Professor Gang Li
Gang gained his BA in Economics, majoring 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 pursue his doctoral degree in tourism forecasting, then in September 2003 Gang started his academic career as a lecturer at Surrey. Gang has participated in a number of international collaborative projects on economic analysis of tourism demand and tourism forecasting.
Tourism economics with particular interests in econometric modelling and forecasting of tourism demandTourism competition and destination competitivenessQuantitative research methods for tourism studiesChinese economic issues, especially socio-economic development assessment
Director of International Relations, School of Hospitality and Tourism Management
Affiliations and memberships
Tourism economics with particular interests in econometric modelling and forecasting of tourism demand
Tourism competition and destination competitiveness
Quantitative research methods for tourism studies
Chinese economic issues, especially socio-economic development assessment
Mobility and migrataion
- Identifying, and quantifying, the main types of international youth mobility in the EU, and their key characteristics
- Understanding what determines which individuals do and which do not participate in international mobility as personal and professional development strategies: their motives, migration channels and information sources
- Analysing the individual outcomes in terms of both employability and careers (skills and competences) and non-economic terms (welfare and identities)
- Analysing the territorial outcomes for the regions of both origin and destination, in economic, demographic and cultural terms
- Differentiating between short-term and long-term outcomes, taking into account return migration and future intentions to migrate
- Identifying implications for policies in migration but also of education, the economy and housing
Business of Tourism
Tourism Policy and Development
Applied Research in Hospitality, Tourism and Events
Tourism Social Science
International Tourism Management
Courses I teach on
Postgraduate research supervision
In the areas of:
- Tourism economics with particular interests in econometric modelling and forecasting of tourism demand
- Tourism competition and destination competitiveness
- Quantitative research methods for tourism studies
- Chinese economic issues, especially socio-economic development assessment
- Mobility and migrataion
Postgraduate research supervision
(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.
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.
Australia: A system-of-equations approach, Tourism Management 45 pp. 159-170
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
Consumption Dynamics: A Time-varying Parameter Demand System Approach, Annals of Tourism Research 39 (2) pp. 667-685
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
of Tourism: Case Study from a
Mediterranean Island, Journal of Travel Research 53 (5) pp. 610-624 SAGE
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.
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
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.
While existing tourism demand models are successful in measuring the causal effects of economic variables on tourism demand for a single origin-destination pair, they tend to miss the spillover effects onto other countries. In the era of globalisation, tourism destinations become interdependent on each other. Impacts of a distant event can be transmitted across borders and be felt globally. Hence, modelling international
tourism demand requires one to go beyond a particular origin-destination pair, and take into account the interdependencies across multiple countries. The proposed approach overcomes the ?curse of dimensionality? when modelling a large set of endogenous variables.
The empirical results show that, to different extents, co-movements of international tourism demand and of macroeconomic variables are observed across all the 24 countries. In the event of a negative shock to China?s real income level and that to China?s own price level, it is found that in the short run, almost all countries will face fluctuations in their international tourism demand and their own price. But in the long
run the shocks will impact on developing countries and China?s neighbouring countries more deeply than on developed countries in the West.
The current research contributes to the knowledge on tourism demand. It models tourism demand in the setting of globalisation and quantifies the interdependencies across major countries. On the practical front, tourism policy makers and business practitioners can make use of the model and the results to gauge the scale of impacts of unexpected events on the international tourism demand of their native markets.
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.
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
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.
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.
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.
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 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.
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.
From this perspective, the research involves examination of secondary data (e.g. TDCs` websites) and initial semi-structured interviews with professional participants in the tourism development industry in order to map the framework of TDC project ecologies in China. However, the core of the methodology is multiple case studies conducted in three contrasting TDCs over a period of 10 months. Data were collected through participant observation and informal interview during the case study process, focusing on how knowledge is managed within the project ecologies.
This research provides a relatively detailed description about the entities, and their interrelationships, involved in the project ecology of TDP. Building on the multilevel nature of project ecology, this research discusses the effects of various factors on KM at four different contextual levels (i.e. the individual level, the team level, the organizational level, and the external environment level). Four key findings serve to synthesize the roles of the factors in the four levels respectively: (1) the diverse and intrinsic effects of individual level factors on the individuals` performance in KM-related activities; (2) the aligning functions of the team level factors in configuring individuals` project work and their corresponding KM-related activities; (3) the organizational level factors which exert relatively more ongoing and sustained influences on KM activities despite the varied features of the various projects;(4)the characteristics of the external environment which can also exert latently ongoing, and sometimes notable influences on the interactions and dynamics of these relevant entities in terms of their performance in project and KM-related issues.
As well as bringing a new theoretical perspective to knowledge management in tourism, this research exhibits the ways in which the project ecology of TDPs in China are different from previous models of project ecologies developed in the literature. It does represent a substantial analysis of this topic within the field of tourism. Furthermore, it is intended that the research will also contribute to enhancing the performance of the case study firms, as well as the TDC sector generally.
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,
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.
From a reference point theory perspective, we test a ?three reference point? framework for executive compensation contracting: in external reference to peer firms, internally to other executives on board, and in the time dimension referring to top executives? previous pay. We find that as a superior alternative to agency theory, reference point theory can provide a better explanation for top executive compensation contracting. In our three empirical chapters, we mainly find that: (1) Chinese executive pay levels are externally heavily influenced in their setting by the pay level of executive peer groups; internally by other executives within firms and by the pay level of an individual executive in the previous period. It should also be noted that the loss aversion effect exists in top executive compensation contracting. (2) We test the three reference point effect and use Principal Component Analysis (PCA) to determine the systematic effects of these reference points to empirically test the behavioural determinants of executive pay in China; the three reference points and the systematic effect on top executive compensation are determined to be robust. (3) We document a negative association between top executive pay and a politically-connected board. However, after controlling for the three reference points, this association becomes positive and further strengthens the pay-performance link. We also find that in state-owned and politically connected firms the pay?performance link is the strongest, especially after adjusting for the effect of the three reference points. In private firms that are not politically connected, meanwhile, the pay-for-performance link is weak.
Our findings have important policy implications. They highlight the defects of current corporate governance mechanisms, particularly in executive compensation design and monitoring. The current Chinese Corporate Governance Code largely mimics corporate governance practice in the Anglo-American business environment and relies heavily on agency theory; therefore, to a large extent, it does not work in its own context. Our findings identify that in practice, the ?three reference point? framework and political connections play very important roles in influencing Chinese executive pay setting, in addition to the link to pay-for-performance and corporate governance mechanisms. It is therefore indicated that the Chinese Government should consider developing policies and regulations in addition to its Corporate Governance Code in order to effectively control the problems of the departure of executive compensation from firm performance.
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.
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.