From Intention to Loyalty: Unfolding Tokenized Smart Contract Incentives in Ecotourism via Agent-Based Modeling

From Intention to Loyalty: Unfolding Tokenized Smart Contract Incentives in Ecotourism via Agent-Based Modeling | 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 From Intention to Loyalty: Unfolding Tokenized Smart Contract Incentives in Ecotourism via Agent-Based Modeling Majid Heidari, Hossein Hosseinalibeiki This is a preprint; it has not been peer reviewed by a journal.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-8021398/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates how tokenized incentives supported by blockchain-enabled smart contracts can influence eco-tourists’ sustainable behaviors and loyalty. Even though the importance of environmental sustainability has been recognized, the tourism industry is still prone to an intention–action gap, because conventional loyalty structures do not have the features of immediacy, personalization, and transparency. An agent-based modeling (ABM) simulation was thus created to overcome these constraints and it was based on the Stimulus–Organism–Response (S-O-R) framework. The model is primarily derived from the Theory of Planned Behavior (TPB) to elucidate the impact of perceived behavioral control on eco-intention and subsequently eco-action, with intrinsic motivation from Self-Determination Theory (SDT) serving as another psychological variable impacting persistence. A total of 200 synthetic agents were simulated over ten decision rounds when conditions of visibility, perceived value, and reward format were manipulated using gamification design principles. The results suggest the conditions of visibility and value increase eco-intention and eco-action; however, repeated actions and satisfaction in redeeming tokens emerge as more substantial predictors of loyalty intentions. The research indicates that tokenized incentives, with the help of smart contracts as automated and transparent tools, have the potential to increase the behavioral engagement of users and can serve as a foundation for a lasting loyalty program in context of ecotourism. The study applies a combination of TPB, ABM, and blockchain-enabled token systems, which add methodological and practical dimensions while providing actionable recommendations to address the intention-action gap in sustainable tourism through a programmable platform. Tokenized Incentives Ecotourism Behavior Agent-Based Modeling (ABM) Sustainable Loyalty Blockchain Smart Contracts Theory of Planned Behavior (TPB) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Although awareness of sustainability is a significant component of engaging in ecotourism marketing, an established intention–action gap persists. Travelers frequently endorse eco-friendly principles but rarely act upon them, demonstrating that traditional pre-engagement cues may not lead to successful commitment by travelers (Viglia & Acuti, 2023 ; Nieto-García et al., 2024 ). These strategies may be beneficial in activating cognitive awareness, but almost never succeed at ongoing commitment, or engage an emotional motivation towards 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. This challenge is approached here through S-O-R as an overarching structure, applying TPB to explain how perceived behavioral control (PBC) drives eco-intention and eco-action in tourism contexts. 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 ). Conventional loyalty programs are non-personalized, non-immediate, and non-psychologically meaningful to a traveler's motivational profile. Although such programs may achieve immediate compliance, they rarely sustain intention and commitment. This aspect is particularly relevant in cases where incentives seem to be generalized or delayed (Koo et al., 2020 ); furthermore, legacy systems tend to be centralized and opaque, leading to a loss of trust among users (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 ought to engage a user in a way that generates value for them and modifies the cognitive and emotional feedback loops that are directly related to that engagement (Rodrigues et al., 2023 ). An effective engagement system should provide incentives that are meaningful, observable, and based on rules that respond to user behavior in real time. Transparency can be provided through blockchain-based smart contracts, which can deliver tokens automatically, delivering trust and responsivity in ecotourism engagement (Balasubramanian et al., 2022 ). Smart contracts enabled by blockchain technology have gained traction as a viable solution to address the inefficiencies that have impeded traditional incentive structures in ecotourism. They function as self-executing, rules-based contracts that terminate the delivery of transparent and verifiable tokens directly and increasingly toward sustainable behavior (Balasubramanian et al., 2022 ; Barclay et al., 2022 ). Smart contracts, rather than a centralized loyalty program with non-transparent third-party intermediaries, build trust and accountability into their design through decentralization and are secured in a blockchain ledger that makes all transactions immutable. This feature provides functionality in that reliable rewards are delivered in real time, thereby narrowing the intention–action gap often presented in sustainability settings. Gamification-based design elements—such as badges, progression levels, symbolic rewards, and milestone triggers—are more than simply emotionally engaging rewards for repeating desirable behaviors that will further long-term engagement (Pasca, 2021). These elements, in conjunction with blockchain-based transparency, also serve to personalize the eco-rewards, which can now be aligned with your own motivational and behavioral profiles (Hosseinalibeiki & Heidari, 2024 ; Fotiou et al., 2025 ). In this way, blockchain and gamification are positioned as complementary enablers that operationalize incentive delivery within the S-O-R framework, rather than as independent theoretical constructs (Lim et al., 2025 ; Mountije et al., 2025 ). While the conceptual appeal of blockchain mechanisms and gamified incentives is strong, their practical testing remains constrained by development costs, ethical dilemmas, and user heterogeneity (Chica et al., 2023 ). For this reason, simulation data serves as a valid avenue for hypothesis testing. ABM, incorporated into the S-O-R framework, offers a dynamic approach to modeling the interactions between incentive stimuli and psychological characteristics over time (Wallinger et al., 2023 ). This research utilizes TPB as the main theoretical base, illustrating how PBC influences eco-intention and eco-action, with SDT included as an additional element that reflects the continuity of intrinsic motivation (Baktash et al., 2023 ). The simulation features 200 agents with unique psychographic characteristics, allowing for a structured examination of how tokenized rewards affect intention, eco-action, satisfaction, and loyalty within a regulated setting. This framework creates a theoretically based structure that produces consistent, behaviorally realistic data. Overall, the framework shows how programmable, psychologically informed systems bridge the intention–action gap in ecotourism. This research enhances eco-friendly tourism by recognizing eco-engagement as a continuous process rather than a fixed link between motivation and action. Combining TPB and SDT in the S-O-R framework reveals the changes in intention, behavior, and loyalty through constant interaction with tokenized rewards. Moreover, the ABM introduces a novel method by illustrating the gradual behavioral changes over time and different profiles, thus enabling the safe testing of various incentive systems. The framework is a customizable test environment for modifying token visibility, valuation, and redemption barriers to discover optimal settings. In contrast to previous survey methods, this approach accounts for temporal adaptation and path-dependence, illustrating when and for whom particular incentives maintain ecological commitment. The article, therefore, links behavioral theory to programmable design, associating psychological realism with blockchain-facilitated execution. 2 Behavioral Theories and Tokenized Eco-Engagement 2.1 Blockchain-Based Incentives and Smart Contract Design Blockchain technology has evolved from backend efficiency uses to serve as a foundational infrastructure for sustainable incentive frameworks in tourism, especially in contexts demanding transparency and verifiable processes (Balasubramanian et al., 2022 ). Blockchain originally concentrated on applications like secure reservations and decentralized transactions, before transitioning to support decentralized incentive structures in ecotourism (Fotiou et al., 2025 ). In ecotourism, blockchain will influence through its permanence in documenting actions and immediate confirmation of sustainable practices, like carbon-neutral travel and eco-lodge accommodations (Rodrigues et al., 2023 ). Research on blockchain in tourist activities has affirmed that perceived utility and usability impact actual systems readiness (Corne et al., 2023 ). Beyond being a technical upgrade, blockchain enables decentralized tracking of verifiable eco-actions (Thanasi-Boçe & Hoxha, 2025 ). In this setting, smart contracts facilitate incentive distribution by connecting eco-friendly actions to set rewards. For instance, conservation efforts or community service can be tracked on-chain and automatically compensated with token credits (Barclay et al., 2022 ). The merging of smart contracts with tokenized incentives represents a shift from static incentives to dynamic behavior alignment. In this context, users anticipate incentives in the form of tokens that serve as integrated nudges to influence their actions based on established reasoning and offer prompt 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 can deliver personalized micro-incentives by drawing on behavioral economic principles resulting from user behavioral history, autonomy, and trust (Chan, 2024 ; Hosseinalibeiki & Heidari, 2024 ). This decentralization implies shifts in sustainability governance, including moving design authority away from centralized systems towards more agent-mediated structures (Thanasi-Boçe & Hoxha, 2025 ). 2.2 Gamified Token Logic as Behavioral Ecotourism Stimuli Gamification has effectively supported pro-environmental actions in tourism, turning abstract sustainability goals into interactive and emotionally captivating experiences (Pradhan et al., 2025 ). In this research, gamification is regarded not as a conceptual framework but as a design tool that implements incentives within the S-O-R architecture. Gamification systems utilize game components such as points, badges, milestones, social comparisons, and prompt feedback to encourage cognitive and emotional participation, leading to action-driven engagement for eco-friendly behavior (Abou Kamar et al., 2024 ). These design features can improve PBC by making the visualization and repetition of eco-actions easier, thereby reinforcing the intention–action connection highlighted in TPB. Frequent sporadic ecotourism can gain from consistent actions that strengthen engagement and commitment (Sesliokuyucu & Cobanoglu, 2025 ). Similarly, token-driven resources are integrating gamification into a tangible framework that offers specific rewards for performance-related behaviors (e.g., environmentally-friendly actions, sustainable travel options), aiding in the development of sustainability habits in the user (Choirisa et al., 2025 ). Token systems can implement reinforcement strategies, employing structured reward mechanisms and adaptable value, to promote the development of lasting habits (Yu et al., 2024 ). Blockchain-based token systems simplify reward distribution, reduce administrative workload, and improve trust (Wang & Huang, 2025 ). In this way, gamification acts as a facilitating factor within the stimulus layer of S-O-R, engaging with TPB-based intentions instead of existing as a separate theory. 2.3 Intrinsic Motivation and PBC As per SDT, intrinsic motivation provides a strong psychological basis for sustaining pro-environmental actions in ecotourism (Patwary et al., 2024 ). The greater the intrinsic values tourists assign to their eco-actions (e.g., duration at eco-lodges, readiness to selflessly contribute time and resources to conservation), the more probable they are to act in an ecological manner. Extrinsic tools, such as gamified incentives or symbolic eco-rewards, can complement rather than replace intrinsic initiatives (Bhartiya et al., 2025 ). Still, these external tools can help stimulate self-actualization regarding eco-engagement. Digital engagement systems that respect SDT principles of autonomy and relevance may leverage intrinsic motivation and promote long-term eco-engagement. In simulations, intrinsic motivation is portrayed as a consistent characteristic, largely unaffected by the timing of external rewards (Baktash et al., 2023 ). When engagement systems resonate with users’ values and autonomy requirements, they strengthen motivational consistency and sustainable eco-actions over time (Wang & Huang, 2025 ). PBC, a key element of TPB, strengthens this framework by affecting tourists' belief in their ability to engage in sustainable actions (Abou Kamar et al., 2024 ). In ecotourism, PBC encompasses perceived awareness and the ease of engaging in low-impact actions—elements that strongly correlate with behavioral intention and actual execution. ABM simulations indicate that agents with higher PBC respond more decisively to eco-stimuli and display consistent patterns over various decisions (Bhartiya et al., 2025 ). This control is engaging and can be efficiently modified through careful design, soliciting feedback, and ease of use (Chan, 2024 ). User-friendly systems that enhance PBC and offer actionable feedback can reinforce the alignment between intention and action in ecotourism decisions. These processes function dynamically and could influence elements of substantial, lasting ecological engagement (Schönherr & Pikkemaat, 2024 ). Table 1 offers a summary of key studies regarding token incentives and motivational elements in tourism, highlighting their frameworks, approaches, and relevance to the existing simulation-based technique. Table 1 Comparative Studies on Tokenized Incentives and Psychological Drivers in Tourism Behavior. Study Theory & Key Constructs Method Key Findings Limitation vs Present Study Koo et al. (2020) Loyalty programs; Satisfaction → Commitment → Loyalty; Switching Barriers. Survey (PLS-SEM) Satisfaction boosts loyalty; switching barriers shape dynamics. No dynamic behavior modeling; no token variables or ABM. Corne et al. (2023) TAM + Trust + Promotion; Blockchain adoption (loyalty, booking, reviews). Survey (fsQCA) Usefulness and trust form sufficient adoption paths. Focus on adoption by managers; no eco-behavior constructs; no ABM. Qiu et al. (2023) SOR; Credibility → Image → Attachment → TERB. 3 Surveys; Mediation (Bayesian) Credibility strengthens TERB via place attachment. Destination-level stimulus; static; no tokens or agent feedback. Nguyen et al. (2023) Extended TPB; PBC, Motivation, Moral Reflectiveness. Survey (SEM) Moral reflectiveness and TPB factors predict intention. No gamification/tokens; no dynamic modeling or ABM. Abou Kamar et al. ( 2024 ) TAM + TPB; Eco-gamification → Sustainability Knowledge → TPB Constructs. App trial + Post-survey (SEM) Gamification improves TPB-related intentions by increasing knowledge. Short-term trial; no satisfaction or loyalty; no token logic or ABM. Boukis (2024) Self-enhancement; Novelty & Ownership mediate token effects. 