Professor Gang Li

Purpose – Although artificial intelligence (AI) is an essential component of hospitality in the technological empowerment era, AI's effectiveness as an attraction in this context remains unclear. Grounded in Herzberg's motivation theory and complexity theory, this study explores configurational paths whereby combinations of qualities lead to success for different types of AI-themed hotels. Design/methodology/approach – This study innovatively blends topic modeling and fuzzy-set qualitative comparative analysis (fsQCA) to investigate configurational paths whereby combined qualities produce positive guest evaluations of 12 AI-themed hotels as evidenced by 7,431 customer reviews. Findings – The results indicate that AI could serve as a " theme " to attract customers under certain circumstances. First, " attractive " and " must-be " qualities are first identified for different types of AI-themed hotels. Furthermore, 6, 15, and 15 configurational paths inspiring favorable guest evaluations of luxury independent, budget independent, and chain AI-themed hotels, respectively. Technology-related qualities are found to be especially attractive for luxury independent AI-themed hotels, whereas the role of technology is minimal for budget AI-themed hotels. The impact of technology is salient for chain AI-themed hotels when combined with other factors. Additionally, the effect of price differs among the configurational paths for the three hotel types. Research limitations/implications – This study expands the understanding of AI applications within the hospitality context by exploring the role of AI in AI-themed hotels and comparing its effectiveness in attracting customers across various hotel types. It also provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors. Originality – This study represents an early attempt to integrate topic modeling and fsQCA to clarify customers' perceptions of AI-themed hotels and the combined impacts of various qualities. The findings expand on Kano's model by classifying technology-related qualities into attractive qualities within AI-themed hotels.

The paper addresses how individual rankings of destinations change between non-crisis and crisis conditions, and across different types of crises under conditions of bounded rationality in the face of shifting risks and uncertainties. The impacts of three types of potential crises—heatwaves, epidemics and terrorism—are analysed using novel multi-perspective experimental methods, linked to a five-country international survey of risk attitudes and knowledge. Destination preferences, including strong persistence during crises, are explained by how the influence of tacit and codified knowledge is shaped by the familiarity heuristic and recency bias. Individual responses are also moderated by tolerance of and competence to manage risks. The findings have useful implications for destination management and marketing.

China's vast urban and rural building clusters face a significant risk of structural damage and collapse when subjected to strong earthquake forces. Current research methods fall short in simultaneously meeting the demands of obtaining comprehensive building information at the county level and accurately simulating seismic events.. To achieve rapid and accurate seismic performance assessment of urban and rural building clusters, a method combining regional architectural characteristics and GIS data for intelligent acquisition of building information and seismic capacity assessment was proposed. Roof information and external dimensions of building clusters in counties and districts were obtained through remote sensing methods such as GIS data and high-resolution satellites. Leveraging the above information and the actual characteristics of regional urban and rural per capita GDP, geographical location, and construction customs, a fuzzy inference model based on fuzzy theory and expert system was established to determine the internal information such as building structure type, material properties, and opening conditions, and numerical simulation is used to realize seismic damage analysis of regional building clusters. This method was applied to acquire parameter and seismic damage analysis of building clusters in a county town in Southwestern China. The actual seismic damage situation in the partially affected areas of the Ludian earthquake was used to verify the applicability of this method. Results demonstrate that this method exhibits strong applicability in the seismic performance assessment of both urban and rural building clusters.

The present research conducted four experiments and found that virtual influencers are less effective than human influencers in endorsing cultural heritage destinations. Drawing on social categorization theory, we suggest that this effect occurs because tourists perceive virtual (vs. human) influencers as outsiders of humankind with a limited understanding of human culture. Consistent with this mechanism, our findings further reveal that this low effectiveness would be mitigated when virtual influencers showcase the ability to comprehend human culture. This research makes theoretical contributions to the literature on social perceptions of virtual influencers and social media marketing in the tourism industry. Also, these findings provide managerial implications regarding the effective usage of virtual influencers in destination marketing.

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.

The presence of hosts' profile photo on peer-to-peer accommodation platforms is likely to influence consumers' judgments and purchase behavior. Based on the stimulus–organism–response theory and mental imagery theory, this study examines the existence and mechanisms of beauty premium via experimental designs. Results indicate that consumers tend to book and pay more for an accommodation offered by an attractive host, and these effects are mediated by potential consumers' perceived enjoyment and threats regarding their future stay. The study also highlights two factors that can weaken consumers' reliance on hosts' facial attractiveness when making purchase decisions: hosts' reputation and self-disclosure. This study enriches the literature on the beauty premium and ways to reduce consumers' reliance on hosts' facial attractiveness.

Although there has been increasing focus on the employment mobility associated with migration and return, a number of important research gaps can be identified. First, there has been greater focus on occupational mobility than on changes in economic activity, although it is their interaction which determines welfare outcomes. Moreover, most studies of economic activity have focused on either self-employment, or the simple dichotomy between being employed versus unemployed, neglecting the shifts between full-time, part-time, and casual employment. Secondly, research on the determinants of these different types of employment mobility has been relatively narrowly focused on individual economic factors. Most studies have been fragmented, especially lacking a comparative element. To address these gaps, descriptive statistics and Bayesian multilevel models are applied to a pan-European panel survey of 3851 young returned migrants. The findings disclose that positive shifts in employment mobility are more evident in economic activity than in occupations, and for those with a lower occupational status prior to migration. Although a range of significant determinants of employment mobility are identified, the findings also demonstrate that education is a major driver of occupational mobility, while marital and family status are important influences on economic activity shifts.

This study investigated whether regional differences in economic, socio-psychological, and environmental distance affect tourists' destination choices. Taking Hangzhou, China, as a case, macro-and micro-level data were integrated to examine the effects of multi-dimensional distance on the city's tourism demand via a panel gravity model. All six distance variables were identified as influencing factors, but their effects varied in size and direction. Tourists' behavior has changed since COVID-19; as such, distance effects before and after its emergence were identified. Tourists were less sensitive to economic distance and price differences following the pandemic and tended to favor more culturally and climatically different destinations. The terror management theory was introduced to explain the shift in tourists' choices. Findings provide implications for destination management and marketing amid the pandemic.

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.

I-ImaS (Intelligent Imaging Sensors) is a European project which has designed and developed a new adaptive X-ray imaging system using on-line exposure control, to create locally optimized images. The I-ImaS system allows for real-time image analysis during acquisition, thus enabling real-time exposure adjustment. This adaptive imaging system has the potential of creating images with optimal information within a given dose constraint and to acquire optimally exposed images of objects with variable density during one scan. In this paper we present the control system and results from initial tests on mammographic and encephalographic images. Furthermore, algorithms for visualization of the resulting images, consisting of unevenly exposed image regions, are developed and tested. The preliminary results show that the same image quality can be achieved at 30-70% lower dose using the I-ImaS system compared to conventional mammography systems. © Springer-Verlag Berlin Heidelberg 2007.

