The spatial heterogeneity and dynamics of tourism-flow spillover effect: The role of high-speed train in China

Abstract

The existing literature has not fully explored the spatial heterogeneity and dynamics of spillover effects of tourism-flow patterns, and this article makes contributions to addressing this issue. Using spatial autoregressive models and comparing the results for the non-high-speed train (HST) era (2002–2006) and those for the HST era (2011–2015) of China, this article finds that in the HST era, the spillover effect of tourism flows from neighboring regions has changed from positive to negative. Moreover, in the HST era, the cities with HST services have significantly positive effects on for a particular city, while those cities without HST have negative effects. These results reveal the spatial heterogeneity and dynamics of the tourism-flow spillover. After evaluating the variation of other factors which potentially contribute to the change of spillover patterns, this study identifies that the HST service plays an important role in such changes. Finally, this article uses geographically weighted regressions to explore the spatial heterogeneity in an elaborative way.

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

“…Specifically, competitive effect (positive spillover effect) is detected in luxury, upper upscale and upscale segments, but contagious effect (negative spillover effect) is found in low-class hotel segments, such as upper midscale and midscale. Unlike the single-direction spillover effect found in prior studies (Seo et al, 2014;Lei et al, 2008;Zhou et al, 2017), the present results show that the magnitude and direction of spillover effects may differ depending on hotel class. Positive spillover effects become smaller as the hotel class decreases, and negative spillover effects become larger.…”

Spatial spillover of transport improvement on tourism growth

“…Furthermore, some variables, such as hotel infrastructure, may suffer from endogeneity issues, which contaminate the consistency of our estimates. Lastly, we did not assess heterogeneity across Chinese regions in terms of geography or socioeconomics when investigating transport spillover (Zhou et al, 2017). We, therefore, call for future studies extending our research method and better specify the combination of transport modes in spillovers to incorporate these concerns.…”

“…ln GDP_per it is the log of GDP per capita of the region (in CNY). This variable measures the region’s income level; a more affluent region is expected to have more robust infrastructure to support tourism growth (Zhou et al, 2017). …”

[Retracted] An Intelligent Analysis Model in the Environment of Regional Economic Development and Regional Economic Differences in China Using Big Data Analysis Technology

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