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A platform for research: civil engineering, architecture and urbanism
Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model
Travel reservation is an important way to improve tourist experiences and digitally manage tourist attractions in the COVID-19 context. However, few studies have focused on the online reservation intentions of tourist attractions and its influencing factors. Based on the theory of the technology acceptance model (TAM), two variables (perceived risk and government policy) are introduced to expand on the theoretical model. This study investigates the influence of subjective norms, government policy, perceived usefulness, perceived ease of use, and perceived risk on reservation intentions of tourist attractions. An online survey was conducted in China, and 255 questionnaires were collected. The data were analysed using SPSS 26.0 and AMOS 28.0 to construct a structural equation modelling and analyse the path. The findings show that (1) subjective norms have no significant impact on reservation behaviours under voluntary situations; (2) perceived usefulness positively affects tourists’ reservation intention; and (3) perceived risk has a significant negative impact on reservation intention, and government policy is the main factor affecting tourists’ reservation intentions. These findings enhance the understanding of tourists’ reservation intentions and extend the TAM theory. From the practice perspective, tourist attraction operators should continue to strengthen the construction of the reservation system, improve tourists’ experiences, reduce the perceived risk of tourists, and other stakeholders such as the government should strengthen cooperation, promote the reservation system, and create a good reservation atmosphere.
Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model
Travel reservation is an important way to improve tourist experiences and digitally manage tourist attractions in the COVID-19 context. However, few studies have focused on the online reservation intentions of tourist attractions and its influencing factors. Based on the theory of the technology acceptance model (TAM), two variables (perceived risk and government policy) are introduced to expand on the theoretical model. This study investigates the influence of subjective norms, government policy, perceived usefulness, perceived ease of use, and perceived risk on reservation intentions of tourist attractions. An online survey was conducted in China, and 255 questionnaires were collected. The data were analysed using SPSS 26.0 and AMOS 28.0 to construct a structural equation modelling and analyse the path. The findings show that (1) subjective norms have no significant impact on reservation behaviours under voluntary situations; (2) perceived usefulness positively affects tourists’ reservation intention; and (3) perceived risk has a significant negative impact on reservation intention, and government policy is the main factor affecting tourists’ reservation intentions. These findings enhance the understanding of tourists’ reservation intentions and extend the TAM theory. From the practice perspective, tourist attraction operators should continue to strengthen the construction of the reservation system, improve tourists’ experiences, reduce the perceived risk of tourists, and other stakeholders such as the government should strengthen cooperation, promote the reservation system, and create a good reservation atmosphere.
Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model
Yuzong Zhao (author) / Hui Wang (author) / Zhen Guo (author) / Mingli Huang (author) / Yongtao Pan (author) / Yongrui Guo (author)
2022
Article (Journal)
Electronic Resource
Unknown
reservation intentions , tourist attraction reservation , technology acceptance model (TAM) , perceived risk , government policy , structural equation modelling (SEM) , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
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