This study examines the demand for Thai tourism by seven major origin countries – Australia, Japan, Korea, Singapore, Malaysia, the UK and the USA. The general-to-specific modelling approach is followed in the construction, estimation, testing and selection of the tourism demand models. The empirical results show that habit persistence is the most important factor that influences the demand for Thai tourism by residents from all origin countries. The income, own price, cross price and trade volume variables are also found to be significant in the demand models, but the explanatory power of these variables, judged by the number of times they appear in the models, varies from origin to origin. The Asian financial crisis that occurred in late 1997 and early 1998 also appears to have had a significant impact on tourist arrivals from Singapore, Malaysia, Korea and the UK, but the magnitude and direction of influence are not the same for all models. The models that performed relatively well for each of the origin countries, according to both economic and statistical criteria, are selected to generate ex ante forecasts for the period up to 2010. The results suggest that Korea, Malaysia and Japan are expected to be the largest tourism generating countries by the end of the forecasting period, while the growth rate of tourist arrivals from Korea to Thailand is likely to be the highest among the seven origin countries.

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.

The purpose of this study is to test a two-step tourist satisfaction index framework empirically. The first step estimates sectoral-level satisfaction indexes based on a structural equation model, and the second obtains an overall tourist satisfaction index by conducting second-order confirmatory factor analysis. This study is a pilot test of the theoretical framework based on three tourism-related service sectors in Hong Kong. The results indicate that mainland Chinese tourists are most satisfied with the hotel sector in Hong Kong, followed by the retail sector, and least satisfied with local tour operators. The aggregate tourist satisfaction index is 74.04 out of 100. The results of this study have important practical implications for long-term destination management.

The online P2P accommodation market, including Airbnb, encourages accommodation hosts to upload profile photos. However, the inclusion of a profile photo may carry consequences such as appearance discrimination. Using secondary Airbnb data from Beijing, China, this study investigates the presence of the “beauty premium” in the relatively low-priced accommodation market and examines the extent to which consumers discriminate based on hosts’ facial appearance from a supply perspective. Three experiments were conducted to respectively examine the impacts of hosts’ facial beauty on customers’ willingness-to-pay, boundary conditions, and underlying mechanisms. The findings emphasize the importance of hosts’ visual self-disclosure in reducing appearance-based discrimination. By providing practical implications for P2P platform operators and accommodation hosts, this research contributes to a better understanding of appearance-based biases in the online accommodation market and reveals strategies for mitigating negative effects.

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.

Service providers in tourism and hospitality are beginning to welcome robots as a customer service option. Given this trend, it is important to explore the factors driving tourists' willingness to adopt such new technology. This study focuses on the role of crowding, an environmental factor widely observed in destinations susceptible to over-tourism, in shaping tourists' willingness to adopt service robots. Based on one survey and two experiments, the present research demonstrates that a destination which is more (vs. less) crowded generally motivates tourists to favor robot-provided services rather than those from human staff. Furthermore, findings reveal that this pattern manifests because more (vs. less) social crowding reduces tourists’ motivation to interact with others, as evidenced by social withdrawal tendency.

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.

This study develops a global vector autoregressive (global VAR or GVAR) model to quantify the cross-country co-movements of tourism demand and simulate the impulse responses of shocks to the Chinese economy. The GVAR model overcomes the endogeneity and over-parameterisation issues found in many tourism demand models. The results show the size of co-movements in tourism demand across 24 major countries in different regions. In the event of negative shocks to China’s real income and China’s tourism price variable, almost all of these countries would face fluctuations in their international tourism demand and in their tourism prices in the short run. In the long run, developing countries and China’s neighbouring countries would tend to be more negatively affected than developed countries.

Using survey data from the China Family Panel Studies project, this study explores factors influencing Chinese households' tourism consumption. The Heckman model is employed to decompose households' travel decision‐making process into two stages: first deciding whether to engage in travel and tourism and then determining the level of tourism consumption. We find that sociodemographic, economic, and psychological factors shape Chinese households' tourism consumption. Our results paint a vivid picture of Chinese households as travel consumers and offer valuable insight for governmental policymaking, tourism marketing strategy design, and business organizations' development planning.

Corporate philanthropy (CP) is receiving increased attention, especially in transitional countries, such as China. Focusing on Chinese tourism attraction companies that operate on public tourism resources and have close relationships with their surrounding communities, this study investigates the factors driving firms’ CP behaviour from a community perspective. Hypotheses are developed under the legitimacy framework. Probit and tobit regression models are used with data gathered from listed tourism attraction companies in China between 2000 and 2015. Three main findings are obtained. First, tourism attraction companies engage in CP more actively than other companies in the tourism industry. Second, three community-related features are significant drivers of tourism attraction firms’ CP: unbalanced economic development, fierce business competition within the community and scarce educational resources. Third, the economic contribution of tourism partially moderates the associations between community features and tourism attraction firms’ CP. Further interviews with top managers of selected companies confirm the findings of the above statistical analysis. Both the theoretical and practical implications of the findings are discussed at the end of the paper.

This study considers the dynamics of the consumption behaviour of tourists from an economic perspective. The evolution of various demand elasticities is explored using a time-varying parameter almost ideal demand system model. The top four source markets for tourism in Hong Kong are examined, and three major tourist expenditure categories, including shopping, hotel accommodation and meals outside hotels, are investigated for each market. Elasticity analysis reveals different consumption trends and patterns across the source markets. The findings will serve as a useful reference for Hong Kong tourism-related industries and the government in their efforts to enhance the competitiveness of Hong Kong as an international tourism destination.

This paper reviews literature on travel destination choice and organizes these studies systematically. A “cell–system” structure is proposed to describe the psychological process of travel destination choice. When forming decisions on vacations, tourists gather information on potential destinations and evaluate visit intentions among potential destinations (“cell”). The visit intentions are successively compared while information is updated in the process (“system”). The “cell–system” structure provides a clear view of the psychological process of travel destination choice. Empirical studies based on the structure can provide further insights into why and how tourists choose travel destinations.

The economic effects of tourism industry during periods of crisis, such as the COVID-19 pandemic, have received significant attention in recent years. The future is likely to pose a range of new challenges and opportunities to sustainable tourism. This paper employs the Markov-switching vector autoregressions (MSVAR) model to investigate the sustainability of tourism’s economic effects in Hong Kong, both during periods of crisis and in the absence of crises. The empirical results show that: (1) The MSVAR model is effective in capturing the nonlinear relationship between the economy and tourism and allows for the categorizing of this relationship into four regimes, for example, the “major event crises” regime and the “economic crises” regime; (2) The economic effects of tourism differ noticeably across the four different regimes, and sustainability varies depending on the presence and type of crisis; (3) The Hong Kong economy, and the tourism industry in particular, exhibits high levels of stability and sustainability. In short, economic growth in Hong Kong’s tourism industry is capable of rapid recovery following major crisis events, and it has the capacity to rebound quickly into new periods of rapid growth.

