this setting, stimuli are tokenized incentives, delivered as blockchain smart contracts, with differences in visibility, value, and gamification structure. These act as the initial input, triggering internal organismic states, including motivation activation, perceived behavioral control, and eco-affective appraisal (Qiu et al., 2023). These organism-level reactions are modeled in agents\u0026rsquo; cognitive-affective parameters and are empirically mirrored in moderating and mediating variables such as intrinsic motivation and action frequency. The final response phase encompasses the behavioural outcomes: intention, eco-action frequency, satisfaction with token redemption, and loyalty following long-term engagement. This three-pronged approach aligns with the six hypotheses, the input-output logic of the simulation, and a consistent interpretation of behavioral clustering across the results. Furthermore, the S-O-R framework provides a structure to the agent-based model; each agent receives inputs, processes these through its states, and produces observable behaviors (Baktash et al., 2023). In the analysis phase, S-O-R also aids with interpreting differences in behavioural engagement across profiles\u0026mdash;an essential analytic pillar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Translating Cognitive Models into Smart Contract Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo operationalize the multidimensional theoretical integration and S-O-R framework, this study designed a simulation-ready behavioral architecture that integrates psychological modeling with programmable smart contract logic. Incentive visibility was a strong moderator where smart contracts accounted for social-visible gamification (Figure 1). When tourists recognize the relevant token-linked behavior, eco-intentionality will continue to grow based on expectancy disconfirmation or peer signaling (Choirisa et al, 2025). Transparency in token design also promotes behavioral salience and can motivate a participant to adopt sustainable behaviors even before they receive any direct benefit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e \u003cem\u003eToken visibility significantly increases tourists\u0026apos; intentions to perform eco-friendly behaviors.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBeyond mere awareness, the perceived value of the token itself can influence actual behavior. If tourists believed that the tokens earned offer a form of utility in their own right, whether accounting for benefits that can be redeemed, reputation, or social status, it\u0026apos;s more likely that they will be incentivized to engage in eco-tourism behaviour. Perceived value triggers both extrinsic motivation and evaluation of cognitive cost-benefit, ultimately increasing the intention-action link (Choirisa et al., 2025). In our simulation, we parameterized the perceived token value through smart contract logic and examined how agent behavior changes under different incentive values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e \u003cem\u003eHigher perceived token value positively influences tourists\u0026apos; actual engagement in eco-tourism activities.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe impact of incentives is not homogeneous, as it varies based on each person\u0026apos;s sense of controllability. Perceived behavioral control\u0026mdash;a central concept within the Theory of Planned Behavior\u0026mdash;can increase the motivational impact of tokens; if a person is confident about their ability to act sustainably, e.g., they possess time, access, and knowledge, then tokens can reinforce motivation, as opposed to simply bringing attention to a prompt (Akter \u0026amp; Hasan, 2023). This moderation is modeled in the agents with internal PBC weights, which determine how the agent responds to decision points created by incentives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e \u003cem\u003ePerceived behavioral control strengthens the relationship between token incentives and eco-behavioral intentions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlong with perceived control, the quality of the motivation is also relevant. According to SDT, individuals with a high level of intrinsic motivation towards caring for the environment may be more reliably engaged in sustainable behaviors, particularly if tokenized incentives align with their values. Rather than undermining tourists\u0026rsquo; autonomy, this alignment may reinforce continued engagement through their sense of internal satisfaction and in realizing mutual goals (Patwary et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e \u003cem\u003eIntrinsic motivation moderates the relationship between token incentives and sustained eco-tourism engagement.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBehavioural change is not an event but a process of repetition and habitual behaviour. In ecotourism, the frequency of tourist behavior mediates the course from a one-time experience to loyalty. Tourists who engage in high-frequency interactions are more likely to form habitual behaviors, which develop familiarity (Habits and Routines) and become additional behavioral contexts for positive brand experiences and intentions. For developmental continuity in a behavioural context, action frequency serves as the behavioural bridge between initial tourism incentives for visitation and long-term destination commitment (Fr\u0026iacute;as-Jamilena et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5:\u003c/strong\u003e \u003cem\u003eFrequency of performed eco-tourism actions mediates the relationship between token-based incentives and tourists\u0026apos; loyalty intentions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLoyalty is dependent not only on frequency of behaviour but also on affective satisfaction and, in particular, affective satisfaction with the experience of token redemption. Should the processes of the claims and subsequent use of tokens be effortless, transparent, and rewarding, tourists are more likely to form intentions to return to the destination and recommend it to others. Satisfaction can be considered as a reward cycle, and this feeling of attachment is important in a gamified approach (Choirisa et al., 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6:\u003c/strong\u003e \u003cem\u003eSatisfaction with the token redemption process significantly predicts tourists\u0026rsquo; future loyalty and revisit behaviors.