An Exploration of Behavioral Intention towards Participation in Sustainable Tourism Activities: A Response to the Coming of Smart Technology

An Exploration of Behavioral Intention towards Participation in Sustainable Tourism Activities: A Response to the Coming of Smart Technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An Exploration of Behavioral Intention towards Participation in Sustainable Tourism Activities: A Response to the Coming of Smart Technology Tai-Kuei Yu, Jeou-Shyan Jeou-Shyan, Yen-Po Fang, Chih-Hsing Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7492880/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 In the face of escalating climate risks, advancing sustainable tourism has become a critical imperative for the global tourism industry. This study addresses this challenge by exploring how smart technologies can enhance tourist engagement with sustainable destinations. Focusing on sport tourism centers as a key sector, this research synthesizes insights from interviews with 63 senior managers to analyze the determinants of visitor participation. It examines the interplay of key constructs—namely, 7P marketing strategy, information technology application, cognitive image, cognitive benefit, and risk perception—in shaping pro-sustainability intentions. The findings reveal a strong managerial consensus that smart technology is a powerful catalyst for sustainable engagement. Specifically, information technology application and perceived cognitive benefits are identified as significant direct drivers of a tourist's intention to participate in sustainable tourism. Furthermore, the study delineates the pathways to these outcomes: a well-executed 7P marketing strategy and effective management of risk perception positively influence cognitive benefits, while both information technology application and risk perception are crucial in shaping a favorable cognitive image. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology sustainable tourism behavior information technology application cognitive image cognitive benefit marketing strategy Figures Figure 1 1. Introduction Smart technology, including artificial intelligence, VR/AR, blockchain, big data, and sharing economy, has become an important trend in recent years in tourism industry, taking lead of the development of global smart industry. Through instant feedback, transparency, market segmentation, decision-making support, product and service innovation, smart technology can improve the commercial value of knowledge-intensive industries. The era of Internet technology has seen the prosperity of online communities, in which consumers often disclose their personal emotions and make comments on consumer products, creating a forum of word of mouth where enterprises can obtain direct access to consumer evaluations. As a result, businesses have attached great importance to online brand marketing and have regarded it as one of the most important methods of corporate marketing; at the same time, they have started to pay attention to the impacts and effects of smart technology on their businesses, especially the business rends brought by smart technology (Jenong & Shin, 2020). Sustainability, on the other hand, is an important issue for the environment of current global enterprises and businesses. It can facilitate the performance improvement of the tourism industry and the acquisition of competitive advantages, and it can be a marketing issue for the industry (Sørensen & Grindsted, 2021 ). From the perspective of an operator in the sustainable tourism industry, it is challenging to investigate and learn about the factors that affect consumers' participation in sustainable tourism activities and the correlation between these different factors. This study takes senior managers of the sustainable tourism industry as its subjects, trying to construct a behavioral theory that clarifies how different impact constructs—marketing strategy, information technology application, cognitive image, risk perception, and cognitive benefit—can influence customers' intention to engage in sustainable tourism activities and how these important constructs can be correlated. The results of this study can be an important reference for people to understand and organize sustainable tourism activities. 2. Literature review 2.1 Behavioral intentions of sustainable tourism activities A green and sustainable tourism environment offers people a great variety of benefits, such as reducing stresses, improving cognitive functions, maintaining healthy psychology, strengthening social ties, increasing safety, and reducing crime. Nisbet & Zelenski ( 2011 ) argue that a happy path to sustainable development is to encourage people to get close to nature rather than adopt an ecologically sustainable lifestyle through fears, obligations, or economic incentives. Sustainable goals have become the focus of studies on tourism's contribution to sustainable development; it is now also a way for companies to increase the competitive advantages and profits of their businesses (Padgett & Moura- Leite, 2012). Sustainable tourism aims for consumers engaging in tourism activities to protect the environment, promote economic benefits, strike a balance among different constructs such as social justice and cultural integrity, improve people's living standards, and ultimately develop long-term sustainability (Liu et al., 2013 ). Inducing the concept of sustainability into the tourism industry can reduce the negative impacts that may come along with the development of tourism industry. Therefore, sustainable tourism has become an ideal goal for the development of tourism industry. For the sustainable tourism industry, there are several possible development strategies, such as (1) developing local tourism, (2) reducing group travel, (3) opting for sustainable tourism, and (4) opting for more sustainable activities. In hospitality and tourism industries, there have been quite a few studies discussing the relationship between industry and sustainability. Some studies have focused on the significance and difficulty of improving sustainability, while others have identified sustainable tourism as a key tool for improving competitiveness (Leonidou et al., 2013). Dudensing et al. ( 2011 ) find that the marketing strategies that stakeholders of various tourisms take are of a great diversity, resulting in the heterogeneity in tourism consumption and conflicting expectations among stakeholders. Various tourisms have taken sustainability into consideration, developing a series of sustainable products and various products related to sustainable tourism, ensuring a fair economy with high standards (e.g. local sourcing), a positive socio-cultural impact (e.g. protection of cultural heritage), and a friendly environment (e.g. green management) (della Corte & Aria, 2016 ; Golding et al., 2025 ). 2.2 Marketing strategy and information technology application Early marketing strategies came with the key elements of 4Ps (McCarthy, 1979), which have been widely used in businesses and many other fields. The 4Ps refer to promotion, place, product, and price, which Kolter (1999) believes to have become inadequate for a marketing strategy in a society of great complexity and diversity. He adds politics and public opinion to the original 4Ps, forming a marketing mix with 6Ps. Booms & Bitner (1981) revise the 4Ps marketing mix proposed by McCarthy (1979) into one with 7Ps, adding the other 3Ps—people, process, and physical evidence. Pomering ( 2017 ) adds sustainable participants, processes, and physical evidence to the traditional 4P marketing strategy, together with the three key elements of promise, principles, and partnership. He argues that each element can be viewed as a controllable marketing variable that contributes to the creation/co-creation of individual and social values. Information technology application and marketing strategy play key roles in business operations in the digital era. Most consumers tend to acquire product knowledge through online information before making purchases rather than merely rely on on-site marketing services. As consumers pay more and more attention to online word of mouth, the influence of the consumption experience of strangers or Internet celebrities on consumers has also increased. In terms of word-of-mouth marketing, the physical world needs populace, while the virtual world needs popularity. In the digital era, big data has been integrated with marketing to generate new marketing theories. Fan et al. ( 2015 ) propose a new 5P marketing mix (people, product, promotion, price, place), arguing that big data can provide consumers with detailed personalized information and personalized marketing, turning every consumer into a niche market. Inanc–Demir & Kozak (2019) believe that in the context of the traditional marketing mix with 4Ps (product, promotion, price and place), big data analysis and technology can be used to update and maintain the operation and management of the tourism and hospitality industry. Information sharing can be generally defined as the exchange of meaningful and timely information between participants in a formal or informal way. In the digital era, the rapid development of technology has improved the speed and convenience of globalization, and the information sharing across different cultures, regions, societies, and individuals has become more important and more common. Therefore, information sharing is now one of the key factors in marketing strategy. When an adequate number of individuals invest a considerable amount of genuine human interest in information sharing or open discussion over an adequate length of time, a network of individual relationships will be formed in cyberspace, and social aggregation will occur in the network. They can share similar virtual enjoyment without limitation to geographical boundary. They can continuously participate in discussions, sharing their information beyond the limit of time and space (Milne & Callahan, 2006 ). For operators of sustainable tourism in the digital era, providing consumers with personalized information is a key factor to consumers’ participation in sustainable tourism activities. This is because the consumption process of the digital era has changed from a purely linear model to a cyclical purchasing process. As a result, information technology application plays a very important role in the tourism industry (de Kervenoael, 2020). 2.3 Behavioral theory of engaging in sustainable tourism activities Early research on sustainable education and environmental education mainly focused on knowledge, attitude, and behavior (K-A-B), as well as the interrelationships of these three elements (Hart, 1981 ). A lot of environmental education and environmental literacy frameworks (Hungerford & Volk, 1990 ) particularly concentrated on attitude and behavior (Simmons, 1995 ). In general, factors that may influence people’s engagement in sustainable tourism can be broadly categorized into cognitive factors and affective factors. Some studies have found that environmental knowledge can directly affect environmental behavior (Frick et al., 2004 ). However, other studies have indicated that environmental knowledge affects behavioral intention or pro-environmental behavior through affective factors—value, attitude, sensitivity, and a sense of responsibility (Wang et al., 2014 ). Some studies have also found that their empirical results do not conform to the traditional K-A-B environmental education model, with a gap either between knowledge and behavior or between attitude and behavior (Young et al., 2010 ). Ram et al. ( 2013 ) constructs a conceptual model suitable for sustainable tourism, a model with happiness, travel motivations, and perception of distance. The model shows that happiness, which is indeed an integral part of tourist experience, calls for a need to formulate effective strategies to break the traditional speed-distance-demand loop, bring changes to transport and infrastructure policies, and recognize the key role of happiness in sustainable tourism strategies. For the behavioral patterns of sustainable tourism consumers or pro-environmental behaviors, the Theory of Planned Behavior (TPB) and Value-Belief-Norm theory (VBN) are two common theoretical models. TPB (Ajzen, 2015 ) divides the factors that affect behavioral intentions into three cognitive constructs: attitude, subjective norm, and perceived behavioral control. The VBN model (Stern, 2000 ) divides the factors that affect environmentally responsible behavior into four constructs: value, belief, norm, and behavior. Over the past years, TPB has been applied to discussing pro-environmental behaviors, such as consumers’ pro-environmental behaviors and sustainable tourism behaviors (Kaplan et al., 2015 ; ud Din et al., 2025 ). Some scholars have enhanced and expanded TPB, taking into consideration the influence of preceding, mediating, or intervening variables on behavioral intention, such as emotions, desires, service quality, customer satisfaction, overall image, and the frequency of past behavior (Han & Kim, 2010 ). The key factors affecting consumer behavior in the tourism industry, as Cohen et al. ( 2014 ) point out, are decision-making, value, motivation, self-concept and personality, expectation, attitude, perception, satisfaction, trust and loyalty. When consumers change their lifestyles or take any alternative ones with sustainable features to promote sustainable development, environmental loads can be significantly alleviated (Stern, 2000 ), and the ecological footprint of products in production, use, or disposal can be reduced. However, the decision-making process in which green consumers participate has become increasingly complex, involving not only individual needs and desires but also corporate social responsibility (Young et al., 2010 ; Park et al., 2022 ). Schaefer & Crane ( 2005 ) argue that green consumers “are thought to be motivated by strong environmental values and attitudes, therefore seeking environmental product information, rationally weighing the utility provided by a particular product against the environmental cost attached and making a purchasing decision based on these environmental criteria in conjunction with more conventional considerations of price, quality, and convenience.” Risk perception is often used to describe people's attitudes and intuitive judgments on risk, and tourism risk has received extensive attention from scholars working on cognitive psychology and consumer behavior (Somez & Graefe 1998). Tourism risk perception can directly affect customers' intention to purchase tourism goods (Cui et al., 2016 ; Wang et al., 2019 ); however, it is susceptible to subjective