Although there has been increasing focus on the employment mobility associated with migration and return, a number of important research gaps can be identified. First, there has been greater focus on occupational mobility than on changes in economic activity, although it is their interaction which determines welfare outcomes. Moreover, most studies of economic activity have focused on either self-employment, or the simple dichotomy between being employed versus unemployed, neglecting the shifts between full-time, part-time, and casual employment. Secondly, research on the determinants of these different types of employment mobility has been relatively narrowly focused on individual economic factors. Most studies have been fragmented, especially lacking a comparative element. To address these gaps, descriptive statistics and Bayesian multilevel models are applied to a pan-European panel survey of 3851 young returned migrants. The findings disclose that positive shifts in employment mobility are more evident in economic activity than in occupations, and for those with a lower occupational status prior to migration. Although a range of significant determinants of employment mobility are identified, the findings also demonstrate that education is a major driver of occupational mobility, while marital and family status are important influences on economic activity shifts. Supplemental data for this article can be accessed online athttps://doi.org/10.1080/1369183X.2022.2142104.

Tourism is becoming more and more important in the global economy, and its longterm prosperity is desired by every tourism destination. Prosperity, however, cannot be achieved successfully without the involvement of those influenced by the industry, so, evaluating residents’ perceptions of tourism and involving them in as many aspects of planning and policymaking as possible are important steps in creating sustainability in tourism destination development. In attempting to fill in the research gaps in social impact analysis of urban tourism development in the Chinese context, a face-to-face survey was carried out to explore residents’ perceived impacts of tourism development in Harbin, a famous tourist destination in north-eastern China. The findings of this survey suggest that residents’ reaction towards local tourism development varies between different interest groups. Age, income and personal connections with local tourism were found to influence residents’ perceptions to some extent.

This paper aims to provide the most up-to-date survey of tourism economics research and to summarise the key trends in its recent development. Particular attention is paid to the research progress made over the last decade in respect of approaches, methodological innovations, emerging topics, research gaps, and directions for future research. Remarkable but unbalanced developments have been observed across different sub-research areas in tourism economics. While neoclassical economics has contributed the most to the development of tourism economics, alternative schools of thought in economics have also emerged in advancing our understanding of tourism from different perspectives. As tourism studies are multi- and inter-disciplinary, integrating economics with other social science disciplines will further contribute to knowledge creation in tourism studies.

This study extends tourism demand research by focusing on how an important psychological factor, risk aversion, affects tourism participation and expenditure. Based on five waves of national longitudinal data from the China Household Financial Survey, this study represents an initial attempt in the tourism literature to integrate a Heckman model with a hierarchical age–period–cohort model to decompose the effects of time-related factors (age, period, and birth cohorts) when analyzing the role of risk aversion in tourism decisions. Findings showed that risk aversion significantly influenced tourism participation and expenditure. The propensity to travel and the amount of tourism expenditure each declined over one's lifetime. Period and cohort effects differentially affected tourism participation and expenditure. •Risk aversion affects tourism participation and expenditure negatively.•Age, period and cohort significantly impact tourism participation and expenditure.•Demographic variables moderate the effects of risk aversion on tourism decisions.•Age-period-cohort analysis integrating with a Heckman model is novel in tourism.

Limitations in statistical data and differences in accounting methods have hindered the accuracy of tourism carbon emissions accounting. In this research, based on the Tourism Satellite Account (TSA) and underpinned by the logic of “accounting basis–key coefficient–accounting objective,” a comprehensive decomposition accounting method is built from a consumption stripping perspective. First, it classifies the tourism industry by the “sector–industry–product” structure into seven sectors, 13 industries, and 22 characteristic products/services. Next, it strips the actual tourism consumption from the tourism industry by constructing two key coefficients. Finally, it transforms tourism consumption data into carbon emissions data by introducing tourism ecological efficiency. Taking Guangdong province of China as an example, its tourism carbon emissions are calculated from 2010 to 2020 using the proposed method. The results reveal the distribution structure of tourism carbon emissions and confirm the scientific and accurate nature of this accounting method.

The advantages of error correction models (ECMs) and time varying parameter (TVP) models have been discussed in the tourism forecasting literature. These models are now combined to give a new single-equation model, the time varying parameter error correction model (TVP-ECM), which is applied for the first time in the context of tourism demand forecasting. The empirical study focuses on tourism demand, measured by tourism spending per capita, by UK residents for 5 key Western European destinations. Based on the discussion of how the series considered related to most, the empirical results show that the TVP-ECM can be expected to outperform a number of alternative econometric and time series models in forecasting the demand for tourism. By measuring performance in terms of the accuracy of the forecasts of growth (rates of change) and showing that TVP-ECM performs very well for this as well as conventional assessment of the level of demand in this study, it is suggested that forecasters of tourism demand levels and growth rates can feel comfortable using TVP-ECM given that it is expected to perform well.

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.

Tourism forecasting plays an important role in tourism planning and management. Various forecasting techniques have been developed and applied to the tourism context, amongst which econometric forecasting has been winning an increasing popularity in tourism research. This paper therefore aims to introduce the latest developments of econometric forecasting approaches and their applications to tourism demand analysis. Particular emphases are placed on the time varying parameter (TVP) forecasting technique and its application to the almost ideal demand system (AIDS). The discussions in this paper fall into two main parts, in line with the two broad categories of econometric forecasting approaches: the first part refers to the single-equation forecasting techniques, focusing particularly on both long-run and short-term TVP models. The second part introduces the system-of-equations forecasting models, represented by the AIDS and its dynamic versions including the combination with the TVP technique, will be discussed one by one following the order of methodological developments.

Air pollution is becoming a serious socio-environmental problem in many modern societies and poses significant economic threats to popular tourism destinations. Despite the documented consequences of air pollution on tourism demand, studies have seldom examined its impact on individuals’ psychological states, especially in the tourism context. Through a correlational study and two experiments, our findings indicate that tourists are more likely to be suspicious of local service providers when travelers perceive a destination as having heavy air pollution (vs. one without such pollution). This relationship presumably exists because tourists experience greater pessimism in an environment with high air pollution, which in turn influences their evaluations of service providers. Following this logic, we show that the effect diminishes when tourists are cognizant of (and thus rely less on) their pessimistic feelings when evaluating service providers. Finally, we offer theoretical and practical implications of this effect in tourism.

