Dr Anyu Liu

This study developed a perceived intelligence scale for artificially intelligent (AI) assistants and investigated its impact on users’ travel-related behavioral intentions. A four-stage study with a mixed-methods design was conducted. Study 1 identified three dimensions and 26 initial items through a systematic literature review, interviews, and focus group discussions. Study 2 used exploratory factor analysis to refine the items. Through composite confirmatory analysis, Study 3 confirmed an 18-item and three-dimensional scale (conversational intelligence, information quality, anthropomorphism). Study 4 established the scale’s predictive validity in travelers’ intentions to use AI assistants to search for travel information and make travel bookings. This research made the first attempt to identify factors shaping users’ perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector. Furthermore, it provides actionable recommendations for the travel industry and AI developers when designing and deploying AI assistant services.

Competitiveness is a well-discussed research topic in various disciplines and fields, amongst which competitiveness in the visitor economy is a prominent research stream. With rapid transformations in the visitor economy, destinations, regions, sectors and businesses have had to adapt – with varying degrees of success – to internal and external changes, significantly affecting their competitiveness. Existing studies are dominantly based on a few pioneering models and indicators and relatively few empirically challenge the assumed causality of competitiveness factors at different scales. This article, therefore, conducts a systematic literature review of competitiveness in the visitor economy post-2005 and examines the intellectual and conceptual structures of the extant literature as a platform to identify knowledge gaps and emerging trends and perspectives for future research.

Although numerous studies have focused on forecasting international tourism demand, minimal light has been shed on the factors influencing the accuracy of real-world ex-ante forecasting. This study evaluates the forecasting errors across various prediction horizons by analyzing the annually published forecasts of the Pacific Asia Tourism Association (PATA) from 2013 to 2017, comprising 765 origin-destination pairs covering 31 destinations in the region. The regression analysis shows that the variation in tourism demand and gross domestic product (GDP), covariation between tourism demand and GDP, order of lagged variables, origin, destination, and forecasting method all have significant effects on the forecasting accuracy over different horizons. This suggests that tourism forecasting should account for these factors in the future.

This review provides novel and timely insights into research in the field of diaspora tourism. The principal originality of this work lies in capturing the full extent and richness of research on this topic by looking beyond tourism journals and the term “diaspora tourism” to situate the review in a broader social science research domain. It also adds to the small number of systematic syntheses of existing research on diaspora tourism by addressing both the geographical dimensions of diaspora tourism and the superdiversity of diasporas. Uneven research coverage exists between diaspora tourism studies across disciplines and continents, reflecting the superdiversity of diasporas and creating challenges, as well as opportunities for theoretical and methodological discussion and convergence as the topic matures. Future research can address these issues through research on irregular immigrants with diverse immigration histories, cross-continental comparative studies, and longitudinal methods.

With the tourism and hospitality sector reopening post-lockdown of COVID-19, the recovery of customers' purchase intentions is essential to reboot the sector. This study aims to examine the relationship between social distancing measures and purchase intentions in the UK's restaurant and hotel sectors using a propensity score weighting experimental design method. The findings suggest that the impact of social distancing measures on purchase intentions is mediated by the trust in the targeted restaurant and hotel. Risk tolerance significantly moderates the influence of social distancing measures on trust; (non-) cash promotions have an insignificant impact on purchase intentions. The introduction of the propensity score weighting scheme addresses the endogeneity caused by the sampling bias in non-probability sampling experiment studies.

This study applies 3x2 between-subjects design to examine the effects of service failure dimensions and recovery strategies on satisfaction and customer repurchase intention in peer-to-peer accommodation. The preliminary results, which is based on an online survey with 107 respondents, revealed that the three different dimensions of service failure did not yield significant differences in satisfaction and repurchase intention. However, a significant difference was found between “compensation” and “no compensation” recovery strategies in satisfaction and repurchase intention. The types of service failure only affect the relationship between customer satisfaction and repurchase intention when a compensation is provided. This may be due to the limited response in each scenario. Therefore, in future studies, a larger sample is needed to confirm these preliminary results.

Combination is an effective way to improve tourism forecasting accuracy. However, empirical evidence is limited to point forecasts. Given that interval forecasts can provide more comprehensive information, it is important to consider both point and interval forecasts for decision-making. Using Hong Kong tourism demand as an empirical case, this study is the first to examine if and how the combination can improve interval forecasting accuracy for tourism demand. Winkler scores are employed to measure interval forecasting performance. Empirical results show that combination improves the accuracy of tourism interval forecasting for different forecasting horizons. The findings provide government and industry practitioners with guidelines for producing accurate interval forecasts that benefit their policy-making for a wide array of applications in practice.