3 Experiments Tokens increase loyalty via novelty and ownership. Short-term effects; no eco-action constructs; no dynamic feedback. Choirisa et al. ( 2025 ) Gamification → Engagement → Loyalty. Survey (SmartPLS) Gamification boosts emotional and cognitive loyalty pathways. No eco-behavior constructs; static design; no token parameters. While tourism research has examined token visibility, reward framing, and motivational pathways, most studies employ cross-sectional designs that limit causal inference and overlook feedback mechanisms. Previous research, such as Boukis ( 2024 ), Lim et al. ( 2025 ), and Koo et al. ( 2020 ), predominantly conceptualizes behavior as a fixed intention instead of an outcome of changing exposure to incentives. Even research utilizing SDT and TPB seldom models how constructs engage dynamically under different token circumstances. Importantly, the design principles of tokens — visibility, symbolic compared to material value, and user alignment — are still insufficiently explored regarding personalization, satisfaction cycles, and loyalty development. This research tackles these shortcomings by modeling a dynamic eco-behavioral process through ABM. The model incorporates psychological factors into a s S-O-R framework, enabling parameterized evaluation of token strategies and their resulting influence on sustainable behavior and intention throughout various interaction stages. 3 Simulating Eco-Behavioral Dynamics through Tokenized Incentives 3.1 Theoretical Foundations The conceptual framework incorporates TPB as the main theory for predicting behavior, while SDT offers a different perspective to understand intrinsic motivation. SDT shows that intrinsic motivation encourages eco-engagement, particularly when tokenized rewards are perceived as promoting autonomy rather than being controlling (Patwary et al., 2024 ). TPB suggests that attitudes, social norms, and perceived behavioral control forecast eco-intentions and following eco-behaviors, with programmable incentives affecting these connections (Nguyen et al., 2023 ). Four essential concepts are defined to merge theory with simulation. Eco-intention refers to an individual's inherent commitment to sustainability, influenced by acknowledged social norms and the demands of their surroundings. Eco-action encompasses the significant, environmentally conscious choices made by individuals, such as conserving resources or opting for sustainable alternatives. Token redemption satisfaction encompasses the user's overall experience with tokens, taking into account both positive and negative aspects, highlighting usability and perceived fairness. Loyalty intention refers to an individual's inclination to continue utilizing or endorsing the platform, influenced by ongoing engagement and common values. The S–O–R framework organizes the impact of external token-related incentives on internal decision-making processes. Stimuli—defined by token visibility and value, and delivered via smart contracts—activate internal states such as PBC and intrinsic drive. These internal reactions subsequently influence behaviors such as environmental intentions, the execution of eco-friendly actions, satisfaction, and loyalty. This causal chain is illustrated within the ABM framework, where every agent senses external factors, evaluates internal states, and generates varied results across time (Baktash et al., 2023 ). This micro-level, trait-focused decision model allows ABM to replicate behaviorally informed differences among agents successfully. In the analysis phase, the same S-O-R framework supports the interpretation of behavioral clustering and engagement variation across profiles. 3.2 S–O–R Framework in Token-Based Agent Simulation The simulation employs the S-O-R framework by converting TPB and SDT components into agent-based decision-making logic, using smart-contract parameters: visibility and value. Visibility serves as a stimulus-level signal, with social observability integrated into smart contracts as a toggle for public display. In actual platforms, this demonstrates how sustainability badges or eco-points are visibly presented to promote peer influence and social comparison. When tourists identify behaviors associated with tokens, eco-intention increases due to the salience of these behaviors and peer influence (Choirisa et al., 2025 ). Incentive intensity represents the combined effect of token visibility and perceived value as active stimuli within the simulation. Visibility is portrayed as a categorical input with three tiers (low/medium/high) in the ABM, influencing agent salience weighting in every round. H1 Under repeated exposure, token visibility increases eco-intention by enhancing salience and social signaling. Beyond awareness, the perceived token value (implemented as a smart contract payout parameter) influences eco-action. If tourists believed that the tokens 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 ecotourism behavior. Perceived value triggers both extrinsic motivation and evaluation of cognitive cost-benefit, ultimately increasing the intention-action link (Choirisa et al., 2025 ). Value is represented as a payout variable in a smart contract that affects agents' cost-benefit threshold for taking action. H2 Higher perceived token value strengthens the intention–action link, increasing eco-action likelihood across rounds. PBC influences how tokenized incentive intensity (visibility and value) affects eco-intention, since individuals vary in perceived controllability (Akter & Hasan, 2023 ). Each agent has a PBC trait (0–1 scale) that adjusts intention strength in response to incentive cues. H3 PBC moderates the effect of incentive intensity on eco-intention, amplifying the impact for agents with higher PBC. In addition to perceived control, the nature of the motivation is also significant. According to SDT, greater intrinsic motivation boosts the transformation of tokenized incentive intensity into continuous eco-action, especially when incentives resonate with personal values (Patwary et al., 2024 ). In the ABM, motivation emerges as a trait of the agent (low/medium/high), shaped by previous experiences and the alignment of values throughout time. H4 Intrinsic motivation moderates the effect of incentive intensity on sustained eco-action, reinforcing persistence when values align. Behavioral change is a process marked by repetition and the establishment of habits, not a single event. In ecotourism, the rate of eco-action influences the route from initial exposure to incentives to the intention of loyalty. Tourists who participate in regular interactions are more inclined to form habitual behaviors. In terms of developmental continuity within a behavioral framework, the frequency of actions serves as the link between initial tourism motivations for visiting and sustained commitment to a destination (Frías-Jamilena et al., 2022 ). In the ABM, action frequency is tracked per agent across rounds and statistically modeled as a mediator between early exposure and loyalty output. H5 Eco-action frequency mediates the pathway from initial incentive exposure to loyalty intention. Loyalty is dependent not only on frequency of behavior but also on affective satisfaction and, in particular, affective satisfaction with the experience of token redemption. Contentment with token redemption reflects the emotional assessment of the incentive experience and is anticipated to influence loyalty intentions (Choirisa et al., 2025 ). In the simulation, satisfaction is derived from the ease of redemption and friction; loyalty intention is a cumulative function assessed in the final rounds H6 Satisfaction with token redemption predicts loyalty intention by reinforcing affective evaluation of the platform. In this context, smart contracts serve only as programmable delivery mechanisms for token visibility and value; they are not modeled as constructs or variables within the research framework (Fig. 1 ). 4 Methodology 4.1 Simulation and Token Stimulus Logic ABM was preferred to survey-based or structural equation methods because of its ability to perform the dynamic analysis of causal mechanisms resulting from ongoing interactions. This choice ensures timing precision and allows intentional modifications of theoretical frameworks without the ethical or practical constraints of real-world testing (Baktash et al., 2024). Various simulation methods were tested and finally discarded because they cannot provide the same level of control over specific psychological characteristics and decision-making processes. In contrast, ABM allows each simulated tourist to operate based on a unique profile, including psychological traits. This renders ABM particularly appropriate for theory-based simulation consistent with TPB and SDT, wherein behavior arises from dynamic, multi-round interactions with stimuli. The capacity to simulate diverse agents engaging with changing incentives offers a realistic basis for investigating ecotourism choices. This research utilized a simulation-driven experimental approach, a variant of ABM, to explore the impact of tokenized smart contract incentives on engagement in ecotourism. The research created 200 artificial agents intended to model conceptual ecotourists. Eco-identity was identified as a characteristic not included in the model, used only for clustering (Section 5.3 ), and not part of the theoretical model or hypotheses. TPB and SDT were operationalized through agent-level constructs guiding decision rules. The broader S-O-R architecture structures the simulation flow. Gamification is treated strictly as a delivery mechanism for incentive format (e.g., tiered rewards), not a theory or variable in the S-O-R model. Throughout the simulation, agents engaged in ecotourism decision-making processes across 10 rounds. Each round featured exposure to flexible incentive stimuli, which included token visibility levels (low/medium/high), perceived token value (low/medium/high), and gamification frameworks (fixed versus tiered reward systems based on eco-action frequency). The smart contract's parameters regulated the tokens' visibility and perceived worth, while the gamification format defined the reward system's structure (either fixed or tiered). Agents’ reactions were designed based on the prominence of stimuli and internal motivational alignment, producing behavioral choices that align with TPB concepts and practical decision-making reasoning. Although no real participant data were used, the simulation generated agent- and round-level behavioral outputs within a controlled experimental setting. 4.2 Agent Profiling and Variable Mapping 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 and perceived token value), organism-level data (intrinsic motivation and PBC), and response-level data including eco-intention, eco-action, eco-action frequency, satisfaction with token redemption, and loyalty intention. Clustering profiles additionally included eco-identity as a segmentation trait (see Section 5.3 ). While the data were synthetic, they were grounded in transparent, rule-based logic aligned with the S-O-R architecture, and modeled realistic behavior through time-series logging at both the agent and round levels (see Fig. 2 for validation loop). The examination consisted of four phases: (1) descriptive statistics to condense behavioral distributions; (2) regression and path analysis to evaluate hypotheses H1–H2 (main effects) and H3–H6 (moderation and mediation); (3) psychographic grouping centered on intrinsic motivation, PBC, and eco-identity; and (4) sensitivity testing through alteration of incentive thresholds and motivation weights to gauge simulation reliability. The dataset for the simulation was created using a constant random-seed setting, guaranteeing precise reproducibility of all results presented (refer to Table 3 for parameter information). Although based on simulation, this study’s behavioral architecture is grounded in empirically supported psychological theories, ensuring cognitive and affective realism in the modeled pathways typical of sustainable tourism behavior. The model outcomes matched empirical results, validating that gamified stimuli affected eco-behaviors (Frías-Jamilena et al., 2022 ). The intrinsic motivation and PBC moderating roles also appeared in the agent clusters and interaction models (Patwary et al., 2024 ). The loyalty effect corresponds with Boukis ( 2024 ), affirming that satisfaction with token redemption greatly impacted loyalty intention, consistent with the simulation’s attitudinal results. Figure 2 models the simulation-empirical alignment and proposed validation loop. 5 Theoretical-to-Operational Mapping This section describes how theoretical concepts relate to practical use, connecting the behavioral constructs mentioned in Section 3 to their execution in the agent-based simulation. Each component of the S-O-R framework is linked to its parameterized counterpart in the model to ensure clear understanding in both concept and analysis. Table 2 summarizes how token-level incentives, organismic traits, and behavioral responses were encoded as simulation variables and how each variable contributes to the tested hypotheses (H1–H6). This mapping serves as the interpretive bridge between the conceptual model and the analytical outputs presented in the following section. Table 2 Construct-to-ABM Mapping. Variable Role (S/O/R) Operationalization in ABM Linked Hypothesis Analytical Role / Outcome Token Visibility S 3-level input (low/medium/high); public-display toggle. H1 Predictor of Eco-Intention Token Value S Smart-contract payout variable (low/medium/high). H2 Predictor of Eco-Action Perceived Behavioral Control (PBC) O Agent trait (0–1); wights intention formation. H3 Moderator of Eco-Intention Intrinsic Motivation O Agent trait (low/medium/high). H4 Moderator of Sustained Eco-Action Eco-Intention R Continuous score per round. H1–H3 Intermediate outcome Eco-Action R Binary indicator of eco-behavior per round. H2-H4 Behavioral Outcome Eco-Action Frequency R Count of eco-actions per agent across rounds. H5 Mediator (link to Satisfaction) Token Redemption Satisfaction R 1–5 scale derived from ease and clarity; final state represents loyalty intention. H6 Predictor of Loyalty Intention 5.1 Simulation Output and Behavioral Overview The simulation involved 200 agents executing 10 decision rounds under token-based incentive