factors, objective factors, and visitors’ perception of the negative impact on tourism activities. Subjective factors include physiological characteristics and psychological processes, while objective factors include physical risk, economic risk, equipment risk, social risk, psychological risk, time risk, and opportunity loss. Yang et al. (2014) found that uncertainty, worry, fear, and anxiety are closely related to risk perception as they review the concepts and theories of risk perception, conceptualize various constructs of risk perception, and enhance the understanding of tourists' risk perception. Mohaidin et al. ( 2017 ) indicate that those who engage in tourism activities have a specific threshold for risk perception; therefore, quantitative assessment of tourism risk can help customers deal with tourism decision-making and destination management. They also find that environmental attitude, motivation, and word-of-mouth have a significant impact on tourists’ intention to select sustainable tourist destinations. Cognitive image is an important key for consumers to evaluate product quality. When consumers do not have sufficient knowledge about a product and its functions, unable to evaluate product quality correctly, their perceived risk will increase. At this time, if there is a better brand image or a better cognitive image to be a product endorsement or quality assurance, consumers' perception of product quality will be improved (Woosnam et al., 2020 ). Zhang et al. ( 2014 ) applies meta-analysis to investigate the relationship between tourist destination image and tourist loyalty, finds that tourist destination image has a significant impact on tourist loyalty and that overall image, affective image, and cognitive image have the greatest impact on tourist loyalty. As for the cognitive image of sustainable tourism and pro-environmental behavior, Lee et al. (2010) indicate that the cognitive image (including value and quality) has a positive impact on the affective image and overall image of a green hotel, while the affective image has a positive impact on the overall image of the green hotel. Peña et al. ( 2012 ) explore the constructs of cognitive image for rural tourism destinations and find that the positive cognitive image of a tourist destination—a group of symbolic aspects that encompass destinations, services, culture, natural events, local products and gastronomy—has an important incentive effect on consumer behavior (such as satisfaction and loyalty). The cognitive benefits of participating in sustainable tourism activities can be divided into two categories: (1) economic benefits measured by money; (2) positive improvement benefits for individuals (Driver et al., 1991 ). Public participation in tourism activities brings forth a lot of positive effects, including enhancing the image of the organizer or the destination, generating income, improving economic and social development, and many others. However, it also produces such negative effects as traffic congestion and real estate speculation at tourist destinations (Kim & Petrick, 2005 ). These will form an impact on the cognitive benefits of individuals participating in tourism activities. For an organizer of tourism activities, the cognitive benefits derived from the activities can be classified into economic benefits, structural benefits, and self-efficacy. Tourists participating in the tourism activities of multicultural festivals can obtain natural recovery, transformation, cognition, and social and emotional benefits (Liu et al, 2016 ). Tourists can also gain cognitive benefits from participating in tourism activities that encourage pro-environmental behavior. For example, Zeppel ( 2008 ) shows in a meta-analysis that cognitive and affective empathy with marine wildlife contributes to creating longer-term intention to engage in marine conservation actions. Participation in sustainable tourism activities (hikers, runners, and walkers in national parks) that offer cognitive benefits helps create relaxation, pleasure, improved moods, and other health effects. Cognitive benefit is also an important factor that can influence one's intention to participate in sustainable tourism activities or take pro-environmental behaviors (Oppewal et al., 2015 ). The Social Cognitive Theory was applied to examine the motivation of sustainable tourism and different types of sustainable tourism people undertake. They find that those who take sustainable action are subject to the level of their individual's empathy with and attachment to sustainability, which can be explained in relation to the two constructs of personal benefits and cultural focus (Font et al., 2016 ). The Social Capital Theory to community-based ecotourism, could be utilized to understand and improve the cooperation between the residents of host communities and the development of community-based ecotourism, thereby encouraging residents to take pro-environmental behaviors. The model results represented that economic benefits have a direct impact on residents' pro-environmental behaviors, while cognitive social capital has a partial mediating effect on the relationship between the residents and the ecotourism (Liu et al., 2014 ). 3. Research Materials and Methodology 3.1 Research Materials Taking the management-level personnel of the industry as the respondents of the questionnaire survey, this study evaluates and predicts the influencing factors that will affect consumers' participation in sustainable tourism activities and the correlation among different influencing factors. It further employs structural equations (statistical tools using SmartPLS model) to verify the causal patterns of participation in sustainable tourism activities. For the situational design of this questionnaire experiment, first, the potential variables and measurement variables of the sustainable tourism behavioral model were collected, the experimental situation framework was constructed, and the questionnaire content was formulated. Next, experts were invited to conduct three inspections of the content of the experimental situation, followed by a pre-test and a post-test. After the results of the questionnaire research had been converged and a certain degree of consensus had been reached, a behavioral model for participation in sustainable tourism activities was established in accordance with the theoretical basis of the literature review. Sustainable tourism was defined as tourism that meets the needs of present tourists and host regions while protecting and enhancing opportunity for the future (World Tourism Organization. 1993). Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes (World Tourism Organization. 1993). As for the selection of sustainable tourism industries, this study took as its survey respondents the management-level personnel of the sport centers in New Taipei City and Taipei City, Taiwan. Based on the definition of sustainable tourism above, sport centers are clear and suitable targets. Since the first sport center opened in Taipei in 2003, sport centers in Taiwan have become the best venues for sports tourism to the public. In the evolving discourse of sustainable tourism, the focus is increasingly shifting towards models that enhance the well-being of local populations while championing environmental stewardship. A compelling case study in this domain emerges from Taiwan, where municipal sports centers (climate service institutions) (Mahon et al., 2021 Riach & Glaser, 2024 ) represent a unique and replicable model of urban-centric, participatory sustainable tourism. This analysis examines the sports centers of Taipei and New Taipei City, positing that their long-term viability and contribution to urban sustainability are intrinsically linked to a strategic embrace of digital transformation. By leveraging smart technologies, these facilities can transcend their traditional roles, evolving into dynamic hubs for community health, operational efficiency, and environmental responsibility. The establishment of these sports centers was a deliberate act of public policy aimed at fostering a healthier citizenry. The initiative was rooted in a 2002 policy vision by the Taipei City Government to establish at least one accessible sports facility in each administrative district. The objective was clear: to cultivate a pervasive sports culture and transform Taipei into a "Healthy City." This vision gained national momentum with the 2010 "Sports Island Plan," which funded the construction of dozens of national sports centers. The goal was to provide diverse and affordable athletic options, thereby encouraging regular exercise habits among the public. From a sustainable tourism perspective, these centers are exemplary. They cater primarily to the local community, reducing the carbon footprint associated with long-distance travel. Their operational model, where service consumption and delivery occur simultaneously, fosters direct engagement and community building—hallmarks of a socially sustainable enterprise. Furthermore, many were conceived with green building principles in mind, incorporating features like solar power, water-saving fixtures, rainwater harvesting systems, and natural ventilation to minimize their ecological impact. Despite their public mandate and inherent sustainability credentials, these centers operate within a highly competitive marketplace. They face significant operational challenges, including intense competition from private gyms, overlapping service areas, and the sophisticated marketing strategies of rivals. To thrive, operators must move beyond simply offering facilities; they must deeply understand and anticipate consumer needs, enhance customer loyalty, and attract new demographics. The central question becomes: How can these centers overcome market pressures while simultaneously deepening their commitment to sustainability and improving the user experience? The answer lies in the strategic integration of smart technology. The application of smart technology offers a powerful, multi-faceted solution to these challenges, creating a symbiotic relationship between operational excellence, user engagement, and sustainability. Within the designated area of study, the application of smart technologies in sports centers can be broadly classified into three main categories: (1) Intelligent Infrastructure for Sustainable Operations: At the most fundamental level, technology can optimize the physical management of the centers. The Internet of Things (IoT) is paramount here. By embedding sensors throughout a facility, operators can gather real-time data on everything from occupancy in the swimming pool to the usage of specific gym equipment. This data, integrated with an IoT platform, enables dynamic resource allocation. For instance, HVAC (heating, ventilation, and air conditioning) and lighting systems can be automatically adjusted based on real-time occupancy, drastically reducing energy consumption. Advanced systems for voltage regulation and smart water management further contribute to environmental goals, aligning operations with UN Sustainable Development Goal 7 (Affordable and Clean Energy) and making these centers showcases for sustainable urban infrastructure. (2) Data-Driven Operations and Personalized Wellness: Beyond infrastructure, technology revolutionizes the understanding of the customer. By utilizing cloud computing platforms, sports centers can aggregate and analyze vast amounts of user data, including visit frequency, class participation rates, and equipment usage patterns. Leading operators are already using mobile applications that integrate with cloud backends to provide management with insightful operational reports and users with personalized progress tracking. The application of Artificial Intelligence (AI) and Machine Learning (ML) takes this a step further. When integrated with wearable devices, the center's app can capture biometric data like heart rate and calories burned. AI algorithms can then analyze this information to generate optimized and scientifically grounded exercise prescriptions tailored to specific user groups, such as senior citizens or women. This data-driven approach to health management embodies the trend toward "precision health," elevating the center from a simple sports venue to a sophisticated wellness partner. (3)Enhancing the User Experience through Seamless Digital Integration: Digital tools are critical for streamlining the entire customer journey. Mobile applications, connected via APIs to the core management system, have become central to the user experience. They offer frictionless services such as online class booking, secure digital payments through popular platforms, and QR code-based facility access. This user-centric design not only significantly enhances convenience and satisfaction by reducing waiting times but also improves service accessibility. From an operational standpoint, this digital-first approach streamlines check-in processes, reduces administrative labor costs, and represents a classic example of successful digital transformation in the service industry. 3.2 Research design In the face of smart technology era, this study aims to construct and verify the behavioral model of participation intention in sustainable tourism activities. According to the literature review above, there are six major interacting constructs: information technology application, cognitive image, risk perception, marketing strategy, cognitive benefit, and sustainable tourism behavior. The measurement scales related to smart technology and sustainable tourism are described as follows: (1) Information technology application includes information sharing, website quality (including website design and overall information function of the Internet), with a measurement scale of information sharing modified from Bock et al. ( 2005 ) and a measurement scale of website quality modified from the scale established by Tsai et al. ( 2010 ); (2) Cognitive image includes attraction characteristics, service atmosphere, and activities and events, with a measurement scale based on Royo-Vela ( 2009 ); (3) Risk perception is given a measurement scale based on the concepts of risk perception proposed by Cui et al. ( 2016 ); (4) Marketing strategy is given a measurement scale modified from those proposed by Pogorelova et al. ( 2016 ), including promotion, place, product, price, people, process, and physical evidence; (5) Cognitive benefits of tourism activities include relaxation benefits, health benefits, and experiential benefits, with a measurement scale modified from Chen & Petrick ( 2016 ); (6) Behavioral intention of sustainable tourism activities is given a measurement scale based on Han et al. ( 2011 ). The constructs above were measured in accordance with the extent to which the respondents regard the question item as important, on a 7-point Likert scale from "Not important" to "very important," with 1 to 7 points in sequence. 