The conditions which determine the acquisition of skills by migrants are still poorly understood. This paper addresses two of those conditions: the temporality of the acquisition of competences, whether the number and duration of migrations matter, as well as the spatiality, or the variation across countries of origin and return. Based on a large-scale online panel survey of returned young migrants in nine European countries, the significance of time (duration) and space (number of migrations) in the acquisition of skills and competences are examined. The findings reveal that young European returnees' experiences gained abroad result in largely positive outcomes but with significant differences between formal qualifications, language skills and personal and cultural competences. However, their acquisition of skills and competences is mediated by temporality - the combination of number of trips, and duration of migration. Spatiality is also important, with outcomes depending on the destination countries, and whether migration and return are from or to rural versus urban areas. These indicate that structural considerations continue to shape individual migration experiences within the EU's freedom of movement space.

The COVID-19 pandemic and corresponding border control policies of various destinations have had a profound impact on the tourism industry worldwide. Although a few destinations adopted a ‘co-existence’ policy by partially re-opening their borders, changes in the COVID-19 situation, particularly with respect to the spread of new variants, have caused major uncertainties for the tourism industry. Against this background, with the goal of advancing tourism forecasting methodologies and informing industrial practitioners about good practices in tourism forecasting and the predicted impact of COVID-19, in July 2020 the Curated Collection of Annals of Tourism Research on Tourism Forecasting announced a forecasting competition. Three competing teams, namely the Asia and Pacific team (Qiu et al., 2021), the Europe team (Liu et al., 2021) and the Africa team (Kourentzes et al., 2021), implemented two stages of tourism demand forecasting (ex post forecasts for 2019Q1–2019Q4 and ex ante forecasts for 2020Q1–2021Q4), represented by inbound visitor arrivals or hotel nights, for 20 countries/regions. The competition rules for both stages and the results of the accuracy evaluation of the first-stage forecasting (i.e., ex post forecasting for the period 2019Q1–2019Q4) were described in Song and Li (2021). The Asia and Pacific team won Stage 1 of the competition by stacking five time-series models, which outperformed the benchmark seasonal naïve model by 22 % in terms of accuracy, evaluated by the relative mean absolute scaled error (MASE) against seasonal naïve model. The forecasting evaluation results for Stage 2 and the overall competition results of the three teams are set forth in this commentary and will be presented at the 8th Conference of the International Association for Tourism Economics in Perpignan, France in June 2022.

Greater China, including Mainland China, Hong Kong, Macau, and Taiwan, contributes significantly to both regional and global tourism developments. Empirical research on tourism demand modeling and forecasting has attracted increasing attention of scholars both within and beyond this region. One hundred eighty articles are identified that were published in both English‐ and Chinese‐language journals since the beginning of the 1990s. This study presents the largest scale of literature survey on tourism demand studies. Furthermore, this is the first attempt in tourism demand review studies that focuses exclusively on one geographic region and covers bilingual literature. Particular emphasis of this review is placed on research development, geographic focus, data type and frequency, measurement of tourism demand, modeling and forecasting techniques, demand elasticity analysis, forecasting exercises, and emerging research trends. Comparisons between the two bodies of literature published in two languages show a number of research gaps, such as the diversity and sophistication of the research methodology, rigor of the modeling and forecasting process, and theoretical foundations of demand analysis. Correspondingly, constructive recommendations are made to further advance tourism demand studies related to Greater China.

In this study, we investigate the causal relationships between international tourism growth and regional economic expansion in China, and more importantly, disclose the factors determining the occurrence of these relationships. The empirical results reveal that 10 out of 29 regions experienced tourism-led growth (TLG) during 1978 to 2013, whereas nine regions experienced economic-driven tourism growth (EDTG). Different from the past literature, this study uses Bayesian probit models to unveil the factors influencing these different growth patterns. Our results suggest that regions with less-developed economies, larger economic sizes, and covering larger geographic areas are more likely to experience TLG, and regions with less-developed economies are more likely to experience EDTG as well. Lastly, practical implications are provided.

Purpose – This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available. Design/methodology/approach – This study is the first attempt to employ the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data. Findings – The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are employed. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that this kind of penalty-based MIDAS model is a useful option when using high-dimensional mixed frequency data. Originality/value – This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data, and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. The study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.

Globalization characterizes the economic, social, political, and cultural spheres of the modern world. Tourism has long been claimed as a crucial force shaping globalization, while in turn the developments of the tourism sector are under the influences of growing interdependence across the world. As globalization proceeds, destination countries have become more and more susceptible to local and global events. By linking the existing literature coherently, this study explores a number of themes on economic globalization in tourism. It attempts to identify the forces underpinning globalization and assess the implications on both the supply side and the demand side of the tourism sector. In view of a lack of quantitative evidence, future directions for empirical research have been suggested to investigate the interdependence of tourism demand, the convergence of tourism productivity, and the impact of global events.

Recall of tourism experiences is a decisive factor in tourists’ future behavior and decision making when choosing destinations. Understanding the phenomenology of tourism memory can enable tourism organizations to enter a more competitive marketplace. Although extensive literature has addressed how to provide memorable tourism experiences, limited studies have focused on the autobiographical memories associated with these experiences. This research employed rigorous scale development procedures to establish the Tourism Memory Characteristics Scale (TMCS). Findings point to a seven-dimension scale consisting of accessibility, trip details, vividness, sensory details, valence, emotional intensity, and sharing. Tourism memory characteristics were found to support the scale’s dimensional structure, validity, and reliability. It was also found that tourism memory influences revisit intention and word of mouth. Results present opportunities for tourism organizations to capture the fundamental characteristics of their products by using the TMCS.

This study uses a gravity framework to model tourism demand for the Caribbean. The basic model is augmented by Linder’s hypothesis—tourist flows are partly determined by the similarity in preferences between the destination and source markets—and climate distance, which measures the gap between climate conditions in origin and destination countries. The results indicate that traditional gravity variables are significant in explaining demand for the region. Habit persistence has the largest impact on demand, a result that holds promise for regional policy makers. Evidence is also unearthed that similarity in preferences between the region and its source markets, as well as climate distance, are important demand determinants.

Research on migration intentions is relatively fragmented, traditionally drawing conclusions from relatively small survey samples, focussing on individual countries, or relying on public opinion polls which provide very few explanatory variables. This paper addresses these limitations by developing a multi-level model of an extensive range of macro, meso and micro determinants of migration intentions across different time frames. The paper utilises an online panel survey of 20,473 non-student respondents aged 16-35 from 9 EU countries. Ordinal multi-level modelling, with post-stratification weighting, is used to determine the key drivers of, and barriers to, migration intentions in both a pan-European model, and nine separate national-scale models. The findings confirm the significance of macro, meso and micro factors. While socio-economic factors emerge as powerful explanatory factors, non-pecuniary factors are also important, including sensation seeking. There are broad similarities in the findings across the separate national-level models, but also differences in the relative importance of socio-economic, gender, and personality factors. Migration intentions were highly dependent on the decision-making time frame: 17 per cent of respondents over one year, but 30 per cent over five years, are likely to migrate or to have made firm plans to migrate. The rank ordering of the countries challenges the notion of there being a simple differentiation between the newer and older member states of EU.

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.