conditions defined by the parameters in Table 3 . Two psychological traits—intrinsic motivation and perceived behavioral control (PBC)—were modeled as organism-level variables, while eco-identity was retained solely for post-simulation clustering (Section 5.3 ). All incentive parameters and behavioral thresholds were applied using a consistent simulation logic to ensure internal consistency and replicability. Token conditions (visibility and value) were modeled as stimuli, and gamification together with eco-identity were treated as contextual elements rather than analytical variables. The experiment tested six structured hypotheses derived from the S-O-R framework and the TPB–SDT cognitive–affective architecture. Table 3 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 Presence 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 results arising from the tokenized incentive conditions showed clear and consistent impacts on metrics related to participation and loyalty (Table 4 ). Agents subjected to high-value token stimuli engaged in more eco-actions per round (7.2) than those in medium (5.9) and low-value (3.5) conditions. Increased involvement resulted in enhanced token acquisition and better redemption rates. In the high-value condition, 79% of agents reached the redemption threshold (≥ 60% eco-actions), while only 28% in the low-value group did. Emotional reactions showed a comparable trend: satisfaction with redemption grew from 2.7 (low) to 4.5 (high), while loyalty intention elevated from 2.8 to 4.6 on a 5-point scale. These results validate that greater incentives not only boost the frequency of actions but also improve satisfaction and loyalty after the behavior. Table 4 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 Note: Token Uptake Rate refers to agent interactions with tokenized incentives across rounds. Figure 3 validates these trends, illustrating a distinct rise in average eco-actions per agent with the increase in both token value and visibility. These variations in behavior provide descriptive backing for hypotheses H1 (visibility → intention) and H2 (value → action), establishing the foundation for future assessment of structural paths and moderation effects. The results support the fundamental reasoning of the S-O-R simulation, demonstrating that more prominent and valuable stimuli enhance behavioral engagement. 5.2 Token Effects and Psychological Modulation of Eco-Intentions The initial stage of the causal analysis focused on the direct impacts of the tokenized incentive elements—mainly token visibility (H1) and perceived token value (H2)—on ecotourism engagement outcomes (Table 5 ). Both the predictors showed statistically significant relationships with their dependent variables. Token visibility brought about a standardized beta coefficient of β = 0.42 (p < .001); thus, it is proven that a higher visibility contributes to the cognitive accessibility and motivational salience of eco-intentions. In the same way, token value strongly influenced eco-behavior (β = 0.57, p < .001), which means that the perceived reward utility is the main factor that leads to the activation of sustainable behavior. The R² values (0.34 for intention; 0.41 for action) indicate that token visibility and value explain a considerable portion of the behavioral variance. These results support the core reasoning of H1–H2 and also verify the S-O-R stimulus–response model, where perceptual cues (visibility) and evaluative expectancy (value) jointly lead to intention formation and behavioral execution. Table 5 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-Action 0.57 0.05 < .001 0.41 The subsequent phase of analysis examined how internal psychological factors—PBC and intrinsic motivation—moderate the effect of token rewards on outcomes related to ecotourism engagement (Table 6 ). PBC boosted to a great extent the impact of the visibility of the token on eco-intention (β = 0.21, p < .01), thus showing that those who had more volitional confidence were able to assimilate token signals and mentally convert them into intention quite frequently. The results are consistent with TPB, which shows that PBC influences the perception of being easy and having the necessary skills for eco-friendly behaviors. Similarly, intrinsic motivation served as a moderator in the influence of token value on continuous behavior (β = 0.26, p < .01); hence, those individuals who had more environmentally oriented values internally were the ones who repeated and consistent eco-friendly behaviors. The impact of these moderations goes deeper into the S-O-R organismic layer, suggesting that internal states play a major role in determining the occurrence of behaviors. Besides that, mediation analysis also showed that the frequency of eco-actions was a behavioral bridge between token exposure and loyalty intention; thus, a significant indirect effect was revealed (β = 0.31, p < .001). These findings reinforce the "Organism → Response" transition in the S-O-R framework, highlighting the vital role of psychological state variables in affecting and promoting engagement. Table 6 Moderation and Mediation results for H3-H5. Effect Type Predictor Outcome Variable Interaction / Indirect Effect (β) p-Value Moderation (H3) Token Visibility × PBC Eco-Intention 0.21 < .01 Moderation (H4) Token Value × Intrinsic Motivation Eco-Action 0.26 < .01 Mediation (H5) Token Visibility & Value → Eco-Action Frequency Loyalty Intention 0.31 < .001 The final phase of analysis examined whether satisfaction with token redemption directly predicts loyalty intention (H6), demonstrating an affective–behavioral connection within the response layer of the S-O-R framework. The results of the regression analysis are shown in Table 7 , illustrating a strong direct effect (β = 0.52, p < .001). Namely, a clear, trusted, and emotionally fulfilling assessment of the redemption experience served as a significant predictor of loyalty. This finding highlights the crucial issue of emotional resolution, essential for maintaining behavioral commitments in ecotourism contexts. In alignment with H6, the emotional resolution that follows token engagement is vital for transforming behavioral involvement into attitudinal commitment. Table 7 Direct Effect Analysis: Satisfaction as a Predictor of Loyalty Intention (H6). Effect Type Predictor 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 Distinct engagement profiles among simulated ecotourists were psychographically clustered to identify differences in the organizational traits of intrinsic motivation, eco-identity presence, and PBC. A diagnostic assessment was performed to confirm the clustering arrangement and to decide the quality and the stability of the four-cluster solution. Table 8 demonstrates that the silhouette coefficient (0.71) and the average cluster stability (> 0.92) are the indicators of strong internal cohesion and external separation. The differences between clusters accounted for 63.5% of the total variation, thus being a double confirmation of the segmentation validity and the interpretative power. Table 8 Clustering Diagnostics and Stability. Metric Value Interpretation Number of Clusters (K) 4 Retained based on interpretability and ABM logic Average Silhouette Score 0.71 Very good cluster separation Between-Cluster Variance (%) 63.5% Strong group separation Within-Cluster Variance (%) 36.5% Acceptable intra-group cohesion Stability Across 10 Runs (Rand Index) > 0.92 High segmentation consistency Four clusters were identified based on the stability of convergence and usability of the profile (Table 9 ); 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) achieved average scores on the three engagement positions and consists of a group of simulated ecotourists with reduced psychological connection to sustainability goals. 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 ecotourism 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. This psychographic segmentation supports the organism layer of the S-O-R framework by identifying how internal traits condition responses to the same external stimuli, enabling more precise modeling of stimulus–organism interactions. Table 9 Cluster Profiles and Descriptive Characteristics. Cluster ID Cluster Label Intrinsic 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 assess how psychological profiles influenced behavioral engagement, each cluster was examined using three criteria: frequency of eco-actions, token uptake rate, and redemption success (Fig. 4 ); clusters 1 and 2 exhibited greater engagement across all measures. High Resonance Seekers engaged in the most eco-friendly activities (14.2) and attained the highest overall token gain (92%) and redemption percentage (88%), demonstrating their strong motivation and stable identity. Nostalgic Resonators were involved too (13.1 eco-actions, 87% uptake), but their participation likely stemmed from an emotional connection to their identity rather than increased self-efficacy or personal agency. Cluster 3 ranked in the center (with 9.8 eco-actions) and was only marginally lower in uptake, suggesting a more disengaged connection to their response to the stimuli. Cluster 4 demonstrated the lowest level of engagement (7.4 eco-actions, 51% uptake, 47% success), likely linked to diminished intrinsic motivation and perceived control, which lessened involvement with tokenized incentives. This corresponds with TPB, where restricted perceived control diminishes intention development; identity characteristics are utilized solely for segmentation. Such differences demonstrate an internal psychographic framework of strategic potential, based on the idea of customizing strategies for specific segments. In the context of platform design, these cluster differences in technical deliberation highlight the need for multiple variations in the incentive. Individuals with high PBC may seek a symbolic form of incentive acknowledgment. In contrast, those with low PBC may benefit from more obvious and meaningful cue suggestions to help turn intention into action. In addition to the differences in behavioral engagement, the clusters showed differences in affective engagement and loyalty potential (Fig. 5 ). Cluster 1 had the highest average token redemption satisfaction of 4.6, and an average loyalty intention score of 4.5, which suggest a high level of overlap between prior psychological dispositions and responses to the incentives. Nostalgic Resonators had lower satisfaction and loyalty at 4.3 and 4.2 respectively. Therefore, their autonomy may have decreased slightly relative to C1's, but an emotional bond persisted sufficiently to maintain a commitment to behavioral engagement. Satisfaction (3.7) and loyalty (3.5) scores at C3 indicated that their engagement could be described as fairly neutral. The low scores of Cluster 4 (satisfaction: 3.1; loyalty: 2.9) suggest that token rewards remain ineffective without a connection to authentic psychological needs, particularly when motivation and eco-identified self-concept are lacking, leading to diminished emotional commitment and sustained engagement. Results highlight the importance of psychographic segmentation in predicting both behavior and subsequent loyalty in attitudes after intervention. Segment-aware delivery changes in token systems might more effectively align the incentives with the user profiles, thus adjusting the smart contract outcomes to the changing user profiles in order to increase the engagement sustainability. 5.4 Sensitivity Analysis and Simulation Robustness A sensitivity analysis was performed to assess the robustness and generalizability of the simulation outcomes, during which five parameters were altered independently: token values, token visibility, intrinsic motivation weight, redemption threshold, and agent decision rounds. For each parameter, we changed one at a time while keeping the others constant to analyze the marginal effect of the parameter on eco-action frequency, token redemption satisfaction, and loyalty intention. The impacts of token value and motivation weight represent the most significant influences (26% and 22%, respectively). The modifications to token visibility resulted in significant increases in behavior (+ 18%). Increasing the redemption threshold from 50% to 80% also led to a decrease in satisfaction (− 0.3) and loyalty (− 0.4) behaviors and indicates the negative behavioral impact of implementing overly stringent reward criteria. This reinforces the notion that excessively high thresholds may inhibit the emotional account closure and behavioral reinforcement the system is intended to provide and strengthen the need for awareness-focused redemption strategies. The sensitivity analysis validated that behavioral and attitudinal results stemmed from structured psychographic-incentive interactions rather than random parameter changes—demonstrating internal consistency and simulation validity. Table 10 Sensitivity Test Results by Parameter Set. Parameter Varied Δ Eco-Actions (%) Δ Satisfaction Score Δ 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 results revealed that the simulation maintained structural stability and directional consistency over a wide range of input configurations and also served as a source of strategic insights for parameter tuning. The segmented view provided in Fig. 6 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 produced moderate yet reliable enhancements across behavioral and affective metrics, functioning as a perceptual cue that complemented motivational depth and perceived value without dominating the response architecture. 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. 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 the S-O-R framework. The visual appearance of tokens (H1) significantly increased eco-intention. The results indicate that these actions significantly influence the initiation of sustainability-oriented decision-making. The aspects of visibility, clarity, and shared token rewards significantly influenced cognitive accessibility and motivational importance for pre-behavior planning. 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 normative ecological attitudes into sustained behavioral participation (Qiu et al., 2023 ). 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) supported sustained behavior, revealing that agents with internal motivational alignment maintained consistent eco-actions across rounds, even as token formats and redemption thresholds varied. This indicates that efficient tokenization is more about interpersonal relationships related to social-psychosocial fit than about structural design (Chan, 2024 ). The research additionally indicated that the frequency with which individuals act and re-engage in pro-environmental behaviors is an associative variable (H5), which supports the frequency equating normality over time leading to a habit of learnt exposure to a stimulus and loyalty intention (Frías-Jamilena et al., 2022 ). Moreover, satisfaction with the redemption process (H6) significantly predicted future loyalty concerning emotions, where elevated satisfaction with the redemption process correlated positively with the intention to recommend and return (Boukis, 2024 ). This study verifies that sustainable practices in ecotourism involve more than just receiving rewards; there exist additional intangible aspects in the form of (1) cognitive, (2) affective, and (3) behavioral components, all of which are represented in the intricate model comprising six primary hypotheses. These dynamics showcase the architectural reasoning of S-O-R, where external signals (Stimuli), internal states (Organism), and behavioral outcomes (Response) interact through established psychological channels. 