\u003c/em\u003e\u003c/p\u003e"},{"header":"4 Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Simulation Architecture and Token Stimulus Logic\u003c/h2\u003e \u003cp\u003eThis study employed a simulation-based experimental design, an application of ABM, to investigate whether and how tokenized smart contract incentives influence eco-tourist involvement within evolving psychographic and contextual contexts. The study generated 200 synthetic agents (N\u0026thinsp;=\u0026thinsp;200), designed to represent a hypothetical eco-tourist, and created psychological profiles of each agent that consisted of three primary psychological characteristics: intrinsic motivation levels (low, medium, high), eco-identity orientation (present or absent), and perceived behavioral control (a continuous scale from 0.0 to 1.0). The psychological profiles were based on SDT, TPB, Gamification Theory, and the VBN Theory as the underlying systems of the S-O-R framework. In the simulation, agents completed eco-tourism decision-making tasks across 10 sequential rounds that included exposure to different incentive stimuli: token visibility (low to high), token value (low to high), and gamification format (fixed vs. tiered rewards based on cumulative eco-action frequency). Motivational triggers, which are grounded in the recognition of and consideration for environmental stimuli by the agents, were equivalent to simulating world decision-making. While we did not acquire in-situ behavior data from real human participants, the simulation produced behavior data outputs with a predefined level of resolution, due to the modeling parameters: fixed token thresholds, eligibility to redeem with limits, and a constant random seed to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Agent Profiling, Variable Mapping, and Data Structuring\u003c/h2\u003e \u003cp\u003eThe simulation generated a multi-structured dataset that recorded agent behavior across all profiles, incentive conditions, and rounds of decision-making. These data were organized using the S-O-R framework, which extracted stimulus-level data (token visibility, perceived value, and gamification format), organism-level data (internal intrinsic motivation, eco-identity, and perceived behavioral control), and response-level data (eco-action frequency, token redemption, satisfaction, and loyalty intention). While the data were synthetic, they were grounded in a transparent rule-based logic that modeled realistic behavior through time-series logging at both the agent and round levels. This enabled profile-specific tracking and aggregation while ensuring reproducibility. The analysis was performed in a four-step process: (1) descriptive statistics to provide summary statistics of agent behavior and imitation of the realistic aspects of simulation; (2) regression and path modeling to evaluate main effects (H1-H2) and mediation and moderation theories (H3-H6); (3) psychographic clustering to demonstrate patterns of behavior brought on by motivational identity or contact with motivational forces; and (4) sensitivity analysis by changing simulation input parameters (incentive thresholds, motivation weight) to assess robustness of the simulation. All of these methods together contribute to a precise examination of the impact of token-based micro-incentives on the behavior of simulated eco-tourists.\u003c/p\u003e \u003cp\u003eAlthough based on simulation, this study's behavioral architecture is well-established by empirically grounded psychological theories that lend cognitive and affective verisimilitude to the agent-based logic, ensuring that the simulated behaviors follow pathways typically observed in sustainable tourism environments. The model predictions reflected the empirical findings, which showed that gamified stimuli impacted eco-actions, consistent with the cue-based behavior of Fr\u0026iacute;as-Jamilena et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, the moderators of intrinsic motivation and perceived behavioural control identified by Patwary et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) can be found in the study's agent clusters and interaction models. The loyalty effect also aligned with Boukis's (2024) findings regarding reward satisfaction and backs the attitudinal outputs of the simulations. While the model was not developed to mirror a single empirical context, it was designed for theoretical consistency and behavioural plausibility. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e diagrammatically models the simulation-empirical alignment and proposed validation loop.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Data analysis and results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Simulation Output and Behavioral Overview\u003c/h2\u003e \u003cp\u003eTo develop a controlled simulation that is valid and replicable, 200 agents (N\u0026thinsp;=\u0026thinsp;200) were instantiated in an agent-based model to demonstrate decision making in an eco-tourism context. The agents made decisions in 10 decision rounds that were exposed to tokenized incentive conditions structured by programmable smart contract logic. Incentive conditions were presented under various conditions, including three levels of token visibility (low, medium, high), three levels of perceived token value (low, medium, high), and two competing versions of gamification logic (fixed reward vs. tiered incentive). Agent profiles were algorithmically categorized based on intrinsic motivation (low, medium, high), presence of eco-identity (positive or negative), and perceived behavioral control (indexed between 0.0 and 1.0). All the various offers of incentive triggers, behavioral thresholds, and environmental conditions were consistently, validly, and reliably presented through a pre-arranged logic of parametric control, ensuring internal consistency and replicability. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the simulation constructs embody a factorial approach that aims to represent combinations of psychological and structural features with respect to the variables and conditions of the S-O-R framework and the theoretical model. In this way, the study examined six structured hypotheses regarding the combinations of internal cognitive\u0026ndash;affective profiles and external intensities of incentives, which logically resembled behavioral heterogeneity and analytical generalizability in the synthetic data set used in the study.\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\u003eSimulation Parameters and Configuration Settings.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Number of Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 agents\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation Rounds per Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 rounds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Visibility Levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow, Medium, High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Token Value Levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow, Medium, High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamification Formats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Rewards, Tiered Rewards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrinsic Motivation Levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow, Medium, High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Identity Orientation Types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent, Absent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control Index Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u0026ndash;1.0 (continuous scale)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRedemption Threshold (Eco-Actions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60% cumulative eco-action compliance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Seed for Reproducibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSet for consistency across runs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBehavioral outcomes resulting from the token conditions present an increasingly clear effect of incentive structure on participation and loyalty-related variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean number of eco-actions per round was higher for agents in the high-value token incentive (7.2) compared to the medium (5.9) and low (3.5) incentives. This increased participation led to a more frequent uptake of tokens and improved redemption rates. 79% of agents in the high-token group qualified for the specified cash redemption, compared to only 28% of agents in the low-token group. The mean satisfaction with the redemption experience also increased with the value of token redemption from 2.7 (low) to 4.5 (high) on a 5-point Likert scale, which also represented a greater intent to return related to satisfaction, in this case, mean loyalty intentions rated highest at 4.6 in the high tokens group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Agent Behavior by Token Condition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvg. Token Uptake Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvg. Eco-Actions per Agent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRedemption Success Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvg. Satisfaction Score (1\u0026ndash;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAvg. Loyalty Intention (1\u0026ndash;5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates these trends by demonstrating a clear upward trend in the average number of eco-actions per agent as both visibility and the value of the tokens increased. These findings provide preliminary confirmation of the directional assumptions listed in H1 and H2, providing a descriptive framework for the eventual testing of structural and moderated hypotheses. The tendency of agents to engage in more eco-actions with league tokens merely confirms the decision logic of the SOR-driven simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamining the behavior dynamics within the S-O-R framework shows a logical flow from incentive exposure to loyalty-aligned outcomes. The simulation, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, was justified to model this behavior stream organized to show that tokenized incentives/gamification logic (stimuli) initiated internal states (organism) like intrinsic motivation, perceived behavioral control, ecological identity salience, that led to some eco-tourism action, satisfaction with redeemed tokens, and stated loyalty intentions (response). The converging behavior trajectories suggest that agents behaved according to theoretically expected patterns, with the higher visibility and value of the tokens acting to influence active motivation and decision-making confidence. This flow reinforces the internal validity of the simulation as well as the theoretical strength of S-O-R for modeling decision outcomes. Additionally, this framework provides a conceptual basis for structural hypothesis testing as discussed in section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5.2\u003c/span\u003e, where causal relationships are considered. As such, the simulation supported behavioral outcomes aligned with the S-O-R framework and offered a logical psychological flow from incentive design to sustainable eco-tourist behavior.