63 questionnaires were administered, and 63 questionnaires were returned, with a response rate of 100%. The paper questionnaires were distributed mainly at the Taipei sport tourism centers, and the subjects of the questionnaires were the management-level personnel of several sport tourism centers. 3.3 Statistical methods, reliability analysis, and validity analysis In the sample analysis, this study used two sets of tools—SPSS 22.0 for Windows and Smart PLS—for the statistical analysis after data collection. In terms of descriptive statistics, this study mainly used SPSS 22.0 to analyze the variables of population sample, since its structural equations are suitable for predicting highly complex patterns (Chin, 1998 ). The main reasons why this study used PLS for structural and measurement model analysis were: (1) Compared with LISREL or AMOS, PLS has fewer restrictions on sampling, and the sample distribution requires no more than normal distribution (Gefen, Straub, & Boudreau, 2000 ); (2) the PLS structural model can support confirmatory and exploratory research without a strong theoretical foundation. However, LISREL requires a strong theoretical foundation to conduct empirical research (Gefen et al., 2000 ). In this study, the PLS structural equation was used for model construction and data analysis. In the actual data analysis, SmartPLS 3.0, developed by Ringle et al. ( 2015 ), was used to analyze the measurement model and structural model. The bootstrapping analysis was used to obtain standard errors and t-values of parameter estimates. Since it would be easier to find a fitting pattern when the research pattern identification was repeated, 500 bootstrap samples were applied in this paper. Anderson & Gerbing ( 1988 ) suggest that a measurement model analysis should (1) ensure that, in terms of the overall model, each measurement variable of the verification model can correctly measure its latent variable; (2) determine the complex measurement variables of the test load value in different patent variables, which are the two important construct validities of the test model: Convergent validity—when related variables are measured in different ways, their correlation with each other must be high; in other words, when the same thing is measured, the measurement score and the result should be the same; Discriminant validity—when two different concepts were measured, the correlation analysis of the measurement results must show a low correlation, regardless of whether the measurer used the same method or different ones. 4. Results and discussions 4.1 Reliability and validity Based on the recommendation of Bagozzi & Yi ( 1988 ), this study evaluated the measurement model of reflective indicators by the three most commonly used indicators in the following: (1) Individual item reliability: It measures the factor loadings of the variables against latent variable, and examines the statistical significance of each variable loading. All the factor loadings in this study were significantly higher than the recommended value of 0.5, which was in line with the value recommended by Hair et al. ( 2010 ), and the factor loading coefficients of the tested samples ranged from 0.689 to 0.922. (2) Composite reliability (CR): The CR value of a latent variable is the composition of the reliability of all its measurement variables, and its index significance is similar to Cronbachs Alpha, which is used to indicate the internal consistency of the construct index. Fornell & Larcker ( 1981 ) suggest that the CR value should be above 0.6, and the parameter estimation should be based on different measurements (standardized or unstandardized). In addition, SmartPLS provides the reliability index of Rho_a. Dijkstra & Henseler ( 2015 ) suggest that the Rho_a coefficient should be above 0.7. The higher reliability means a greater internal consistency for latent variables. In this study, the CR values of the tested samples ranged from 0.830 to 0.925, and the Rho_a coefficients ranged from 0.811 to 0.924. All the indicators above showed that the internal consistency of the research model was good, which met the reliable recommended value—greater than 0.7. When measured by the Cronbachs Alpha coefficient, the Cronbachs Alpha of each variable in the research model was between 0.730 and 0.901, which met the criterion that the Cronbachs Alpha needs to be greater than 0.7. (3) Average variance extracted (AVE): It measures the variance explanatory power of each measurement variable in the calculation of the latent variable. The higher AVE means better discriminant validity and convergent validity for the latent variable. The standard value, as Fornell & Larcker ( 1981 ) suggest, should be greater than 0.5. In this study, the AVE value of each latent variable in the tested sample was between 0.623 and 0.804. Relevant data of reliability and validity tests are shown in Table 1 . As the analyses above show, the latent variables in the measurement model of this study had good convergent validity. Table 1 Reliability and validity test of the measurement model Latent Variable CA rho_A CR AVE Information Technology Application 0.878 0.891 0.925 0.804 Risk Perception 0.890 0.892 0.919 0.695 Cognitive Image 0.730 0.836 0.830 0.623 Marketing Strategy 0.901 0.924 0.923 0.635 Cognitive Benefit 0.810 0.811 0.887 0.724 Behavioral Intention of Sustainable Tourism 0.825 0.831 0.885 0.658 CA: Cronbach's Alpha; rho_A: rho_A reliability coefficient; CR: Composite Reliability; AVE: Average Variance Extracted Insert Table 1 here Table 1 Reliability and validity test of the measurement model In addition, the measurement model needed to be tested for discriminant validity. As stated above, when two different concepts are measured, the correlation analysis of the measurement results must show a low correlation, regardless of whether the measurer uses the same method or different ones. When the PLS analysis was adopted in this study, the measurement indicators based on Fornell & Larcker ( 1981 ) (see Table 2 ) showed that the measured result coefficients met the threshold requirement that the diagonal coefficient must be higher than the coefficient of research variables. Table 2 Correlation coefficient matrix of research variables Latent Variable ITA RP CI MS CB BIST Information Technology Application, ITA 0.897 Risk Perception, RP 0.593 0.834 Cognitive Image, CI 0.447 0.431 0.790 Marketing Strategy, MS 0.758 0.718 0.568 0.797 Cognitive Benefit, CB 0.719 0.704 0.495 0.821 0.851 Behavioral Intention of Sustainable Tourism, BIST 0.641 0.501 0.351 0.556 0.680 0.811 Insert Table 2 here Table 2 Correlation coefficient matrix of research variables 4.2 Path analysis and discussion In this study, the bootstrap resampling (with 500 sub-samples) was used to estimate the correlation value between different constructs on the PLS (Chin, 1998 ). The path relationship between different constructs was estimated by PLS, and the individual path values were presented with standardized coefficients (Fig. 1). Among the 10 path relationships hypothesized in the research model, only 1 path relationship failed to reach a significant level of α = 0.05, 2 path relationships reached a significant level of α = 0.05, and 7 path relationships reached a significant level of α = 0.01. In the research model, the path analysis of pre-variables that affected cognitive image showed that risk perception had a positive impact on cognitive image (0.257) and that information sharing had a positive impact on cognitive image (0.295). The path analysis of pre-variables that affected marketing strategy showed that risk perception had a positive impact on marketing strategy (0.359), that information sharing had a positive impact on marketing strategy (0.450), and that cognitive image had a positive impact on marketing strategy (0.212). The path analysis coefficient between antecedent variables and dependent variables showed that risk perception had a positive impact on cognitive benefit (0.238) and information technology application had a positive impact on the behavioral intention of sustainable tourism (0.387). The path analysis coefficients of marketing strategy for cognitive benefit and sustainable behavior showed that marketing strategy had a positive impact on cognitive benefit (0.650) and that marketing strategy had a positive impact on the behavioral intention of sustainable tourism (-0.204). The final path analysis coefficients among endogenous variables showed that cognitive benefit had a positive impact on the behavioral intention of sustainable tourism (0.568). The 9 path relationships discussed above were supported by positive and significant empirical data, while the path coefficients of the marketing 7P for behavioral intention of sustainable tourism were negative and insignificant. Therefore, this path was still uncertain. The explanatory power of the endogenous variables of the research model was 0.242 ~ 0.720, and the explanatory power of most variables met the requirement of more than 0.5. This showed that the research model proposed in this study achieved a good model explanatory power. All the relevant path relationships can be found in Fig. 1. Insert Fig. 1 here Judging from the sustainable tourism behaviors discussed in this study, managers of the sustainable tourism industry tend to believe that information technology application and cognitive benefit have a significant positive impact on the behaviors of individuals engaging in sustainable tourism, thanks to the influence of intelligent technology and technological environment. Information technology application has a significant positive impact on the behavioral intention of the sustainable tourism industry. In the digital era, information technology application (information sharing) has a driving force and yet a certain dependence on sustainable tourism, which can help improve the sustainable performance of the industry. With information technology application in social media, the sustainable tourism industry is no longer an isolated business goal. Rather, it can be a shared goal for the stakeholders of sustainable tourism industry. Similar to the research results of Gössling ( 2017 ), it is necessary to strengthen the marketing strategy of sustainable tourism industry and reduce the contradictions or disadvantages of the sustainable tourism industry. The cognitive benefits of the tourism industry usually include relaxation benefits, health benefits, and experiential benefits. The results of this study show that cognitive benefit has a significant positive impact on participation in sustainable tourism activities. Puhakka et al. ( 2017 ) indicated that cognitive benefit has a positive impact on sustainable tourism activities, emphasizing that it is necessary to incorporate the cognitive benefit of health and well-being into the economic benefit of the protected areas. Drawing from Social Cognitive Theory, Font (2016) claims that an individual's responsibility for sustainability depends on his or her empathy with sustainability and attachment to sustainability. As for the sustainable tourism industry, the public in Taiwan has already had a certain degree of understanding of sustainability, so that putting stress only on the sustainability of sustainable tourism activities is not attractive enough to the public. Therefore, it is necessary to emphasize the cognitive benefits (relaxation, wellness, and experience) of sustainable tourism activities and increase individual intention to participate in sustainable tourism activities. For the empirical model of this study, the path coefficient of marketing strategy for the behavioral intention of sustainable tourism activities was negative and insignificant. However, some studies have indicated that there is a positive relationship between the traditional marketing strategy and the intention to take pro-environmental action or participate in sustainable tourism activities. Marketing strategy serves not only as a functional indicator of tourism, but also as a key indicator to measure the sustainability of tourism industry, which is very important for consumers in identifying the key performance of tourism brands (Pomering et al., 2011). Marketing strategy has a positive impact on the environmental, economic, and social dimensions of the hospitality industry, and at the same time plays a successful mediator among these three dimensions and pro-environmental behaviors. On the other hand, for affluent individuals, marketing strategies that emphasize environmental appeal have proved to be effective (Moser, 2015 ). Research results of some scholars, however, are in line with the present study, showing that there may not be a positive relationship. For example, Pomering ( 2017 ) argues that the original marketing strategy (7P) is obviously insufficient for sustainability, so that he adds to the traditional marketing strategy (7P) three other elements—promise, principles, and partnership—to create a new framework for sustainable marketing theory and practice and provide positive contributions to social and individual values. For price-conscious consumers who occasionally buy green food or engage in pro-environmental behaviors, health marketing appeals should be more appropriate. For the era of smart technology, the results of this study show that among the marketing strategies that affect the sustainable tourism industry, information technology application, risk perception, and cognitive image had a positive impact on marketing strategy in a declining scale: information technology application (0.450), risk perception (0.359), and cognitive image (0.212). Information technology application (Navío-Marco et al., 2018 ) has the highest coefficient for marketing strategy, which reflects that in the era of smart technology, the impact of information technology application on sustainable industries should be higher than that of risk perception and cognitive image. This shows the importance of information technology application for marketing strategy in the sustainable tourism industry. Even if the cognitive image of the tourism destination is improved, the risk perception caused by the environmental impact and health impact of air pollution still seriously affects individual intention to participate in sustainable tourism activities. Likewise, in the atmosphere of severe public health incidents due to covid-19, the influence of individual risk perception on marketing strategy has increased (O'Connor & Assaker, 2021). It is worth noting that industry managers tend to believe that