6.2 Theoretical and Practical Contributions By integrating TPB and SDT constructs into an agent-based modeling framework based on S-O-R logic, this research provides three main theoretical contributions. Firstly, it portrays the behavioral change over time, thus illustrating how eco-intention, action, satisfaction, and loyalty arise through repeated interactions rather than being single exposures. In contrast to static SEM models, ABM allows for process tracing across rounds and decision points (Baktash et al., 2023 ; Wallinger et al., 2023 ). Secondly, it depicts agent heterogeneity in the model, thus enabling differential responses across motivational and control traits and, therefore, the inclusion of psychographic segmentation that traditional models hardly account for (Patwary et al., 2024 ). Thirdly, the ABM design facilitates the examination of causal paths beyond correlation because the simulation encodes and performs the mediators and moderators that are explicitly in line with the TPB and SDT logic (Boukis, 2024 ; Chan, 2024 ). Hence, these contributions, as a whole, demonstrate how ABM can be used by tourism researchers to study the dynamic formation of behavior, identify heterogeneous response pathways, and exemplify design-level interventions with theoretical clarity. This article provides tangible tips to those who create ecotourism websites and are striving to increase sustainable participation. To begin with, token visibility carries out the role of activation and thus points out the need for eco-reward indicators that are not only visually powerful but also socially recognizable and “speaking” through symbolic actions (Choirisa et al., 2025 ). On the other hand, the setting of token value must match the effort and reward expectations of the users in order to keep the link between intention and action strong (Yu et al., 2024 ). One factor leading to customer loyalty is the creation of a continuous cycle of satisfaction through the reduction of redemption barriers via simple rules and fast implementation of smart contracts which in turn leads to customer loyalty growth (Boukis, 2024 ). Personalization of psychographics should be taken into account by the platforms, giving users incentives based on their characteristics such as intrinsic motivation and PBC (Patwary et al., 2024 ). If the behavioral limits of rewards like the cap or the fatigue threshold are applied, the token design will all the more adhere to ethical principles (Chica et al., 2023 ). These control mechanisms intertwined create a modular design structure for the tokenized sustainability systems that are not only psychologically appealing and ethically sound but also technologically adaptable. 6.3 Limitations and Future Research Directions The simulation-based framework, although it is considered strong with respect to internal validity and theoretical alignment, has a number of limitations as well. The phenomenon of token fatigue, which is when users get disengaged because of repetitive or too frequent rewards, was not simulated but it might still have a negative impact on long-term effectiveness in real-world settings (Chica et al., 2023 ). The second limitation is that the study does not consider ethical resistance, which may be perceived by individuals as the use of tokens being manipulative or transactional, thereby leading to the reduction of engagement among the users who are driven by their values (Rodrigues et al., 2023 ). The third limitation is that since the simulation is based on controlled and idealized profiles of the agents, the external validity is quite limited. Real-world behavior might vary due to differences in socio-cultural norms, as well as the design of the interface or network effects, which are not accounted for here. The limitations only reinforce the need for hybrid validation frameworks that go beyond the previous empirical surveys — combining agent-based models with behavioral experiments, A/B tests, and digital twin simulations. This would allow the incentive designs to be progressively calibrated across the controlled environments, while at the same time, taking into consideration user-level ethical acceptability and the variability of the real world. Future work would be well served with a mixed validation strategy that pairs ABM with empirical approaches to enhance realism and generalizability. First, lab experiments can be constructed to verify in controlled settings how individuals respond to tokenized incentive prompts that are substantiated in simulations to confirm causal links identified in simulations. Secondly, an A/B test of real-world ecotourism platforms can explore the changes in user behavior brought about by the different ways in which tokenized incentive cues are presented, valued, and redeemed. Thirdly, the involvement of travelers as co-creators can help to create the incentive system that is consistent with user valuation and morality, and is sensitive to the peculiarities of the different cultural contexts. Lastly, digital twins can be used to enable continuous adaptation of smart contracts that deliver personalized experience while ensuring behavioral integrity. This step-wise approach (simulation → lab → A/B field test → digital twin) provides both theoretical justification as well as practical foundation for progression from simulated real world engagement systems. Declarations Compliance with Ethical Standards: The authors declare no financial or personal conflicts of interest that could have influenced the research presented. This research did not involve any studies with human participants or animals. All data used in the simulation were synthetically generated and do not pertain to any real individuals or living subjects. Informed consent was not applicable, as the study did not involve any human participants or the collection of personal data. Funding Declaration: The authors declare that no funding was received for the development, execution, or publication of this research. 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05:57:00","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147026,"visible":true,"origin":"","legend":"","description":"","filename":"781cfe3a2623455eac6332a36276e8a51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/7fb711ab02067b8cf9343df4.xml"},{"id":95079818,"identity":"b60e8ed4-3fe3-4d9a-89ee-0368dc1a81bd","added_by":"auto","created_at":"2025-11-04 05:57:00","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153741,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/95b5fe185b6f9d600ad420a2.html"},{"id":95079796,"identity":"584919ab-a68f-4fc2-93cd-61bb14f669c1","added_by":"auto","created_at":"2025-11-04 05:57:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165510,"visible":true,"origin":"","legend":"\u003cp\u003eS–O–R simulation model of tokenized smart contract incentives and ecotourism engagement.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/c9226a3f6b1498d5976d86f3.png"},{"id":95223573,"identity":"e7a97d77-1a3c-44d9-ab07-095c62aa914b","added_by":"auto","created_at":"2025-11-05 16:22:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84744,"visible":true,"origin":"","legend":"\u003cp\u003eValidation loop linking simulation outputs to empirical ecotourism behavior patterns.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/db5196555287fcc4e7623faf.png"},{"id":95079798,"identity":"47b1b6be-e57f-465a-a0df-2909e03b7d7a","added_by":"auto","created_at":"2025-11-04 05:57:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28050,"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-8021398/v1/5f6b07a28e2dfb5cd66c35ce.png"},{"id":95225266,"identity":"9a3fbe76-35a9-468d-983b-bc5692d4170e","added_by":"auto","created_at":"2025-11-05 16:24:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57200,"visible":true,"origin":"","legend":"\u003cp\u003eBehavioral Engagement Metrics by Cluster.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/68284840362e51787b91f7ca.png"},{"id":95079801,"identity":"6fc7f7f8-f6d4-4778-9a54-f61fe8b5aa51","added_by":"auto","created_at":"2025-11-04 05:57:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55071,"visible":true,"origin":"","legend":"\u003cp\u003eLoyalty and Satisfaction by Cluster.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/a771eaa8a47fee4705dee245.png"},{"id":95223825,"identity":"8daa1e16-6e2a-49c4-8ed3-dbfc9d73857c","added_by":"auto","created_at":"2025-11-05 16:22:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80497,"visible":true,"origin":"","legend":"\u003cp\u003eOutcome Variability by Simulation Parameter.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/4ec75b7371243fb2f5b9a646.png"},{"id":97664708,"identity":"84f4a95b-d018-41df-bb7d-4fbe99c9730d","added_by":"auto","created_at":"2025-12-08 09:13:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1668789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8021398/v1/e7982ce6-fb45-4427-be07-b169e71ce738.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Intention to Loyalty: Unfolding Tokenized Smart Contract Incentives in Ecotourism via Agent-Based Modeling","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAlthough awareness of sustainability is a significant component of engaging in ecotourism marketing, an established intention\u0026ndash;action gap persists. Travelers frequently endorse eco-friendly principles but rarely act upon them, demonstrating that traditional pre-engagement cues may not lead to successful commitment by travelers (Viglia \u0026amp; Acuti, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nieto-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These strategies may be beneficial in activating cognitive awareness, but almost never succeed at ongoing commitment, or engage an emotional motivation towards 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=\"CR21\" 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. This challenge is approached here through S-O-R as an overarching structure, applying TPB to explain how perceived behavioral control (PBC) drives eco-intention and eco-action in tourism contexts.\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). Conventional loyalty programs are non-personalized, non-immediate, and non-psychologically meaningful to a traveler's motivational profile. Although such programs may achieve immediate compliance, they rarely sustain intention and commitment. This aspect is particularly relevant in cases where incentives seem to be generalized or delayed (Koo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); furthermore, legacy systems tend to be centralized and opaque, leading to a loss of trust among users (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 ought to engage a user in a way that generates value for them and modifies the cognitive and emotional feedback loops that are directly related to that engagement (Rodrigues et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An effective engagement system should provide incentives that are meaningful, observable, and based on rules that respond to user behavior in real time. Transparency can be provided through blockchain-based smart contracts, which can deliver tokens automatically, delivering trust and responsivity in ecotourism engagement (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSmart contracts enabled by blockchain technology have gained traction as a viable solution to address the inefficiencies that have impeded traditional incentive structures in ecotourism. They function as self-executing, rules-based contracts that terminate the delivery of transparent and verifiable tokens directly and increasingly toward sustainable behavior (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Barclay et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Smart contracts, rather than a centralized loyalty program with non-transparent third-party intermediaries, build trust and accountability into their design through decentralization and are secured in a blockchain ledger that makes all transactions immutable. This feature provides functionality in that reliable rewards are delivered in real time, thereby narrowing the intention\u0026ndash;action gap often presented in sustainability settings. Gamification-based design elements\u0026mdash;such as badges, progression levels, symbolic rewards, and milestone triggers\u0026mdash;are more than simply emotionally engaging rewards for repeating desirable behaviors that will further long-term engagement (Pasca, 2021). These elements, in conjunction with blockchain-based transparency, also serve to personalize the eco-rewards, which can now be aligned with your own motivational and behavioral profiles (Hosseinalibeiki \u0026amp; Heidari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fotiou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this way, blockchain and gamification are positioned as complementary enablers that operationalize incentive delivery within the S-O-R framework, rather than as independent theoretical constructs (Lim et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mountije et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile the conceptual appeal of blockchain mechanisms and gamified incentives is strong, their practical testing remains constrained by development costs, ethical dilemmas, and user heterogeneity (Chica et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For this reason, simulation data serves as a valid avenue for hypothesis testing. ABM, incorporated into the S-O-R framework, offers a dynamic approach to modeling the interactions between incentive stimuli and psychological characteristics over time (Wallinger et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This research utilizes TPB as the main theoretical base, illustrating how PBC influences eco-intention and eco-action, with SDT included as an additional element that reflects the continuity of intrinsic motivation (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The simulation features 200 agents with unique psychographic characteristics, allowing for a structured examination of how tokenized rewards affect intention, eco-action, satisfaction, and loyalty within a regulated setting. This framework creates a theoretically based structure that produces consistent, behaviorally realistic data. Overall, the framework shows how programmable, psychologically informed systems bridge the intention\u0026ndash;action gap in ecotourism.