Social media had made significant effect on the tourism and hospitality industry. Among diverse types of social media platforms, short video apps (SVA) represented by TikTok or Douyin had brought great changes to the tourism industry. As new mobile technology platform, short video apps had changed the way for user to obtain travel information, make traveling plans and share the travel experience. Considering the new technology of SVA and the influence in tourism, this research aims to explore the SVA users’ behavior intentions and the adopting of SVA for making travel decision. Therefore, the new SVA-TAM model is proposed based on the technology acceptance model (TAM), including two new variables: electronic word of mouth (eWOM) and electronic trust (eTrust). An online survey was conducted to short video apps users. PLS-SEM was implemented for data and structural equations analysis of the final obtained 302 samples. In terms of the relationship between variables, this study found that user perceptions of SVA on usefulness and ease of use are powerful predictors of attitudes toward using SVA for travel planning, which maintains consistency with the outcome of previous TAM studies. Additionally, eWOM and eTrust positively influence user attitudes toward using SVA for travel planning even for destination decisions. Therefore, the short video apps should be taken into consideration for tourism marketing and destination branding owes to the effect on the potential users’ behavior intentions.

Based on the tourism industry economic panel data, this research divides and measures the tourism industry’s economic resilience cycle in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by constructing a counterfactual function and exploring the evolution of its Spatial-Temporal difference characteristics in the past 20 years. Estimation results show that three out of the four Recession–Recovery cycles of GBA have been characterized as “creative destruction”. Moreover, the economic resilience values and fluctuation trends of the individual tourism industries in the GBA are quite different. Additionally, the economic resilience of the urban tourism industry has changed from centralized to discrete, and the trend of economic resilience of the tourism industry has changed from low toughness to concentrated. This study expands the practice of resilience theory in the tourism industry economy, and it reveals the difference of tourism industry resilience in the metropolitan area system of GBA urban agglomeration from the perspective of industrial economic resistance and resilience.

The orderly development of diverse rural industries affects sustainable development in China. This study offers new insight in that the three-industry integration leads the rural economy as a whole system, with the integration between different industries affecting not just a single industrys development but also the sustainability of multiple rural industries. Conghua District is taken as an example in this research, and selected indicators and data from the past 14 years are obtained from the Conghua Statistical Yearbook. The density dependence logistic model is adopted to analyze the symbiosis patterns of rural three-industry integration system (RTIIS). The findings reveal that the symbiosis between the primary industry and the secondary industry in Conghua is poor, resulting in a negative symbiosis relationshipnamely, the mode of mutual competition. However, the symbiotic development model appears to reveal a positive, asymmetrical relationship (mutualism) between primary industry and tertiary industry and between secondary industry and tertiary industry. This study explores the process of rural three-industry integration through to the stage of symbiosis and provides scientific research methods and directions for the sustainable symbiosis of RTIIS. Future research on RTIIS in the framework of symbiosis theory will be of higher theoretical and practical value.

With the urbanization and transformation of consumption styles, the peer-to-peer accommodation (P2PA) industry has emerged to provide more convenient and efficient options for tourists and to inject new vitality into urban economies. This study investigates the spatiotemporal clustering and evolutionary drivers of P2PA in urban and rural areas of Shanghai. Spatial autocorrelation techniques and the Multi-scale Geographically Weighted Regression (MGWR) model are adopted to analyze the Airbnb data from 2016 to 2021 to identify the spatial characteristics of P2PA. The results indicate that high-value P2PA clusters in the city center are gradually breaking apart and shifting to rural areas especially in Pudong New Area. The shift from urban to rural areas makes great contributions to form an integrated urban–rural tourism system. Specifically, the spatial distribution of P2PA in Shanghai exhibits a vertical clustering pattern shaped by resource-oriented and demand-oriented mechanisms. Cultural tourism attractions significantly promote P2PA distribution, while business demands and transportation facilities have inhibiting effects. To support the sustainable growth of P2PA and tourism economy, the government should implement integrated Urban–Rural planning to build distinctive P2PA clusters based on local tourism resource, deepen local industrial linkages to develop new consumption formats and extend industrial chains, encourage practitioners to integrate intangible cultural heritage and folk customs with modern tourism needs, and establish a window for cultural display and inheritance with P2PA model.

This study delves into the pattern of digital economy development in the Guangdong, Hong Kong and Macao Greater Bay Area, and provides detailed forecasts of the development trend of the digital economy index of each prefecture-level city in the next three years. Drawing on authoritative government statistics, this study adopts a multidimensional analytical framework that combines economic indicators and digital economy indexes. Under a robust theoretical framework, this study applies stepwise regression analysis and ARIMA forecasting, and finds that GDP per capita and the tertiary industry have a significant positive impact on economic growth, while the digital economy index exhibits an unexpected negative impact. The innovation lies in combining the prediction model with regional stratification, revealing differences that require strategic policy interventions to achieve balanced development of the digital economy. Future research directions aim to address emerging socioeconomic and policy challenges in the digital era.