risk perception is more influential than cognitive image in developing marketing strategies for sustainable tourism activities. Information technology application has obviously become an important construct. Thanks to the advancement and diversity of information technology in Taiwan, most individuals are able to use social media and websites of tourism industry to obtain tourism information. Adopting the marketing strategy of information technology application (corporate website and social media), therefore, will increase the visibility and engagement of sustainable tourism activities. Both risk perception and information technology application have a significant positive impact on the cognitive image of sustainable tourism activities. The respective correlation coefficients are information technology application (0.295) and risk perception (0.257). The fact that these two coefficients are quite close indicates that industry managers tend to believe that information technology application and risk cognition have similar correlation and importance to cognitive image when individuals participate in sustainable tourism activities. For example, Jalilvand & Heidari ( 2017 ) believe that the word-of-mouth formed by information technology application creates better cognitive image and attitude in a visitor and increase his or her participation intention to visit a tourist destination than does the traditional word-of-mouth. Hyun & O'Keefe ( 2012 ) propose that information technology application (online media) has a positive impact on the cognitive image of tourist destination. By understanding the quality of destination attributes and tourists’ risk perception of the destination, the sustainable tourism industry can design appropriate marketing strategies based on tourists' risk perception and their cognitive images of tourist destinations. Perpiña et al. ( 2021 ) examine the potential travel motivation of tourists and find that risk perception has a significant negative moderating effect on destination image. On the other hand, risk perception has a significant positive impact on the cognitive image of sustainable tourism activities. Therefore, when organizing a sustainable tourism activity, it is necessary to describe and provide detailed risk factors, risk management strategies, and risk information that may be encountered in the event. This will show that the organizer has a high risk-awareness of the event, provide tourists with sufficient risk information, and help implement good risk management strategies. On the other hand, the results of this study show that both risk perception and marketing strategy have a positive impact on the cognitive benefit of sustainable tourism activities. The respective correlation coefficients are marketing strategy (0.650) and risk perception (0.238). The correlation of marketing strategy is much higher than that of risk perception, indicating that industry managers tend to believe that the correlation and importance of marketing strategy and cognitive benefit are much higher than risk perception when individuals participate in sustainable tourism activities. Although the inherent risk characteristics of sustainable tourism activities will affect individual intention to participate in activities, the negative impact of individuals on tourism activities can be effectively mitigated if individuals can improve their risk awareness of the activities. Based on robust empirical data, this study offers three significant theoretical contributions, engaging in a direct dialogue with existing literature in sustainable recreation, environmental psychology, and technology application: (1) Positioning Information Technology Application as a Mediator of "Cognitive Image and Marketing Strategy" within the Theory of Planned Behavior (TPB). Previous research exploring pro-environmental behavioral intentions often confined IT application to a mere tool for information dissemination or convenience. This study significantly deepens the theoretical role of technology within behavioral models. This research identifies IT application as a crucial "mediator of cognitive image and marketing strategy" within the context of sustainable tourism. This finding extends Ajzen's (2015) classic Theory of Planned Behavior (TPB) by empirically demonstrating that IT application can directly influence behavioral intentions. More profoundly, it shows how IT can substantially enhance visitors' cognitive image and perceptions of a site's sustainable performance—such as "carbon reduction" and "climate adaptation"—thereby increasing perceived cognitive benefits and ultimately driving visitation intentions. (2) Proposing a "Value-Resonance Model" applicable to Sustainable Sites, Challenging the Universality of Traditional Marketing Theory. Our research reveals a notable negative relationship between traditional marketing strategies and the visit intentions of target audiences (β = -0.204). This counter-intuitive finding critically challenges the universal applicability of traditional marketing theories in specific sustainability contexts. The core contribution here is the empirical support for a "Value-Resonance Model" more attuned to sustainability issues. This model posits that for individuals highly conscious of sustainability, consumption decisions are not primarily driven by external incentives but by a profound alignment between a site's cognitive impression and their intrinsic values. This perspective strongly corroborates Stern's (2000) Value-Belief-Norm Theory (VBN), which emphasizes the pivotal role of personal values in activating pro-environmental behavioral norms. Moreover, this study provides concrete empirical evidence for Vargo and Lusch's (2004) Service-Dominant Logic within climate action, illustrating that a site's value is not unidirectionally transmitted but realized through "value co-creation" with value-aligned visitors. (3) Constructing and Validating the Conceptual Framework of a "Virtuous Cycle of Sustainable Conviction." Another significant theoretical contribution is the integration of climate risk perception, information technology application, cognitive benefits, and behavioral intention into a novel conceptual framework: the "Virtuous Cycle of Sustainable Conviction." This framework delineates a dynamic psychological pathway where an individual's prior beliefs, particularly a high perception of climate risk (as illuminated by Slovic's 1987 research on risk perception), serve as key psychological antecedents influencing subsequent judgments and behaviors. The strategic intervention of IT application acts as a catalyst, transforming a site's sustainable actions into credible cognitive benefits. This process solidifies an individual's "sustainable conviction," which, in turn, strongly drives their behavioral intention to support the site. The completion of this cycle can also be viewed as a successful instantiation of the Experience Economy (Pine & Gilmore, 1999 ), transforming a visit into a profound, educational experience that reinforces personal values. This cyclical model not only elucidates "why" visitors choose sustainable sites but also details "how" this process can be achieved through technological mediation, offering a dynamic and explanatory theoretical tool for understanding the decision-making psychology of sustainable recreation consumers. 5. Conclusion Sustainable tourism stands as a paramount trend for the future of the tourism industry, driven by both its potential as a profitable business investment and its capacity to deliver physical and mental well-being to individuals. In this evolving landscape, particularly with the advent of smart technology, industry managers must meticulously understand the critical factors influencing individuals' participation in sustainable tourism activities and climate services, as well as the intricate interactions among these factors. Our findings underscore that in an era shaped by business ethics, corporate social responsibility, and rapid digital transformation, concerns regarding climate change are globally accelerating the development of the sustainable tourism industry. Specifically, cognitive benefit and information technology (IT) application emerge as key intention factors for current tourism managers organizing sustainable tourism activities, with cognitive benefit holding greater salience than specific aspects of IT application, such as visitor information sharing and information personalization. From a strategic marketing perspective, operators in sustainable tourism must not only emphasize the inherent environmental sustainability advantages of their activities and climate services but also actively promote the cognitive benefits and IT applications associated with them. This dual approach is crucial for fostering the industry's growth. Firstly, effectively promoting cognitive benefits—such as relaxation, health, and experiential value—is indispensable for the success of sustainable tourism. As evidenced in regions like Taiwan, where media coverage of environmental issues significantly impacts public awareness, marketing strategies should explicitly highlight both the green, sustainable aspects and the personal, cognitive rewards of engagement. Secondly, strengthening the content and expanding the channels of IT application will immensely benefit future tourism operations. Furthermore, we advocate for cross-industry collaborations in IT application to leverage diverse digital platforms, ultimately enabling consumers to access sustainable tourism activities at more attractive prices. Declarations Ethics approval All study procedures were approved by Research Ethics Committee National Taiwan University Approve number: 202205ES018, Approve time: May, 09, 2023. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent Informed consent was obtained from individual participants or their legal guardians through oral communication. Approve Time: April, 28, 2023 Author Contribution Yu Tai-Kuei: Writing original & editing, Visualization & Methodology. Horng Jeou-Shyan: Conceptualization & Editing. Fang Yen-Po: Data curation, Methodology. Liu Chih-Hsing: Supervision & Project administration. Chou Sheng-Fang: Writing – review & editing, Supervision. Yu Tai-Yi: Writing – Investigation, original draft, review & editing. Acknowledgements The authors would like to thank anonymous reviewers for useful suggestions and the National Science and Technology Council of Taiwan for financial support [Grant number: MOST 111-2410-H-130 -011] Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability All data generated or analyzed during this study are included in this published paper. References Ajzen, I. 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Manag. 40:213–223. https://doi.org/10.1016/j.tourman.2013.06.006 Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jeou-Shyan","middleName":"","lastName":"Jeou-Shyan","suffix":""},{"id":524952846,"identity":"b5ac2adf-b221-468a-8529-2e87db8d5050","order_by":2,"name":"Yen-Po Fang","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Yen-Po","middleName":"","lastName":"Fang","suffix":""},{"id":524952847,"identity":"2fb69648-9c9a-40b0-a971-8a01355dc636","order_by":3,"name":"Chih-Hsing Liu","email":"","orcid":"","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chih-Hsing","middleName":"","lastName":"Liu","suffix":""},{"id":524952848,"identity":"3894dce8-7469-4e3a-83dc-f9557ee8ac36","order_by":4,"name":"Sheng-Fang Chou","email":"","orcid":"","institution":"Ming Chuan 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18:16:01","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163371,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7492880/v1/7b85e80cc8befa556bb155a1.html"},{"id":93071338,"identity":"c6333a63-66bd-42ac-92cf-f056033ffe7d","added_by":"auto","created_at":"2025-10-08 17:56:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258175,"visible":true,"origin":"","legend":"\u003cp\u003eThe path model of this study\u003c/p\u003e","description":"","filename":"figure1A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7492880/v1/7ea4ead0819123848ca66253.jpg"},{"id":104240559,"identity":"76a71ac4-3823-43ed-b24c-141487e6f363","added_by":"auto","created_at":"2026-03-09 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17:56:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16719,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7492880/v1/4c2e8c432494d079037b4667.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Exploration of Behavioral Intention towards Participation in Sustainable Tourism Activities: A Response to the Coming of Smart Technology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmart technology, including artificial intelligence, VR/AR, blockchain, big data, and sharing economy, has become an important trend in recent years in tourism industry, taking lead of the development of global smart industry. Through instant feedback, transparency, market segmentation, decision-making support, product and service innovation, smart technology can improve the commercial value of knowledge-intensive industries. The era of Internet technology has seen the prosperity of online communities, in which consumers often disclose their personal emotions and make comments on consumer products, creating a forum of word of mouth where enterprises can obtain direct access to consumer evaluations. As a result, businesses have attached great importance to online brand marketing and have regarded it as one of the most important methods of corporate marketing; at the same time, they have started to pay attention to the impacts and effects of smart technology on their businesses, especially the business rends brought by smart technology (Jenong \u0026amp; Shin, 2020).\u003c/p\u003e\u003cp\u003eSustainability, on the other hand, is an important issue for the environment of current global enterprises and businesses. It can facilitate the performance improvement of the tourism industry and the acquisition of competitive advantages, and it can be a marketing issue for the industry (S\u0026oslash;rensen \u0026amp; Grindsted, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From the perspective of an operator in the sustainable tourism industry, it is challenging to investigate and learn about the factors that affect consumers' participation in sustainable tourism activities and the correlation between these different factors. This study takes senior managers of the sustainable tourism industry as its subjects, trying to construct a behavioral theory that clarifies how different impact constructs\u0026mdash;marketing strategy, information technology application, cognitive image, risk perception, and cognitive benefit\u0026mdash;can influence customers' intention to engage in sustainable tourism activities and how these important constructs can be correlated. The results of this study can be an important reference for people to understand and organize sustainable tourism activities.