\u003c/p\u003e\u003cp\u003eThis research enhances eco-friendly tourism by recognizing eco-engagement as a continuous process rather than a fixed link between motivation and action. Combining TPB and SDT in the S-O-R framework reveals the changes in intention, behavior, and loyalty through constant interaction with tokenized rewards. Moreover, the ABM introduces a novel method by illustrating the gradual behavioral changes over time and different profiles, thus enabling the safe testing of various incentive systems. The framework is a customizable test environment for modifying token visibility, valuation, and redemption barriers to discover optimal settings. In contrast to previous survey methods, this approach accounts for temporal adaptation and path-dependence, illustrating when and for whom particular incentives maintain ecological commitment. The article, therefore, links behavioral theory to programmable design, associating psychological realism with blockchain-facilitated execution.\u003c/p\u003e"},{"header":"2 Behavioral Theories and Tokenized Eco-Engagement","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Blockchain-Based Incentives and Smart Contract Design\u003c/h2\u003e\u003cp\u003eBlockchain technology has evolved from backend efficiency uses to serve as a foundational infrastructure for sustainable incentive frameworks in tourism, especially in contexts demanding transparency and verifiable processes (Balasubramanian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Blockchain originally concentrated on applications like secure reservations and decentralized transactions, before transitioning to support decentralized incentive structures in ecotourism (Fotiou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In ecotourism, blockchain will influence through its permanence in documenting actions and immediate confirmation of sustainable practices, like carbon-neutral travel and eco-lodge accommodations (Rodrigues et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\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). Beyond being a technical upgrade, blockchain enables decentralized tracking of verifiable eco-actions (Thanasi-Bo\u0026ccedil;e \u0026amp; Hoxha, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this setting, smart contracts facilitate incentive distribution by connecting eco-friendly actions to set rewards. For instance, conservation efforts or community service can be tracked on-chain and automatically compensated with token 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 shift from static incentives to dynamic behavior alignment. In this context, users anticipate incentives in the form of tokens that serve as integrated nudges to influence their actions based on established reasoning and offer prompt feedback (Lim et al., \u003cspan citationid=\"CR20\" 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=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Smart contracts can deliver personalized micro-incentives by drawing on behavioral economic principles 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). This decentralization implies shifts in sustainability governance, including moving design authority away from centralized systems towards more agent-mediated structures (Thanasi-Bo\u0026ccedil;e \u0026amp; Hoxha, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Gamified Token Logic as Behavioral Ecotourism Stimuli\u003c/h2\u003e\u003cp\u003eGamification has effectively supported pro-environmental actions in tourism, turning abstract sustainability goals into interactive and emotionally captivating experiences (Pradhan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this research, gamification is regarded not as a conceptual framework but as a design tool that implements incentives within the S-O-R architecture. Gamification systems utilize game components such as points, badges, milestones, social comparisons, and prompt feedback to encourage cognitive and emotional participation, leading to action-driven engagement for eco-friendly behavior (Abou Kamar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These design features can improve PBC by making the visualization and repetition of eco-actions easier, thereby reinforcing the intention\u0026ndash;action connection highlighted in TPB. Frequent sporadic ecotourism can gain from consistent actions that strengthen engagement and commitment (Sesliokuyucu \u0026amp; Cobanoglu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, token-driven resources are integrating gamification into a tangible framework that offers specific rewards for performance-related behaviors (e.g., environmentally-friendly actions, sustainable travel options), aiding in the development of sustainability habits in the user (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Token systems can implement reinforcement strategies, employing structured reward mechanisms and adaptable value, to promote the development of lasting habits (Yu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Blockchain-based token systems simplify reward distribution, reduce administrative workload, and improve trust (Wang \u0026amp; Huang, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this way, gamification acts as a facilitating factor within the stimulus layer of S-O-R, engaging with TPB-based intentions instead of existing as a separate theory.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Intrinsic Motivation and PBC\u003c/h2\u003e\u003cp\u003eAs per SDT, intrinsic motivation provides a strong psychological basis for sustaining pro-environmental actions in ecotourism (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The greater the intrinsic values tourists assign to their eco-actions (e.g., duration at eco-lodges, readiness to selflessly contribute time and resources to conservation), the more probable they are to act in an ecological manner. Extrinsic tools, such as gamified incentives or symbolic eco-rewards, can complement rather than replace 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 engagement systems that respect SDT principles of autonomy and relevance may leverage intrinsic motivation and promote long-term eco-engagement. In simulations, intrinsic motivation is portrayed as a consistent characteristic, largely unaffected by the timing of external rewards (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When engagement systems resonate with users\u0026rsquo; values and autonomy requirements, they strengthen motivational consistency and sustainable eco-actions over time (Wang \u0026amp; Huang, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePBC, a key element of TPB, strengthens this framework by affecting tourists' belief in their ability to engage in sustainable actions (Abou Kamar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In ecotourism, PBC encompasses perceived awareness and the ease of engaging in low-impact actions\u0026mdash;elements that strongly correlate with behavioral intention and actual execution. ABM simulations indicate that agents with higher PBC respond more decisively to eco-stimuli and display consistent patterns over various decisions (Bhartiya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This control is engaging and can be efficiently modified through careful design, soliciting feedback, and ease of use (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). User-friendly systems that enhance PBC and offer actionable feedback can reinforce the alignment between intention and action in ecotourism decisions. These processes function dynamically and could influence elements of substantial, lasting ecological engagement (Sch\u0026ouml;nherr \u0026amp; Pikkemaat, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e offers a summary of key studies regarding token incentives and motivational elements in tourism, highlighting their frameworks, approaches, and relevance to the existing simulation-based technique.\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\u003eComparative Studies on Tokenized Incentives and Psychological Drivers in Tourism Behavior.\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTheory \u0026amp; Key Constructs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLimitation vs Present Study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKoo et al.\u003c/p\u003e\u003cp\u003e(2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoyalty programs; Satisfaction \u0026rarr; Commitment \u0026rarr; Loyalty; Switching Barriers.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvey (PLS-SEM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfaction boosts loyalty; switching barriers shape dynamics.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo dynamic behavior modeling; no token variables or ABM.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorne et al.\u003c/p\u003e\u003cp\u003e(2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAM\u0026thinsp;+\u0026thinsp;Trust\u0026thinsp;+\u0026thinsp;Promotion; Blockchain adoption (loyalty, booking, reviews).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvey (fsQCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUsefulness and trust form sufficient adoption paths.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFocus on adoption by managers; no eco-behavior constructs; no ABM.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQiu et al.\u003c/p\u003e\u003cp\u003e(2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOR; Credibility \u0026rarr; Image \u0026rarr; Attachment \u0026rarr; TERB.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 Surveys; Mediation (Bayesian)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCredibility strengthens TERB via place attachment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDestination-level stimulus; static; no tokens or agent feedback.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNguyen et al.\u003c/p\u003e\u003cp\u003e(2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtended TPB; PBC, Motivation, Moral Reflectiveness.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvey (SEM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMoral reflectiveness and TPB factors predict intention.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo gamification/tokens; no dynamic modeling or ABM.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbou Kamar et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAM\u0026thinsp;+\u0026thinsp;TPB; Eco-gamification \u0026rarr; Sustainability Knowledge \u0026rarr; TPB Constructs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eApp trial\u0026thinsp;+\u0026thinsp;Post-survey (SEM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGamification improves TPB-related intentions by increasing knowledge.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eShort-term trial; no satisfaction or loyalty; no token logic or ABM.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoukis\u003c/p\u003e\u003cp\u003e(2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-enhancement; Novelty \u0026amp; Ownership mediate token effects.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 Experiments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTokens increase loyalty via novelty and ownership.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eShort-term effects; no eco-action constructs; no dynamic feedback.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChoirisa et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGamification \u0026rarr; Engagement \u0026rarr; Loyalty.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvey (SmartPLS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGamification boosts emotional and cognitive loyalty pathways.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo eco-behavior constructs; static design; no token parameters.\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\u003eWhile tourism research has examined token visibility, reward framing, and motivational pathways, most studies employ cross-sectional designs that limit causal inference and overlook feedback mechanisms. Previous research, such as Boukis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lim et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Koo et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), predominantly conceptualizes behavior as a fixed intention instead of an outcome of changing exposure to incentives. Even research utilizing SDT and TPB seldom models how constructs engage dynamically under different token circumstances. Importantly, the design principles of tokens \u0026mdash; visibility, symbolic compared to material value, and user alignment \u0026mdash; are still insufficiently explored regarding personalization, satisfaction cycles, and loyalty development. This research tackles these shortcomings by modeling a dynamic eco-behavioral process through ABM. The model incorporates psychological factors into a s S-O-R framework, enabling parameterized evaluation of token strategies and their resulting influence on sustainable behavior and intention throughout various interaction stages.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Simulating Eco-Behavioral Dynamics through Tokenized Incentives","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Theoretical Foundations\u003c/h2\u003e\u003cp\u003eThe conceptual framework incorporates TPB as the main theory for predicting behavior, while SDT offers a different perspective to understand intrinsic motivation. SDT shows that intrinsic motivation encourages eco-engagement, particularly when tokenized rewards are perceived as promoting autonomy rather than being controlling (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). TPB suggests that attitudes, social norms, and perceived behavioral control forecast eco-intentions and following eco-behaviors, with programmable incentives affecting these connections (Nguyen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Four essential concepts are defined to merge theory with simulation. Eco-intention refers to an individual's inherent commitment to sustainability, influenced by acknowledged social norms and the demands of their surroundings. Eco-action encompasses the significant, environmentally conscious choices made by individuals, such as conserving resources or opting for sustainable alternatives. Token redemption satisfaction encompasses the user's overall experience with tokens, taking into account both positive and negative aspects, highlighting usability and perceived fairness. Loyalty intention refers to an individual's inclination to continue utilizing or endorsing the platform, influenced by ongoing engagement and common values.