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Background Regular monitoring of static lacunar infarction (SLI) lesions plays an important role in preventing disease development and managing prognosis. Magnetic resonance imaging is one method used to monitor SLI lesions. Purpose To evaluate the image quality of the T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence using artificial intelligence-assisted compressed sensing (ACS) in detecting SLI lesions and assess its clinical applicability. Methods A total of 42 patients were prospectively enrolled and scanned by T2-FLAIR. Two independent readers reviewed the images acquired with accelerated modes 1D (acceleration factor 2) and ACS (acceleration factors 2, 3, and 4). The overall image quality and lesion image quality were analyzed, as were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and number of lesions between groups. Results The subjective assessment of overall brain image quality and lesion image quality was consistent between the two readers. The lesion display quality and the overall image quality were better with the traditional 1D acceleration method than with the ACS accelerated method. There was no significant difference in the SNR of the lacunar infarction in the images between the groups. The CNR of the images with the 1D acceleration mode was significantly lower than that of images with the ACS acceleration mode. Images with the 1D, ACS2, and ACS3 acceleration modes showed no significant differences in terms of detecting lesions but scan time can be reduced by 40% (1D vs. ACS3). Conclusion ACS acceleration mode can greatly reduce the scan time. In addition, the images have good SNR, high CNR, and strong SLI lesion detection ability.

During a pandemic, tourism can inflict negative social costs on communities in tourist destinations. This study examines factors affecting residents’ responses to policies to mitigate the social costs of tourism during a pandemic. Two hypothetical scenarios are analyzed. Study 1 investigates framing effects on residents’ attitudes toward the effectiveness of policy measures; study 2 explores the impact of mental accounting on residents’ willingness to pay. Findings show that residents perceive policy measures as more effective if their positive outcomes of such measures are highlighted. Also, residents are more willing to fund social cost mitigation with unearned income, such as anti-pandemic bonds, than through their salaries. This article contributes to academic debate on the efficacy of public policies in combating pandemics and extends the literature on framing and mental accounting in tourism research. Policy implications of these findings are also discussed.

This study investigates the impacts of global economic policy uncertainty (GEPU) and domestic (Chinese) economic policy uncertainty (CEPU) on the long-run volatility of the tourism stock market in China based on an improved GARCH–MIDAS–X model. Empirical results reveal that both CEPU and GEPU have significant negative effects on the long-run volatility of China’s tourism stock market. It is further identified that the impact of GEPU on tourism companies’ performance is short-lived. The findings suggest that tourism-related practitioners should monitor both CEPU and GEPU when conducting risk assessments related to tourism investment and policymaking.

Prospect theory has been an essential theoretical foundation for behavioral economics, as recognized with the Nobel Prize in economic sciences in 2002. The growing interest in behavioral economics among tourism researchers necessitates a systematic assessment of prospect theory and its application in tourism research to critically examine the current status of tourism decision-making studies. This study therefore clarifies the theoretical background of prospect theory and analyzes 93 published studies to examine how prospect theory has performed in explaining tourism decision-making. The study also evaluates the application of prospect theory in tourism research and provides future research directions with respect to under-researched dimensions, reference points, dynamic decision-making processes, and the logical continuity and systemization of prospect theory.

The aim of this study is to investigate the long-term determinants of China’s imported wine demand and to forecast wine imports from 2019 to 2023 using econometric methods. Auto-regressive distributed lag models are developed based on neoclassical economic demand theory to investigate the long-term determinants of China’s demand for imported bottled, bulk, and sparkling wine from the top five countries of origin. The empirical results demonstrate that income is the most important determinant of China’s imported wine demand, and that price only plays a significant role in a few markets. Substitute and complement effects are identified between wines from different countries of origin and between imported wines and other liquids. China’s imported wine demand is expected to maintain its rapid growth over the forecast period. Bottled wine will continue to dominate China’s imported wine market. France will have the largest market share in the bottled wine market, Spain will be the largest provider of bulk wine, and Italy will hold the same position for sparkling wine. This is the first study to use a single equation with the general to specific method rather than a system of equations to estimate and forecast China’s demand for imported bottled, bulk, and sparkling wines from different countries of origin. The more specific model setting for each country of origin improves forecasting accuracy.

