Dr Jason Chen

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.

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.

[This dataset contains all data used for Studies 2 (qualitative), 3 (quantitative survey) and 4 (longitudinal) in my PhD research.]Thesis abstract:This thesis explores the potential positive impact of artificial intelligence (AI) technology on sustainability in and outside of the tourism industry through four studies. Study 1 introduced the AI4GoodTourism framework, emphasising the need for sustainability inclusion and tourist involvement to achieve a successful sustainability transition. Five themes were identified through a systematic review: intelligent automation to enhance tourist experience, preserve heritage, promote quality of life, measure tourist experience, and preserve the environment. The latter theme was the least explored scholarly topic. Study 2 conceptualised a conversational AI chatbot to promote pro-environmental behaviour spillover among tourists visiting the Gili Islands, Indonesia. A theoretical model was proposed, highlighting factors influencing chatbot usage and spillover effects. Study 3 identified relationships between factors from Study 2, revealing that factors such as performance expectancy, timing, and credibility significantly influenced people’s intention to use the proposed chatbot technology. A significant relationship was established between people’s intentions to use the chatbot and environmentally friendly transport. Scenario-based experiments showed that using the chatbot with educational information on sustainability was sufficient to trigger behaviour change. Study 4 explored the underlying mechanism of pro-environmental behaviour spillover through human-chatbot interactions using flashback nudging. A longitudinal experiment involving the Gili tourists demonstrated that flashback nudging delivered through chatbot technology strengthened their environmental self-identity, leading to significant differences in self-reported pro-environmental behaviour between treatment and control groups. In conclusion, the thesis demonstrates that AI technology, designed with high sustainability inclusion, can positively impact sustainability through tourists’ marginal contributions. The proposed AI4GoodTourism framework and the conceptualised chatbot technology, especially with flashback nudging, show potential for facilitating pro-environmental behaviour spillovers among tourists. All four studies in this thesis highlight the importance of prioritising sustainability in AI innovations for the tourism industry, offering insights for future AI development and adoption to support the global sustainability agenda.

Given proper facilitation, pro-environmental behaviour in tourist destinations may spill over to the daily lives of tourists. Recent advancements in conversational artificial intelligence (AI) may lead to the emergence of more effective technology that will encourage people to become more environmentally friendly. However, limited research has been devoted to understanding the complexity of using such technologies for pro-social nudging. This study applies rigorous scale development procedures to test how advanced conversational AI, like chatbots, can be effective in encouraging pro-environmental behaviour spillover. The conceptual model introduces seven factors that predict people's intention to use the chatbot and three factors that predict their intention to embrace the target behaviour. Performance expectancy and timing were the most significant in predicting the intention of individuals to adopt the technology. Efficiency and government support were the chief drivers of the intention to adopt the pro-environmental behaviour. The findings suggest that although introducing a technology-mediated nudging mechanism can trigger people's intention to behave pro-environmentally, other factors must be fulfilled in order to ensure sustained behaviour change in society.

This study constructed hotel demand curves at the disaggregate level to uncover the heterogeneity of demand curves across consumers and during both normal periods and times of crisis, exemplified by the pandemic. The novel demand modeling technique fits nonlinear demand curves, parameterizes elasticity dynamics, and enables the comparison of demand curves by essential value. The demand curves for three hotel types in normal and pandemic situations were fitted and decomposed by consumers' socio-demographics, preferences, and risk tolerance. A pandemic made the demand curve for midscale (upscale) hotels more inelastic (elastic) and mitigates (amplifies) the influence of individual differences on the demand curve, whereas the demand curve for economy hotels was unaffected by a pandemic. The findings offer insights into the business operations of different hotels, including optimal pricing, customized marketing across consumer segments, and business strategies in case of a health crisis.

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specifica-tion of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance. (c) 2021 Elsevier Ltd. All rights reserved.

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.

Researchers have confirmed the substitution of sharing accommodation for hotels. The existing assessments of the substitution have primarily focused on the inverse relationship between sharing accommodation supply and hotel performance, with a lack of examination based on demand curve analysis. This study utilizes behavioral economic demand models to construct alone-price/own-price demand curves for hotels and cross-price demand curves for sharing accommodation to quantify the substitutive relationship between sharing accommodation and different hotel types. Furthermore, we explore the variations in this substitutive relationship by travel companion and customer group. The analysis is dual-directional, including both the substitutability of sharing accommodation for hotels and the reverse relationship. The findings inform market competition strategies for hotels and sharing accommodation.

This study examines the role of tourism development in reducing regional income inequality in China. First, the theoretical foundation for how tourism affects regional income inequality is discussed. Second, based on the conditional convergence framework, this study proposes a spatiotemporal autoregressive model to capture spatial and temporal dependence as well as spatial heterogeneity. Tourism development is introduced as a conditional convergence factor in an attempt to examine whether the convergence speed is accelerated by regional tourism development. Third, the effects of international and domestic tourism in narrowing regional inequality are compared both globally and locally. The empirical results indicate that tourism development contributes significantly to the reduction of regional inequality, with domestic tourism making a greater contribution than international tourism.