\u003c/p\u003e\u003cp\u003eThe S\u0026ndash;O\u0026ndash;R framework organizes the impact of external token-related incentives on internal decision-making processes. Stimuli\u0026mdash;defined by token visibility and value, and delivered via smart contracts\u0026mdash;activate internal states such as PBC and intrinsic drive. These internal reactions subsequently influence behaviors such as environmental intentions, the execution of eco-friendly actions, satisfaction, and loyalty. This causal chain is illustrated within the ABM framework, where every agent senses external factors, evaluates internal states, and generates varied results across time (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This micro-level, trait-focused decision model allows ABM to replicate behaviorally informed differences among agents successfully. In the analysis phase, the same S-O-R framework supports the interpretation of behavioral clustering and engagement variation across profiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 S\u0026ndash;O\u0026ndash;R Framework in Token-Based Agent Simulation\u003c/h2\u003e\u003cp\u003eThe simulation employs the S-O-R framework by converting TPB and SDT components into agent-based decision-making logic, using smart-contract parameters: visibility and value. Visibility serves as a stimulus-level signal, with social observability integrated into smart contracts as a toggle for public display. In actual platforms, this demonstrates how sustainability badges or eco-points are visibly presented to promote peer influence and social comparison. When tourists identify behaviors associated with tokens, eco-intention increases due to the salience of these behaviors and peer influence (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Incentive intensity represents the combined effect of token visibility and perceived value as active stimuli within the simulation. Visibility is portrayed as a categorical input with three tiers (low/medium/high) in the ABM, influencing agent salience weighting in every round.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eUnder repeated exposure, token visibility increases eco-intention by enhancing salience and social signaling.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eBeyond awareness, the perceived token value (implemented as a smart contract payout parameter) influences eco-action. If tourists believed that the tokens 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 ecotourism behavior. Perceived value triggers both extrinsic motivation and evaluation of cognitive cost-benefit, ultimately increasing the intention-action link (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Value is represented as a payout variable in a smart contract that affects agents' cost-benefit threshold for taking action.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003e\u003cem\u003eHigher perceived token value strengthens the intention\u0026ndash;action link, increasing eco-action likelihood across rounds.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003ePBC influences how tokenized incentive intensity (visibility and value) affects eco-intention, since individuals vary in perceived controllability (Akter \u0026amp; Hasan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each agent has a PBC trait (0\u0026ndash;1 scale) that adjusts intention strength in response to incentive cues.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003ePBC \u003cem\u003emoderates the effect of incentive intensity on eco-intention, amplifying the impact for agents with higher PBC.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn addition to perceived control, the nature of the motivation is also significant. According to SDT, greater intrinsic motivation boosts the transformation of tokenized incentive intensity into continuous eco-action, especially when incentives resonate with personal values (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the ABM, motivation emerges as a trait of the agent (low/medium/high), shaped by previous experiences and the alignment of values throughout time.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003e\u003cem\u003eIntrinsic motivation moderates the effect of incentive intensity on sustained eco-action, reinforcing persistence when values align.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eBehavioral change is a process marked by repetition and the establishment of habits, not a single event. In ecotourism, the rate of eco-action influences the route from initial exposure to incentives to the intention of loyalty. Tourists who participate in regular interactions are more inclined to form habitual behaviors. In terms of developmental continuity within a behavioral framework, the frequency of actions serves as the link between initial tourism motivations for visiting and sustained commitment to a destination (Fr\u0026iacute;as-Jamilena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the ABM, action frequency is tracked per agent across rounds and statistically modeled as a mediator between early exposure and loyalty output.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003cp\u003e\u003cem\u003eEco-action frequency mediates the pathway from initial incentive exposure to loyalty intention.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eLoyalty is dependent not only on frequency of behavior but also on affective satisfaction and, in particular, affective satisfaction with the experience of token redemption. Contentment with token redemption reflects the emotional assessment of the incentive experience and is anticipated to influence loyalty intentions (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the simulation, satisfaction is derived from the ease of redemption and friction; loyalty intention is a cumulative function assessed in the final rounds\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e\u003cp\u003e\u003cem\u003eSatisfaction with token redemption predicts loyalty intention by reinforcing affective evaluation of the platform.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn this context, smart contracts serve only as programmable delivery mechanisms for token visibility and value; they are not modeled as constructs or variables within the research framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Simulation and Token Stimulus Logic\u003c/h2\u003e\u003cp\u003eABM was preferred to survey-based or structural equation methods because of its ability to perform the dynamic analysis of causal mechanisms resulting from ongoing interactions. This choice ensures timing precision and allows intentional modifications of theoretical frameworks without the ethical or practical constraints of real-world testing (Baktash et al., 2024). Various simulation methods were tested and finally discarded because they cannot provide the same level of control over specific psychological characteristics and decision-making processes. In contrast, ABM allows each simulated tourist to operate based on a unique profile, including psychological traits. This renders ABM particularly appropriate for theory-based simulation consistent with TPB and SDT, wherein behavior arises from dynamic, multi-round interactions with stimuli. The capacity to simulate diverse agents engaging with changing incentives offers a realistic basis for investigating ecotourism choices.\u003c/p\u003e\u003cp\u003eThis research utilized a simulation-driven experimental approach, a variant of ABM, to explore the impact of tokenized smart contract incentives on engagement in ecotourism. The research created 200 artificial agents intended to model conceptual ecotourists. Eco-identity was identified as a characteristic not included in the model, used only for clustering (Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5.3\u003c/span\u003e), and not part of the theoretical model or hypotheses. TPB and SDT were operationalized through agent-level constructs guiding decision rules. The broader S-O-R architecture structures the simulation flow. Gamification is treated strictly as a delivery mechanism for incentive format (e.g., tiered rewards), not a theory or variable in the S-O-R model.\u003c/p\u003e\u003cp\u003eThroughout the simulation, agents engaged in ecotourism decision-making processes across 10 rounds. Each round featured exposure to flexible incentive stimuli, which included token visibility levels (low/medium/high), perceived token value (low/medium/high), and gamification frameworks (fixed versus tiered reward systems based on eco-action frequency). The smart contract's parameters regulated the tokens' visibility and perceived worth, while the gamification format defined the reward system's structure (either fixed or tiered). Agents\u0026rsquo; reactions were designed based on the prominence of stimuli and internal motivational alignment, producing behavioral choices that align with TPB concepts and practical decision-making reasoning. Although no real participant data were used, the simulation generated agent- and round-level behavioral outputs within a controlled experimental setting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Agent Profiling and Variable Mapping\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 and perceived token value), organism-level data (intrinsic motivation and PBC), and response-level data including eco-intention, eco-action, eco-action frequency, satisfaction with token redemption, and loyalty intention. Clustering profiles additionally included eco-identity as a segmentation trait (see Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5.3\u003c/span\u003e). While the data were synthetic, they were grounded in transparent, rule-based logic aligned with the S-O-R architecture, and modeled realistic behavior through time-series logging at both the agent and round levels (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for validation loop). The examination consisted of four phases: (1) descriptive statistics to condense behavioral distributions; (2) regression and path analysis to evaluate hypotheses H1\u0026ndash;H2 (main effects) and H3\u0026ndash;H6 (moderation and mediation); (3) psychographic grouping centered on intrinsic motivation, PBC, and eco-identity; and (4) sensitivity testing through alteration of incentive thresholds and motivation weights to gauge simulation reliability. The dataset for the simulation was created using a constant random-seed setting, guaranteeing precise reproducibility of all results presented (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for parameter information).\u003c/p\u003e\u003cp\u003eAlthough based on simulation, this study\u0026rsquo;s behavioral architecture is grounded in empirically supported psychological theories, ensuring cognitive and affective realism in the modeled pathways typical of sustainable tourism behavior. The model outcomes matched empirical results, validating that gamified stimuli affected eco-behaviors (Fr\u0026iacute;as-Jamilena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The intrinsic motivation and PBC moderating roles also appeared in the agent clusters and interaction models (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The loyalty effect corresponds with Boukis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), affirming that satisfaction with token redemption greatly impacted loyalty intention, consistent with the simulation\u0026rsquo;s attitudinal results. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e models the simulation-empirical alignment and proposed validation loop.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Theoretical-to-Operational Mapping","content":"\u003cp\u003eThis section describes how theoretical concepts relate to practical use, connecting the behavioral constructs mentioned in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e to their execution in the agent-based simulation. Each component of the S-O-R framework is linked to its parameterized counterpart in the model to ensure clear understanding in both concept and analysis. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes how token-level incentives, organismic traits, and behavioral responses were encoded as simulation variables and how each variable contributes to the tested hypotheses (H1\u0026ndash;H6). This mapping serves as the interpretive bridge between the conceptual model and the analytical outputs presented in the following section.\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\u003eConstruct-to-ABM Mapping.\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRole\u003c/p\u003e\u003cp\u003e(S/O/R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOperationalization in ABM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinked Hypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnalytical Role / Outcome\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3-level input (low/medium/high); public-display toggle.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredictor of Eco-Intention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToken Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmart-contract payout variable (low/medium/high).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredictor of Eco-Action\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgent trait (0\u0026ndash;1); wights intention formation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerator of Eco-Intention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntrinsic Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgent trait (low/medium/high).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerator of Sustained Eco-Action\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEco-Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous score per round.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH1\u0026ndash;H3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIntermediate outcome\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEco-Action\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBinary indicator of eco-behavior per round.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH2-H4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBehavioral Outcome\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEco-Action Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount of eco-actions per agent across rounds.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMediator (link to Satisfaction)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToken Redemption Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;5 scale derived from ease and clarity; final state represents loyalty intention.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredictor of Loyalty Intention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Simulation Output and Behavioral Overview\u003c/h2\u003e\u003cp\u003eThe simulation involved 200 agents executing 10 decision rounds under token-based incentive conditions defined by the parameters in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Two psychological traits\u0026mdash;intrinsic motivation and perceived behavioral control (PBC)\u0026mdash;were modeled as organism-level variables, while eco-identity was retained solely for post-simulation clustering (Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5.3\u003c/span\u003e). All incentive parameters and behavioral thresholds were applied using a consistent simulation logic to ensure internal consistency and replicability. Token conditions (visibility and value) were modeled as stimuli, and gamification together with eco-identity were treated as contextual elements rather than analytical variables. The experiment tested six structured hypotheses derived from the S-O-R framework and the TPB\u0026ndash;SDT cognitive\u0026ndash;affective architecture.