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.

Commercial hosts are becoming increasingly common in peer-to-peer (P2P) accommodation sharing. Yet the interplay between commercial and individual hosts has been unclear. This study investigates the effect of properties managed by commercial hosts on the individual hosts in the neighborhood. Specifically, we hypothesize that an increase in commercial properties, which have competitive advantages, would penetrate neighborhood markets and cannibalize the online popularity of individual properties. We test these hypotheses using a large-scale, longitudinal data set collected from a leading P2P accommodation-sharing platform in Beijing. The findings show that an increase in commercial properties is associated with a decline in the popularity of individual properties in the neighborhood. However, the negative effect of commercial properties is weakened when there is a higher price difference between the two ownership types and a higher density of tourist attractions. The implications for service operations and strategies for P2P accommodation-sharing businesses are discussed.

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.

ObjectiveTo delineate the response of migrating chondrogenic progenitor cells (CPCs) that arose from the surface of mechanically injured articular cartilage to proinflammatory damage-associated-molecular-patterns (DAMPs).DesignBovine CPCs and non-CPC chondrocytes isolated from either impacted or scratched articular cartilage were studied. Those 2 types of cells were treated with mitochondrial DAMPs (MTDs; 10 nM fMLF and 10 µg/mL CpG DNA), or 10 nM HMGB1, or 10 ng/mL IL-1b for 24 hours. At the end of experiments, conditioned media and cell lysates were collected for analysis of expression levels of matrix metalloproteinases (MMPs), chemokines, and cytokines that are associated with cartilage degeneration with Western blotting and quantitative polymerase chain reaction. The difference of expression levels was compared by Welch’s t-test.ResultsOur data indicated that HMGB1 and MTDs remarkably upregulated pro-MMP-13 expression in CPCs. Compared with non-CPCs, CPCs expressed significantly more baseline mRNAs of MMP-13, CXCL12, and IL-6. MTDs greatly increased the expression of MMP-13 and IL-6 in CPCs by over 100-fold ( P < 0.001). MTDs also significantly increased IL-8 expression in CPCs to a similar extent ( P < 0.001). However, when IL-1b was present, CPCs expressed less MMP-3 and active MMP-13 proteins as well as less CCL2 and IL-6 than did non-CPCs.ConclusionsWe concluded that CPCs were more sensitive than non-CPCs in response to DAMPs, especially MTDs. The proinflammatory nature of CPCs implied their critical role in the early phase of posttraumatic osteoarthritis development.

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.

This study is aimed at investigating what has happened to the dynamic linkages between foreign direct investment (FDI) and tourism development in China since the emergence of the so-called new normal economy. A time-varying parameter vector autoregressive (TVP-VAR) model is used for the first time to analyze the equi-spaced and time-point impulse responses between FDI, foreign exchange earnings from international tourism (FEE), and gross domestic product using annual data taken from 1983 to 2017. The results for the equi-spaced impulse response show that a difference in intensity for the interaction effect between FDI and FEE will change with different intervals. In addition, impulse response diagrams for FDI and FEE based on changes in economic development at three significant points in time reveal that the effect FDI in the new normal period has had the greatest impact on FEE in 2012, followed in decreasing impact by 2003 and then 1997.

Hosting mega-events is often perceived as a way to stimulate economic growth through tourism. However, the cost of infrastructure investments and promotion may outweigh the benefits generated by the mega-events. Measuring the impact of such events on a hosting country’s economy is not easy, especially as mega-events generally involve many sectors of a destination’s economy. In this study, we adopt a panel data approach to evaluate the impact of the London Olympic Games, Brazil World Cup, and Rio Olympic Games on the economic growth of the respective destinations. Using cross-sectional correlations between countries, we construct scenarios in which the hosting countries did not hold the mega-events and then estimate the time-varying impact of the events on the economy. The developed and developing countries exhibit different results.

Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

This paper studies a hyperbolic tangent oscillator arising in a nonlinear packaging system. The high-order nonlinearity makes it difficult to study the system analytically. A modified homotopy perturbation method is proposed, where the initial guess is an approximate solution of the well-known Duffing oscillator; the solution accuracy can be improved by either improvement of the initial guess or continuous iteration. This paper gives a general approach to high-order nonlinear oscillators.