We revisit the problem of secure cross-domain communication between two users belonging to different security domains within an open and distributed environment. Existing approaches presuppose that either the users are in possession of public key certificates issued by a trusted certificate authority (CA), or the associated domain authentication servers share a long-term secret key. In this article, we propose a generic framework for designing four-party password-based authenticated key exchange (4PAKE) protocols. Our framework takes a different approach from previous work. The users are not required to have public key certificates, but they simply reuse their login passwords, which they share with their respective domain authentication servers. On the other hand, the authentication servers, assumed to be part of a standard PKI, act as ephemeral CAs that certify some key materials that the users can subsequently use to exchange and agree on as a session key. Moreover, we adopt a compositional approach. That is, by treating any secure two-party password-based key exchange (2PAKE) protocol and two-party asymmetric-key/symmetric-key-based key exchange (2A/SAKE) protocol as black boxes, we combine them to obtain generic and provably secure 4PAKE protocols.

The unprecedented COVID-19 pandemic reversed the ongoing upsurge in the global tourism industry. Yet compared with still-stagnant international tourism, domestic tourism has shown signs of recovery. This study takes Guangdong Province, China as a case for regional domestic tourism and adopts the tourism satellite account (TSA) method to assess domestic tourism's status. A pre- and post-pandemic comparison is conducted to map the impacts of the COVID-19 outbreak on domestic tourism's economic contribution. The TSA results show that the direct contribution of domestic tourism to Guangdong's economy fell from 2.53% to 1.20% across these timeframes. Findings also reveal changes in visitor composition by places of origin and in industries' proportional contributions to tourism. •The study investigates how COVID-19 affects tourism contribution to economics.•Tourism Satellite Account framework is applied for evaluation.•The economic contribution of domestic tourism is 2.53% in 2019, and 1.20% in 2020.•The composition of economic contribution across industries does not change a lot.

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

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 investigated the two main dimensions of STEs’ community social responsibility and their impact on firms’ objective and subjective performance, respectively. It also explored the moderating effects of STE owners’ demographics on the relationships between the two community social responsibility dimensions and firm performance. By the survey data from STEs in the historical towns in southwestern China, the empirical findings suggested that engaging in socially responsible behavior at the community level contributes to STEs’ subjective performance; and the influence of community engagement on STEs’ performance is moderated by the owners’ demographic characteristics, such as age, gender, ethnicity, and birthplace.

This chapter introduces a general form of the time-varying parameter (TVP) model. Unlike most traditional econometric models, which are based on fixed-parameter estimation, the TVP model can capture the dynamics of parameters over the sample period based on the recursive Kalman filter (KF) algorithm. When applied to tourism demand analysis, this unique technical feature of the TVP model provides insights into the evolution of demand elasticities. Furthermore, the TVP model generally produces accurate and stable tourism demand forecasts across different forecasting horizons. Future research could explore further integration of the TVP technique with more advanced econometric models.

In this work, we present a new generic polymorphic routing protocol tailored for vehicular ad hoc networks (VANETs). Similar to the case of mobile ad hoc networks, the routing task in VANETs comes under various constraints that can be environmental, operational, or performance based. The proposed Polymorphic Unicast Routing Protocol (PURP) uses the concept of polymorphic routing as a means to describe dynamic, multi-behavioral, multi-stimuli, adaptive, and hybrid routing, that is applicable in various contexts, which empowers the protocol with great flexibility in coping with the timely requirements of the routing tasks. Polymorphic routing protocols, in general, are equipped with multi-operational modes (e.g., grades of proactive, reactive, and semi-proactive), and they are expected to tune in to the right mode of operation depending on the current conditions (e.g., battery residue, vicinity density, traffic intensity, mobility level of the mobile node, and other user-defined conditions). The objective is commonly maximizing and/or improving certain metrics such as maximizing battery life, reducing communication delays, improving deliverability, and so on. We give a detailed description and analysis of the PURP protocol. Through comparative simulations, we show its superiority in performance to its peers and demonstrate its suitability for routing in VANETs. © 2011 John Wiley & Sons, Ltd.

Most tourism programs today have an international component in their curriculum, usually including a global tourism class. This book serves as an excellent supplemental reading for students in these classes.

