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This study investigated the travel intentions (TIs) of Chinese travelers and their utilization of virtual tourism technology during the Zero COVID-19 policy period by using a stimulus-organism-response (SOR) model. The study specifically examines the interplay between knowledge of policy, perception of risk, TI, usage of virtual tourism technology (UVTT) and the mediating role of personal values.

The data were collected from 333 Chinese travelers through an online questionnaire, and structural equation modeling (SEM) was employed to test the proposed hypotheses.

The study suggests that knowledge of policy and risk perception increase changes in personal values (PVs), which, in turn, affect Chinese travelers' TIs and the UVTT, with PVs playing a mediating role. Risk perception has a positive effect on the UVTT.

This study highlights the positive impact of tourism policy knowledge and risk awareness on individual values as a stimulus. Stakeholders need to implement industry-specific policies that are in line with scientific developments. Tourism managers should prioritize understanding the psychological reactions of tourists in crises and provide support to mitigate negative emotions. Anticipating changes in PVs is crucial, as instability affects tourists' behavior. The findings of the study also provide valuable insights for technology designers and underscore the substitutability of virtual technologies in improving the tourism experience.

This study is the first to examine the mediating role of PVs in the relationship between knowledge of policy, tourism risk perception (TRP), TI and the UVTT based on the SOR model. The insights gained from this analysis can assist policymakers and tourism managers in understanding the psychological changes of tourists, thereby facilitating the development of appropriate tourism planning.

This study aims to investigate the process of developing loyalty in the Chinese mobile health community from the information seeking perspective.

A covariance-based structural equation model was developed to explore the mobile health community loyalty development process from information seeking perspective and tested with LISREL 9.30 for the 191 mobile health platform user samples.

The empirical results demonstrate that the information seeking perspective offers an interesting explanation for the mobile health community loyalty development process. All hypotheses in the proposed research model are supported except the relationship between privacy and trust. The two types of mobile health community loyalty—attitudal loyalty and behavioral loyalty are explained with 58 and 37% variance.

This paper has brought out the information seeking perspective in the loyalty formation process in mobile health community and identified several important constructs for this perspective for the loyalty formation process including information quality, communication with doctors and communication with patients.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.