Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.     

Modelling and forecasting the demand for Hong Kong tourism

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.     

Academic tourism demand in Galicia,Spain

The last few decades have witnessed a dramatic increase in the mobility of higher education students. When fulfilling certain conditions, this type of mobility can actually be considered a type of tourist activity. This paper justifies the choice of the term “academic tourism” to describe such a form of tourism. Further to this, its primary purpose is to identify the main determinants that drive the demand of academic tourism in Galicia. An empirical analysis has been carried out using a dynamic panel data model by a generalized method of moments (GMM). Contrary to what can be observed in other types of tourism, the results suggest that academic tourism depends mainly on determinants that are not strictly economic; namely, the relevance of the habits and preferences of students, the potential for differential attractiveness of the University of Santiago de Compostela, and the significant impact of the Erasmus programme. In light of these results, policy implications are then discussed.     

Progress in tourism demand research: Theory and empirics

To explore recent progress in tourism demand research, we comprehensively survey current studies in the leading tourism and hospitality journals, asking six evaluative questions about the scientific merits of the studies and three explorative questions about emerging areas in the literature. The examination identifies potential flaws and their consequences in the field of tourism demand. A theoretical foundation is recommended for future tourism demand studies with a view to reduce bias in the empirical analysis of tourism demand. Several emerging areas of analysis in the field of tourism demand are recognized and discussed. Our study provides critical insights that will enable future tourism demand research to generate more reliable, impactful information than in the past.     

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

A meta-analysis of international tourism demand forecasting and implications for practice

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

Remodeling international tourism demand: Old theory and new evidence

The purpose of this study is to advance the tourism demand theory by excluding simultaneous effects of exchange rates and prices in empirical models, formulating an alternative pricing modus operandi consistent with recent research in the area, and demonstrating the efficacy of the use of an industrial production index (IPI) as a proxy for income. A panel fully modified ordinary least squares (FMOLS) method is employed to estimate the inbound tourism demand for Turkey. Study findings suggest that the inclusion of exchange rates and prices, as mutually exclusive components, can be misleading; the IPI is not a good proxy for income; and country-specific coefficients need to be analyzed to accurately explain determinants of tourism demand for countries in the panel.     

Dynamic heterogeneous panel data analysis of tourism demand for Singapore

The objective of this study is to investigate tourism demand for Singapore using heterogeneous dynamic panel estimation methods. A binary variable representing the operation of Integrated Resorts (IRs), as well as income and real exchange rate, are included as determinants of tourism demand growth. Using seasonally adjusted and actual/raw quarterly data of 16 origin countries, our study shows that the pooled mran group (PMG) estimator is able to provide consistent and efficient estimates of long-run relationships between tourism demand and the determinants. The PMG results show that the long-run income (price) elasticity is positively (negatively) significant, with an elasticities range between 0.915 and 3.05 (?0.275 and ?0.34). In addition, the study reveals that the IRs contribute to tourism demand growth, especially in attracting tourists from Asia. In the long term, tourism demand by the Asian markets is less income-sensitive than that of the non-Asian counterparts.     

Immigration and international inbound tourism: Empirical evidence from Australia

Immigration can potentially influence tourism flows. However, in spite of the vast number of studies on tourism demand modelling, the immigration-tourism linkage has not received much attention in the empirical literature. This paper seeks to address this gap. A dynamic demand model is developed and estimated using data from 1980 to 2008 for the 15 main markets of Australia. The explanatory variables included are income, own price, price of a substitute destination, airfare and immigration. The estimation results empirically establish the connection between immigration and inbound tourism. The short run and long-run immigration elasticities generated are 0.028 and 0.09 respectively. Additionally this paper demonstrates that omission of prices of substitutes affects the value of the own price elasticity of demand. The results have implications for future research and for stakeholders who can improve the efficiency of their planning exercises by taking into account additional information on immigration trends.     

Yearly,monthly and weekly seasonality of tourism demand: A decomposition analysis

Annual seasonal variations in tourism demand have been a central theme in literature. However, annual seasonality is not the only time-based inequality in tourism flows that has important implications on policy-making decisions at destinations. Within the context of tourism, this study aims to make an in-depth analysis of intra-monthly and intra-weekly tourism demand using the entropy and relative redundancy measures as alternative seasonality indicators to the Gini coefficient in order to provide new tools to manage tourism and propose new action policies at these frequencies. In comparison with the Gini coefficient, the entropy measure is simpler to compute and it is easily decomposable. Using the case study of air arrivals and departures to and from the Balearic Islands, results show the appropriateness of entropy and relative redundancy as seasonal indicators but also as a new information tools for tourism seasonality analysis.     

Navigating through the complex transport system: A heuristic approach for city tourism recommendation

The fast development of machine learning and artificial intelligence has led to a great improvement of the smart tourism recommendation system, however many problems associated with the choice of transport modes in city tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized day itineraries with consideration of transport mode choice. With improved particle swarm optimization and differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was carried out in Chengdu, China to examine the performance of our approach. The results show that compared with extant methods, our approach achieves better performance. In addition, our approach can create more sensible, multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app providers could integrate our proposed model into their existing smart service systems, as part of their e-business or digital strategy for enhancing tourist experience.     

Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.     

The impacts of governance and institutions on inbound tourism demand: evidence from a dynamic panel data study

This study explores the impact of governance and institutions on inbound tourism demand in Malaysia using a dynamic panel data approach for 45 tourism source countries over the period 2005–2015. The results show that institutions play a very important role in explaining the behaviour of inbound tourism demand. To obtain a better picture, we investigate the response of international tourists to disaggregated institutional quality. We find that international tourists are more concerned about political stability, governmental effectiveness, regulations, laws, and corruption than voice and accountability. Therefore, policymakers should focus on ways to improve institutional quality to significantly increase international tourist arrivals.     

Productivity growth and income in the tourism sector: Role of tourism demand and human capital investment

This paper proposes a model for the demand for tourism in the context of a developing country. The parameters of the model are a tourist sector characterised by monopolistic competition, where human capital is the main factor of production and hotels have market power. Additionally land use is marked by demand from both agricultural and tourism sectors. From the household side, a simplified OLG approach is developed to consider consumption, human activity and the number of children. A dynamic framework is therefore identified to investigate the long-run consequences of increasing labor productivity and lowering the fertility rate. If the supply-side policy leads to economic growth, the tourism led growth hypothesis is theoretically confirmed. It is concluded that an increase in labor productivity generates positive growth effects only if the demand for tourism is elastic, otherwise negative results arise.     

Short-run and long-run performance of international tourism: Evidence from Bayesian dynamic models

Measuring the technical efficiency of the tourism industry is essential for evaluating tourism sustainability and reshaping tourism activities. This paper introduces for the first time a new dynamic stochastic frontier model to 1-measure and compare the short-run and long-run technical efficiencies of leading tourism destinations, and 2-provide impulse response functions and persistence measures to trace out the dynamic effect of shocks in technical inefficiency. We develop our model in a Bayesian framework using carefully constructed Markov Chain Monte Carlo (MCMC) techniques. We report efficiency results and persistence scores for individual destinations and discuss how different destinations recover from shocks in tourism performance.     

The effects of the military in politics on the inbound tourism: evidence from Turkey

In this paper, we analyze the effects of the military in politics on the number of tourist inflows from 71 countries to Turkey for the period from 1984 to 2014. We use the fixed-effects and the random-effects as well as the dynamic generalized methods of moments estimations. We find that a lower level of the relative military in politics (the difference between the source country and Turkey) positively affects the tourism inflows to Turkey. Specifically, one standard deviation reduction in the index of the relative military intervention in politics in Turkey leads to almost 7% increase in the tourism inflows.     

Seasonal concentration of tourism demand: Decomposition analysis and marketing implications

This paper analyzes seasonality in the United Kingdom, specifically the English regions in relation to tourists' place of origin and main travel motivation. The method used is a decomposition of the Gini index, which provides relative marginal effects that facilitate the identification of market segments open to counter-seasonal marketing efforts. This method has been combined with a graphical multivariate technique (biplot), which groups segments according to their seasonality characteristics. Seasonal patterns associated with particular segments differ significantly when studied on a disaggregated basis. Therefore, an adequate level of disaggregation is essential in the design of counter-seasonal strategies. Although this study focuses on British destinations, this methodology could be used as a control and monitoring measure in the regional analysis of any destination, facilitating regular adjustment of regional tourism marketing campaigns to minimize seasonality effects, specifically by targeting the types of tourists less prone to seasonality.     

Standardization of tourism education and training to address the increasing demand for tourism staff in the East Asia/pacific region

Major travel and tourism organizations, including the World Tourism Organization (WTO) and the World Travel and Tourism Council (WTTC), agree that tourism will continue to grow significantly throughout the next two decades, with the most prolific inbound and outbound growth occurring in that part of the world (Northeast and Southeast Asia) from which most East Asian Tourism Forum (EATOF) members are drawn. This paper argues that the limiting factor to successful growth is the availability of sufficient skilled staff, with appropriate technical and interpersonal skills, to enable tourism expansion to proceed without loss of service standards. Properly devised and adequately resourced education and training programmes are essential to provide such skilled staff. This paper will elaborate on some of the tourism growth statistics and related increases in staffing needs, and raise the idea of creating regional standards of education and training. Such regional standards could be used fundamentally to help destinations to ensure the quality of tourism staff. In addition, such regional schemes could be used for marketing purposes, to assure international visitors and travel organizers of the high standards available in those destinations.     

Using a network data envelopment analysis model to assess the efficiency and effectiveness of cultural tourism promotion in Taiwan

The importance of tourism promotion has been discussed throughout past studies, and the consequences of ineffective promotion have also been investigated. While tourism promotion is a topic of interest to many researchers, performance measurement used to assess the effectiveness of tourism promotion is a topic seldom discussed. The purpose of this study is to develop an approach based on the data envelopment analysis (DEA) model to evaluate performance for tourism promotion. The main difference compared with previous DEA models is the evaluation of multiple efficiencies in a single DEA implementation, and the assessment of overall efficiency in a ratio index. In the assumption of variables, we use cultural tourism promotion as a foundation in formulating the empirical evaluation, and we test the proposed model by using data across 20 regions in Taiwan.