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

Early-stage tourism destinations often seek external capital to establish, invest and participate in tourism businesses at various levels. Entrepreneurial mobility at these destinations is thus an important phenomenon in need of further exploration both empirically and theoretically. This study uses an early-stage destination in rural China as a case to explore the experiences of inward entrepreneurial migrants in tourism development and associates their mobility, geographical and organisational, with dimensions of social capital, including institutional support, community openness and personal social networks. Primary interview data were collected from inward tourism entrepreneurs, key local community members and government officials related to tourism projects. The findings suggest that the inward entrepreneurs had unrealistic perceptions of the tourism industry before they entered. Entrepreneurial mobility in tourism development is influenced by all three social capital dimensions. The integration with the locality enhances the potential contribution of entrepreneurial mobility to tourism development.

This study proposes a hierarchical pattern recognition method for tourism demand forecasting. The hierarchy consists of three tiers: the first tier recognizes the calendar pattern of tourism demand, identifying work days and holidays and integrating “floating holidays.” The second tier recognizes the tourism demand pattern in the data stream for different calendar pattern groups. The third tier generates forecasts of future tourism demand. Evidence from daily tourist visits to three attractions in China shows that the proposed method is effective in forecasting daily tourism demand. Moreover, the treatment of “floating holidays” turns out to be more effective and flexible than the commonly adopted dummy variable approach.

Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.

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.

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.

Governments spend considerable public resources, including tax dollars, on marketing their cities or countries in order to become a strong brand attractive for exports, foreign direct investment and tourism. Ironically at the same time, self-imposed and strict visa regulations that can potentially hamper the productivity of marketing and branding are fairly common. There are valid reasons for imposing strict visa regulations, two of the most important being national and economic security. Although strict visa regulations are a common phenomenon around the world, the tourism academia have yet to argue against strict visa regulations by providing empirical evidence on their potential impact on tourism demand, and hence the economy. The current study is a pioneer attempt to generate awareness about the potential detrimental impacts of visa restrictions on a country’s economy, by using the case of People’s Republic of China, one of the countries known to have a history of strict visa regulations. The research reported in this paper was based on the standard tourism demand function, which models the causal relationship between tourism demand and a number of macroeconomic variables. The autoregressive distributed lag model (ADLM) was used since it has a dynamic specification that takes the time path of the tourist decision-making process into consideration by using both current and lagged values of variables. Results show the negative effect of visa regulations on both country-level and prefecture-level economies. Implications are discussed and recommendations provided.

Package-tour operators often tailor trips to fit travelers’ personal requirements to create higher customer value. In practice, there are various options available for presenting an identical package tour to potential customers. This research investigates how option framing influences travelers’ decision making in a package-tour customization task. Based on a behavioral study and a logistic regression analysis, our results show that: (1) when both upgrading (i.e., starting from an economic package and customizing upwards) and downgrading (i.e., beginning with a luxury package and customizing downwards) methods are available, more consumers prefer upgrading to downgrading; (2) consumers customize their tour packages to a higher total price in the downgrading condition; (3) after separating peripheral service from core service attributes, our results further reveal that option framing affects peripheral services more than core services; and (4) consumers who are quality conscious and promotion-focused are more likely to choose a downgrading method for package-tour customization. Combined, these findings offer important managerial implications for travel companies.

The aim of this study is to determine the factors influencing the development of medical tourism in Hong Kong. A qualitative research method was adopted to collect data from representatives of private and public hospitals, government bodies, and medical institutions. The results reveal that policies and regulations, government support, costs, capacity problems, and the healthcare needs of the local community are the main barriers to the development of such tourism. Several strategies for lifting these barriers are suggested, such as new promotional activity policies, government action to encourage investment in the medical tourism market, and cooperative efforts by the hospitality sector and medical institutions to develop medical tourism products.

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.

Forecast combination in tourism has emerged as an important research area due to its relevance to tourism decision making. This paper further investigates the impact of forecast combination on forecast accuracy by applying a quadratic programming approach to determine the combination weights for individual forecasts. In particular, we introduce three novel ideas which have not been found in previous tourism forecasting studies. First, we introduce a quality control technique, CUSUM, to determine the time for updating the weights. Next, we develop a hybrid method (using quadratic programming) to combine the forecasts to reduce forecasting errors. Thirdly, we investigate whether different performance measures yield different results. Thus, instead of comparing different weighting methods using only one performance measure, we introduce several indicators for performance comparisons. The empirical results suggest that the controlled weighting method both saves time in updating the combination weights and improves the overall performance of the combined forecasts. The method is also easy-to-implement and should be used to improve forecasting accuracy in practice.