Limited historical data are the primary cause of the failure of tourism forecasts. Bayesian bootstrap aggregation (BBagging) may offer a solution to this problem. This study is the first to apply BBagging to tourism demand forecasting. An analysis of annual and quarterly tourism demand for Hong Kong shows that BBagging can, in general, improve the forecasting accuracy of the econometric models obtained using the general‐to‐specific (GETS) approach by reducing, relative to the ordinary bagging method, the variability in the posterior distributions of the forecasts it generates.

In recent years, travel vlogs are prevalent on social media, they are projected as an important marketing tool to attract tourists to destinations in the post-COVID-19 era. However, the underlying mechanism of how travel vlogs affect prospective tourists’ behaviours remains unclear. To address this gap, this paper discusses the applicability of the Stimulus-Organism-Response (SOR) model to travel vlog research and proposes a SOR based theoretical framework. Moreover, this paper highlights the increasing trend of the SOR model in both e-tourism and wider tourism and hospitality research.

This article uses the dataset of tourist satisfaction index of Hong Kong to investigate the impact of culture difference on the gap between tourists’ expectations and their perceptions of actual service performance. When the demographic profile and travel experience are controlled, it is found that small culture difference (Hong Kong and China) has positive impact on Expectation-Performance Gap (EPG); whereas negative relationship with EPG is identified for large culture difference (Hong Kong and western countries). The practical implication for tourism managers in Hong Kong is that service providers should manage EPG in accordance with the aspects of the culture difference between the destination and the source markets.

A survey of 780 UK residents was conducted to identify the extent to which perceived peer-to-peer (P2P) accommodation development is associated with changes in community members’ well-being from economic, social and environmental perspectives, and to understand in which circumstances P2P listings have positive and negative effects on 7 community members’ well-being. Partial least squares analysis demonstrates that the 8 perceived positive community impacts of P2P accommodation are more pronounced than the 9 perceived negative impacts. Additionally, weak but statistically significant effects of 10 perceived P2P accommodation prevalence on residents’ social and environmental well-being 11 are observed. Based on these findings and in accordance with social exchange theory, both 12 policy makers and the P2P accommodation sector should develop strategies to enhance the 13 perceived positive impacts on residents’ well-being and mitigate the perceived negative 14 impacts.

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.

The aim of this study is to investigate the determinants of attraction demand and shed light on the spillover effects of visitor flows between/across attractions in London using spatial econometric modelling. Both global and local models reveal that income and search queries are significant determinants to attraction demand, while distance from city centre is only significant in the global model. Visitor flow spillovers from neighbouring attractions are found to have significant effects on attraction demand. The intensity and direction of visitor flows’ spillover effects vary by attraction locations. Furthermore, asymmetric spillover effects of visitor flows between a pair of attractions have been identified for the first time in the tourism literature. The adoption of novel spatial estimation methods generates a new dimension to investigate intra-destination demand across attractions. This can provide empirical evidence for decision-makers to optimise visitor flows within a destination.

This study aims to investigate the contribution of aviation related travel restrictions to control the spread of COVID-19 in Europe by using quasi-experiment approaches including the regression discontinuity design and a two-stage spatial Durbin model with an instrumental variable. The study provides concrete evidence that the severe curtailing of flights had a spontaneous impact in controlling the spread of COVID-19. The counterfactual analysis encapsulated the spillover effects deduced that a 1% decrease in flight frequency can decrease the number of confirmed cases by 0.908%. The study also reveals that during the lockdown, the aviation industry cancelled over 795,000 flights, which resulted in averting an additional six million people being from being infected and saving 101,309 lives.

The aim of this study was to explore UK residents’ opinions of how peer-to-peer (P2P) accommodation listings within their communities impact upon their quality of life (QoL). Seven hundred and eighty open-ended questions were collected across the UK and content analysis was conducted to investigate the textual data. It is found that 13% of UK residents held positive opinion on P2P accommodation whereas another 13% expressed negative attitude and the rest kept neutral opinions. More people believed P2P accommodation brought positive economic and negative environmental impacts on the QoL, while the social influence was neutral. Opinions of London residents on P2P accommodation are different from those of non-London residents. Practical implications are provided to policymakers based on the empirical findings.