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Token Effects and Psychological Modulation of Eco-Intentions\u003c/h2\u003e \u003cp\u003eThe first stage of the causal analysis explored the direct predictive effects of the tokenized incentive elements, specifically token visibility (H1) and perceived value of the token (H2), on the outcome variables of eco-tourism engagement (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The findings for both predictor variables was statistically significant in relation to their dependent variables. token visibility produced a standardized beta coefficient β\u0026thinsp;=\u0026thinsp;0.42 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and was an important predictor of eco-intention, indicating prior exposure to the incentive mechanisms positively influences cognitive accessibility and the motivational salience of pro-environmental intentions. The token value produced a strong positive effect on eco-behavior (β\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming the perceived functional usefulness of the token reward is a salient driver of behavioral activation. The R-squared values for both models were moderate (0.34 for intention and 0.41 for behavior), suggesting that tokenized incentive measures explain a fair amount of the variance in simulated decision-making outcomes. These findings provide empirical evidence for the theoretical rationale of the simulation that perceptual salience (H1) and value expectancy (H2) play an ontological role in converting incentive stimuli into sustainable behavioral responses. This stage confirms the stimulus\u0026ndash;response processes in S-O-Rand serves as a preamble to investigating how internal, psychological traits may shape or mediate these relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for H1 and H2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Visibility (H1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEco-Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Value (H2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEco-Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePhase two of the analysis explored how internal psychological characteristics could help moderate the association between tokenized incentives and eco-tourism engagement outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The allocation of PBC substantially moderated the association between incentive stimulation and eco-intention (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), whereby individuals with higher levels of volitional confidence demonstrated greater sensitivity to the incentives; highlighting the fact that when someone believes they can act sustainably, they are more likely to turn stimuli into a behavioral intention. Additionally, intrinsic motivation moderated the token incentive on sustained behavior, with a small, positive interaction (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), suggesting that active individuals motivated by intrinsic environmental values also exhibited eco-actions that demonstrated sustained behavior and affective reinforcement. A further mediation analysis identified that the frequency of eco-action played a central role in delineating the link between token exposure and loyalty intention. The statistically significant indirect effect of eco-action was 0.31 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that while this path is important, performing the behaviour repeatedly is crucial for sustaining longitudinal commitments. This analysis validates the \"Organism-Response\" level of the S-O-R framework, confirming that the intersectional practices of the agents' responses to incentives depend on the internal cognitive-affective state of the agent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModeration and Mediation results for H3-H5.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerator / Mediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutcome Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInteraction / Indirect Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModeration (H3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToken Incentive \u0026times; PBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived Behavioral Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEco-Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModeration (H4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToken Incentive \u0026times; Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntrinsic Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEco-Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediation (H5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToken Incentive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEco-Action Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoyalty Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo assess the final stage of the behavioral response cycle, a direct effect analysis was conducted to test whether satisfaction with the token redemption process predicts tourists\u0026rsquo; loyalty intentions. As conceptualized in H6, this path represents an affective\u0026ndash;behavioral linkage within the Response layer of the S-O-R framework. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e outlines the outcome of the regression computations and shows that the regression model yielded a statistically significant direct effect (β\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); indicating that the more tourists perceive the token redemption process as being clear, rewarding, and enjoyable, the more likely they are to form intentions to revisit or remain loyal to a sustainable tourism platform. Therefore, this result further highlights the significance of emotional resolution and post-engagement evaluation, considering the endurance required for eco-tourism. The data empowers the emerging structural logic of the simulation by showing what constitutes meaningful affectual experiences, rather than simply symbolic exposure or the number of actions undertaken, as influential factors affecting loyalty-related outcomes in sustainability systems where the mediating environment is digital.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect Effect Analysis: Satisfaction as a Predictor of Loyalty Intention (H6).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerator / Mediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutcome Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect (H6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToken Redemption Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoyalty Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Psychographic Clustering and Segment-Level Analysis\u003c/h2\u003e \u003cp\u003ePsychographic clustering was employed to assess whether distinct engagement profiles among simulated eco-tourists could be determined based on three organization-level traits: intrinsic motivation, eco-identity presence, and perceived behavioral control (PBC). Four clusters were retained based on the stability of convergence and usability of the profile (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e); Cluster 1 (High Resonance Seekers) had the highest motivation (0.89), strong PBC (0.83), and the near-universal presence of eco-identity (94%), suggesting the presence of a profile of intrinsically aligned and self-determining agents. Cluster 2 (Nostalgic Resonators) had similar high eco-identity (87%) as well as moderately high motivation (0.78), indicating reflective dealing due to some identity resonance as opposed to pure behavioral autonomy. Cluster 3 (Emotionally Neutral Responders) had middle-ranking scores on the three engagement positions and represents a cohort of simulated ecotourists with lesser psychological alignment to sustainability ambitions. Cluster 4 (Low Identity\u0026ndash;Affect Alignment) had the lowest motivation score (0.43) and the lowest eco-identity presence (33%), suggesting limited affective or cognitive resonance with eco-tourism incentives. The three profiles outline how the underlying psychological configurations of simulated agents indicate the level to which agents are stratified prior to behavioral exposure, laying the groundwork for differential responses explored in the following paragraphs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster Profiles and Descriptive Characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMotivation Level (0\u0026ndash;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEco-Identity Presence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePBC Score (0\u0026ndash;1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Resonance Seekers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNostalgic Resonators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmotionally Neutral Responders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Identity\u0026ndash;Affect Alignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo determine how psychological profiles manifested in behavioral engagement, each cluster was analyzed based on three measures: frequency of eco-actions; token uptake rate, and redemption success (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e); clusters 1 and 2 had higher levels of engagement on all measures. High Resonance Seekers completed the highest number of eco-actions (14.2) and had the best overall token uptake (92%) and redemption rate (88%), in line with their high motivation and consistency of identity. Nostalgic Resonators were also engaged (13.1 eco-actions, 87% uptake), although their engagement was probably based more on emotional resonance than actional assertiveness. Cluster 3 was in the middle (with 9.8 eco-actions) and only slightly lower in uptakes, indicating a more detached relationship between their orientation to the stimuli. Cluster 4 exhibited the least engagement (7.4 eco-actions, 51% uptake, 47% success) arguably demonstrating behavior costs associated with low cognitive and affective engagement. These differences indicate that internal psychographic architecture not only influenced intention formation, but also influenced downstream implications with respect to incentive systems, thereby highlighting one potential strategic advantage of segment-specific personalization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the difference in behaviour engagement, the clusters also differed considerably in their affective engagement and loyalty formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e); C1 had the highest mean evaluation of satisfaction with the token redemption (4.6) and loyalty intention scores (4.5), suggesting a strong congruence of pre-existing psychological dispositions and incentive responses. Nostalgic Resonators had slightly lower satisfaction and loyalty rate scores of 4.3 and 4.2, respectively, indicating that, although they had somewhat lower autonomy relative to C1, perhaps emotional resonance will be sufficient to sustain future behavioural engagement commitment. C3 had a moderate amount of loyalty (3.5) and satisfaction (3.7), consistent with their relatively disengaged style of behaviour. In contrast, cluster 4 demonstrated the lowest satisfaction (3.1) and loyalty (2.9), indicating that while they may be affected, motivational resonance and identity transformation are critical for achieving positive affective and long-term engagement outcomes. Results highlight the utility of psychographic segmentation in predicting not only behaviour but also future attitudinal loyalty post-intervention. Strategic implications suggest creating future tokenized offerings not just to incite actions, but rather to layer meaning, emotional fortification, and value congruence in the eco-tourism journey.