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified, and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies that compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. The time-varying parameter (TVP) model and structural time-series model with causal variables, however, perform consistently well. © 2005 Sage Publications.

Business failure prediction or survival analysis can assist corporate organizations in better understanding their performance and improving decision making. Based on aspect-based sentiment analysis (ABSA), this study investigates the effect of customer-generated content (i.e., online reviews) in predicting restaurant survival using datasets for restaurants in two world famous tourism destinations in the United States. ABSA divides the overall review sentiment of each online review into five categories, namely location, tastiness, price, service, and atmosphere. By employing the machine learning–based conditional survival forest model, empirical results show that compared with overall review sentiment, aspect-based sentiment for various factors can improve the prediction performance of restaurant survival. Based on feature importance analysis, this study also highlights the effects of different types of aspect sentiment on restaurant survival prediction to identify which features of online reviews are optimal indicators of restaurant survival.

This paper examines the convergence process of industrial productivity between Chinese regions. Both σ- and β-convergences are investigated using a panel data set of 30 provinces and autonomous regions over the period 1985–1999. Unconditional σ- and β-convergence methods fail to detect productivity convergence over the whole sample period, although they suggest convergence during a sub-period 1985–1990. The estimates of a human capital enhanced production function, with the constant return to scale constraint, show that productivity gaps between Chinese regions declined during 1985–1999 with a rate of convergence of around 1.3% per annum. Similar results are also found when the data are disaggregated into three broader geographic regions.

This study examined the relationships between abusive supervision, subordinates' work engagement and their emotional labour on a daily basis. Based on an experience sampling study of 95 frontline hospitality employees over 10 working days, the results revealed the complex consequences of abusive supervision on subordinates in the hospitality industry. The results showed that daily abusive supervision was positively related to employees' daily surface acting through their daily work engagement, but it was not significantly related to daily deep acting. In addition, subordinates' mindfulness moderated the relationship between daily abusive supervision and subordinates' daily work engagement. These findings reveal employees' daily responses to abusive supervision and can help tourism and hospitality managers develop relevant training programmes and policies to reduce the negative impact of abusive supervision and thus protect employee well-being.

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.

Purpose – This study aims to provide researchers in hospitality management with a comprehensive understanding of the experience sampling method (ESM) and to engage them in the use of ESM in their future research. With this critical discussion of the advantages and challenges of the method, researchers can apply it appropriately to deepen and broaden their research findings. Design/methodology/approach – This study chooses an empirical example in the context of hotel employees’ surface acting, tiredness and sleep quality to illustrate the application of ESM. Based on the example, this paper conducts two-level modeling in Mplus, including a cross-level mediation analysis and mean centering. Findings – This paper demonstrates the applicability and usefulness of ESM for hospitality research and provides a detailed demonstration of how to use the statistical program Mplus to analyze ESM data. With this paper, researchers will be able to consider how to engage ESM in their future studies. Originality/value – This paper is among the first to provide a hands-on demonstration of ESM to hospitality researchers. We call for more research in hospitality management to use ESM to answer complex and pressing research questions.

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.

This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms other models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events' impact assessment and risk forecasting. (C) 2007 Elsevier Ltd. All rights reserved.

Purpose: This paper aims to provide researchers and practitioners with an understanding of abusive supervision in the context of hospitality. It seeks to conduct a comprehensive review of the area and offer recommendations for future research by exploring the antecedents, consequences, mechanisms, and designs of research on abusive supervision. Design/methodology/approach: Content analysis was conducted to review and analyze studies on abusive supervision in the context of hospitality. Previous studies were searched in the EBSCO, Scopus, Web of Science, and Google Scholar electronic databases. Findings: Thirty-six referred articles related to abusive supervision in hospitality were reviewed across four key areas, namely, antecedents, consequences, mechanisms, and research design. After reviewing the research on abusive supervision in the context of hospitality, this paper offers future research directions with respect to research focus and research design. Research limitations/implications: This paper only included English articles from peer-reviewed journals on abusive supervision. The number of reviewed articles was relatively small. This limitation may have arisen because abusive supervision is a new research field and is still a sensitive topic. Practical implications: The results of this work may encourage managers to minimize or even halt abusive supervision. From an organizational perspective, formal policies may be developed to regularize supervisors’ behavior. In turn, employees could use this paper to learn further about abusive behavior and how to handle it effectively. Social implications: The review highlighted the negative consequences of abusive supervision. Managers should urgently realize the seriousness of abusive supervision and develop effective policies to minimize its negative effect. Originality/value: This paper contributes to the emerging literature on abusive supervision in the context of hospitality by identifying key research trends and framing the outlines of empirical studies. It identifies research gaps, and as the first review of abusive supervision in hospitality, it may encourage researchers to explore the topic on the basis of the characteristics of the sector and offer suggestions for future research.

Written by a plethora of worldwide experts on this topic, this book offers a comprehensive approach to tourism econometrics. Accurate demand forecasts are crucial to decision-making in the tourism industry and this book provides real-life tourism applications and the corresponding R code alongside theoretical foundations, in order to enhance understanding and practice amongst its readers. The methodologies introduced include general to specific modelling, cointegration, vector autoregression, time-varying parameter modelling, spatiotemporal econometric models, mixed-frequency forecasting, hybrid forecasting models, forecasting combination techniques, density forecasting, judgemental forecasting, scenario forecasting under crisis, and web-based tourism forecasting.

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.

Tourism forecasting is one of the longest standing areas in tourism economics research, with over half a century of history already. The development of tourism forecasting research responds and contributes to the industry practice. Accurate demand forecasts are the foundation of tourism-related business decisions on pricing and operation strategies, and for governments on infrastructure investment and tourism policymaking. In recent years, tourism forecasting has received more attention from industry practitioners. First, echoing the increasing numbers of international and domestic tourists, the tourism industry has continuously grown and become more dynamic. As a result, industry practitioners have hoped to understand the market and predict future trends more accurately and comprehensively. Second, in recent years, decision makers have realized the increasing importance of quantitative evidence and have become more likely to rely on or refer to it for their strategy and policy formulations. Finally, the development of big data based on Internet technology has made it possible for the industry to obtain more accurate forecasts. Data on online tourist behaviour can be traced and retrieved. With greater understanding of it, the industry can then use it to forecast future trends.

In this work, we study the isospin-violating decay of phi-->omegapi0 and quantify the electromagnetic (EM) transitions and intermediate meson exchanges as two major sources of the decay mechanisms. In the EM decays, the present datum status allows a good constraint on the EM decay form factor in the vector meson dominance model, and it turns out that the EM transition can only account for about 1/4~1/3 of the branching ratio for phi-->omegapi0. The intermediate meson exchanges, K[overline K](K*) (intermediate K[overline K] interaction via K* exchanges), K[overline K]*(K) (intermediate K[overline K]* rescattering via kaon exchanges), and K[overline K]*(K*) (intermediate K[overline K]* rescattering via K* exchanges), which evade the naive Okubo-Zweig-Iizuka rule, serve as another important contribution to the isospin violations. They are evaluated with effective Lagrangians where explicit constraints from experiment can be applied. Combining these three contributions, we obtain results in good agreement with the experimental data. This approach is also extended to J/psi(psi[prime])-->omegapi0, where we find contributions from the K[overline K](K*), K[overline K]*(K), and K[overline K]*(K*) loops are negligibly small, and the isospin violation is likely to be dominated by the EM transition.