\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\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 Presence\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 results arising from the tokenized incentive conditions showed clear and consistent impacts on metrics related to participation and loyalty (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Agents subjected to high-value token stimuli engaged in more eco-actions per round (7.2) than those in medium (5.9) and low-value (3.5) conditions. Increased involvement resulted in enhanced token acquisition and better redemption rates. In the high-value condition, 79% of agents reached the redemption threshold (\u0026ge;\u0026thinsp;60% eco-actions), while only 28% in the low-value group did. Emotional reactions showed a comparable trend: satisfaction with redemption grew from 2.7 (low) to 4.5 (high), while loyalty intention elevated from 2.8 to 4.6 on a 5-point scale. These results validate that greater incentives not only boost the frequency of actions but also improve satisfaction and loyalty after the behavior.\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\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\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Token Uptake Rate refers to agent interactions with tokenized incentives across rounds.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e validates these trends, illustrating a distinct rise in average eco-actions per agent with the increase in both token value and visibility. These variations in behavior provide descriptive backing for hypotheses H1 (visibility \u0026rarr; intention) and H2 (value \u0026rarr; action), establishing the foundation for future assessment of structural paths and moderation effects. The results support the fundamental reasoning of the S-O-R simulation, demonstrating that more prominent and valuable stimuli enhance behavioral engagement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Token Effects and Psychological Modulation of Eco-Intentions\u003c/h2\u003e\u003cp\u003eThe initial stage of the causal analysis focused on the direct impacts of the tokenized incentive elements\u0026mdash;mainly token visibility (H1) and perceived token value (H2)\u0026mdash;on ecotourism engagement outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both the predictors showed statistically significant relationships with their dependent variables. Token visibility brought about a standardized beta coefficient of β\u0026thinsp;=\u0026thinsp;0.42 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001); thus, it is proven that a higher visibility contributes to the cognitive accessibility and motivational salience of eco-intentions. In the same way, token value strongly influenced eco-behavior (β\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), which means that the perceived reward utility is the main factor that leads to the activation of sustainable behavior. The R\u0026sup2; values (0.34 for intention; 0.41 for action) indicate that token visibility and value explain a considerable portion of the behavioral variance. These results support the core reasoning of H1\u0026ndash;H2 and also verify the S-O-R stimulus\u0026ndash;response model, where perceptual cues (visibility) and evaluative expectancy (value) jointly lead to intention formation and behavioral execution.\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\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; .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-Action\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; .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\u003eThe subsequent phase of analysis examined how internal psychological factors\u0026mdash;PBC and intrinsic motivation\u0026mdash;moderate the effect of token rewards on outcomes related to ecotourism engagement (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). PBC boosted to a great extent the impact of the visibility of the token on eco-intention (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), thus showing that those who had more volitional confidence were able to assimilate token signals and mentally convert them into intention quite frequently. The results are consistent with TPB, which shows that PBC influences the perception of being easy and having the necessary skills for eco-friendly behaviors. Similarly, intrinsic motivation served as a moderator in the influence of token value on continuous behavior (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;.01); hence, those individuals who had more environmentally oriented values internally were the ones who repeated and consistent eco-friendly behaviors. The impact of these moderations goes deeper into the S-O-R organismic layer, suggesting that internal states play a major role in determining the occurrence of behaviors. Besides that, mediation analysis also showed that the frequency of eco-actions was a behavioral bridge between token exposure and loyalty intention; thus, a significant indirect effect was revealed (β\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings reinforce the \"Organism \u0026rarr; Response\" transition in the S-O-R framework, highlighting the vital role of psychological state variables in affecting and promoting engagement.\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\u003eModeration and Mediation results for H3-H5.\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=\"left\" 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\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\u003eOutcome Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteraction / Indirect Effect (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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 Visibility \u0026times; PBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEco-Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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 Value \u0026times; Intrinsic Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEco-Action\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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 Visibility \u0026amp; Value \u0026rarr; Eco-Action Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLoyalty Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eThe final phase of analysis examined whether satisfaction with token redemption directly predicts loyalty intention (H6), demonstrating an affective\u0026ndash;behavioral connection within the response layer of the S-O-R framework. The results of the regression analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, illustrating a strong direct effect (β\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Namely, a clear, trusted, and emotionally fulfilling assessment of the redemption experience served as a significant predictor of loyalty. This finding highlights the crucial issue of emotional resolution, essential for maintaining behavioral commitments in ecotourism contexts. In alignment with H6, the emotional resolution that follows token engagement is vital for transforming behavioral involvement into attitudinal commitment.\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\u003eDirect Effect Analysis: Satisfaction as a Predictor of Loyalty Intention (H6).\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=\"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\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\u003eOutcome Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect Size (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eLoyalty Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .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=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Psychographic Clustering and Segment-Level Analysis\u003c/h2\u003e\u003cp\u003eDistinct engagement profiles among simulated ecotourists were psychographically clustered to identify differences in the organizational traits of intrinsic motivation, eco-identity presence, and PBC. A diagnostic assessment was performed to confirm the clustering arrangement and to decide the quality and the stability of the four-cluster solution. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e demonstrates that the silhouette coefficient (0.71) and the average cluster stability (\u0026gt;\u0026thinsp;0.92) are the indicators of strong internal cohesion and external separation. The differences between clusters accounted for 63.5% of the total variation, thus being a double confirmation of the segmentation validity and the interpretative power.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClustering Diagnostics and Stability.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Clusters (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRetained based on interpretability and ABM logic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Silhouette Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery good cluster separation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween-Cluster Variance (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrong group separation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin-Cluster Variance (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcceptable intra-group cohesion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStability Across 10 Runs (Rand Index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh segmentation consistency\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\u003eFour clusters were identified based on the stability of convergence and usability of the profile (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\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) achieved average scores on the three engagement positions and consists of a group of simulated ecotourists with reduced psychological connection to sustainability goals. 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 ecotourism 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. This psychographic segmentation supports the organism layer of the S-O-R framework by identifying how internal traits condition responses to the same external stimuli, enabling more precise modeling of stimulus\u0026ndash;organism interactions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\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\u003eIntrinsic Motivation Level\u003c/p\u003e\u003cp\u003e(0\u0026ndash;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEco-Identity Presence\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePBC Score\u003c/p\u003e\u003cp\u003e(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 assess how psychological profiles influenced behavioral engagement, each cluster was examined using three criteria: frequency of eco-actions, token uptake rate, and redemption success (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e); clusters 1 and 2 exhibited greater engagement across all measures. High Resonance Seekers engaged in the most eco-friendly activities (14.2) and attained the highest overall token gain (92%) and redemption percentage (88%), demonstrating their strong motivation and stable identity. Nostalgic Resonators were involved too (13.1 eco-actions, 87% uptake), but their participation likely stemmed from an emotional connection to their identity rather than increased self-efficacy or personal agency. Cluster 3 ranked in the center (with 9.8 eco-actions) and was only marginally lower in uptake, suggesting a more disengaged connection to their response to the stimuli. Cluster 4 demonstrated the lowest level of engagement (7.4 eco-actions, 51% uptake, 47% success), likely linked to diminished intrinsic motivation and perceived control, which lessened involvement with tokenized incentives. This corresponds with TPB, where restricted perceived control diminishes intention development; identity characteristics are utilized solely for segmentation. Such differences demonstrate an internal psychographic framework of strategic potential, based on the idea of customizing strategies for specific segments. In the context of platform design, these cluster differences in technical deliberation highlight the need for multiple variations in the incentive. Individuals with high PBC may seek a symbolic form of incentive acknowledgment. In contrast, those with low PBC may benefit from more obvious and meaningful cue suggestions to help turn intention into action.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition to the differences in behavioral engagement, the clusters showed differences in affective engagement and loyalty potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cluster 1 had the highest average token redemption satisfaction of 4.6, and an average loyalty intention score of 4.5, which suggest a high level of overlap between prior psychological dispositions and responses to the incentives. Nostalgic Resonators had lower satisfaction and loyalty at 4.3 and 4.2 respectively. Therefore, their autonomy may have decreased slightly relative to C1's, but an emotional bond persisted sufficiently to maintain a commitment to behavioral engagement. Satisfaction (3.7) and loyalty (3.5) scores at C3 indicated that their engagement could be described as fairly neutral. The low scores of Cluster 4 (satisfaction: 3.1; loyalty: 2.9) suggest that token rewards remain ineffective without a connection to authentic psychological needs, particularly when motivation and eco-identified self-concept are lacking, leading to diminished emotional commitment and sustained engagement. Results highlight the importance of psychographic segmentation in predicting both behavior and subsequent loyalty in attitudes after intervention. Segment-aware delivery changes in token systems might more effectively align the incentives with the user profiles, thus adjusting the smart contract outcomes to the changing user profiles in order to increase the engagement sustainability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Sensitivity Analysis and Simulation Robustness\u003c/h2\u003e\u003cp\u003eA sensitivity analysis was performed to assess the robustness and generalizability of the simulation outcomes, during which five parameters were altered independently: token values, token visibility, intrinsic motivation weight, redemption threshold, and agent decision rounds. For each parameter, we changed one at a time while keeping the others constant to analyze the marginal effect of the parameter on eco-action frequency, token redemption satisfaction, and loyalty intention. The impacts of token value and motivation weight represent the most significant influences (26% and 22%, respectively). The modifications to token visibility resulted in significant increases in behavior (+\u0026thinsp;18%). Increasing the redemption threshold from 50% to 80% also led to a decrease in satisfaction (\u0026minus;\u0026thinsp;0.3) and loyalty (\u0026minus;\u0026thinsp;0.4) behaviors and indicates the negative behavioral impact of implementing overly stringent reward criteria. This reinforces the notion that excessively high thresholds may inhibit the emotional account closure and behavioral reinforcement the system is intended to provide and strengthen the need for awareness-focused redemption strategies. The sensitivity analysis validated that behavioral and attitudinal results stemmed from structured psychographic-incentive interactions rather than random parameter changes\u0026mdash;demonstrating internal consistency and simulation validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\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\u003eΔ Eco-Actions (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔ Satisfaction Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔ 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 results revealed that the simulation maintained structural stability and directional consistency over a wide range of input configurations and also served as a source of strategic insights for parameter tuning. The segmented view provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\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 produced moderate yet reliable enhancements across behavioral and affective metrics, functioning as a perceptual cue that complemented motivational depth and perceived value without dominating the response architecture. 