Tourism demand nowcasting is generally carried out using econometric models that incorporate either macroeconomic variables or search query data as explanatory variables. Nowcasting model accuracy is normally evaluated by traditional loss functions. This study proposes a novel statistical method, the monotonicity test, to assess whether the nowcasting errors obtained from the ordinary least squares, generalised dynamic factor model and generalised dynamic factor model combined with mixed data sampling model are monotonically decreasing when new data on explanatory variables become available, based on the mixed frequency data between 1 January 2011 and 31 December 2019. The results of the empirical analysis show that nowcasts generated results based on two data sources combined are superior to that based on a single data source. Compared with traditional loss functions, the monotonicity test leads to a more objective and convincing nowcasting model performance. This study is the first attempt to evaluate tourism demand nowcasting performance using a monotonicity test.

© The Author(s) 2018. Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

The relationship between tourism and economic growth has created a large body of literature investigating the hypotheses of tourism-led economic growth (TLEGH) and economy-driven tourism growth (EDTGH). In this article, we use mixed-frequency Granger causality tests to investigate the relationship between the two types of growth in Hong Kong from 1974 to 2016. Our analysis reveals the following empirical regularities. First, the hidden short-run causality of TLEGH is detected, and EDTGH is proved in the short run and also in the long run when Granger causality tests are performed in a mixed-frequency framework. Second, mixed-frequency Granger tests demonstrate more power in testing the TLEGH and EDTGH via the rejection frequencies (bootstrap p value). Finally, rolling Granger causality tests reveal an unstable relationship between tourism and economic growth in both magnitude and direction, and the relationship is highly economic- and tourism-event-dependent.

This study introduces bootstrap aggregation (bagging) in modeling and forecasting tourism demand. The aim is to improve the forecast accuracy of predictive regressions while considering fully automated variable selection processes which are particularly useful in industry applications. The procedures considered for variable selection is the general-to-specific (GETS) approach based on statistical inference and stepwise search procedures based on a measure of predictive accuracy (MPA). The evidence based on tourist arrivals from six source markets to Australia overwhelmingly suggests that bagging is effective for improving the forecasting accuracy of the models considered.

International tourism is considered an effective means of economic development. However, the effects of tourism are not evenly distributed between rural and urban households in China. In the wake of significant socioeconomic events, the uneven distribution of the economic effects has huge welfare implications for Chinese households. This study is the first attempt to evaluate the distributional effect of two large, recent, sequential events on China’s rural and urban households. It adopts an innovative approach that combines an econometric model and a two-household computable general equilibrium model. The results show that in terms of welfare, urban households were more adversely affected by the events than rural households. To mitigate the loss of welfare, measures should be taken to continually promote China as a destination and attract tourists after such events occur. Meanwhile, training and education should be made more accessible to rural households to increase their job opportunities.

A new model that links climate and seasonal tourism demand is developed to study the effects of home climate, destination climate, and climate difference between destinations and source markets on seasonal tourism demand. Using the dynamic panel data technique, the study focuses on the demand of tourists from Hong Kong for 19 of the major tourism cities in Mainland China. The results show that the home climate, destination climate, and the difference in climate between home and destination cities all have significant influence on tourism demand. Furthermore, demand for Mainland Chinese tourism among Hong Kong residents is found to be driven by the climate at the place of origin, while the effects of destination climate and climate difference are weaker.

Tourism satellite accounts (TSAs) have been widely recognized as standard tools to measure the contribution of tourism to destination economies. However, issues such as high costs of data collection and delayed release of TSAs have limited their regular compilation and practical application in some countries/regions. This study, therefore, introduces an innovative Web-based TSA information system that integrates all functions of the entire TSA compilation process chain, covering data input, data storage and management, TSA table compilation, statistical analysis and other extended applications. The system not only improves the efficiency of tourism data management and TSA compilation but also enhances and extends the usefulness of TSAs for assessing the economic contribution of tourism to destinations. This Web-based TSA information system is established and discussed for a case study in Guangdong Province, China. Overall, the methodology and results reported herein provide academics and practitioners with new perspectives on regional TSA development and applications.

