The main purpose of this research is to investigate and estimate the spillover effects in inbound and domestic tourism flows to 341 cities in mainland China. In conjunction with this, the key determinants of tourism flows are also studied in the spatial econometric model. The results confirm the existence of spillover effects in both inbound and domestic tourism flows, and suggest that physical infrastructure factors, tourist attractions, and the SARS outbreak are significant determinants of inbound and domestic tourism flows. In addition, it is found that although the degree of openness to inbound tourists is important for inbound tourism flows, a city’s income is the key to enhancing domestic tourism flows. Significant differences in spillover effects and determinants of tourism flows are also discovered between cities in different regions.

This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable.These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses’ future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.