Tourism is acknowledged as a contributor to destination economies in many countries. However, COVID-19 has devastated the tourism industry in numerous national economies. Although the economic impact of tourism on destinations has been examined in a large body of tourism literature, most studies have utilized the tourism-led economic growth hypothesis and traditional methods and data rather than cutting-edge economic methods. This study conducts a systematic literature review on tourism economic impact between 1975 and 2020, analyzing the general bibliometrics and examining the key themes and methods of assessing tourism economic impact. It contributes to an accurate assessment of tourism economic impact, works to identify gaps in the literature, highlights emerging trends in the field, and proposes directions for future research. •Assessing the impact of tourism on destinations has long been studied.•Past studies rely on traditional hypotheses, methods and data.•Cutting-edge economic methods to accurately assess tourism impact is lacking.•Assessing dynamic causal relationships between tourism and economic growth is needed.•Spillover effects of tourism impact across space and time is recommended.

This study applies 3x2 between-subjects design to examine the effects of service failure dimensions and recovery strategies on satisfaction and customer repurchase intention in peer-to-peer accommodation. The preliminary results, which is based on an online survey with 107 respondents, revealed that the three different dimensions of service failure did not yield significant differences in satisfaction and repurchase intention. However, a significant difference was found between “compensation” and “no compensation” recovery strategies in satisfaction and repurchase intention. The types of service failure only affect the relationship between customer satisfaction and repurchase intention when a compensation is provided. This may be due to the limited response in each scenario. Therefore, in future studies, a larger sample is needed to confirm these preliminary results.

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.

The COVID-19 pandemic has brought sweeping changes to global tourism alongside large-scale travel restrictions, posing complex challenges to entrepreneurs and firms seeking to find their footing in a turbulent climate. This study presents a theoretical framework linking uncertainty, capital, and innovation to analyse how bed-and-breakfast small and medium-sized enterprises have innovatively responded to unprecedented obstacles during COVID-19 recovery. Three-stage longitudinal interviews were conducted with more than 30 entrepreneurs between April and November 2020 to unpack their ongoing responses to the pandemic. The recovery process was found to be non-linear due to the shifting nature of sources of uncertainty and changes in entrepreneurs' capital. These alterations shaped interviewees’ responses, especially in terms of product and marketing innovations, which ultimately generated new uncertainty.

In a context in which the tourism industry is jeopardised by the COVID-19 pandemic, and potentially by other pandemics in the future, the capacity to produce accurate forecasts is crucial to stakeholders and policy-makers. This paper attempts to forecast the recovery of tourism demand for 2021 in 20 destinations worldwide. An original scenario-based judgemental forecast based on the definition of a Covid-19 Risk Exposure index is proposed to overcome the limitations of traditional forecasting methods. Three scenarios are proposed, and ex ante forecasts are generated for each destination using a baseline forecast, the developed index and a judgemental approach. The limitations and potential developments of this new forecasting model are then discussed. •A new method is developed to provide forecasts under extreme uncertainty.•Uncertainly is addressed by applying judgemental adjustments to scenarios.•A two steps method combines forecasting, judgemental adjustment and scenarios.•A COVID-19 risk exposure index objectively ranks different destinations.•The twenty destinations studied will have different recovery patterns.

Purpose Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study applies the signaling theory to the booking of Airbnb listings and explores the influence of quality signals on the odds of an Airbnb listing being booked. Methodology A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt. Findings Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments. Value This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

A supply-side structural reform (SSR) has been carried out in China since late 2015, with a view to reducing overproduction in selected products such as coal, iron and steel. This paper examines whether the development of international tourism in China could support SSR, using an multi-methods approach that combines an econometric model and a computable general equilibrium model. It finds that the development of tourism can reduce the outputs of overcapacity industries and reallocate surplus labour to tourism-related industries. The calibration of 30 provincial CGE models demonstrates that the impact of tourism on reform in provinces with severer industry overcapacities is much stronger. This study contributes to the literature on the spillover effects of tourism on non-tourism sectors through its combination of econometric and CGE models. Practical implications are also presented.

Host attitudes towards tourists are critical to the sustainable development of the tourism industry. Although numerous studies have focused on investigating host attitudes towards tourists and tourism development, the theoretical support from an economic perspective in this field is still underdeveloped. By following the social exchange theory and applying a utility maximization model, the current study not only explains Doxey’s Irridex model from an economic perspective but also complements the findings of the tourism area life cycle model proposed by Butler. Results show that the public resources at the destination, along with the ability of local community in channeling (foreign) tourism income into productivity advancement, influence the optimal level of tourism development in a destination.