This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms other models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events’ impact assessment and risk forecasting.

This work examines key competition issues in the areas of transport for tourism, the accommodation sector and the travel distribution, drawing examples and case ...

The purpose of this research is to propose an index approach to study the impact of travel experience on tourists' satisfaction and the further impact on their sense of well‐being. Based on the latest development of tourist satisfaction research, that is, the tourist satisfaction indices, this innovative study further extends the two‐stage framework of tourist travel experiences to account for subjective well‐being and subsequently calculates a tourist well‐being index. A questionnaire with 496 respondents was used, which focused on four service sectors' tourist satisfaction indices. From this, a destination overall tourist satisfaction index and a tourist well‐being index were produced using the results of structural equation modelling. Some key findings include the higher the impact of the trip on tourist's sense of well‐being the higher the loyalty towards the destination. Different cultures had different results concerning the trip experiences (satisfaction) and the impact of the latter on their subjective well‐being. Group travellers also had a significantly more positive experience compared with solo travellers. A new innovative indices system capturing tourist satisfaction and its causes and outcomes, in particular its impact on tourist's subjective well‐being, was developed. This research therefore extends work done on the impact of tourist experience and quality of life/subjective well‐being.

This study investigates the performance of combination forecasts in comparison to individual forecasts. The empirical study focuses on the UK outbound leisure tourism demand for the USA. The combination forecasts are based on the competing forecasts generated from seven individual forecasting techniques. The three combination methods examined in this study are: the simple average combination method, the variance-covariance combination method and the discounted mean square forecast error method. The empirical results suggest that combination forecasts overall play an important role in the improvement of forecasting accuracy in that they are superior to the best of the individual forecasts over different forecasting horizons. The variance-covariance combination method turns out to be the best among the three combination methods. Another finding of this study is that the encompassing test does not contribute significantly to the improved accuracy of combination forecasts. This study provides robust evidence of the efficiency of combination forecasts.

Recall of tourism experiences evokes pleasant affect tied to the trip, which leads to mood and behavioral intentions. Based on experimental design with two studies, this research investigates the mood-repairing role of tourism memory, memory characteristics, and affective and behavioral consequences of tourism memory. Study 1 confirmed that both positive and negative mood groups recall positive tourism memories, and the effect of mood repair motivation on tourism memory valence is moderated by mood state. Study 2 identified tourism memory characteristics and the effect of tourism memory valence on mood and behavioral intentions. Findings contribute to the literature on relationships between tourism memories, mood and behavioral intentions, and inform tourism organizations on how to use tourism memories for experience management.

This study uses a system-of-equations approach to model the substitution relationship between Australian domestic and outbound tourism demand. A new price variable based on relative ratios of purchasing power parity index is developed for the substitution analysis. Short-run demand elasticities are calculated based on the estimated error correction almost ideal demand systems. The empirical results reveal significant substitution relationships between Australian domestic tourism and outbound travel to Asia, the UK and the US. This study provides scientific support for necessary policy considerations to promote domestic tourism further.

Increasing levels of global and regional integration have led to tourist flows between countries becoming closely linked. These links should be considered when modeling and forecasting international tourism demand within a region. This study introduces a comprehensive and accurate systematic approach to tourism demand analysis, based on a Bayesian global vector autoregressive (BGVAR) model. An empirical study of international tourist flows in nine countries in Southeast Asia demonstrates the ability of the BGVAR model to capture the spillover effects of international tourism demand in this region. The study provides clear evidence that the BGVAR model consistently outperforms three other alternative VAR model versions throughout one- to four-quarters-ahead forecasting horizons. The potential of the BGVAR model in future applications is demonstrated by its superiority in both modeling and forecasting tourism demand.

As the global tourism industry continues to expand and to become more complex, it is vital that those in the industry identify trends early and design proactive ...

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.

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified, and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies that compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. The time-varying parameter (TVP) model and structural time-series model with causal variables, however, perform consistently well.

The linear almost ideal demand system (LAIDS), in both static and dynamic forms, is examined in the context of international tourism demand. The superiority of the dynamic error correction LAIDS compared to its static counterpart is demonstrated in terms of both the acceptability of theoretical restrictions and forecasting accuracy, using a data set on the expenditure of United Kingdom tourists in twenty-two Western European countries. Both long-run and short-run demand elasticites are calculated. The expenditure elasticities show that travelling to most major destinations in Western Europe appears to be a luxury for UK tourists in the long run. The demand for travel to these destinations by UK tourists is also likely to be more price elastic in the long run than in the short run. The calculated cross-price elasticites suggest that the substitution/complementarity effects vary from destination to destination.

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.

As one of the first studies to explore the joint consumption of both leisure and pandemic-related tourism products in former pandemic epicenters, this research expands the scope of dark tourism to include former pandemic epicenters. The motivational determinants of intention to visit leisure and pandemic-related sites are empirically identified using an ordered logit model. This is the first study which formally proposes patriotism as a new push motive in stimulating people to visit post-disaster destinations. The identified segmentations of tourists with different levels of push-pull motives and socio-demographic features provides key stakeholders and practitioners in former epicenters with a systematic recovery plan in the post-pandemic era. Key Words Post-disaster tourism, intention to visit, push and pull motives, ordered logit regression, factor analysis, cluster analysis Introduction Although the development of the tourism industry significantly contributes to economic growth in various economies, it has been catastrophically interrupted by the outbreak of the COVID-19 pandemic. The global deluge of the pandemic has led to worldwide travel restrictions, which not only hindered the flow of international tourists but also (due to epidemic control policies such as the 14-day quarantine in China) drastically reduced the number of tourists within each country. The tourism industry and overall economic prosperity were seriously harmed by the pandemic, especially in former epicenters which had the majority of confirmed cases or COVID-related deaths compared to other cities. With ongoing vaccination programs and the increase in herd immunity, it is likely that epidemic control policies will be relaxed. It is thus essential for key stakeholders in the tourism industry to put forward a systematic recovery plan for the post-pandemic era, especially within former epicenters.

