Dr Eden Jiao

Purpose: This study aims to extend the conceptualization of hotel demand resilience from the spatial and temporal perspectives and operationalize the conceptualization by developing a systematic analytical framework to comprehensively examine the spatial and temporal dynamics hotel demand resilience.Methodology: To operationalize the extended conceptualization of hotel demand resilience, this study extends the standard spatial model by integrating time-varying features to accurately capture the spatial and temporal dynamics of hotel demand resilience among Asian destinations as a panel.Findings: The spillover interaction of hotel demand resilience varied as the pandemic evolved, with a higher magnitude at the beginning of the shock. Breaking down the analysis of policy stringency and hotel resilience at the local level reveal heterogeneous policy effects.  Implications: This study offers practical insights for hotel industry stakeholders by highlighting how varying levels of demand resilience across Asian destinations in response to crises can inform more effective crisis management and recovery strategies, emphasizing the importance of spatial collaboration, policy alignment, and diversification of tourist source markets. Originality/Value: This study is the first to apply a time-varying parameter (TVP) spatial modeling framework to tourism and hospitality research. The findings concerning the evolving nature of hotel resilience spillover contribute to the hospitality and resilience literature. Keywords: Hotel resilience, spatial spillovers, time-varying parameter, policy stringency, spatial heterogeneity

In light of the spatial interdependence between neighbouring destinations in terms of their international tourism demand and the importance of spillovers to destination competitiveness, this study aims to introduce a novel conceptualisation of relative destination competitiveness that moves beyond the traditional isolated approach by explicitly considering the dynamic interplay between competing and complementary destinations and the unique characteristics of each source market. The conceptualisation is operationalised through an advanced spatiotemporal econometric framework with a broader definition of spillovers extracted from each demand system to gauge destination competitiveness relative to both source markets and other destinations in the system. Global and local estimations are performed to extract the destination-level net spillover effects of tourism demand and price, serving as indicators of relative competitiveness. This framework is applied to empirically assess the competitiveness of Asian destinations across various source markets, offering valuable insights into both destination competitiveness and tourism demand.

To improve the quality of life of destination communities in the context of an expanding global economy, tourist destinations must sharpen their competitive advantages through increased productivity. In this study, we investigate the effect of sectoral productivity on destination competitiveness, using Hong Kong as an example. The competitiveness of a destination is measured by tourism-contributed quality of life relative to that of competing destinations. An autoregressive distributed lag model with error correction mechanism is used to model the relationship between destination competitiveness and various sectoral productivities. The productivity effects of several related sectors are identified through an empirical econometric analysis and the results show that destination competitiveness is more dependent on the productivities of core tourism sectors than those of other sectors. •Productivity improvement enhances destination competitiveness.•Tourism price of the destination encourages high quality product supply.•Tourism demand increase drives destinations to be more competitive.•Crowding out effect may exist between core and supporting sector productivities.

Economic analysis of the tourism industry is a critical tool for local industries and governments to estimate, understand, and forecast the tourism potential and economic performance of a destination, especially amidst a crisis such as the COVID-19 pandemic. In this study, we take Macao as a case study, perform an analysis using an input-output framework. By constructing a tourism satellite account and a dynamic stochastic general equilibrium model, we estimate the contribution of the tourism industry to the economy of Macao and assess the ramifications of several government policies to mitigate the impact of COVID-19. Our findings provide the Macao government and local industrial stakeholders with critical information for pandemic mitigation and recovery strategies.

Destination choice in multi-destination trips is crucial for understanding tourists' preferences, predicting tourism demand and flow, and fostering collaborative opportunities between destinations. This study utilises prospect theory to examine how tourists select a stop within a multi-destination trip and to explore the role of other chosen stops' attributes as a reference point when tourists plan their itinerary. The results reveal dynamic perceptions of gains and losses relative to reference points that vary across individual characteristics and destination attributes. Additionally, heterogeneity is revealed in tourists' reference-dependent patterns, which can be categorised as immediate- and delayed-gratification reference dependencies. These findings contribute to the destination choice discourse by proposing new directions for comprehending tourists' heterogeneous reference dependencies and attitudes in sequential destination choices.

The tourism industry is vulnerable to external shocks. Various crises inevitably impact the tourism industry and tourist destinations negatively but at the same time bring opportunities to examine destination resilience in response to a real shock that is hard to simulate. To manage a crisis more effectively, two critical issues should be addressed: the duration of the impact of the crisis (i.e., temporal perspective) and the affected geographical scale (i.e., spatial perspective), which have been neglected in previous studies on destination resilience. To address the above gaps, this research develops a comprehensive, multi-stage, dynamic spatiotemporal analytical framework to firstly measure two aspects of tourism resilience (i.e., resistance and recovery), and secondly analyze the influencing factors of tourism resilience. The empirical context of international tourism in Europe during the COVID-19 pandemic is used to demonstrate the applicability of the developed framework and relevant policy implications.

Purpose The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively. Design/methodology/approach First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run. Findings The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025. Originality/value The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.