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Sensitivity Analysis and Simulation Robustness\u003c/h2\u003e \u003cp\u003eTo test the robustness and generalizability of the simulation results, a sensitivity analysis was conducted in which five parameters were independently varied: token values, token visibility, intrinsic motivation weight, redemption threshold, and agent decision rounds. For each parameter, we varied one parameter at a time while holding the others at a constant value to examine the marginal effect of the parameter on the variables of eco-action frequency, token redemption satisfaction, and loyalty intention. The results of the sensitivity analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The effects of token value and motivation weight denote the largest effects (26% and 22% increases, respectively). The changes in token visibility also produced substantial increases in behavior (+\u0026thinsp;18%). However, moving the redemption threshold from 50\u0026ndash;80% resulted in decreases in satisfaction (\u0026minus;\u0026thinsp;0.3) and loyalty (\u0026minus;\u0026thinsp;0.4) behavior, indicating the behavioral cost of having very restrictive reward standards. The sensitivity testing reinforced the point that observed relationships would not result solely from fixed input conditions, providing us with good evidence of patterns of non-arbitrary responses for reasonable incentive configurations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity Test Results by Parameter Set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter Varied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange in Eco-Actions (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChange in Satisfaction Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in Loyalty Intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Value (Low \u0026rarr; High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToken Visibility (20% \u0026rarr; 80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation Weight (0.3 \u0026rarr; 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRedemption Threshold (50% \u0026rarr; 80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRounds per Agent (5 \u0026rarr; 15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity analysis findings confirmed that the simulation exhibited both structural stability and directional consistency in a broad range of input settings, while providing strategic insights for parameter tuning. The segmented view provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e separates the behavioral from the attitudinal responses. The top panel displays the percentage change in frequency of eco-actions, and the bottom panel shows the changes in satisfaction and loyalty intention scores. The token value had the largest impact, increasing eco-actions by 26% and satisfaction and loyalty by 0.7 and 0.6 points, respectively. The next most impactful parameter was motivation weighting, confirming the behavioral and affective acknowledgement that incentive motivation salience must align with intrinsic motivation tendency. Token visibility had a fairly ambivalent influence, but helped boost everything, acting as a consistent but modest lift across all metrics. However, the imposition of stricter redemption thresholds notably decreased both behavioral (\u0026minus;\u0026thinsp;11%) and attitudinal behavior (\u0026minus;\u0026thinsp;0.3 to \u0026minus;\u0026thinsp;0.4) due to over-reward friction. The segmented visualization suggests that purposeful change in a small number of attributes known to have high impacts\u0026mdash;the salience of value and motivation\u0026mdash;can have meaningful and impactful effects on eco-tourism in action, both for behavioral types and for loyalty-type actions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6 Discussion and Implications","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Cognitive\u0026ndash;Affective Pathways in Eco-Engagement and Loyalty Formation\u003c/h2\u003e \u003cp\u003eThe simulations provide strong support for the main premises of S.O.R framework, especially the use of external characteristics that influence an individual\u0026rsquo;s behavioural engagement. The visual appearance of tokens (H1) significantly increased eco-intention. The evidence suggests that behaviours perceived saliently (in this case, visual) help to activate sustainability-based decision-making. Visibility, transparency, and consubstantial token awards directed cognitive access and motivational relevance towards pre-behaviour planning. Similarly, token value (H2) was a significant factor in behavioural execution; higher-scaled incentives supported behavioural involvement and enhanced redemption activation. This aligns with the ecotourism literature on salience and perceived utility in decision-making (Farshbafiyan Hosseininezhad et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This effect was highlighted in the simulations, as there were consistently higher action frequencies and loyalty scores as token tiers fluctuated, suggesting that incentives were intentionally designed to be both psychologically meaningful and that excessive salience can help convert users from normative behaviours towards the environment to engaged participants (Qiu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It can also be considered that the simulation supports the first stage of the S-O-R framework, as stimulus layer variables that are programmatically structured and intentional provide helpful behavioural change interventions within the context of gamified tourism studies (Boukis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the