Due to the limitations of existing tourism demand forecasting models, data with frequencies lower than those of the tourism demand need to be processed in advance and cannot be directly used in a model, which leads to the loss of timeliness and accuracy in tourism demand forecasting. Taking the inbound tourism of the United States prior to and during the COVID-19 pandemic as an example, this study systematically examines the impact of data frequency processing on tourism demand modeling and forecasting, through the construction and comparison of three categories of models, with a particular focus on the first developed multiple mixed-frequency specification of reverse mixed-data sampling (RMIDAS) model. The results confirm the positive effect of multiple mixed-frequency models, which can directly use various original data frequencies, in improving the accuracy of tourism demand forecasting. This study also provides important guidance for future research on high-frequency tourism variables forecasting.

Purpose – The purpose of this paper is to provide a short review of tourism forecasting literature and general summary of the trends and developments in tourism forecasting and point out directions for future research in the next 75 years. Design/methodology/approach – This is a general literature overview. Findings – Key trends are identified for next 75 years. Originality/value – First overview in tourism forecasting that provides foresight on long-term future trends (over next 75 years). Keywords: Tourism forecasting, Automated Paper type General review

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.

Tourist arrivals and tourist expenditure, in both aggregate and per capita forms, are commonly used measures Of tourism demand in empirical research. This study compares these two measures In the context of econometric modelling and the forecasting Of tourism demand. The empirical Study focuses on demand for Hong Kong tourism by residents of Australia, the UK and the USA. Using the general-to-specific modelling approach, key determinants Of tourism demand are identified based on different demand measures. In addition, the forecasting accuracy of these demand Measures is examined. It is found that tourist arrivals in Hong Kong are influenced mainly by tourists' income and 'word-of-month'/habit persistence effects, while the tourism price in Hong Kong relative to that of the tourist origin country is the most important determinant Of tourist expenditure in Hong Kong. Moreover, the aggregate tourism demand models Outperform the per capita models, with aggregate expenditure models being the most accurate. The implications of these Findings for tourism decision making are that the choice of demand measure for forecasting models Should depend on whether the objective of the decision maker is to maximize tourist arrivals or expenditure (receipts), and also that the models should be specified in aggregate form.

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.

This study utilizes almost ideal demand system (AIDS) models to examine Hong Kong’s competitiveness as an international tourist destination in comparison with its competitors. The empirical findings of the study shed new light on the destination competitiveness literature and demonstrate that a destination’s competitiveness should be examined from a market-specific perspective. The results also suggest that Hong Kong is more competitive than Macau, particularly in terms of its ability to attract Australian and mainland Chinese tourists, while price elasticity calculations suggest Singapore and South Korea are more competitive than Hong Kong.

Lifestyle-oriented motivation (LOM) is the reason that the owners of many small enterprises start and operate businesses in the tourism industry. Using a sample of guesthouses in historic Chinese towns, this study examines how LOM affects these small businesses’ corporate social responsibility (CSR), performance, and owners’ intentions to sustain operations. Applying the structural equation modeling approach to a sample of 154 guesthouses, this study finds that LOM positively influences CSR, performance, and owners’ operational intentions. Specifically, LOM promotes each dimension of CSR activities (product, environment, community, employees, and heritage protection); however, it only increases firms’ subjective performance and has no significant influence on their objective performance. The mediating effects of CSR and performance on the path from LOM to owners’ operational intentions are also demonstrated. Lastly, the theoretical and managerial implications of the findings are discussed.

Big data contain a vast amount of information which is valuable for researchers and decision-makers both in normal and crisis situations. This bibliometric study aims to present the progress, theoretical foundations, and intellectual structure of big data analytics in the hospitality and tourism research domain. Literature records were collected via the Web of Science and screened to maximize relevance. The overall literature dataset included 1184 papers, comprising both review and empirical articles. From this dataset, 47 publications related to the COVID-19 pandemic were identified and formed a sub-dataset to capture the specific research focuses during the crisis. The research themes and their evolutionary paths were identified by keyword clustering and keyword Time Zone analysis. Co-citation analysis was implemented to visualize the intellectual structure. Based on the systematic review, this study proposes future research directions.

The ongoing COVID-19 pandemic has negatively influenced the global tourism industry. Despite the documented negative impacts of diseases on tourism demand and people’s perceived health risk, researchers have seldom examined the psychological responses of tourists travelling during an infectious disease outbreak. We therefore conducted three studies to examine this key aspect, and our findings indicate that tourists have a strong negative emotional reaction towards disadvantaged tourism-related prices in response to a high (vs low) infectious disease threat. Furthermore, risk aversion acts as an underlying mechanism driving this effect: tourists are more risk aversive under the threat of an infectious disease, which consequently magnifies their negative emotional reaction. At last, theoretical and practical implications of these findings for tourism are discussed.

This study aims to predict the recovery of the Hong Kong tourism industry from the current global financial and economic crisis. Based on the latest statistics available, this study provides updated forecasts of tourist arrivals to Hong Kong from 10 key source markets over the period 2010-2015. The forecasts include annual and quarterly forecasts of tourist arrivals and the market shares of the source markets concerned. An econometric method is used to estimate the demand elasticities as well as their confidence intervals, followed by the interval demand predictions. The total tourist arrivals to Hong Kong are projected to reach 53.8 million by 2015 with the interval forecasts between 38.4 and 74.4 million, representing an annual growth of 10.48% on average against 2009, with an interval ranging from 4.44% to 16.60%. As far as individual source markets are concerned, their demand recovery takes varying paces. Overall, tourism demand in Hong Kong is relatively resilient to the global financial and economic crisis.

This study develops time varying parameter (TVP) linear almost ideal demand system (LAIDS) models in both long-run (LR) static and short-run error correction (EC) forms. The superiority of TVP-LAIDS models over the original static version and the fixed-parameter EC counterparts is examined in an empirical study of modelling and forecasting the demand for tourism in Western European destinations by UK residents. Both the long-run static and the short-run EC-LAIDS models are estimated using the Kalman filter algorithm. The evolution of demand elasticities over time is illustrated using the Kalman filter estimation results. The remarkably improved forecasting performance of the TVP-LAIDS relative to the fixed-parameter LAIDS is illustrated by a one-year- to four-years-ahead forecasting performance assessment. Both the unrestricted TVP-LR-LAIDS and TVP-EC-LAIDS outperform their fixed-parameter counterparts in the overall evaluation of demand level forecasts, and the TVP-EC-LAIDS is also ranked ahead of most other competitors when demand changes are concerned. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

The linear almost ideal demand system (LAIDS), in both static and dynamic forms, is examined in the context of international tourism demand. The superiority of the dynamic error correction LAIDS compared to its static counterpart is demonstrated in terms of both the acceptability of theoretical restrictions and forecasting accuracy, using a data set on the expenditure of United Kingdom tourists in twenty-two Western European countries. Both long-run and short-run demand elasticites are calculated. The expenditure elasticities show that travelling to most major destinations in Western Europe appears to be a luxury for UK tourists in the long run. The demand for travel to these destinations by UK tourists is also likely to be more price elastic in the long run than in the short run. The calculated cross-price elasticites suggest that the substitution/complementarity effects vary from destination to destination.