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

The package tour market is information intensive in nature. Its complex informational structure is generally inaccessible to tourists, whose purchase behavior might be affected by the extent to which they can access such information. This study shows that package tours can result in low tourist satisfaction and deter tourists’ behavioral intention over time despite the fact that package tours are financially advantageous. An analysis of data based on China’s outbound tourism demand in Hong Kong from 1993 to 2013 shows that tourist satisfaction can be explained by the market share of inclusive package tours. This suggests that the decline in the market share of package tours leads to an increase in tourist satisfaction. As the amount of information in the market increases as a result of accumulated travel experience, package tours are replaced by independent tours or entirely new packages, or by travel to a new destination.

A detailed understanding of decision rules is essential in order to better explain consumption behavior, yet the variety of decision rules used have been somewhat neglected in tourism research. This study adopts an innovative method, greedoid analysis, to estimate a noncompensatory type of decision rule known as lexicographic by aspect (LBA). It is quite different from the weighted additive (WADD) model commonly assumed in tourism studies. By utilizing an experimental research design, this study enables the evaluation of the two types of decision rules regarding their predictive and explanatory power. Additionally, we introduce a novel evaluation indicator (“cost”), which allows further investigation of the heterogeneity in the use of decision rules. The results suggest that although the out-of-sample accuracy is lower, the LBA model has a better explanatory performance on respondents’ preference order. Moreover, the different perspective provided by the LBA model is useful for obtaining managerial implications.

This study uses meta-analysis to examine the relationship between estimated international tourism demand elasticities and the data characteristics and study features that may affect such empirical estimates. By reviewing 195 studies published during the period 1961–2011, the meta-regression analysis shows that origin, destination, time period, modeling method, data frequency, the inclusion/omission of other explanatory variables and their measures, and sample size all significantly influence the estimates of the demand elasticities generated by a model. Moreover, the demand elasticities at both product and destination levels are generalized by statistically integrating previous empirical estimates. The findings of this meta-analysis will be useful wherever an understanding of the drivers of tourism demand is critically important.

This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable.These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses’ future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.

Over the past two decades, researchers and practitioners have given considerable attention to issues of tourism value chain governance. To establish a systematic understanding of the current research contributions and gaps, this study provides a review of published studies on the topic. Various dimensions of tourism value chain governance, namely, policy, destination management and marketing, integration of distribution channels, and sustainability of the tourism value chain, are examined. Content analysis is used to provide quantitative evidence and hence a more objective evaluation. The results show that research on tourism value chain governance remains limited, and the development of each dimension unbalanced. An agenda for future research is proposed, given the indications that both qualitative and quantitative investigations are needed to establish the most appropriate governance models for tourism value chains.

This paper studies the impact of the involvement of tour operators in a tourism supply chain with multiple hotels and travel agencies. Tour operators negotiate with upstream hotels on the quantity of hotel rooms and promote the accommodation to downstream travel agencies. Two types of game models are formulated to analyze quantity competition in tourism supply chains with and without a tour operator. We conduct a comparative study of the two cases and explore the effects of various parameters. The results show that when the market size of travel agencies is lower than a certain level, then both travel agencies and hotels benefit more from the presence of a tour operator in a tourism supply chain. Furthermore, in this situation, the fluctuations in the profits of both travel agencies and hotels are more violent as the supply chain’s membership changes.

The recent economic crisis and swine flu pandemic have had significantly negative impacts on global tourism. Tourism in the United Kingdom has also suffered as a result of the two crises, although their actual impacts have yet to be evaluated. This study analyzes the impacts of these two phenomena on the demand for U.K. inbound tourism during the 2008Q1-2009Q2 period among visitors from the country’s 14 major visitor source markets. An econometric framework is proposed to separate and estimate the impacts of swine flu and the economic crisis on U.K. tourism demand.

