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Gang LI

University of Surrey, School of Hospitality and Tourism, Faculty Member

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Gang gained his BA in Economics with his major in Investment and Accounting in DUFE, China. From the same university, he also obtained an MSc (distinction) in Econometrics and Statistics. In 2000 Gang came to the University of Surrey to conduct his PhD research on tourism forecasting. In September 2003 Gang joined the faculty. Gang has participated in a number of international collaborative projects on economic analysis of tourism demand and tourism forecasting.
Phone: +44 (0)1483 686356
Address: School of Hospitality and Tourism Management
Faculty of Business, Economics and Law
University of Surrey
Guildford, Surrey
GU2 7XH, UK

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Sirnak University

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Books by Gang LI

Forecasting Tourism Demand Using Econometric Models

Trends in European Tourism Planning and Organisation, 2012

This study evaluates the forecasting accuracy of six alternative econometric models in the contex... more This study evaluates the forecasting accuracy of six alternative econometric models in the context of predicting the quarterly international tourism demand in 25 countries/country groupings. Tourism demand is measured in terms of tourist expenditure by inbound international visitors in a destination. Two univariate time series models are included in the forecasting comparison as benchmarks. Forecast accuracy is assessed in terms of error magnitude and directional change. Seasonality is an important feature of forecasting models and requires careful handling. The empirical results show that the time-varying parameter (TVP) model provides the most accurate short-term forecasts, whereas the naïve (no-change) model performs best in longer term forecasting up to two years, in terms of the error magnitude accuracy. This study provides new evidence of the TVP model’s superior performance in short-term seasonal tourism demand forecasting.

The Advanced Econometrics of Tourism Demand

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest.... more Tourism demand is the foundation on which all tourism-related business decisions ultimately rest. Governments and companies such as airlines, tour operators, hotels, cruise ship lines, and recreation facility providers are interested in the demand for their products by tourists. The success of many businesses depends largely or totally on the state of tourism demand, and ultimate management failure is quite often due to the failure to meet market demand. This book introduces students, researchers and practitioners to the modern developments in advanced econometric methodology within the context of tourism demand analysis, and illustrates these developments with actual tourism applications. The concepts and computations of modern advanced econometric modelling methodologies are introduced at a level that is accessible to specialists and non-specialists alike. The methodologies introduced include general-to-specific modelling, cointegration, vector autoregression, time varying parameter modelling, panel data analysis and the almost ideal demand system (AIDS). In order to help the reader understand the various methodologies, extensive tourism demand examples are provided throughout the volume.

Papers by Gang LI

Tourism Demand Forecasting with Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Journal of Travel Research, 2023

Due to the limitations of existing tourism demand forecasting models, data with frequencies lower... more 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.

Intellectual Landscape and Emerging Trends of Big Data Research in Tourism and Hospitality: A Scientometric Analysis

International Journal of Hospitality Management, 2024

Big data contain a vast amount of information which is valuable for researchers and decision-make... more 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 1,184 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.

IMPACTS OF RISK AVERSION ON TOURISM CONSUMPTION: A hierarchical age-period-cohort analysis

Annals of Tourism Research, 2023

This study extends tourism demand research by focusing on how an important psychological factor, ... more 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.

TOURISM FORECASTING COMPETITION IN THE TIME OF COVID-19: AN ASSESSMENT OF EX ANTE FORECASTS

Annals of Tourism Research, 2022

BACKGROUND The COVID-19 pandemic and corresponding border control policies of various destination... more BACKGROUND 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 reopening 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.

Impact of decomposition on time series bagging forecasting performance

Tourism Management, 2023

Time series bagging has been deemed an effective way to improve unstable modelling procedures and... more 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.

Do looks matter for hosts on the peer-to-peer sharing accommodation market

by Hengyun Li, Qian Wang, Gang LI, and Danting Cai

Annals of Tourism Research, 2022

The presence of hosts' profile photo on peer-to-peer accommodation platforms is likely to influen... more The presence of hosts' profile photo on peer-to-peer accommodation platforms is likely to influence consumers' judgments and purchase behavior. Based on the stimulusorganism-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 selfdisclosure. This study enriches the literature on the beauty premium and ways to reduce consumers' reliance on hosts' facial attractiveness.

RISK, UNCERTAINTY AND AMBIGUITY AMID COVID-19: A multi-national analysis of international travel intentions1

This study analyses how Covid-19 shapes individuals' international tourism intentions in context ... more 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.