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Behavioral intentions of sustainable tourism activities\u003c/h2\u003e\u003cp\u003eA green and sustainable tourism environment offers people a great variety of benefits, such as reducing stresses, improving cognitive functions, maintaining healthy psychology, strengthening social ties, increasing safety, and reducing crime. Nisbet \u0026amp; Zelenski (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argue that a happy path to sustainable development is to encourage people to get close to nature rather than adopt an ecologically sustainable lifestyle through fears, obligations, or economic incentives. Sustainable goals have become the focus of studies on tourism's contribution to sustainable development; it is now also a way for companies to increase the competitive advantages and profits of their businesses (Padgett \u0026amp; Moura- Leite, 2012).\u003c/p\u003e\u003cp\u003eSustainable tourism aims for consumers engaging in tourism activities to protect the environment, promote economic benefits, strike a balance among different constructs such as social justice and cultural integrity, improve people's living standards, and ultimately develop long-term sustainability (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Inducing the concept of sustainability into the tourism industry can reduce the negative impacts that may come along with the development of tourism industry. Therefore, sustainable tourism has become an ideal goal for the development of tourism industry. For the sustainable tourism industry, there are several possible development strategies, such as (1) developing local tourism, (2) reducing group travel, (3) opting for sustainable tourism, and (4) opting for more sustainable activities. In hospitality and tourism industries, there have been quite a few studies discussing the relationship between industry and sustainability. Some studies have focused on the significance and difficulty of improving sustainability, while others have identified sustainable tourism as a key tool for improving competitiveness (Leonidou et al., 2013). Dudensing et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) find that the marketing strategies that stakeholders of various tourisms take are of a great diversity, resulting in the heterogeneity in tourism consumption and conflicting expectations among stakeholders. Various tourisms have taken sustainability into consideration, developing a series of sustainable products and various products related to sustainable tourism, ensuring a fair economy with high standards (e.g. local sourcing), a positive socio-cultural impact (e.g. protection of cultural heritage), and a friendly environment (e.g. green management) (della Corte \u0026amp; Aria, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Golding et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Marketing strategy and information technology application\u003c/h2\u003e\u003cp\u003eEarly marketing strategies came with the key elements of 4Ps (McCarthy, 1979), which have been widely used in businesses and many other fields. The 4Ps refer to promotion, place, product, and price, which Kolter (1999) believes to have become inadequate for a marketing strategy in a society of great complexity and diversity. He adds politics and public opinion to the original 4Ps, forming a marketing mix with 6Ps. Booms \u0026amp; Bitner (1981) revise the 4Ps marketing mix proposed by McCarthy (1979) into one with 7Ps, adding the other 3Ps\u0026mdash;people, process, and physical evidence. Pomering (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) adds sustainable participants, processes, and physical evidence to the traditional 4P marketing strategy, together with the three key elements of promise, principles, and partnership. He argues that each element can be viewed as a controllable marketing variable that contributes to the creation/co-creation of individual and social values.\u003c/p\u003e\u003cp\u003eInformation technology application and marketing strategy play key roles in business operations in the digital era. Most consumers tend to acquire product knowledge through online information before making purchases rather than merely rely on on-site marketing services. As consumers pay more and more attention to online word of mouth, the influence of the consumption experience of strangers or Internet celebrities on consumers has also increased. In terms of word-of-mouth marketing, the physical world needs populace, while the virtual world needs popularity. In the digital era, big data has been integrated with marketing to generate new marketing theories. Fan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) propose a new 5P marketing mix (people, product, promotion, price, place), arguing that big data can provide consumers with detailed personalized information and personalized marketing, turning every consumer into a niche market. Inanc\u0026ndash;Demir \u0026amp; Kozak (2019) believe that in the context of the traditional marketing mix with 4Ps (product, promotion, price and place), big data analysis and technology can be used to update and maintain the operation and management of the tourism and hospitality industry.\u003c/p\u003e\u003cp\u003eInformation sharing can be generally defined as the exchange of meaningful and timely information between participants in a formal or informal way. In the digital era, the rapid development of technology has improved the speed and convenience of globalization, and the information sharing across different cultures, regions, societies, and individuals has become more important and more common. Therefore, information sharing is now one of the key factors in marketing strategy. When an adequate number of individuals invest a considerable amount of genuine human interest in information sharing or open discussion over an adequate length of time, a network of individual relationships will be formed in cyberspace, and social aggregation will occur in the network. They can share similar virtual enjoyment without limitation to geographical boundary. They can continuously participate in discussions, sharing their information beyond the limit of time and space (Milne \u0026amp; Callahan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For operators of sustainable tourism in the digital era, providing consumers with personalized information is a key factor to consumers\u0026rsquo; participation in sustainable tourism activities. This is because the consumption process of the digital era has changed from a purely linear model to a cyclical purchasing process. As a result, information technology application plays a very important role in the tourism industry (de Kervenoael, 2020).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Behavioral theory of engaging in sustainable tourism activities\u003c/h2\u003e\u003cp\u003eEarly research on sustainable education and environmental education mainly focused on knowledge, attitude, and behavior (K-A-B), as well as the interrelationships of these three elements (Hart, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). A lot of environmental education and environmental literacy frameworks (Hungerford \u0026amp; Volk, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) particularly concentrated on attitude and behavior (Simmons, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In general, factors that may influence people\u0026rsquo;s engagement in sustainable tourism can be broadly categorized into cognitive factors and affective factors. Some studies have found that environmental knowledge can directly affect environmental behavior (Frick et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, other studies have indicated that environmental knowledge affects behavioral intention or pro-environmental behavior through affective factors\u0026mdash;value, attitude, sensitivity, and a sense of responsibility (Wang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Some studies have also found that their empirical results do not conform to the traditional K-A-B environmental education model, with a gap either between knowledge and behavior or between attitude and behavior (Young et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Ram et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) constructs a conceptual model suitable for sustainable tourism, a model with happiness, travel motivations, and perception of distance. The model shows that happiness, which is indeed an integral part of tourist experience, calls for a need to formulate effective strategies to break the traditional speed-distance-demand loop, bring changes to transport and infrastructure policies, and recognize the key role of happiness in sustainable tourism strategies.\u003c/p\u003e\u003cp\u003eFor the behavioral patterns of sustainable tourism consumers or pro-environmental behaviors, the Theory of Planned Behavior (TPB) and Value-Belief-Norm theory (VBN) are two common theoretical models. TPB (Ajzen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) divides the factors that affect behavioral intentions into three cognitive constructs: attitude, subjective norm, and perceived behavioral control. The VBN model (Stern, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) divides the factors that affect environmentally responsible behavior into four constructs: value, belief, norm, and behavior. Over the past years, TPB has been applied to discussing pro-environmental behaviors, such as consumers\u0026rsquo; pro-environmental behaviors and sustainable tourism behaviors (Kaplan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; ud Din et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Some scholars have enhanced and expanded TPB, taking into consideration the influence of preceding, mediating, or intervening variables on behavioral intention, such as emotions, desires, service quality, customer satisfaction, overall image, and the frequency of past behavior (Han \u0026amp; Kim, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe key factors affecting consumer behavior in the tourism industry, as Cohen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) point out, are decision-making, value, motivation, self-concept and personality, expectation, attitude, perception, satisfaction, trust and loyalty. When consumers change their lifestyles or take any alternative ones with sustainable features to promote sustainable development, environmental loads can be significantly alleviated (Stern, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the ecological footprint of products in production, use, or disposal can be reduced. However, the decision-making process in which green consumers participate has become increasingly complex, involving not only individual needs and desires but also corporate social responsibility (Young et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Schaefer \u0026amp; Crane (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) argue that green consumers \u0026ldquo;are thought to be motivated by strong environmental values and attitudes, therefore seeking environmental product information, rationally weighing the utility provided by a particular product against the environmental cost attached and making a purchasing decision based on these environmental criteria in conjunction with more conventional considerations of price, quality, and convenience.\u0026rdquo;\u003c/p\u003e\u003cp\u003eRisk perception is often used to describe people's attitudes and intuitive judgments on risk, and tourism risk has received extensive attention from scholars working on cognitive psychology and consumer behavior (Somez \u0026amp; Graefe 1998). Tourism risk perception can directly affect customers' intention to purchase tourism goods (Cui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); however, it is susceptible to subjective factors, objective factors, and visitors\u0026rsquo; perception of the negative impact on tourism activities. Subjective factors include physiological characteristics and psychological processes, while objective factors include physical risk, economic risk, equipment risk, social risk, psychological risk, time risk, and opportunity loss. Yang et al. (2014) found that uncertainty, worry, fear, and anxiety are closely related to risk perception as they review the concepts and theories of risk perception, conceptualize various constructs of risk perception, and enhance the understanding of tourists' risk perception. Mohaidin et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicate that those who engage in tourism activities have a specific threshold for risk perception; therefore, quantitative assessment of tourism risk can help customers deal with tourism decision-making and destination management. They also find that environmental attitude, motivation, and word-of-mouth have a significant impact on tourists\u0026rsquo; intention to select sustainable tourist destinations.\u003c/p\u003e\u003cp\u003eCognitive image is an important key for consumers to evaluate product quality. When consumers do not have sufficient knowledge about a product and its functions, unable to evaluate product quality correctly, their perceived risk will increase. At this time, if there is a better brand image or a better cognitive image to be a product endorsement or quality assurance, consumers' perception of product quality will be improved (Woosnam et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Zhang et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) applies meta-analysis to investigate the relationship between tourist destination image and tourist loyalty, finds that tourist destination image has a significant impact on tourist loyalty and that overall image, affective image, and cognitive image have the greatest impact on tourist loyalty. As for the cognitive image of sustainable tourism and pro-environmental behavior, Lee et al. (2010) indicate that the cognitive image (including value and quality) has a positive impact on the affective image and overall image of a green hotel, while the affective image has a positive impact on the overall image of the green hotel. Pe\u0026ntilde;a et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) explore the constructs of cognitive image for rural tourism destinations and find that the positive cognitive image of a tourist destination\u0026mdash;a group of symbolic aspects that encompass destinations, services, culture, natural events, local products and gastronomy\u0026mdash;has an important incentive effect on consumer behavior (such as satisfaction and loyalty).