This study discusses the comparative efficiency of hotels using integer data envelopment analysis (IDEA), which is an extension to the traditional DEA models. This model is particularly useful when the integer input or output data are used in the evaluation of hotel performance. Specifically, IDEA can identify more appropriate and feasible input and output targets than the traditional DEA models. The empirical analysis of IDEA is carried out based on a data set related to 23 tourist hotels in Taipei. The contributions and managerial implications for efficiency improvement of this study are also presented.

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.

The purpose of this study is to forecast inbound tourism to Asia and outbound tourism from Asia by considering the possible impacts of the current financial and economic crisis. The autoregressive distributed lag model is used to calculate the interval demand elasticities, which are then used to generate interval forecasts for inbound tourism to and outbound tourism from Asia. Interval forecasts reduce the risk of complete forecasting failure arising from the uncertainties associated with the crisis. The forecast results suggest that the financial and economic crisis will have a negative impact on both inbound and outbound tourism in Asia but the demand will rebound from 2010.

This study provides a comprehensive comparison of the performance of the commonly used econometric and time-series models in forecasting seasonal tourism demand. The empirical study is carried out based on the demand for outbound leisure tourism by UK residents to seven destination countries: Australia, Canada, France, Greece, Italy, Spain and the USA. In the modelling exercise, the seasonality of the data is treated using the deterministic seasonal dummies, seasonal unit root test techniques and the unobservable component method. The empirical results suggest that no single forecasting technique is superior to the others in all situations. As far as overall forecast accuracy is concerned, the Johansen maximum likelihood error correction model outperforms the other models. The time-series models also show superior performance in dealing with seasonality. However, the time-varying parameter model performs relatively poorly in forecasting seasonal tourism demand. This empirical evidence suggests that the methods of seasonality treatment affect the forecasting performance of the models and that the pre-test for seasonal unit roots is necessary and can improve forecast accuracy.

The performance of forecast combination techniques is explored at different time horizons in the context of tourism demand forecasting. Statistical comparisons between the combination and single-model forecasts show that the combined forecasts are significantly more accurate than the average single-model forecasts across all forecasting horizons and for all combination methods. This provides a strong recommendation for forecast combination in tourism. In addition, the empirical results indicate that forecast accuracy does not improve as the number of models included in the combination forecasts increases. It also appears that combining forecasts may be more beneficial for longer-term forecasting.

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest and so accurate forecasts of tourism demand are crucial for tourism industry practitioners. From the functional point of view, a tourism demand forecasting system (TDFS) is a forecasting support system capable of providing quantitative tourism demand forecasts and allowing users to make their own ‘what-if’ scenario forecasts. From the technical point of view, a TDFS is an information system consisting of a set of computer-based modules or components that support tourism demand forecasting and scenario analysis. This paper establishes a widely accessible Web-based TDFS which not only takes advantage of advanced econometric tourism demand forecasting techniques but also incorporates the real-time judgemental contribution of experts in the field. Furthermore, scenario forecasts are permitted within the system. Built on Web-based technology, the system provides advanced information sharing and communication and brings considerable convenience to various stakeholders engaged in tourism demand forecasting at different locations. In attempting to generate more accurate tourism demand forecasts, the system is designed to incorporate a two-stage forecasting methodology, which integrates judgemental adjustments with statistically based forecasts. The software architecture, detailed components and development environment of the Web-based TDFS are described in detail. A three-tiered client–server architecture is employed, which offers great flexibility, reusability and reliability. The prototype system has been developed and screen shots of interaction with the system are presented using Hong Kong tourism as an example.

This article considers two tourism supply chains (TSCs). Each TSC is assumed to consists of three sectors with the following service providers—a theme park operator, accommodation providers, and tour operators. Game theory is used to investigate the cooperation and competition between these two TSCs, between the three sectors within each TSC, and between the enterprises within each sector when configuring and marketing package holidays. Several important findings are obtained. First, a larger membership in each of the TSC sectors strengthens the sector's overall capacity and intensifies the internal competition, thus reducing members' profits while other sectors benefit from this internal competition. Second, decision makers of the two competing TSCs should adopt appropriate product differentiation strategies by carefully positioning their package holiday products to optimize their performance. Third, the theme park would benefit from integration with the accommodation provider. There exists a win-win situation in which the performances of both TSCs could be improved, if the integration adequately increases the TSCs' preference.