Understanding the landscape of inbound tourism measurement

VisitBritain project report, 2021

Commissioned by VisitBritain, this research project firstly overviews the International Passenger... more Commissioned by VisitBritain, this research project firstly overviews the International Passenger Survey (IPS) used in the UK as a tool to measure inbound arrivals and expenditure. It then summarises the approaches used by twelve destinations that share some similarities to the UK: Australia, New Zealand, Japan, Ireland, South Korea, USA, South Africa, Hong Kong, the EU as a whole as well as France, Austria and Saudi Arabia. Details of official measures such as the data collecting methods, data processing, publishing schedules are provided and mapped.
Additionally, given the interruption during the Covid-19 crisis, the study explores complementary data sources that can be applied as mitigation when traditional data collection is substantially interrupted. Alternative potential or emerging types of measures considered or used by these destinations, including mobile positioning data, bank card transaction data and Google trends data are investigated. Further details of the approaches used by each of the selected destinations are provided in the form of case studies.

Do Crises Affect the Sustainability of the Economic Effects of Tourism? A Case Study of Hong Kong

by Wu Peihuang and Gang LI

Journal of Sustainable Tourism, 2021

The economic effects of tourism industry during periods of crisis, such as the COVID-19 pandemic,... more 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.

Tourism Demand Nowcasting Using a LASSO-MIDAS Model

International Journal of Contemporary Hospitality Management, 2021

Purpose-This study aims to examine whether and when real-time updated online search engine data s... more 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.

Post-pandemic Dark Tourism in Former Epicenters

Tourism Economics, 2021

As one of the first studies to explore the joint consumption of both leisure and pandemic-related... more 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 pandemicrelated 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.

Tourism Memory Characteristics Scale: Development and Validation

by Kim Youngsoo and Gang LI

Journal of Travel Research, 2021

Recall of tourism experiences is a decisive factor in tourists' future behavior and decision maki... more 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.

Review of Evidence of Elasticities Relevant to Tourism in Scotland

Project report, 2020

Editorial: Tourism Forecasting Competition in the Time of COVID-19

Annals of Tourism Research, 2021

His research focuses on economic analysis and forecasting of tourism demand.

Bayesian Bootstrap Aggregation for Tourism Demand Forecasting

by Gang LI and Xinyang Liu

International Journal of Tourism Research, 2021

Limited historical data are the primary cause of the failure of tourism forecasts. Bayesian boots... more 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.

Ex-ante Tourism Forecasting Assessment

Journal of Travel Research, 2020

Although numerous studies have focused on forecasting international tourism demand, minimal light... more 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.

Determinants of Chinese households' tourism consumption: Evidence from China Family Panel Studies

by Yuan Qin, Gang LI, and Jinsong Wu

International Journal of Tourism Research, 2020

Using survey data from the China Family Panel Studies project, this study explores factors influe... more 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.

Service Robots or Human Staff: How Social Crowding Shapes Tourist Preferences

Tourism Management, 2020

Service providers in tourism and hospitality are beginning to welcome robots as a customer servic... more 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.

Forecasting Tourism Demand Using Econometric Models

Trends in European Tourism Planning and Organisation, 2012

This study evaluates the forecasting accuracy of six alternative econometric models in the contex... more This study evaluates the forecasting accuracy of six alternative econometric models in the context of predicting the quarterly international tourism demand in 25 countries/country groupings. Tourism demand is measured in terms of tourist expenditure by inbound international visitors in a destination. Two univariate time series models are included in the forecasting comparison as benchmarks. Forecast accuracy is assessed in terms of error magnitude and directional change. Seasonality is an important feature of forecasting models and requires careful handling. The empirical results show that the time-varying parameter (TVP) model provides the most accurate short-term forecasts, whereas the naïve (no-change) model performs best in longer term forecasting up to two years, in terms of the error magnitude accuracy. This study provides new evidence of the TVP model’s superior performance in short-term seasonal tourism demand forecasting.

The Advanced Econometrics of Tourism Demand

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest.... more Tourism demand is the foundation on which all tourism-related business decisions ultimately rest. Governments and companies such as airlines, tour operators, hotels, cruise ship lines, and recreation facility providers are interested in the demand for their products by tourists. The success of many businesses depends largely or totally on the state of tourism demand, and ultimate management failure is quite often due to the failure to meet market demand. This book introduces students, researchers and practitioners to the modern developments in advanced econometric methodology within the context of tourism demand analysis, and illustrates these developments with actual tourism applications. The concepts and computations of modern advanced econometric modelling methodologies are introduced at a level that is accessible to specialists and non-specialists alike. The methodologies introduced include general-to-specific modelling, cointegration, vector autoregression, time varying parameter modelling, panel data analysis and the almost ideal demand system (AIDS). In order to help the reader understand the various methodologies, extensive tourism demand examples are provided throughout the volume.

Tourism Demand Forecasting with Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Journal of Travel Research, 2023

Due to the limitations of existing tourism demand forecasting models, data with frequencies lower... more 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.

Intellectual Landscape and Emerging Trends of Big Data Research in Tourism and Hospitality: A Scientometric Analysis

International Journal of Hospitality Management, 2024

Big data contain a vast amount of information which is valuable for researchers and decision-make... more 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 1,184 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.

