Li Chen

This study analyses how Covid-19 shapes individuals’ international tourism intentions in context of bounded rationality. It provides a novel analysis of risk which is disaggregated into tolerance/aversion of and competence to manage risks across three different aspects: general, domain (tourism) and situational (Covid-19). The impacts of risk are also differentiated from uncertainty and ambiguity. The empirical study is based on large samples (total=8,962) collected from the world’s top five tourism source markets: China, USA, Germany, UK and France. Various risk factors show significant predictive powers of individual’s intentions to defer international tourism plans amid Covid-19. Uncertainty and ambiguity intolerance is shown to lead to intentions to take holidays relatively sooner rather than delaying the holiday plans. 

This research investigates the direct and (indirect) spatial spillover effects of agglomeration economies on the productivity of the tourism industry. With increasing concerns about the persistence of low (labour) productivity in tourism across many developed economies, there is an urgent need to address this productivity challenge. Using major under-exploited UK microeconomic panel data, spatial econometric modelling is employed to estimate the effects of agglomeration economies on productivity. Findings reveal the significant effects of agglomeration economies on productivity within a specific region, but also significant spatial spillover effects across neighbouring regions, suggesting the possibility of productivity convergences. Competitive and complementary effects of agglomeration economies on productivity are identified.

This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.

Multivariate forecasting methods are intuitively appealing since they are able to capture the inter-series dependencies, and therefore may forecast more accurately. This study proposes a multi-series structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the component series are then restacked to build a multi-series structural time-series model. Empirical evidence from Hong Kong inbound tourism demand forecasting shows that the newly proposed approach improves the forecast accuracy, compared with traditional univariate models.

This study proposes a relative climate index based on the push and pull theory to assess the effects of relative climate variability on seasonal tourism demand. The relative climate index measures the climatic comfort of a destination relative to that of the tourist origin. Using the proposed approach, the effects of the relative climate comfort on seasonal tourism demand are empirically tested based on a quarterly panel data set of visitor arrivals from Hong Kong to 13 major Chinese cities. The intra-annual seasonality and interannual variability are both tested in the model. The results indicate that the intra-annual relative climate positively influences tourism demand in Mainland regions, where the climate is significantly different from that of Hong Kong.