Most existing studies on the impact of tourism on economic growth adopt an econometric approach that is insufficient to confirm that tourism actually leads to economic growth. Moreover, it cannot explain the causalities of different variables. Taking Mauritius as an example, this study uses the dynamic stochastic general equilibrium approach to investigate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector. A two-sector, small, open economy is modelled under the Dynamic Stochastic General Equilibrium framework. The model is estimated using the Bayesian method based on real tourism and macroeconomic data from Mauritius for the period from 1999 to 2014. The impulse response functions are used to simulate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector. The simulation results show that the Mauritian GDP would increase by 0.09% if the productivity of tourism improved by 1%, indicating that tourism could lead to economic growth. Considering the average annual growth rate of the Mauritian GDP, the contribution of tourism to its economic growth is significant. Furthermore, the effects of tourism on economic growth are moderated by price elasticities in international tourism demand. This is the first study that estimates the dynamic stochastic general equilibrium model using the Bayesian method. By correcting the prior information with real tourism and macroeconomic data, the estimation and simulation results are more robust compared with the calibration method, which has been used frequently in tourism studies.

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro-level by predicting the number of visits to London museums. The number of visits to London museums is forecasted and the predictive powers of Naïve I, seasonal Naïve, SARMA, SARMAX, SARMAX-MIDAS and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher-frequency search query data allows generation of weekly predictions, which are essential for attraction- and destination-level planning.

The potential offered by the China outbound tourism market will create pressure on destinations to relax their visa requirements in order to capitalize on the opportunities it presents. Prior research into the impacts of visa liberalization indicates that the volume of arrivals increases substantially. However, little research has been conducted examining changes in profile of visitors and their resultant behaviors. This study examines changes in the Chinese pleasure visitor market to Hong Kong from 1998 to 2012. The study is grounded in market access theory. In addition to the significant increase of arrivals during that time, behavior patterns have changed almost completely, as independent tourists have been freed from the constraints imposed by tour group participation. The study has valuable implications for other destinations that are likely to relax visa requirements for markets like China.

Personal security is a major concern for tourists. Most tourists will seek safe and secure destinations and avoid those that have been plagued by terrorism. This research quantifies the relationship between terrorism and tourism in 95 different countries and territories using international tourism demand models. After controlling for income, we find there is no long run effect of terrorism on international tourism demand and the short run effect is quite limited from a global perspective using panel data models. Only nine countries out of the 95 show a long run impact of terrorism on tourism and 25 countries out of the 95 show a short run impact using time series models, implying that international tourism is resilient to terrorism. The influence of terrorism is diverse in destinations with different political instability, income levels and tourism intensities.

There has been much rhetoric about tourism's role in promoting world peace. This research takes a global perspective examining the relationship between peace and tourism across 111 different countries using a panel data model using two indicators, international tourist arrivals and the Institute for Economics and Peace's Global Peace Index. The results indicate that tourism is the beneficiary of peace rather than grounds for peace. Peace is most important to tourism in medium income destinations but still important for high income nations. No relationship exists between peace and tourism arrivals for low income nations.

This paper aims to examine the demand for outbound tourism by mainland Chinese residents to 11 international destinations, and provide long-run forecasts up to the year 2020. The empirical results suggest that the income level and the cost of a stay at a tourism destination compared with that of staying at a Chinese tourism destination are two important factors that affect Chinese residents’ traveling abroad. Results also show that the long-run income elasticities for all destinations range from 0.406 to 1.785, while the own price elasticities vary from −9.490 to −0.152. Based on the response patterns of tourism demand to income and price changes, five main categories of Chinese outbound tourism markets are identified. An ex ante forecasting exercise is also carried out which shows that outbound tourists from China mainly travel to Asian countries/regions during the period 2012–2020. The total number of outbound tourists is expected to reach 138.7 million by 2020.

Tourism satellite accounts (TSAs) have been widely recognised as standard tools to measure the contribution of tourism to destination economies. However, issues such as high costs of data collection and delayed release of TSAs have limited their regular compilation and practical application in some countries/regions. This study therefore introduces an innovative Web-based TSA information system that integrates all functions of the entire TSA compilation process chain, covering data input, data storage and management, TSA table compilation, statistical analysis and other extended applications. The system not only improves the efficiency of tourism data management and TSA compilation, but also enhances and extends the usefulness of TSAs for assessing the economic contribution of tourism to destinations. This Web-based TSA information system is established and discussed for a case study in Guangdong Province, China. Overall, the methodology and results reported herein provide academics and practitioners with new perspectives on regional TSA development and applications.