This study examines the mutual relationships among tourists' perceived service quality, value, satisfaction, and intentions to repurchase packaged tour services from travel agents. A mediation model where tourist satisfaction is hypothesized as a key mediator of the relationships between perceived service quality and repurchase intentions, and between perceived value and repurchase intentions, is developed and tested using a substantial data set with a time span of 9 consecutive years, available from a well-established regional consumer satisfaction survey. While previous literature has suggested that quality perceptions impose a direct effect on tourists' repurchase intentions, this study shows that such effect is fully mediated by tourists' satisfaction. The mediating role of satisfaction is further supported by examining the direct and indirect effects of perceived value on tourists' repurchase intentions.

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

By analyzing 766 articles from travel trade magazines published in 2005, this article aims to identify tourism trends in the Asia—Pacific region. The qualitative analysis sketches a holistic conceptual network of relationships among trends, whereas the quantitative analysis visually presents the trends' commonalities or differences among four subregions (Northeast Asia, Southeast Asia, South Asia, and Oceania). This article differs from previous studies in that it counts the frequencies of quotations associated with codes rather than the commonly used keywords. Quotations are composed of sentences and paragraphs that provide a deeper and richer understanding of the context. And as the title implies, it visualizes the relationships among trends and between trends and sub-regions. Through visualizing the qualitative data holistically and individually, this approach is intended to “see the forest for the trees.”

This article presents the results of content analysis of 373 referees' reports of manuscripts submitted to 35 hospitality and tourism journals where rejection or major revision was recommended. Failed manuscripts had multiple shortcomings, with referees identifying an average of 6.2 deficiencies per article. The most common areas where referees found fault with manuscripts were methodology (74% of papers), failure to elucidate significance effectively (60%), poor writing style (58%) and a weak literature review (50%). The study concluded that communications' problems were more common than technical flaws.

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

This article examines the relationship between macroeconomic and the hospitality stock variables using the vector autoregressive (VAR) modeling approach. The empirical results based on the U.S. data show that the hospitality stock indices largely follow an autoregressive process, and they are not entirely independent from some key macroeconomic variables. Specifically, the BOND variable explains a substantial proportion of the forecast error variance among the stock indices for restaurants, lodging, and casinos. The consumer price index, money supply, and industrial production variables, however, provide a relatively smaller contribution toward explaining the forecast error variance in these hospitality stock indices.

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

The empirical results from a forecasting competition show that the unrestricted vector autoregressive model is likely to generate the most accurate forecasts of international tourist expenditure in Denmark. This model is therefore estimated (using data for 1969–99) and is used to generate tourism expenditure forecasts for Denmark to 2010. The employment requirements (direct, indirect and induced) associated with these expenditure forecasts are then estimated using an input–output model. The forecasts of employment demands are shown across all industrial sectors, and linked to qualifications data in respect of the labour force. The major impacts of foreign tourist expenditure on employment in Denmark occur in the retail, hotel and restaurant sectors. Foreign tourist expenditure is also significantly associated with graduate employment.

The ability of various econometric and univariate time-series models to generate accurate forecasts of international tourism demand is evaluated. Accuracy is assessed in terms of error magnitude and also directional change error. Statistical testing for both forecasting bias and directional change forecasting performance is introduced. The empirical results show that for 1-year-ahead forecasting, the time-varying parameter model performs consistently well. However, for 2- and 3-years-ahead forecasting, the best model varies according to the forecasting error criterion under consideration. This highlights the importance (for longer term forecasts) of selecting a forecasting method that is appropriate for the particular objective of the forecast user.

Traditional tourism demand analysis uses ordinary least squares or maximum likelihood methods to estimate demand models, assuming that the parameters of the models remain constant over the sample period. This assumption is too restrictive, as it does not allow for behavioral changes of tourists over time. This study proposes a new methodology—the time-varying parameter (TVP) approach to tourism demand modeling. This method relaxes the assumption of parameter constancy, and the behavioral change of tourists over time is traced using a statistical estimator known as a Kalman filter. The appropriateness of the TVP approach to tourism demand modeling is then tested based on a data set of the demand for Hong Kong tourism by residents from six major tourism origin countries.