Abstract
Social networks are new channels for travelers to obtain or share travel information, which has important impacts on their travel decision-making behavior. Therefore, the psychological feelings of travelers and their acceptance intention (AI) of this type of travel information should be explored. In this study, certain psychological latent variables were incorporated into a technology acceptance model to construct an extended model that explores the factors influencing the travelers’ AI of travel information on social networks. This model was validated using survey data collected in Chongqing, China. The influence of each factor on the AI and the interaction between factors were quantitatively described using the structural equation modeling method. The results showed that the perceived risk, perceived trust, and perceived usefulness are the most important factors affecting travelers’ AI; the subjective norm, hedonic motivation, and perceived ease of use also exert a certain degree of influence; the proposed research model has a good interpretation ability for AI, and the explanatory power has reached 52%. This study confirmed the applicability of the constructed model in this research field on the basis of survey data and provided a theoretical reference for ascertaining the attitude of travelers toward travel information available on social networks.
Highlights
With the development of information technology and the popularization of smart terminal devices, the way people use the Internet has gradually shifted from simple information search and web browsing to information interaction based on social media. us, social media has become an important service platform for people to publish, acquire, and disseminate information
E mass information on social networks involves a significant amount of travel information. e content of travel information on social networks mainly includes two types: one is the information obtained by the traveler via social media during the travel process, concerning the traffic system, such as road conditions, transit time, accidents, and weather information; the other concerns information on related experiences shared by other travelers regarding travel destinations, travel modes, and travel route decisions [4,5,6,7]
The residents of Chongqing were selected as the survey targets, and a combination of online and offline questionnaires was randomly distributed to investigate the acceptance intention (AI) of travel information on social networks. e online questionnaire is distributed through the “Questionnaire Star” network platform, and the network link is shared through social software, such as Sina Weibo, WeChat, and QQ. e area of the offline survey was selected in the business districts, bus stations, rail stations, etc., of the central city’s administrative districts, where there were many people and travel, and the samples were obtained in the form of random interviews by investigators to ensure the randomness of the sample collection and reduce bias
Summary
With the development of information technology and the popularization of smart terminal devices, the way people use the Internet has gradually shifted from simple information search and web browsing to information interaction based on social media. us, social media has become an important service platform for people to publish, acquire, and disseminate information. Us, social media has become an important service platform for people to publish, acquire, and disseminate information. Is travel information has an important influence on the travel decisionmaking behavior of travelers It can affect people’s attitudes regarding travel plans, allowing them to prejudge the travel environment before travel. Travel information on social networks is qualitatively different from the travel information published on traditional channels such as TV and radio It has a faster propagation speed, higher timeliness, wider coverage, and stronger interactive function between the publisher and the receiver. To simultaneously explore the influence relationships between latent variables—that are relatively abstract in concept and cannot be measured directly—the structural equation modeling (SEM) method, a multivariate statistical analysis technique, was applied to analyze the data. ΛX and ΛY are the factor loading coefficient matrices of X and Y, respectively; B is the path coefficient matrix between endogenous latent variables; Γ is the path coefficient matrix between exogenous
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