\u003c/p\u003e\u003cp\u003eThe cognitive benefits of participating in sustainable tourism activities can be divided into two categories: (1) economic benefits measured by money; (2) positive improvement benefits for individuals (Driver et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Public participation in tourism activities brings forth a lot of positive effects, including enhancing the image of the organizer or the destination, generating income, improving economic and social development, and many others. However, it also produces such negative effects as traffic congestion and real estate speculation at tourist destinations (Kim \u0026amp; Petrick, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These will form an impact on the cognitive benefits of individuals participating in tourism activities. For an organizer of tourism activities, the cognitive benefits derived from the activities can be classified into economic benefits, structural benefits, and self-efficacy. Tourists participating in the tourism activities of multicultural festivals can obtain natural recovery, transformation, cognition, and social and emotional benefits (Liu et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Tourists can also gain cognitive benefits from participating in tourism activities that encourage pro-environmental behavior. For example, Zeppel (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) shows in a meta-analysis that cognitive and affective empathy with marine wildlife contributes to creating longer-term intention to engage in marine conservation actions. Participation in sustainable tourism activities (hikers, runners, and walkers in national parks) that offer cognitive benefits helps create relaxation, pleasure, improved moods, and other health effects. Cognitive benefit is also an important factor that can influence one's intention to participate in sustainable tourism activities or take pro-environmental behaviors (Oppewal et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Social Cognitive Theory was applied to examine the motivation of sustainable tourism and different types of sustainable tourism people undertake. They find that those who take sustainable action are subject to the level of their individual's empathy with and attachment to sustainability, which can be explained in relation to the two constructs of personal benefits and cultural focus (Font et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Social Capital Theory to community-based ecotourism, could be utilized to understand and improve the cooperation between the residents of host communities and the development of community-based ecotourism, thereby encouraging residents to take pro-environmental behaviors. The model results represented that economic benefits have a direct impact on residents' pro-environmental behaviors, while cognitive social capital has a partial mediating effect on the relationship between the residents and the ecotourism (Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research Materials and Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Materials\u003c/h2\u003e\u003cp\u003eTaking the management-level personnel of the industry as the respondents of the questionnaire survey, this study evaluates and predicts the influencing factors that will affect consumers' participation in sustainable tourism activities and the correlation among different influencing factors. It further employs structural equations (statistical tools using SmartPLS model) to verify the causal patterns of participation in sustainable tourism activities. For the situational design of this questionnaire experiment, first, the potential variables and measurement variables of the sustainable tourism behavioral model were collected, the experimental situation framework was constructed, and the questionnaire content was formulated. Next, experts were invited to conduct three inspections of the content of the experimental situation, followed by a pre-test and a post-test. After the results of the questionnaire research had been converged and a certain degree of consensus had been reached, a behavioral model for participation in sustainable tourism activities was established in accordance with the theoretical basis of the literature review.\u003c/p\u003e\u003cp\u003eSustainable tourism was defined as tourism that meets the needs of present tourists and host regions while protecting and enhancing opportunity for the future (World Tourism Organization. 1993). Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes (World Tourism Organization. 1993). As for the selection of sustainable tourism industries, this study took as its survey respondents the management-level personnel of the sport centers in New Taipei City and Taipei City, Taiwan. Based on the definition of sustainable tourism above, sport centers are clear and suitable targets. Since the first sport center opened in Taipei in 2003, sport centers in Taiwan have become the best venues for sports tourism to the public. In the evolving discourse of sustainable tourism, the focus is increasingly shifting towards models that enhance the well-being of local populations while championing environmental stewardship. A compelling case study in this domain emerges from Taiwan, where municipal sports centers (climate service institutions) (Mahon et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e Riach \u0026amp; Glaser, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) represent a unique and replicable model of urban-centric, participatory sustainable tourism. This analysis examines the sports centers of Taipei and New Taipei City, positing that their long-term viability and contribution to urban sustainability are intrinsically linked to a strategic embrace of digital transformation. By leveraging smart technologies, these facilities can transcend their traditional roles, evolving into dynamic hubs for community health, operational efficiency, and environmental responsibility.\u003c/p\u003e\u003cp\u003eThe establishment of these sports centers was a deliberate act of public policy aimed at fostering a healthier citizenry. The initiative was rooted in a 2002 policy vision by the Taipei City Government to establish at least one accessible sports facility in each administrative district. The objective was clear: to cultivate a pervasive sports culture and transform Taipei into a \"Healthy City.\" This vision gained national momentum with the 2010 \"Sports Island Plan,\" which funded the construction of dozens of national sports centers. The goal was to provide diverse and affordable athletic options, thereby encouraging regular exercise habits among the public. From a sustainable tourism perspective, these centers are exemplary. They cater primarily to the local community, reducing the carbon footprint associated with long-distance travel. Their operational model, where service consumption and delivery occur simultaneously, fosters direct engagement and community building\u0026mdash;hallmarks of a socially sustainable enterprise. Furthermore, many were conceived with green building principles in mind, incorporating features like solar power, water-saving fixtures, rainwater harvesting systems, and natural ventilation to minimize their ecological impact.\u003c/p\u003e\u003cp\u003eDespite their public mandate and inherent sustainability credentials, these centers operate within a highly competitive marketplace. They face significant operational challenges, including intense competition from private gyms, overlapping service areas, and the sophisticated marketing strategies of rivals. To thrive, operators must move beyond simply offering facilities; they must deeply understand and anticipate consumer needs, enhance customer loyalty, and attract new demographics. The central question becomes: How can these centers overcome market pressures while simultaneously deepening their commitment to sustainability and improving the user experience? The answer lies in the strategic integration of smart technology.\u003c/p\u003e\u003cp\u003eThe application of smart technology offers a powerful, multi-faceted solution to these challenges, creating a symbiotic relationship between operational excellence, user engagement, and sustainability. Within the designated area of study, the application of smart technologies in sports centers can be broadly classified into three main categories: (1) Intelligent Infrastructure for Sustainable Operations: At the most fundamental level, technology can optimize the physical management of the centers. The Internet of Things (IoT) is paramount here. By embedding sensors throughout a facility, operators can gather real-time data on everything from occupancy in the swimming pool to the usage of specific gym equipment. This data, integrated with an IoT platform, enables dynamic resource allocation. For instance, HVAC (heating, ventilation, and air conditioning) and lighting systems can be automatically adjusted based on real-time occupancy, drastically reducing energy consumption. Advanced systems for voltage regulation and smart water management further contribute to environmental goals, aligning operations with UN Sustainable Development Goal 7 (Affordable and Clean Energy) and making these centers showcases for sustainable urban infrastructure. (2) Data-Driven Operations and Personalized Wellness: Beyond infrastructure, technology revolutionizes the understanding of the customer. By utilizing cloud computing platforms, sports centers can aggregate and analyze vast amounts of user data, including visit frequency, class participation rates, and equipment usage patterns. Leading operators are already using mobile applications that integrate with cloud backends to provide management with insightful operational reports and users with personalized progress tracking. The application of Artificial Intelligence (AI) and Machine Learning (ML) takes this a step further. When integrated with wearable devices, the center's app can capture biometric data like heart rate and calories burned. AI algorithms can then analyze this information to generate optimized and scientifically grounded exercise prescriptions tailored to specific user groups, such as senior citizens or women. This data-driven approach to health management embodies the trend toward \"precision health,\" elevating the center from a simple sports venue to a sophisticated wellness partner. (3)Enhancing the User Experience through Seamless Digital Integration: Digital tools are critical for streamlining the entire customer journey. Mobile applications, connected via APIs to the core management system, have become central to the user experience. They offer frictionless services such as online class booking, secure digital payments through popular platforms, and QR code-based facility access. This user-centric design not only significantly enhances convenience and satisfaction by reducing waiting times but also improves service accessibility. From an operational standpoint, this digital-first approach streamlines check-in processes, reduces administrative labor costs, and represents a classic example of successful digital transformation in the service industry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Research design\u003c/h2\u003e\u003cp\u003eIn the face of smart technology era, this study aims to construct and verify the behavioral model of participation intention in sustainable tourism activities. According to the literature review above, there are six major interacting constructs: information technology application, cognitive image, risk perception, marketing strategy, cognitive benefit, and sustainable tourism behavior. The measurement scales related to smart technology and sustainable tourism are described as follows:\u003c/p\u003e\u003cp\u003e(1) Information technology application includes information sharing, website quality (including website design and overall information function of the Internet), with a measurement scale of information sharing modified from Bock et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and a measurement scale of website quality modified from the scale established by Tsai et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e);\u003c/p\u003e\u003cp\u003e(2) Cognitive image includes attraction characteristics, service atmosphere, and activities and events, with a measurement scale based on Royo-Vela (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e);\u003c/p\u003e\u003cp\u003e(3) Risk perception is given a measurement scale based on the concepts of risk perception proposed by Cui et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e);\u003c/p\u003e\u003cp\u003e(4) Marketing strategy is given a measurement scale modified from those proposed by Pogorelova et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), including promotion, place, product, price, people, process, and physical evidence;\u003c/p\u003e\u003cp\u003e(5) Cognitive benefits of tourism activities include relaxation benefits, health benefits, and experiential benefits, with a measurement scale modified from Chen \u0026amp; Petrick (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e);\u003c/p\u003e\u003cp\u003e(6) Behavioral intention of sustainable tourism activities is given a measurement scale based on Han et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe constructs above were measured in accordance with the extent to which the respondents regard the question item as important, on a 7-point Likert scale from \"Not important\" to \"very important,\" with 1 to 7 points in sequence. 