Purpose This study aims examine the impact of authentic leadership on the career satisfaction of hospitality employees through the lens of thriving. The two components of thriving, that is, learning and vitality, are tested as mediators, and psychological contract fulfillment is tested as a boundary factor. Design/methodology/approach Data are collected using an online survey through the Qualtrics panel service in the USA. Structural equation modeling and an invariance test are conducted to investigate the framework. Findings The findings show that authentic leadership can determine career satisfaction through the influence of learning and vitality. Moreover, psychological contract fulfillment exerts a conditional effect on this mediation. Practical implications The findings of this study extend the understanding on authentic leadership and its impact under specific conditions. This study offers several meaningful recommendations to hospitality managers on how to influence employees’ career success to maintain sustainable performance. Detailed approaches include establishing practices for regular and authentic leadership development, increasing attention on employee thriving states and addressing employee psychological contracts. Originality/value This study enriches research on authentic leadership and career management in the hospitality industry. Moreover, this study provides meaningful insights by examining the relationships between authentic leadership, thriving, career satisfaction and psychological contracts.

Purpose: Leader–member exchange (LMX) theory is particularly relevant to the hospitality and tourism industry due to its labor-intensive and service-focused nature. However, the hospitality literature regarding the impact of LMX on its various outcomes have inconsistent results. A holistic review of LMX studies is nonexistent in the current literature. Thus, the purpose of this study is to use a meta approach to quantitatively summarize and examine the relationship between LMX and its outcomes in the hospitality and tourism literature. Design/methodology/approach: A total of 89 individual observations from 36 studies conducted between 1997 and 2018 were identified. A Bayesian random effect model was introduced into the hospitality and tourism literature for the first time to implement the meta-analysis. Findings: Results suggest significant differences in the impact of LMX on various groups of outcomes. LMX has the strongest impact on firms’ practice-related outcomes, such as organizational justice and employee empowerment. Few moderators are identified on the impact of LMX, such as LMX measure, culture, industry sector, and statistical method. Practical implications: Findings yielded several recommendations for both hospitality researchers and organizations in developing LMX related studies as well as managing employees. Originality/value: This study is the first Bayesian meta-analysis in the hospitality and tourism literature; it complements LMX theory by linking it to cognitive appraisal theory. Specific characteristics of LMX in the hospitality and tourism industry, such as the measurement of LMX and the effect of industry sector, are also identified.

Tourism studies commonly focus on the determinants of tourism demand. While most examine factors such as economic determinants, research on the effect of culture on tourism demand remains underdeveloped. This study uses a Bayesian two-stage median regression method to eliminate the potential collinearity between cultural and travel distance and to estimate the impact of cultural distance more appropriately. The results show that while there is a negative relationship between cultural distance and tourism demand, tourism demand is less sensitive to change in cultural distance; the popularity of a travel route moderates the effect of cultural distance on tourism demand; and the influence of cultural distance is different across time and different source markets.

Purpose The impact of demand fluctuation during crisis eventsis crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The aim of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example. Methodology Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy. Findings Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The findings of this research is the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises. Originality The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.

The aim of this study is to investigate the long-term determinants of China’s imported wine demand and to forecast wine imports from 2019 to 2023 using econometric methods. Auto-regressive distributed lag models are developed based on neoclassical economic demand theory to investigate the long-term determinants of China’s demand for imported bottled, bulk, and sparkling wine from the top five countries of origin. The empirical results demonstrate that income is the most important determinant of China’s imported wine demand, and that price only plays a significant role in a few markets. Substitute and complement effects are identified between wines from different countries of origin and between imported wines and other liquids. China’s imported wine demand is expected to maintain its rapid growth over the forecast period. Bottled wine will continue to dominate China’s imported wine market. France will have the largest market share in the bottled wine market, Spain will be the largest provider of bulk wine, and Italy will hold the same position for sparkling wine. This is the first study to use a single equation with the general to specific method rather than a system of equations to estimate and forecast China’s demand for imported bottled, bulk, and sparkling wines from different countries of origin. The more specific model setting for each country of origin improves forecasting accuracy.