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Copyright of Journal of China Tourism Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Search query data reflect users' intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former. [ABSTRACT FROM AUTHOR]

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models. [ABSTRACT FROM AUTHOR]

This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists' online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Copyright of Journal of China Tourism Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The aim was to examine the progression and regression of diabetic retinopathy within a four-year period in a Chinese population with type 2 diabetes mellitus in a community optometry clinic in Hong Kong. Methods: During the period May 2005 to November 2009, 5,160 patients with type 2 diabetes mellitus who had attended at least two diabetic retinopathy screening sessions at a community optometry clinic were included as subjects in this study. All had retinal photographs taken of both eyes, which were of sufficiently good quality for grading. For the purpose of this study, diabetic retinopathy grading was based on the results of the worst eye. The main outcomes were the within four-year incidence of diabetic retinopathy and the incidence of progression and regression of diabetic retinopathy. Results: Of the 5,160 subjects in this study, 3,647 had no diabetic retinopathy, while 1,513 had diabetic retinopathy at the baseline visit. Of those 3,647 subjects with no diabetic retinopathy, the within four-year cumulative incidence of any diabetic retinopathy, mild or moderate non-proliferative diabetic retinopathy and sight-threatening diabetic retinopathy was 15.16 per cent, 14.45 per cent, 0.69 per cent and 0.03 per cent, respectively. Of those 1,513 subjects with diabetic retinopathy at baseline, the within four-year progression incidence of diabetic retinopathy was 6.61 per cent and the regression incidence of diabetic retinopathy was 45.54 per cent. Conclusion: The high regression incidence of diabetic retinopathy suggests that it might not be necessary for all patients with diabetes to be screened annually. Other methods to determine the screening frequency for an individual patient should be explored. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper examines the impact of the global financial/economic crisis on the demand for Hong Kong tourism by residents of 10 major source markets for the period 2009-2012. To capture the influence of this crisis, the Autoregressive Distributed Lag Model (ADLM) is used to calculate the demand elasticities, and four scenarios (ranging from the most pessimistic to the most optimistic) are created to examine the possible impacts of changes in source market income levels and the price of tourism on the demand for Hong Kong tourism in these markets. The demand elasticities reveal that the economic conditions in the source markets are the most significant determinants of demand for Hong Kong tourism. In the most pessimistic scenario, total tourist arrivals to Hong Kong are projected to reach 27.6 million in 2009 and 26.0 million in 2012, whereas in the most optimistic scenario, these numbers are 30.7 million in 2009 and 33 million in 2012. In all of the scenarios, tourist arrivals from the long-haul markets are expected to suffer more losses relative to the short-haul markets during the 2009-2012 forecasting period. The forecasts also indicate that the market shares of the source markets will change slightly over this period, with Mainland China, Taiwan and Japan constituting the dominant markets for Hong Kong tourism. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]

The main objectives of this paper are to identify the factors which contribute to the demand for Hong Kong tourism with the aid of econometric models and to generate forecasts of international tourism arrivals to Hong Kong for the period 2001–2008. The general-to-specific modelling approach is followed to model and forecast the demand for Hong Kong tourism by residents from the 16 major origin countries/regions and the empirical results reveal that the most important factors that determine the demand for Hong Kong tourism are the costs of tourism in Hong Kong, the economic condition (measured by the income level) in the origin countries/regions, the costs of tourism in the competing destinations and the ‘word of mouth’ effect. The demand elasticities and forecasts of tourism arrivals obtained from the demand models form the basis of policy formulations for the tourism industry in Hong Kong. [Copyright &y& Elsevier]