63 questionnaires were administered, and 63 questionnaires were returned, with a response rate of 100%. The paper questionnaires were distributed mainly at the Taipei sport tourism centers, and the subjects of the questionnaires were the management-level personnel of several sport tourism centers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Statistical methods, reliability analysis, and validity analysis\u003c/h2\u003e\u003cp\u003eIn the sample analysis, this study used two sets of tools\u0026mdash;SPSS 22.0 for Windows and Smart PLS\u0026mdash;for the statistical analysis after data collection. In terms of descriptive statistics, this study mainly used SPSS 22.0 to analyze the variables of population sample, since its structural equations are suitable for predicting highly complex patterns (Chin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The main reasons why this study used PLS for structural and measurement model analysis were: (1) Compared with LISREL or AMOS, PLS has fewer restrictions on sampling, and the sample distribution requires no more than normal distribution (Gefen, Straub, \u0026amp; Boudreau, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); (2) the PLS structural model can support confirmatory and exploratory research without a strong theoretical foundation. However, LISREL requires a strong theoretical foundation to conduct empirical research (Gefen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, the PLS structural equation was used for model construction and data analysis. In the actual data analysis, SmartPLS 3.0, developed by Ringle et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), was used to analyze the measurement model and structural model. The bootstrapping analysis was used to obtain standard errors and t-values of parameter estimates. Since it would be easier to find a fitting pattern when the research pattern identification was repeated, 500 bootstrap samples were applied in this paper. Anderson \u0026amp; Gerbing (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) suggest that a measurement model analysis should (1) ensure that, in terms of the overall model, each measurement variable of the verification model can correctly measure its latent variable; (2) determine the complex measurement variables of the test load value in different patent variables, which are the two important construct validities of the test model:\u003c/p\u003e\u003cp\u003eConvergent validity\u0026mdash;when related variables are measured in different ways, their correlation with each other must be high; in other words, when the same thing is measured, the measurement score and the result should be the same;\u003c/p\u003e\u003cp\u003eDiscriminant validity\u0026mdash;when two different concepts were measured, the correlation analysis of the measurement results must show a low correlation, regardless of whether the measurer used the same method or different ones.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Reliability and validity\u003c/h2\u003e\u003cp\u003eBased on the recommendation of Bagozzi \u0026amp; Yi (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), this study evaluated the measurement model of reflective indicators by the three most commonly used indicators in the following: (1) Individual item reliability: It measures the factor loadings of the variables against latent variable, and examines the statistical significance of each variable loading. All the factor loadings in this study were significantly higher than the recommended value of 0.5, which was in line with the value recommended by Hair et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and the factor loading coefficients of the tested samples ranged from 0.689 to 0.922. (2) Composite reliability (CR): The CR value of a latent variable is the composition of the reliability of all its measurement variables, and its index significance is similar to Cronbachs Alpha, which is used to indicate the internal consistency of the construct index. Fornell \u0026amp; Larcker (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) suggest that the CR value should be above 0.6, and the parameter estimation should be based on different measurements (standardized or unstandardized). In addition, SmartPLS provides the reliability index of Rho_a. Dijkstra \u0026amp; Henseler (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) suggest that the Rho_a coefficient should be above 0.7. The higher reliability means a greater internal consistency for latent variables. In this study, the CR values of the tested samples ranged from 0.830 to 0.925, and the Rho_a coefficients ranged from 0.811 to 0.924. All the indicators above showed that the internal consistency of the research model was good, which met the reliable recommended value\u0026mdash;greater than 0.7. When measured by the Cronbachs Alpha coefficient, the Cronbachs Alpha of each variable in the research model was between 0.730 and 0.901, which met the criterion that the Cronbachs Alpha needs to be greater than 0.7. (3) Average variance extracted (AVE): It measures the variance explanatory power of each measurement variable in the calculation of the latent variable. The higher AVE means better discriminant validity and convergent validity for the latent variable. The standard value, as Fornell \u0026amp; Larcker (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) suggest, should be greater than 0.5. In this study, the AVE value of each latent variable in the tested sample was between 0.623 and 0.804. Relevant data of reliability and validity tests are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As the analyses above show, the latent variables in the measurement model of this study had good convergent validity.\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\u003eReliability and validity test of the measurement model\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003erho_A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation Technology Application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Image\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarketing Strategy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Benefit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBehavioral Intention of Sustainable Tourism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCA: Cronbach's Alpha; rho_A: rho_A reliability coefficient; CR: Composite Reliability; AVE: Average Variance Extracted\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eReliability and validity test of the measurement model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn addition, the measurement model needed to be tested for discriminant validity. As stated above, when two different concepts are measured, the correlation analysis of the measurement results must show a low correlation, regardless of whether the measurer uses the same method or different ones. When the PLS analysis was adopted in this study, the measurement indicators based on Fornell \u0026amp; Larcker (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that the measured result coefficients met the threshold requirement that the diagonal coefficient must be higher than the coefficient of research variables.\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\u003eCorrelation coefficient matrix of research variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eITA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBIST\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation Technology Application, ITA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Perception, RP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Image, CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarketing Strategy, MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Benefit, CB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBehavioral Intention of Sustainable Tourism, BIST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.811\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\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eCorrelation coefficient matrix of research variables\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Path analysis and discussion\u003c/h2\u003e\u003cp\u003eIn this study, the bootstrap resampling (with 500 sub-samples) was used to estimate the correlation value between different constructs on the PLS (Chin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The path relationship between different constructs was estimated by PLS, and the individual path values were presented with standardized coefficients (Fig.\u0026nbsp;1). Among the 10 path relationships hypothesized in the research model, only 1 path relationship failed to reach a significant level of α\u0026thinsp;=\u0026thinsp;0.05, 2 path relationships reached a significant level of α\u0026thinsp;=\u0026thinsp;0.05, and 7 path relationships reached a significant level of α\u0026thinsp;=\u0026thinsp;0.01. In the research model, the path analysis of pre-variables that affected cognitive image showed that risk perception had a positive impact on cognitive image (0.257) and that information sharing had a positive impact on cognitive image (0.295). The path analysis of pre-variables that affected marketing strategy showed that risk perception had a positive impact on marketing strategy (0.359), that information sharing had a positive impact on marketing strategy (0.450), and that cognitive image had a positive impact on marketing strategy (0.212). The path analysis coefficient between antecedent variables and dependent variables showed that risk perception had a positive impact on cognitive benefit (0.238) and information technology application had a positive impact on the behavioral intention of sustainable tourism (0.387). The path analysis coefficients of marketing strategy for cognitive benefit and sustainable behavior showed that marketing strategy had a positive impact on cognitive benefit (0.650) and that marketing strategy had a positive impact on the behavioral intention of sustainable tourism (-0.204). The final path analysis coefficients among endogenous variables showed that cognitive benefit had a positive impact on the behavioral intention of sustainable tourism (0.568). The 9 path relationships discussed above were supported by positive and significant empirical data, while the path coefficients of the marketing 7P for behavioral intention of sustainable tourism were negative and insignificant. Therefore, this path was still uncertain. The explanatory power of the endogenous variables of the research model was 0.242\u0026thinsp;~\u0026thinsp;0.720, and the explanatory power of most variables met the requirement of more than 0.5. This showed that the research model proposed in this study achieved a good model explanatory power. All the relevant path relationships can be found in Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert Fig.\u0026nbsp;1 here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eJudging from the sustainable tourism behaviors discussed in this study, managers of the sustainable tourism industry tend to believe that information technology application and cognitive benefit have a significant positive impact on the behaviors of individuals engaging in sustainable tourism, thanks to the influence of intelligent technology and technological environment. Information technology application has a significant positive impact on the behavioral intention of the sustainable tourism industry. In the digital era, information technology application (information sharing) has a driving force and yet a certain dependence on sustainable tourism, which can help improve the sustainable performance of the industry. With information technology application in social media, the sustainable tourism industry is no longer an isolated business goal. Rather, it can be a shared goal for the stakeholders of sustainable tourism industry. Similar to the research results of G\u0026ouml;ssling (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it is necessary to strengthen the marketing strategy of sustainable tourism industry and reduce the contradictions or disadvantages of the sustainable tourism industry. The cognitive benefits of the tourism industry usually include relaxation benefits, health benefits, and experiential benefits. The results of this study show that cognitive benefit has a significant positive impact on participation in sustainable tourism activities. Puhakka et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicated that cognitive benefit has a positive impact on sustainable tourism activities, emphasizing that it is necessary to incorporate the cognitive benefit of health and well-being into the economic benefit of the protected areas. Drawing from Social Cognitive Theory, Font (2016) claims that an individual's responsibility for sustainability depends on his or her empathy with sustainability and attachment to sustainability. As for the sustainable tourism industry, the public in Taiwan has already had a certain degree of understanding of sustainability, so that putting stress only on the sustainability of sustainable tourism activities is not attractive enough to the public. Therefore, it is necessary to emphasize the cognitive benefits (relaxation, wellness, and experience) of sustainable tourism activities and increase individual intention to participate in sustainable tourism activities.\u003c/p\u003e\u003cp\u003eFor the empirical model of this study, the path coefficient of marketing strategy for the behavioral intention of sustainable tourism activities was negative and insignificant. However, some studies have indicated that there is a positive relationship between the traditional marketing strategy and the intention to take pro-environmental action or participate in sustainable tourism activities. Marketing strategy serves not only as a functional indicator of tourism, but also as a key indicator to measure the sustainability of tourism industry, which is very important for consumers in identifying the key performance of tourism brands (Pomering et al., 2011). Marketing strategy has a positive impact on the environmental, economic, and social dimensions of the hospitality industry, and at the same time plays a successful mediator among these three dimensions and pro-environmental behaviors. On the other hand, for affluent individuals, marketing strategies that emphasize environmental appeal have proved to be effective (Moser, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Research results of some scholars, however, are in line with the present study, showing that there may not be a positive relationship. For example, Pomering (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) argues that the original marketing strategy (7P) is obviously insufficient for sustainability, so that he adds to the traditional marketing strategy (7P) three other elements\u0026mdash;promise, principles, and partnership\u0026mdash;to create a new framework for sustainable marketing theory and practice and provide positive contributions to social and individual values. For price-conscious consumers who occasionally buy green food or engage in pro-environmental behaviors, health marketing appeals should be more appropriate.