above direct effects, the model identified more complex organismic processes that mediated and moderated the effects of tokenized incentives. People who felt they controlled their actions (H3) were better at turning exposure to tokens into intentions, and those who felt more in control were more motivated, which helped boost their confidence in engaging with digital environments. Intrinsic motivation (H4) resulted in sustained behaviour, indicating that users who had internalized their values in the environment showed more consistent responses after rounds of exposure to pro-environmental behaviours. This suggests that effective tokenization is more interpersonal concerning social-psychosocial fit rather than structural design (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The research additionally indicated that the frequency with which individuals act and re-engage in pro-environmental behaviours is an associative variable (H5), which supports our thesis on frequency equating normality over time leading to a habit of learnt exposure to a stimulus and loyalty intention (Fr\u0026iacute;as-Jamilena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, satisfaction with the redemption process (H6) was a strong predictor of future loyalty in terms of affect, with high satisfaction with the redemption process being positively linked to the intention to recommend/advice and intention to revisit (Boukis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This research confirms that sustainable behaviour in ecotourism is not only a matter of getting rewards; there are more intangible factors in the form of (1) cognitive, (2) affective, and (3) behavioural factors, each of which is reflected in the complex model that has six main hypotheses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Theoretical, Methodological, and Practical Contributions\u003c/h2\u003e \u003cp\u003eThis research introduces a new lens to view engagement behaviour in ecotourism by utilizing the framework of SDT, TPB and VBN within the S-O-R framework. SDT suggests that intrinsic motivation is more fundamental to understanding commitment than extrinsically driven motivations. TPB outlines the cognitive mechanisms for the development of behavioural intentions through perceived control, as well as attitudinal endorsement (Bhartiya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). VBN provides an ethical and identity-based perspective. This perspective highlights the importance of tokenized incentives that foster alignment with ecological values. Tokenized incentives presented within a VBN framework foster long-term attitudinal loyalty (Landon et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We do not view these dimensions in isolation; rather, they are operationalized into programmable agent logic that allows behaviours to emerge using realistic cognitive-affective traits. This simulation-ready configuration fits with the present trajectories within tourism scholarship that view the potential for blockchain technology to serve as a psychological infrastructure, allowing socio-psychological, value-coherent and ethically constituted digital interventions (Mountije et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The conceptual contribution is reconceptualizing intention and loyalty as patterns, not states, altogether still mutable through iterations of feedback-driven engagement with gamified smart contracts (Yu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMethodologically, this research uses ABM to analyze how tokenized incentives behave across various psychographic characteristics in digitally mediated tourism systems. Smart contracts were configured as programmable logic cages, framing the reward value, the reward visibility, and the parameters of reward engagement to mimic the habitation structures of \"real world\" eco-incentives. This method allowed for the testing of behavioural pathways in a controlled, repeatable way without the ethical and logistical management issues that plague early-stage field implementation studies. In the context of the simulation architecture, the study could engage how an environmental characteristic operates, what user characteristics are and how much participation there is within the participation tier paradigm. The aforementioned methods intend to maximize analytical generalizability and maintain psychological realism. Furthermore, this is of interest to tourism developers and policy designers looking to incorporate behavioural reasoning into blockchain-based sustainability into sustainability modeling. The observable usefulness of these design patterns of tiered rewards, real-time redemption, and psycho-behavioural language provides scalable design patterns for creating loyalty and behavioural compliance, particularly as advancements continue in real-time payments whereby triggering incentives via blockchain transaction execution will be verifiable (Wang \u0026amp; Chan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Limitations, Simulation Scope, and Future Research Directions\u003c/h2\u003e \u003cp\u003eDespite having a strong simulation-based platform for examining tokenized ecotourism behaviour, the research has several limitations related to its virtual experimental conditions. As agents\u0026rsquo; behaviours were modelled based on predetermined parameters and assigned psychological weights that were theoretically grounded, it is acknowledged that human behaviour is often unpredictable, and this research does not intend to model actual tourist behaviour. The research has also used rationality as an abstraction for reward logic but did not include behaviours such as resistance to gamification, token fatigue, or ethical resistance to transactional participation. While we can ensure internal validity by profiling agents and applying the same rules, accepting