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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.

Background Joint replacement surgery provides articular cartilage samples for chondrocyte isolation. To our knowledge, the effect of the collagenase type on releasing of chondrocytes from the extracellular matrix of cartilage is not reported. Objectives To determine whether cartilage digested with collagenase IA yielded more chondrocytes than that digested with collagenase II and determine whether chondrocytes isolated with collagenase IA could be cultured in vitro. Methods Cartilage slices collected from 18 elderly patients who received joint replacement surgery (16 hips, 2 knees) were digested sequentially with 0.4% pronase E and 0.02% collagenase IA, or with 0.15% collagenase II alone, or sequentially with 0.4% pronase E and 0.02% collagenase II. We compared cell yield from each method. Cell viability by the most effective method was calculated and plotted. The morphology of cultured monolayer chondrocytes was recorded with a light microscope. Results Sequential digestion with pronase E and collagenase IA yielded 2566 ± 873 chondrocytes per mg wet cartilage, which was more effective than the other isolation methods (P = 0.018). The average chondrocyte viability could reach 84% ± 8% (n = 11). Light microscopic images showed typical chondrocyte morphology in monolayer cultures. Conclusion Sequential digestion of human articular cartilage with pronase E and collagenase IA was more effective than collagenase II alone or collagenase II combined with pronase E for releasing chondrocytes from extracellular matrix of cartilage. Chondrocytes isolated with this method could be maintained in monolayer cultures for at least 2 passages with unaltered morphology.

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.

Peanut growth in Shandong Province, a major peanut-producing area in China, is greatly affected by drought. The present study uses hierarchical analysis, weighted comprehensive evaluation, and ArcGIS spatial analysis to conduct spatial zoning of peanut drought risk in Shandong Province based on daily precipitation data for the province acquired from 1991 to 2020, the per capita GDP, and the peanut planting area of Shandong Province, so as to quantify the disaster risk of peanut drought and formulate disaster prevention and resilience planning accordingly. The results show the high-drought-risk zone was mainly distributed in the northwestern part of Shandong Province and on the Jiaodong Peninsula, covering 32.4% of the province. Drought risk was concentrated on the Jiaodong Peninsula, covering 20.7% of the province. The high-vulnerability zone was mainly distributed in the cities of Yantai, Weihai, Linyi, and Rizhao, accounting for 26.8% of the total area. The low-disaster-prevention and low-mitigation-capacity zone was mainly distributed in the western part of Shandong Province, covering 38.7% of the province. Medium- and high-risk areas for drought affecting peanuts were widely distributed, while the overall comprehensive risk index was high, covering 76.2% of the province. Spatial analysis to conduct risk zoning and assessment of peanut drought in Shandong Province, so as to provide a basis for peanut drought disaster prevention and safe peanut production in Shandong Province.

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.

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.

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.

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.

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.

Globalization characterizes the economic, social, political, and cultural spheres of the modern world. Tourism has long been claimed as a crucial force shaping globalization, while in turn the developments of the tourism sector are under the influences of growing interdependence across the world. As globalization proceeds, destination countries have become more and more susceptible to local and global events. By linking the existing literature coherently, this study explores a number of themes on economic globalization in tourism. It attempts to identify the forces underpinning globalization and assess the implications on both the supply side and the demand side of the tourism sector. In view of a lack of quantitative evidence, future directions for empirical research have been suggested to investigate the interdependence of tourism demand, the convergence of tourism productivity, and the impact of global events.

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 purpose of this research is to propose an index approach to study the impact of travel experience on tourists' satisfaction and the further impact on their sense of well‐being. Based on the latest development of tourist satisfaction research, that is, the tourist satisfaction indices, this innovative study further extends the two‐stage framework of tourist travel experiences to account for subjective well‐being and subsequently calculates a tourist well‐being index. A questionnaire with 496 respondents was used, which focused on four service sectors' tourist satisfaction indices. From this, a destination overall tourist satisfaction index and a tourist well‐being index were produced using the results of structural equation modelling. Some key findings include the higher the impact of the trip on tourist's sense of well‐being the higher the loyalty towards the destination. Different cultures had different results concerning the trip experiences (satisfaction) and the impact of the latter on their subjective well‐being. Group travellers also had a significantly more positive experience compared with solo travellers. A new innovative indices system capturing tourist satisfaction and its causes and outcomes, in particular its impact on tourist's subjective well‐being, was developed. This research therefore extends work done on the impact of tourist experience and quality of life/subjective well‐being.