\u003c/p\u003e\u003cp\u003eFor the era of smart technology, the results of this study show that among the marketing strategies that affect the sustainable tourism industry, information technology application, risk perception, and cognitive image had a positive impact on marketing strategy in a declining scale: information technology application (0.450), risk perception (0.359), and cognitive image (0.212). Information technology application (Nav\u0026iacute;o-Marco et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) has the highest coefficient for marketing strategy, which reflects that in the era of smart technology, the impact of information technology application on sustainable industries should be higher than that of risk perception and cognitive image. This shows the importance of information technology application for marketing strategy in the sustainable tourism industry. Even if the cognitive image of the tourism destination is improved, the risk perception caused by the environmental impact and health impact of air pollution still seriously affects individual intention to participate in sustainable tourism activities. Likewise, in the atmosphere of severe public health incidents due to covid-19, the influence of individual risk perception on marketing strategy has increased (O'Connor \u0026amp; Assaker, 2021). It is worth noting that industry managers tend to believe that risk perception is more influential than cognitive image in developing marketing strategies for sustainable tourism activities. Information technology application has obviously become an important construct. Thanks to the advancement and diversity of information technology in Taiwan, most individuals are able to use social media and websites of tourism industry to obtain tourism information. Adopting the marketing strategy of information technology application (corporate website and social media), therefore, will increase the visibility and engagement of sustainable tourism activities.\u003c/p\u003e\u003cp\u003eBoth risk perception and information technology application have a significant positive impact on the cognitive image of sustainable tourism activities. The respective correlation coefficients are information technology application (0.295) and risk perception (0.257). The fact that these two coefficients are quite close indicates that industry managers tend to believe that information technology application and risk cognition have similar correlation and importance to cognitive image when individuals participate in sustainable tourism activities. For example, Jalilvand \u0026amp; Heidari (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) believe that the word-of-mouth formed by information technology application creates better cognitive image and attitude in a visitor and increase his or her participation intention to visit a tourist destination than does the traditional word-of-mouth. Hyun \u0026amp; O'Keefe (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) propose that information technology application (online media) has a positive impact on the cognitive image of tourist destination. By understanding the quality of destination attributes and tourists\u0026rsquo; risk perception of the destination, the sustainable tourism industry can design appropriate marketing strategies based on tourists' risk perception and their cognitive images of tourist destinations. Perpi\u0026ntilde;a et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examine the potential travel motivation of tourists and find that risk perception has a significant negative moderating effect on destination image. On the other hand, risk perception has a significant positive impact on the cognitive image of sustainable tourism activities. Therefore, when organizing a sustainable tourism activity, it is necessary to describe and provide detailed risk factors, risk management strategies, and risk information that may be encountered in the event. This will show that the organizer has a high risk-awareness of the event, provide tourists with sufficient risk information, and help implement good risk management strategies.\u003c/p\u003e\u003cp\u003eOn the other hand, the results of this study show that both risk perception and marketing strategy have a positive impact on the cognitive benefit of sustainable tourism activities. The respective correlation coefficients are marketing strategy (0.650) and risk perception (0.238). The correlation of marketing strategy is much higher than that of risk perception, indicating that industry managers tend to believe that the correlation and importance of marketing strategy and cognitive benefit are much higher than risk perception when individuals participate in sustainable tourism activities. Although the inherent risk characteristics of sustainable tourism activities will affect individual intention to participate in activities, the negative impact of individuals on tourism activities can be effectively mitigated if individuals can improve their risk awareness of the activities.\u003c/p\u003e\u003cp\u003eBased on robust empirical data, this study offers three significant theoretical contributions, engaging in a direct dialogue with existing literature in sustainable recreation, environmental psychology, and technology application: (1) Positioning Information Technology Application as a Mediator of \"Cognitive Image and Marketing Strategy\" within the Theory of Planned Behavior (TPB).\u003c/p\u003e\u003cp\u003ePrevious research exploring pro-environmental behavioral intentions often confined IT application to a mere tool for information dissemination or convenience. This study significantly deepens the theoretical role of technology within behavioral models. This research identifies IT application as a crucial \"mediator of cognitive image and marketing strategy\" within the context of sustainable tourism. This finding extends Ajzen's (2015) classic Theory of Planned Behavior (TPB) by empirically demonstrating that IT application can directly influence behavioral intentions. More profoundly, it shows how IT can substantially enhance visitors' cognitive image and perceptions of a site's sustainable performance\u0026mdash;such as \"carbon reduction\" and \"climate adaptation\"\u0026mdash;thereby increasing perceived cognitive benefits and ultimately driving visitation intentions. (2) Proposing a \"Value-Resonance Model\" applicable to Sustainable Sites, Challenging the Universality of Traditional Marketing Theory.\u003c/p\u003e\u003cp\u003eOur research reveals a notable negative relationship between traditional marketing strategies and the visit intentions of target audiences (β = -0.204). This counter-intuitive finding critically challenges the universal applicability of traditional marketing theories in specific sustainability contexts. The core contribution here is the empirical support for a \"Value-Resonance Model\" more attuned to sustainability issues. This model posits that for individuals highly conscious of sustainability, consumption decisions are not primarily driven by external incentives but by a profound alignment between a site's cognitive impression and their intrinsic values. This perspective strongly corroborates Stern's (2000) Value-Belief-Norm Theory (VBN), which emphasizes the pivotal role of personal values in activating pro-environmental behavioral norms. Moreover, this study provides concrete empirical evidence for Vargo and Lusch's (2004) Service-Dominant Logic within climate action, illustrating that a site's value is not unidirectionally transmitted but realized through \"value co-creation\" with value-aligned visitors. (3) Constructing and Validating the Conceptual Framework of a \"Virtuous Cycle of Sustainable Conviction.\" Another significant theoretical contribution is the integration of climate risk perception, information technology application, cognitive benefits, and behavioral intention into a novel conceptual framework: the \"Virtuous Cycle of Sustainable Conviction.\" This framework delineates a dynamic psychological pathway where an individual's prior beliefs, particularly a high perception of climate risk (as illuminated by Slovic's 1987 research on risk perception), serve as key psychological antecedents influencing subsequent judgments and behaviors. The strategic intervention of IT application acts as a catalyst, transforming a site's sustainable actions into credible cognitive benefits. This process solidifies an individual's \"sustainable conviction,\" which, in turn, strongly drives their behavioral intention to support the site. The completion of this cycle can also be viewed as a successful instantiation of the Experience Economy (Pine \u0026amp; Gilmore, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), transforming a visit into a profound, educational experience that reinforces personal values. This cyclical model not only elucidates \"why\" visitors choose sustainable sites but also details \"how\" this process can be achieved through technological mediation, offering a dynamic and explanatory theoretical tool for understanding the decision-making psychology of sustainable recreation consumers.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eSustainable tourism stands as a paramount trend for the future of the tourism industry, driven by both its potential as a profitable business investment and its capacity to deliver physical and mental well-being to individuals. In this evolving landscape, particularly with the advent of smart technology, industry managers must meticulously understand the critical factors influencing individuals' participation in sustainable tourism activities and climate services, as well as the intricate interactions among these factors. Our findings underscore that in an era shaped by business ethics, corporate social responsibility, and rapid digital transformation, concerns regarding climate change are globally accelerating the development of the sustainable tourism industry. Specifically, cognitive benefit and information technology (IT) application emerge as key intention factors for current tourism managers organizing sustainable tourism activities, with cognitive benefit holding greater salience than specific aspects of IT application, such as visitor information sharing and information personalization.\u003c/p\u003e\u003cp\u003eFrom a strategic marketing perspective, operators in sustainable tourism must not only emphasize the inherent environmental sustainability advantages of their activities and climate services but also actively promote the cognitive benefits and IT applications associated with them. This dual approach is crucial for fostering the industry's growth. Firstly, effectively promoting cognitive benefits\u0026mdash;such as relaxation, health, and experiential value\u0026mdash;is indispensable for the success of sustainable tourism. As evidenced in regions like Taiwan, where media coverage of environmental issues significantly impacts public awareness, marketing strategies should explicitly highlight both the green, sustainable aspects and the personal, cognitive rewards of engagement. Secondly, strengthening the content and expanding the channels of IT application will immensely benefit future tourism operations. Furthermore, we advocate for cross-industry collaborations in IT application to leverage diverse digital platforms, ultimately enabling consumers to access sustainable tourism activities at more attractive prices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eAll study procedures were approved by Research Ethics Committee National Taiwan University Approve number: 202205ES018, Approve time: May, 09, 2023. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eInformed consent was obtained from individual participants or their legal guardians through oral communication. Approve Time: April, 28, 2023\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYu Tai-Kuei: Writing original \u0026amp; editing, Visualization \u0026amp; Methodology. Horng Jeou-Shyan: Conceptualization \u0026amp; Editing. Fang Yen-Po: Data curation, Methodology. Liu Chih-Hsing: Supervision \u0026amp; Project administration. Chou Sheng-Fang: Writing \u0026ndash; review \u0026amp; editing, Supervision. Yu Tai-Yi: Writing \u0026ndash; Investigation, original draft, review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to thank anonymous reviewers for useful suggestions and the National Science and Technology Council of Taiwan for financial support [Grant number: MOST 111-2410-H-130 -011]\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjzen, I. (2015) Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions. Ital. Rev. Agric. 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Manag. 40:213\u0026ndash;223. https://doi.org/10.1016/j.tourman.2013.06.006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"sustainable tourism behavior, information technology application, cognitive image, cognitive benefit, marketing strategy","lastPublishedDoi":"10.21203/rs.3.rs-7492880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7492880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the face of escalating climate risks, advancing sustainable tourism has become a critical imperative for the global tourism industry. This study addresses this challenge by exploring how smart technologies can enhance tourist engagement with sustainable destinations. Focusing on sport tourism centers as a key sector, this research synthesizes insights from interviews with 63 senior managers to analyze the determinants of visitor participation. It examines the interplay of key constructs\u0026mdash;namely, 7P marketing strategy, information technology application, cognitive image, cognitive benefit, and risk perception\u0026mdash;in shaping pro-sustainability intentions.\u003c/p\u003e\u003cp\u003eThe findings reveal a strong managerial consensus that smart technology is a powerful catalyst for sustainable engagement. Specifically, information technology application and perceived cognitive benefits are identified as significant direct drivers of a tourist's intention to participate in sustainable tourism. Furthermore, the study delineates the pathways to these outcomes: a well-executed 7P marketing strategy and effective management of risk perception positively influence cognitive benefits, while both information technology application and risk perception are crucial in shaping a favorable cognitive image.\u003c/p\u003e","manuscriptTitle":"An Exploration of Behavioral Intention towards Participation in Sustainable Tourism Activities: A Response to the Coming of Smart Technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 17:56:02","doi":"10.21203/rs.3.rs-7492880/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":"42ac36b7-f59c-4726-8e59-5ad9046be32b","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55792060,"name":"Business and commerce/Business and management"},{"id":55792061,"name":"Social science/Business and management"},{"id":55792062,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-03-09T14:12:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 17:56:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7492880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7492880","identity":"rs-7492880","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}