This paper reviews current research on supply chain management (SCM) within the context of tourism. SCM in the manufacturing industry has attracted widespread research interest over the past two decades, whereas studies of SCM in the tourism industry are very limited. Stakeholders in the tourism industry interact with each other to resolve their divergent business objectives across different operating systems. The potential benefit of considering not only individual enterprises but also the tourism value chain becomes evident. The paper examines the characteristics of tourism products, and identifies and explores core issues and concepts in tourism supply chains (TSCs) and tourism supply chain management (TSCM). Although there is an emerging literature on TSCM or its equivalents, progress is uneven, as most research focuses on distribution and marketing activities without fully considering the whole range of different suppliers involved in the provision and consumption of tourism products. This paper provides a systematic review of current tourism studies from the TSCM perspective and develops a framework for TSCM research that should be of great value not only to those who wish to extend their research into this new and exciting area, but also to tourism and hospitality decision makers. The paper also identifies key research questions in TSCM worthy of future theoretical and empirical exploration.

Introducing entrance fees to natural attractions may help counteract the threat of inadequate public funds for site maintenance and management. The primary objective of this study is to measure visitors’ willingness to pay such fees in Iceland, where no such measurement has previously been undertaken. A questionnaire survey based on the contingent valuation method was carried out at two major natural attractions in Iceland: Gullfoss waterfall and Skaftafell National Park. Over 92% of the 252 respondents were willing to pay an entrance fee. Mean amounts and population consumer surplus estimates per season were ISK 1 333 and ISK 41 million at Gullfoss and ISK 508 and ISK 34 million at Skaftafell, respectively. Modest fees would not significantly decrease the demand for these attractions. Slight differences were found in the willingness to pay according to income, attitude towards environmental protection, number of previous visits, history of paying entrance fees, country of residence, age and education. Implications of the empirical study for policy makers and site managers are provided.

Existing non-tourism related literature shows that forecast combination can improve forecasting accuracy. This study tests this proposition in the tourism context by examining the efficiency of combining forecasts based on three different combination methods. The data used for this study relate to tourist arrivals in Hong Kong from the top ten tourism generating countries/regions. The forecasts are derived from four different forecasting models: autoregressive integrated moving average (ARIMA) model, autoregressive distributed lag model (ADLM), error correction model (ECM) and vector autoregressive (VAR) model. All forecasts are ex post and the empirical results show that the relative performance of combination versus single model forecasts varies according to the origin–destination tourist flow under consideration, which parallels previous findings regarding the relative performance of individual forecasting methods. The results also vary with the combination techniques used. Furthermore, although the combined forecasts do not always outperform the best single model forecasts, almost all the combined forecasts are not outperformed by the worst single model forecasts. This suggests that forecast combination can considerably reduce the risk of forecasting failure. This conclusion also implies that combined forecasts are likely to be preferred to single model forecasts in many practical situations.

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a higher degree of accuracy. The empirical results based on a data set on the demand for Hong Kong tourism show that the Bayesian VAR (BVAR) models invariably outperform their unrestricted VAR counterparts. It is noteworthy that the univariate BVAR was found to be the best performing model among all the competing models examined.

The vector autoregressive (VAR) modelling technique is used to forecast tourist flows to Macau from eight major origin countries/regions over the period 2003–2008. The existing literature shows that the VAR model is capable of producing accurate medium- to long-term forecasts, and also separate forecasts of the explanatory variables are not required. A further justification for using the VAR technique is that it permits an impulse response analysis to be performed in order to examine the ways in which the demand for Macau tourism responds to the ‘shocks’ in the economic variables within the VAR system. The implications of this analysis are discussed. The forecasts generated by the VAR models suggest that Macau will face increasing tourism demand by residents from mainland China. Since the needs of Chinese tourists tend to be different from those from other origin countries/regions, especially Western countries, the business sectors in Macau need to pay considerable attention to catering for the needs of Chinese tourists.