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.

Egon Smeral is a pioneer of tourism economics with a particular focus on modelling and forecasting tourism activities from the international trade perspective. He has been working with the Austrian...

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.

The Delphi technique is the most popular judgemental forecasting method in tourism studies, but theoretical and empirical developments in this area (especially for forecasting purposes) have been slow. This paper analyses published research on Delphi forecasting in tourism and hospitality, explores how the Delphi forecasting method has progressed over the past four decades in terms of topical areas, empirical applications, and issues of reliability and validity, and is thus expected to advance understanding of the Delphi technique, providing topical and methodological recommendations for researchers and industry practitioners for producing accurate forecasts. The literature concerning the qualitative and quantitative applications of Delphi forecasting in tourism is mainly divided into three research themes: event forecasting, forecasting tourism demand, and forecasting future trends/market conditions (the most popular application). Issues of accuracy, reliability, and validity, as well as a group of Delph...

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 assumes that tourists' demand reactions to income and price changes are asymmetric at different phases of the business cycle. In order to test this hypothesis, we analyzed the demand for international tourism in five source markets using a modified growth rate (MGR) model. The empirical evidence demonstrates that income elasticity is indeed asymmetric across the business cycle in four source markets. In addition, asymmetric price effects were found for one source market. To compare forecasting performance, we also estimated a time-varying parameter (TVP) model. The results show that the MGR model generally outperforms the TVP model. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Given the importance of the cruise segment in the tourism industry and the limited number of prior studies in the area, this study empirically explores the structure of customer satisfaction with cruise-line services by evaluating the attributes of cruises that are significant to passengers. Using 44,993 voluntarily provided customer reviews published on a cruise guide website, a stepwise regression analysis is conducted to examine the effects of the attribute performance of cruises on customer satisfaction and dissatisfaction. The findings empirically confirm the validity of the two-factor theory of customer satisfaction in the cruise tourism context. The asymmetric relationship of some attributes makes it possible to identify dissatisfiers, satisfiers, and hybrid factors for the cruise industry overall and cruises on ships of different tonnage. The results can help managers in the cruise industry understand what aspects of cruises should be given more attention to improve their competitive edge. This st...

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 purpose of this study is to test a two-step tourist satisfaction index framework empirically. The fi rst step estimates sectorallevel satisfaction indexes based on a structural equation model, and the second obtains an overall tourist satisfaction index by conducting second-order confi rmatory factor analysis. This study is a pilot test of the theoretical framework based on three tourismrelated service sectors in Hong Kong. The results indicate that mainland Chinese tourists are most satisfi ed with the hotel sector in Hong Kong, followed by the retail sector, and least satisfi ed 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. Refereed/Peer-reviewed

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...

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.

Since the Lao People's Democratic Republic (Lao PDR) first opened its doors to foreign tourists in the early 1990s, the number of tourists to the country has increased significantly. However, the Lao PDR remains one of the poorest countries in the world and tourism is seen by the Laotian government and by non-governmental organizations active in the country as a tool to alleviate poverty. Nonetheless, the Laotian government has until now lacked the financial means and know-how to enact a concerted and effective plan to use tourism to promote poverty alleviation in the country. The paper investigates the current state of pro-poor tourism development in Viengxay and aims to gain an impression of the actual and potential roles and motivations of various stakeholders within the endeavour of tourism for poverty alleviation. The paper concludes with an analysis of the opportunities and barriers to pro-poor tourism in Viengxay, in which the improvement of linkages between the private sector and local development...

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.