The dynamic system-of-equations approach has been used to analyze the demand for outbound tourism among a number of destinations. However, this approach has not been applied to the context of the tourist consumption of different products in a given destination. Given the importance of understanding tourists' consumption behavior to destination management, this study seeks to gain new insights into Hong Kong inbound tourist expenditure patterns using a dynamic system-of-equations approach: the almost ideal demand system model. Based on the estimation of a complete demand system, this study investigates the interactions among the demand for different tourism products (i.e., shopping, hotel accommodation, meals outside hotels, and other) and the impacts of price changes on demand. Tourists from different source markets are examined separately, and the results show that their consumption behavior differs significantly.

In this study, we used deonance theory, attribution theory, spillover effects, and power distance to explore how abusive supervision influences bystanders in the hospitality and tourism industry. In-depth semi-structured interviews revealed an integrated representation of bystanders' emotional and behavioural reactions, ranging from negative emotions to unconcerned and exclusionary feelings, from supportive behaviours to avoidance, gossip, and learning behaviours. We also identified important factors influencing these emotional and behavioural reactions such as trust, power distance, social-cultural context, the tourism and hospitality context, victims' spillover, and bystanders' attribution. This study is one of the first to investigate the influence of abusive supervision from a bystander's perspective. Thus, the findings provide a novel perspective for assessing and understanding abusive supervision through a critical and comprehensive theoretical lens.

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.

This study develops time varying parameter (TVP) linear almost ideal demand system (LAIDS) models in both long-run (LR) static and short-run error correction (EC) forms. The superiority of TVP-LAIDS models over the original static version and the fixed-parameter EC counterparts is examined in an empirical study of modelling and forecasting the demand for tourism in Western European destinations by UK residents. Both the long-run static and the short-run EC-LAIDS models are estimated using the Kalman filter algorithm. The evolution of demand elasticities over time is illustrated using the Kalman filter estimation results. The remarkably improved forecasting performance of the TVP-LAIDS relative to the fixed-parameter LAIDS is illustrated by a one-year- to four-years-ahead forecasting performance assessment. Both the unrestricted TVP-LR-LAIDS and TVP-EC-LAIDS outperform the fixed-parameter counterparts in the overall evaluation of demand level forecasts, and the TVP-EC-LAIDS is also ranked ahead of most other competitors when demand changes are concerned.

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.

This study develops a tourist satisfaction assessment system based on a dual-model framework and demonstrates its general applicability. The first model concerns tourist satisfaction and its key antecedents and consequences. Structural equation modelling is employed to investigate the relationships amongst the constructs in the theoretical framework, and is then used as a basis for the computation of sectoral-level tourist satisfaction indexes. The second model is designed to estimate an aggregate service satisfaction index and an overall destination satisfaction index using a multiple indicator and multiple cause approach. The framework is applied to a large dataset that represents six tourism-related sectors and seven major source markets of inbound tourism to Hong Kong.

The advantages of error correction models (ECMs) and time varying parameter (TVP) models have been discussed in the tourism forecasting literature. These models are now combined to give a new single-equation model, the time varying parameter error correction model (TVP-ECM), which is applied for the first time in the context of tourism demand forecasting. The empirical study focuses on tourism demand, measured by tourism spending per capita, by UK residents for 5 key Western European destinations. Based on the discussion of how the series considered related to most, the empirical results show that the TVP-ECM can be expected to outperform a number of alternative econometric and time series models in forecasting the demand for tourism. By measuring performance in terms of the accuracy of the forecasts of growth (rates of change) and showing that TVP-ECM performs very well for this as well as conventional assessment of the level of demand in this study, it is suggested that forecasters of tourism demand levels and growth rates can feel comfortable using TVP-ECM given that it is expected to perform well.

This study advances the contextual understanding of knowledge management practices adopted by tourism consultants in the setting of tourism development projects. It goes beyond the traditional understanding of the bounded nature of firms to analyse knowledge management issues through a project-based multi-layered perspective, namely project ecology. An innovative participant-observation methodology is utilised to study 15 episodic projects at three tourism development companies over a 12-month period. This provides an insider perspective to enhance understanding of the knowledge management practices and collaborations of tourism consultants. The study reveals two underlying logics that shape knowledge management practices: the logics of creativity and accumulation. The findings exhibit how knowledge management is moulded by the practices within, and interactions among, the four tiers of a multi-level project-specific contextual framework. •Analysis of project knowledge management focussed on the holistic social contexts of episodic projects instead of the bounded firm or the destination networks.•Innovative participant observation fieldwork took place in 15 tourism development projects over 12 months.•This is the first study that utilises the project ecology perspective in tourism research.•Two contrasting but interacted logics of knowledge management practices adopted by tourism consultants in tourism development projects are identified: the logic of creativity and the logic of accumulation.

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.

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.

Addressing the call for a better understanding of tourist behavior in relation to post-disaster destinations, this study explores the motivations and intentions of potential domestic tourists (from non-hit areas) to visit Sichuan, China in the aftermath of an earthquake. Drawing on dark tourism theories, this study offers a more comprehensive insight into the consumption of post-disaster destinations, aiming to capture the impact of the changes to the destination’s attributes on tourist behavior. The findings move beyond the common approach to tourism recovery, which solely focuses on reviving the traditional ‘‘non-dark’’ products. This study reveals the importance of newly formed dark attributes that emerge from the disaster as another means to destination recovery, reflected in the emergence of new tourist segments.

The article introduces an integrated market-segmentation and tourism yield estimation framework for inbound tourism. Conventional approaches to yield estimation based on country of origin segmentation and total expenditure comparisons do not provide sufficient detail, especially for mature destinations dominated by large single-country source markets. By employing different segmentation approaches along with Tourism Satellite Accounts and various yield estimates, this article estimates direct economic contribution for subsegments of the UK market on the Mediterranean island of Cyprus. Overall expenditure across segments varies greatly, as do the spending ratios in different categories. In the case of Cyprus, the most potential for improving economic contribution currently lies in increasing spending on “food and beverages” and “culture and recreation.” Mass tourism therefore appears to offer the best return per monetary unit spent. Conducting similar studies in other destinations could identify priority spending sectors and enable different segments to be targeted appropriately.

This study examines the usefulness of the theory of transaction cost economics (TCE) for the online travel market and investigates customer satisfaction and loyalty with the transaction cost over the Internet taken into account. Using structural equation modelling (SEM), the authors identify the relationships among the antecedents (uncertainty, personal security and buying frequency), the mediating variable (transaction costs) and endogenous constructs (customer satisfaction and loyalty). The findings suggest that the satisfaction and loyalty of customers purchasing travel products over the Internet are affected negatively by transaction costs, which are determined by uncertainty, personal security and buying frequency. Moreover, a significantly negative relationship is identified between buying frequency and customer satisfaction.