IMPACTS OF RISK AVERSION ON TOURISM CONSUMPTION: A hierarchical age-period-cohort analysis

Annals of Tourism Research, 2023

This study extends tourism demand research by focusing on how an important psychological factor, ... more 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.

TOURISM FORECASTING COMPETITION IN THE TIME OF COVID-19: AN ASSESSMENT OF EX ANTE FORECASTS

Annals of Tourism Research, 2022

BACKGROUND The COVID-19 pandemic and corresponding border control policies of various destination... more BACKGROUND 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 reopening 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.

Impact of decomposition on time series bagging forecasting performance

Tourism Management, 2023

Time series bagging has been deemed an effective way to improve unstable modelling procedures and... more 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.

Do looks matter for hosts on the peer-to-peer sharing accommodation market

by Hengyun Li, Qian Wang, Gang LI, and Danting Cai

Annals of Tourism Research, 2022

The presence of hosts' profile photo on peer-to-peer accommodation platforms is likely to influen... more The presence of hosts' profile photo on peer-to-peer accommodation platforms is likely to influence consumers' judgments and purchase behavior. Based on the stimulusorganism-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 selfdisclosure. This study enriches the literature on the beauty premium and ways to reduce consumers' reliance on hosts' facial attractiveness.

RISK, UNCERTAINTY AND AMBIGUITY AMID COVID-19: A multi-national analysis of international travel intentions1

This study analyses how Covid-19 shapes individuals' international tourism intentions in context ... more 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.

Understanding the landscape of inbound tourism measurement

VisitBritain project report, 2021

Commissioned by VisitBritain, this research project firstly overviews the International Passenger... more Commissioned by VisitBritain, this research project firstly overviews the International Passenger Survey (IPS) used in the UK as a tool to measure inbound arrivals and expenditure. It then summarises the approaches used by twelve destinations that share some similarities to the UK: Australia, New Zealand, Japan, Ireland, South Korea, USA, South Africa, Hong Kong, the EU as a whole as well as France, Austria and Saudi Arabia. Details of official measures such as the data collecting methods, data processing, publishing schedules are provided and mapped.
Additionally, given the interruption during the Covid-19 crisis, the study explores complementary data sources that can be applied as mitigation when traditional data collection is substantially interrupted. Alternative potential or emerging types of measures considered or used by these destinations, including mobile positioning data, bank card transaction data and Google trends data are investigated. Further details of the approaches used by each of the selected destinations are provided in the form of case studies.

Do Crises Affect the Sustainability of the Economic Effects of Tourism? A Case Study of Hong Kong

by Wu Peihuang and Gang LI

Journal of Sustainable Tourism, 2021

The economic effects of tourism industry during periods of crisis, such as the COVID-19 pandemic,... more 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.

Tourism Demand Nowcasting Using a LASSO-MIDAS Model

International Journal of Contemporary Hospitality Management, 2021

Purpose-This study aims to examine whether and when real-time updated online search engine data s... more 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.

Post-pandemic Dark Tourism in Former Epicenters

Tourism Economics, 2021

As one of the first studies to explore the joint consumption of both leisure and pandemic-related... more 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 pandemicrelated 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.

Tourism Memory Characteristics Scale: Development and Validation

by Kim Youngsoo and Gang LI

Journal of Travel Research, 2021

Recall of tourism experiences is a decisive factor in tourists' future behavior and decision maki... more 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.

Review of Evidence of Elasticities Relevant to Tourism in Scotland

Project report, 2020

Editorial: Tourism Forecasting Competition in the Time of COVID-19

Annals of Tourism Research, 2021

His research focuses on economic analysis and forecasting of tourism demand.

Bayesian Bootstrap Aggregation for Tourism Demand Forecasting

by Gang LI and Xinyang Liu

International Journal of Tourism Research, 2021

Limited historical data are the primary cause of the failure of tourism forecasts. Bayesian boots... more 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.

Ex-ante Tourism Forecasting Assessment

Journal of Travel Research, 2020

Although numerous studies have focused on forecasting international tourism demand, minimal light... more 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.

Determinants of Chinese households' tourism consumption: Evidence from China Family Panel Studies

by Yuan Qin, Gang LI, and Jinsong Wu

International Journal of Tourism Research, 2020

Using survey data from the China Family Panel Studies project, this study explores factors influe... more 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.

Service Robots or Human Staff: How Social Crowding Shapes Tourist Preferences

Tourism Management, 2020

Service providers in tourism and hospitality are beginning to welcome robots as a customer servic... more 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.

Threat of infectious disease during an outbreak: Influence on tourists' emotional responses to disadvantaged price inequality

Annals of Tourism Research, Jul 9, 2020

The ongoing COVID-19 pandemic has negatively influenced the global tourism industry. Despite the ... more 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.

Forecasting Tourism Demand with Multisource Big Data

Annals of Tourism Research, 2020

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online re... more 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.