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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Discussion and Implications","content":"\u003cdiv id=\"Sec18\" 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 the S-O-R framework. The visual appearance of tokens (H1) significantly increased eco-intention. The results indicate that these actions significantly influence the initiation of sustainability-oriented decision-making. The aspects of visibility, clarity, and shared token rewards significantly influenced cognitive accessibility and motivational importance for pre-behavior planning. 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 normative ecological attitudes into sustained behavioral participation (Qiu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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) supported sustained behavior, revealing that agents with internal motivational alignment maintained consistent eco-actions across rounds, even as token formats and redemption thresholds varied. This indicates that efficient tokenization is more about interpersonal relationships related to social-psychosocial fit than about structural design (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe research additionally indicated that the frequency with which individuals act and re-engage in pro-environmental behaviors is an associative variable (H5), which supports the 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). Moreover, satisfaction with the redemption process (H6) significantly predicted future loyalty concerning emotions, where elevated satisfaction with the redemption process correlated positively with the intention to recommend and return (Boukis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study verifies that sustainable practices in ecotourism involve more than just receiving rewards; there exist additional intangible aspects in the form of (1) cognitive, (2) affective, and (3) behavioral components, all of which are represented in the intricate model comprising six primary hypotheses. These dynamics showcase the architectural reasoning of S-O-R, where external signals (Stimuli), internal states (Organism), and behavioral outcomes (Response) interact through established psychological channels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Theoretical and Practical Contributions\u003c/h2\u003e\u003cp\u003eBy integrating TPB and SDT constructs into an agent-based modeling framework based on S-O-R logic, this research provides three main theoretical contributions. Firstly, it portrays the behavioral change over time, thus illustrating how eco-intention, action, satisfaction, and loyalty arise through repeated interactions rather than being single exposures. In contrast to static SEM models, ABM allows for process tracing across rounds and decision points (Baktash et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wallinger et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Secondly, it depicts agent heterogeneity in the model, thus enabling differential responses across motivational and control traits and, therefore, the inclusion of psychographic segmentation that traditional models hardly account for (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thirdly, the ABM design facilitates the examination of causal paths beyond correlation because the simulation encodes and performs the mediators and moderators that are explicitly in line with the TPB and SDT logic (Boukis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, these contributions, as a whole, demonstrate how ABM can be used by tourism researchers to study the dynamic formation of behavior, identify heterogeneous response pathways, and exemplify design-level interventions with theoretical clarity.\u003c/p\u003e\u003cp\u003eThis article provides tangible tips to those who create ecotourism websites and are striving to increase sustainable participation. To begin with, token visibility carries out the role of activation and thus points out the need for eco-reward indicators that are not only visually powerful but also socially recognizable and \u0026ldquo;speaking\u0026rdquo; through symbolic actions (Choirisa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, the setting of token value must match the effort and reward expectations of the users in order to keep the link between intention and action strong (Yu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One factor leading to customer loyalty is the creation of a continuous cycle of satisfaction through the reduction of redemption barriers via simple rules and fast implementation of smart contracts which in turn leads to customer loyalty growth (Boukis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Personalization of psychographics should be taken into account by the platforms, giving users incentives based on their characteristics such as intrinsic motivation and PBC (Patwary et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). If the behavioral limits of rewards like the cap or the fatigue threshold are applied, the token design will all the more adhere to ethical principles (Chica et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These control mechanisms intertwined create a modular design structure for the tokenized sustainability systems that are not only psychologically appealing and ethically sound but also technologically adaptable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Limitations and Future Research Directions\u003c/h2\u003e\u003cp\u003eThe simulation-based framework, although it is considered strong with respect to internal validity and theoretical alignment, has a number of limitations as well. The phenomenon of token fatigue, which is when users get disengaged because of repetitive or too frequent rewards, was not simulated but it might still have a negative impact on long-term effectiveness in real-world settings (Chica et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The second limitation is that the study does not consider ethical resistance, which may be perceived by individuals as the use of tokens being manipulative or transactional, thereby leading to the reduction of engagement among the users who are driven by their values (Rodrigues et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The third limitation is that since the simulation is based on controlled and idealized profiles of the agents, the external validity is quite limited. Real-world behavior might vary due to differences in socio-cultural norms, as well as the design of the interface or network effects, which are not accounted for here. The limitations only reinforce the need for hybrid validation frameworks that go beyond the previous empirical surveys \u0026mdash; combining agent-based models with behavioral experiments, A/B tests, and digital twin simulations. This would allow the incentive designs to be progressively calibrated across the controlled environments, while at the same time, taking into consideration user-level ethical acceptability and the variability of the real world.\u003c/p\u003e\u003cp\u003eFuture work would be well served with a mixed validation strategy that pairs ABM with empirical approaches to enhance realism and generalizability. First, lab experiments can be constructed to verify in controlled settings how individuals respond to tokenized incentive prompts that are substantiated in simulations to confirm causal links identified in simulations. Secondly, an A/B test of real-world ecotourism platforms can explore the changes in user behavior brought about by the different ways in which tokenized incentive cues are presented, valued, and redeemed. Thirdly, the involvement of travelers as co-creators can help to create the incentive system that is consistent with user valuation and morality, and is sensitive to the peculiarities of the different cultural contexts. Lastly, digital twins can be used to enable continuous adaptation of smart contracts that deliver personalized experience while ensuring behavioral integrity. This step-wise approach (simulation \u0026rarr; lab \u0026rarr; A/B field test \u0026rarr; digital twin) provides both theoretical justification as well as practical foundation for progression from simulated real world engagement systems.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or personal conflicts of interest that could have influenced the research presented.\u003c/p\u003e\n\u003cp\u003eThis research did not involve any studies with human participants or animals. All data used in the simulation were synthetically generated and do not pertain to any real individuals or living subjects.\u003c/p\u003e\n\u003cp\u003eInformed consent was not applicable, as the study did not involve any human participants or the collection of personal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funding was received for the development, execution, or publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution declaration: MH\u003c/strong\u003e wrote the main manuscript text and data analysis, review and editing, \u003cstrong\u003eHH\u003c/strong\u003e was responsible for data management, original draft preparation, visualization, and investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbou Kamar, M., Maher, A., Salem, I. E., \u0026amp; Elbaz, A. M. (2024). 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Tourism Review, 79(4), 939-952. https://doi.org/10.1108/TR-01-2023-0022 \u003c/li\u003e\n\u003cli\u003eSesliokuyucu, O. S., \u0026amp; Cobanoglu, C. (2025). The Implications of Gamification in Tourism. In Leveraging Digital Marketing for Tourism (pp. 243-264). Springer, Cham. https://doi.org/10.1007/978-3-031-88582-2_14 \u003c/li\u003e\n\u003cli\u003eThanasi-Bo\u0026ccedil;e, M., \u0026amp; Hoxha, J. (2025). Blockchain for Sustainable Development: A Systematic Review. Sustainability, 17(11), 4848. https://doi.org/10.3390/su17114848 \u003c/li\u003e\n\u003cli\u003eViglia, G., \u0026amp; Acuti, D. (2023). How to overcome the intention\u0026ndash;behavior gap in sustainable tourism: tourism agenda 2030 perspective article. Tourism Review , 78 (2), 321-325. https://doi.org/10.1108/TR-07-2022-0326 \u003c/li\u003e\n\u003cli\u003eWallinger, S., Grundner, L., Majic, I., \u0026amp; Lampoltshammer, T. J. (2023, January). Agent-based modelling for sustainable tourism. In ENTER22 e-Tourism Conference (pp. 355-360). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-25752-0_40 \u003c/li\u003e\n\u003cli\u003eWang, Y., Huang, L. (2025). Fostering Immersion and Visitor Engagement in Ecotourism Through Gamification and Interaction Design: A Case Study of Baiyun Mountain in Guangzhou. In: Kurosu, M., Hashizume, A. (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":"Tokenized Incentives, Ecotourism Behavior, Agent-Based Modeling (ABM), Sustainable Loyalty, Blockchain, Smart Contracts, Theory of Planned Behavior (TPB)","lastPublishedDoi":"10.21203/rs.3.rs-8021398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8021398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how tokenized incentives supported by blockchain-enabled smart contracts can influence eco-tourists\u0026rsquo; sustainable behaviors and loyalty. Even though the importance of environmental sustainability has been recognized, the tourism industry is still prone to an intention\u0026ndash;action gap, because conventional loyalty structures do not have the features of immediacy, personalization, and transparency. An agent-based modeling (ABM) simulation was thus created to overcome these constraints and it was based on the Stimulus\u0026ndash;Organism\u0026ndash;Response (S-O-R) framework. The model is primarily derived from the Theory of Planned Behavior (TPB) to elucidate the impact of perceived behavioral control on eco-intention and subsequently eco-action, with intrinsic motivation from Self-Determination Theory (SDT) serving as another psychological variable impacting persistence. A total of 200 synthetic agents were simulated over ten decision rounds when conditions of visibility, perceived value, and reward format were manipulated using gamification design principles. The results suggest the conditions of visibility and value increase eco-intention and eco-action; however, repeated actions and satisfaction in redeeming tokens emerge as more substantial predictors of loyalty intentions. The research indicates that tokenized incentives, with the help of smart contracts as automated and transparent tools, have the potential to increase the behavioral engagement of users and can serve as a foundation for a lasting loyalty program in context of ecotourism. The study applies a combination of TPB, ABM, and blockchain-enabled token systems, which add methodological and practical dimensions while providing actionable recommendations to address the intention-action gap in sustainable tourism through a programmable platform.\u003c/p\u003e","manuscriptTitle":"From Intention to Loyalty: Unfolding Tokenized Smart Contract Incentives in Ecotourism via Agent-Based Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 05:56:55","doi":"10.21203/rs.3.rs-8021398/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e06fc7fa-31a3-4782-b9fb-5ae2f05c4ad7","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T16:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 05:56:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8021398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8021398","identity":"rs-8021398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}