the limitations of external validity is more challenging. The research allows for limited generalization and thus demonstrates the need for hybrid methodological frameworks that involve the precision of simulations with the richness of real-world behaviours. Future digital sustainability approaches must also incorporate systemic methods that align technology with user values and ecological outcomes (Rodrigues et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture research may expand on the simulation\u0026rsquo;s conceptual mapping by utilizing hybrid approaches consisting of empirical user studies, A/B testing on ecotourism platforms and participatory design with actual travelers. One promising avenue is the concept of \u0026lsquo;digital twins,\u0026rsquo; which combine real-usage data with simulated environments to provide a richer understanding of behavioural variability and dynamic feedback. Alternatively, one option would be to enhance the agent's architecture further by integrating social identity factors, peer influence structures and cultural significance of incentives and be able to capture the extent to which social norms and local values condition responsiveness to tokenized incentives. Personalization algorithms can also be integrated into the smart contract logic to allow both adaptive gamification and dynamic distributions of tokens. In the future, researchers should also consider adding biometric and affective feedback (i.e., using wearables) to the models to increase their knowledge about emotional gratification and engagement. The future direction should be to combine the accuracy of simulation outputs with the complexity of behaviour\u0026mdash;to produce decision models that are both computationally robust and contextually empathetic. This empirical calibration approach has the added benefit of building upon theoretical alignment and providing legitimacy for the transition from simulation rigor to real-life studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.H. and H.H. contributed equally to the writing of this manuscript. M.H. formulated the theoretical framework and wrote the main body of the manuscript (Section 3: Conceptual Framework, 4: Methodology, and 5: Data Analysis and Results). H.H. wrote the blockchain rules and simulation model architecture based on the smart contracts. The authors wrote the simulation logic, Introduction and Literature Review sections together, collaborated on the results analysis and wrote Section 6 (Discussion and Implications). 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(eds) Human-Computer Interaction. HCII 2025. Lecture Notes in Computer Science, vol 15768. Springer, Cham. https://doi.org/10.1007/978-3-031-93845-0_6 \u003c/li\u003e\n\u003cli\u003eYu, J. J., Hu, J. J., Jiang, W., \u0026amp; Walters, G. (2024). Not just a game: Understanding eco-gamification in sustainable destination development. Journal of Hospitality and Tourism Management, 60, 10-21. https://doi.org/10.1016/j.jhtm.2024.06.005\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":"Blockchain, Smart contract, Tokenized incentives, Eco-tourism behavior, Agent-based modeling, Sustainable loyalty engagement","lastPublishedDoi":"10.21203/rs.3.rs-6939486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6939486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research investigates ways in which blockchain smart contracts and gamified token rewards can increase eco-tourists' engagement and loyalty to online sustainability practices. The tourism industry faces challenges when it comes to converting intentions into sustainable behaviours, despite the obvious value of environmental sustainability. Traditional loyalty frameworks do not provide much real-time feedback or emotional resonance and limit the amount of transparency the agents have. As a result, traditional loyalty frameworks lose their effectiveness over time. Therefore, we developed a simulation-based experimentation model, that was based on Agent-Based Modeling (ABM) and the Stimulus-Organism-Response (SOR) framework that consisted of 200 synthetic agents who have differing psychographic profiles and 10 decision rounds with different token incentive conditions. The simulation model tests six hypotheses from Self-Determination Theory, the Theory of Planned Behavior, and Value-Belief-Norm. The results suggest that the visibility and perceived value of the tokens mostly impact intention and behaviour. There is also a distinction between perceived behavioural control and intrinsic motivation regarding the effects of incentives. The relationship between behavioural repetition and satisfaction with token redemption produced the emergence of loyalty intentions. The proposed methodological approach shows that by controlling tokens according to users' psychological characteristics, gamified smart contracts can foster long-term eco-engagement in users. This research offers a useful plan for testing digital behaviour changes and provides practical advice for building token-based, emotionally conscious ecotourism platforms. Building on cognitive-affective theory, we integrated it with blockchain technology, which will redefine behavioural governance, gamification, and sustainability in future travel ecosystems.\u003c/p\u003e","manuscriptTitle":"Tokenized Incentives and Loyalty: Leveraging Blockchain-Based Smart Contracts for Enhancing Eco-Tourism Behavioral Engagement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:27:25","doi":"10.21203/rs.3.rs-6939486/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":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T06:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:27:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6939486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6939486","identity":"rs-6939486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}