“…Meanwhile, the effect correlation of endogenous components was assessed using Q 2 . In this study, all Q 2 values were greater than 0.000, indicating that the model has impact correlation and further confirming the stability of this study [64].…”
The establishment of a new type of natural protected area system with national parks as the main body is an inevitable trend of current development, and it is also an important ways to build a more beautiful China. During tourist visits, the national park will promote a variety of ways to enhance the ecological values of tourists. Ecological values can strengthen tourists’ sense of identity, but their impact on tourists’ pro-environmental behavior is not discussed. Based on this, Wuyishan National Park, a world natural and cultural heritage, is selected as the case site, and the PLS-SEM analysis method is used. An empirical test was conducted on 358 valid samples collected in the field. The results show the following: (1) tourists’ ecological values and place identity can positively affect their pro-environmental behaviors; (2) place identity plays a mediating role between ecological values and tourists’ pro-environmental behavior; (3) place dependence and place identity play a chain mediating role between ecological values and tourists’ pro-environmental behavior; (4) according to the PLS-MGA test, gender and age can play a moderating role on the influence of ecological values on pro-environmental behavior. Therefore, the managers of national parks should pay attention to the cultivation of ecological values and consider tourist attraction, as well as formulating marketing strategies and other policy suggestions according to the different characteristics of tourists. The findings of this study offer both practical guidance and a theoretical underpinning for advancing ecological tourism within the framework of natural protected areas, with national parks playing a central role.
“…Meanwhile, the effect correlation of endogenous components was assessed using Q 2 . In this study, all Q 2 values were greater than 0.000, indicating that the model has impact correlation and further confirming the stability of this study [64].…”
The establishment of a new type of natural protected area system with national parks as the main body is an inevitable trend of current development, and it is also an important ways to build a more beautiful China. During tourist visits, the national park will promote a variety of ways to enhance the ecological values of tourists. Ecological values can strengthen tourists’ sense of identity, but their impact on tourists’ pro-environmental behavior is not discussed. Based on this, Wuyishan National Park, a world natural and cultural heritage, is selected as the case site, and the PLS-SEM analysis method is used. An empirical test was conducted on 358 valid samples collected in the field. The results show the following: (1) tourists’ ecological values and place identity can positively affect their pro-environmental behaviors; (2) place identity plays a mediating role between ecological values and tourists’ pro-environmental behavior; (3) place dependence and place identity play a chain mediating role between ecological values and tourists’ pro-environmental behavior; (4) according to the PLS-MGA test, gender and age can play a moderating role on the influence of ecological values on pro-environmental behavior. Therefore, the managers of national parks should pay attention to the cultivation of ecological values and consider tourist attraction, as well as formulating marketing strategies and other policy suggestions according to the different characteristics of tourists. The findings of this study offer both practical guidance and a theoretical underpinning for advancing ecological tourism within the framework of natural protected areas, with national parks playing a central role.
“…Future research could use different data to examine both the antecedents and consequences of trust in both dimensions of trust. While the social network concept can measure the degree of trust between experts [91,92], the trust relationship between the telemedicine platform and the user influences the evaluation, and future research is required.…”
Section: Limitations and Future Directionsmentioning
As the COVID-19 pandemic progressed, the resulting demand for telemedicine services increased. This research empirically examines the role of trust, privacy concerns, and perceived usefulness in customer confirmation, satisfaction, and continuing intention in telemedicine. A typology of trust was employed to classify trust into three dimensions and explore the mediating role of the three dimensions of trust in the relationship between satisfaction, perceived usefulness, and continued intention. We also examined the moderating role of personal privacy concerns in the relationship between trust and continued intention. For this study, we developed a structural equation model based on expectation confirmation theory and analyzed 465 questionnaires from Chinese online users. The expectancy confirmation theory (ECT) was reaffirmed by empirical evidence. The results showed that the relationship between perceived usefulness and satisfaction with continued intention is moderated by the three dimensions of trust. Privacy concerns can negatively moderate the relationship between structural assurance-based trust and continued intention. This study also identified potential threats to telehealth market growth alongside new insights.
“…Then algorithms such as TF-IDF are utilized to match user preferences with courses (Ghauth et al 2010 ), thus implementing personalized recommendations for courses (Zhang et al 2017 ); (2) collaborative filtering method (CF). The common practice is to model users based on association rules (Aher and Lobo 2013 ), KNN (Murad et al 2020 ), social networks (Chen et al 2022 ; Zhang et al 2022 ; Zeng et al 2022 ), etc., to depict user portraits (Jing and Tang 2017 ), and then identify users with the same preference based on distance or similarity calculation methods, thus completing course recommendations; (3) machine learning-based recommendations. In this category, researchers mainly use machine learning methods to extract preference information of users (Hu et al 2022 ) or courses (Xu and Zhou 2020 ), and employ Bayesian neural networks (Li et al 2020 ), RNN (Okubo et al 2017 ) to characterize the courses, and make course recommendation; (4) Hybrid-based recommendation.…”
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