Attribute-based authenticated key exchange (AB-AKE) is a useful primitive that allows a group of users to establish a shared secret key and at the same time enables fine-grained access control. A straightforward approach to design an AB-AKE protocol is to extend a key exchange protocol using attribute-based authentication technique. However, insider security is a challenge security issue for AB-AKE in the multi-party setting and cannot be solved using the straightforward approach. In addition, many existing key exchange protocols for the multi-party setting (e.g., the well-known Burmester-Desmedt protocol) require multiple broadcast rounds to complete the protocol. In this paper, we propose a novel one-round attribute-based key exchange (OAKE) protocol in the multi-party setting. We define the formal security models, including session key security and insider security, for OAKE, and prove the security of the proposed protocol under some standard assumptions in the random oracle model.

The controllable linkability of group signatures introduced by Hwang et al. enables an entity who has a linking key to find whether or not two group signatures were generated by the same signer, while preserving the anonymity. This functionality is very useful in many applications that require the linkability but still need the anonymity, such as sybil attack detection in a vehicular ad hoc network and privacypreserving data mining. In this paper, we present a new group signature scheme supporting the controllable linkability. The major advantage of this scheme is that the signature length is very short, even shorter than this in the best-known group signature scheme without supporting the linkability. We have implemented our scheme in both a Linux machine with an Intel Core2 Quad and an iPhone4. We compare the results with a number of existing group signature schemes. We also prove security features of our scheme, such as anonymity, traceability, nonframeability, and linkability, under a random oracle model.

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. 

An overview of estimates of price elasticities of demand (PED) and income elasticities of demand (YED) for tourists to destinations relevant to Scotland; price elasticities of supply (PES) of commercial accommodation relevant to Scotland and other factors influencing the demand and supply of tourism.

This paper provides a novel longitudinal analysis of the stability of risk preferences in the travel domain, and how these are impacted by major life events during a crisis. Analysis of a four-wave survey during COVID-19 demonstrates strong inter-temporal stability of most risk preferences. It also reveals greater stability of generic risk traits and risk and uncertainty tolerance in travel compared to situational risk preferences. An innovative difference-in-differences with multiple time periods analysis is undertaken to examine the oscillating risk preferences of individuals hit hard financially by the pandemic. It reveals they become more tolerant of situational risk and uncertainty over time. Learning that the negative consequences of the pandemic are negotiable plays a key role in changing risk preferences.

The purpose of this research is to investigate and estimate the spill-over effects of online consumer reviews as a proxy to reflect hotel performance, focusing on 689 hotels located in London, UK. This study used a series of data mining approach to collect estimated variables from a travel search engine website (i.e., Kayak.com) and made the first attempt to apply spatial econometric modelling at the firm level in the tourism and hospitality field. The findings of this research identified a complementary effect of consumer rating between neighbouring hotels, and showed the spatial dependency of room prices across hotels at the destination. Furthermore, a local estimation using geographically weighted regression approach allows researchers to understand the spatial variations of the spatial effects. Important implications for tourism and hospitality managers to develop regional marketing and promotions are provided.

Through a systematic review of recent publications on residents’ quality of life (QOL) in relation to tourism development (TD), this study surveys associated dynamics and emerging trends. Several patterns are observed: i) geographic areas of interest have expanded from developed economies to developing economies; ii) an array of theories and concepts have been introduced or merged with classic frameworks; iii) subjective composite approaches have dominated residents’ QOL measurement; and iv) the direct and indirect influences of TD on residents’ QOL constitute a main focus of recent work. Future work can take several directions: i) establishing a conceptual framework to link tourists’ and residents’ perspectives on QOL; ii) combining subjective and objective scales to improve generalizability; iii) employing longitudinal designs with innovative methods to offer insight into the dynamics of the TD–QOL nexus; and iv) investigating QOL/well-being from the eudaimonic tradition to accommodate diversified elements and broader perspectives of QOL.

Background Organisations frequently rely on international business travel when operating in internationalized business environments. Yet, the effectiveness of this mechanism relies on their international business travellers (IBTs) being physically and psychologically well enough to productively perform across different working environments. The salience of this issue has led to increased interest in explaining IBTs’ work-related outcomes and their antecedents. Data and methods This paper tests key assumptions of the job-demands resources theory (JD-R) in the context of international business travel. Based on a sample of 134 IBTs from different national backgrounds, the study analyses the role of prominent job demands and resources for IBTs’ exhaustion and engagement. The study also looks at the presumed moderating role of recovery experiences. Results Support for the JD-R theory when applied to the context of international business travel remains mixed. Results suggest workload as an important predictor of exhaustion. Organisational support predicts engagement. Other typical resources including autonomy and supervisory support do not show a significant relation to work-related outcomes. Apart from recovery relaxation, which significantly weakened the impact of job demands on exhaustion, none of the moderating effects (via coping, buffering or other recovery experiences) are confirmed. Conclusions The findings reflect the unique complexities of international business travel. For professionals working with or as IBTs, this study only suggests workload and support as reliable levers to influence work-related outcomes. For a better understanding of further job demands, resources and moderators within the IBT context, additional JD-R related research and theoretical development is proposed.

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.

Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.