Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mingming Hu ◽  
Mengqing Xiao ◽  
Hengyun Li
Keyword(s):  
Mobile Devices ◽  
Demand Forecasting ◽  
Tourism Industry ◽  
Strategic Decision ◽  
Single Type ◽  
Tourism Demand ◽  
Content Type ◽  
Search Queries ◽  
Search Volume ◽  
Aggregated Search

Purpose While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting. Design/methodology/approach Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting. Findings Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal. Practical implications Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management. Originality/value This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

scholarly journals Forecasting tomorrow’s tourist

2016 ◽  
Vol 8 (6) ◽  
pp. 643-653 ◽  
Author(s):  
Sérgio Moro ◽  
Paulo Rita
Keyword(s):  
Literature Review ◽  
Design Methodology ◽  
Scientific Literature ◽  
Recent Literature ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
Content Type ◽  
Current Trends ◽  
Modeling Techniques ◽  
Excellent Source

Purpose This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.

scholarly journals Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market

2020 ◽  
Vol 12 (8) ◽  
pp. 3243
Author(s):  
Giovanni De Luca ◽  
Monica Rosciano
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Campania Region ◽  
Travel And Tourism ◽  
Tourist Destinations ◽  
High Quantiles ◽  
International Tourist ◽  
Long Time ◽  
Hotel Accommodation

Travel and tourism is an important economic activity in most countries around the world. In 2018, international tourist arrivals grew 5% to reach the 1.4 billion mark and at the same time export earnings generated by tourism have grown to USD 1.7 trillion. The rapid growth of the tourism industry has globally attracted the interest of researchers for a long time. The literature has tried to model tourism demand to analyze the effects of different factors and predict the future behavior of the demand. Forecasting of tourism demand is crucial not only for academia but for tourism industries too, especially in line with the principles of sustainable tourism. The hospitality branch is an important part of the tourism industry and accurate passenger flow forecasting is a key link in the governance of the resources of a destination or in revenue management systems. In this context, the paper studies the interdependence of tourism demand in one of the main Italian tourist destinations, the Campania region, using a quantile-on-quantile approach between overall and specific tourism demand. Data are represented by monthly arrivals and nights spent by residents and non-residents in hotels and complementary accommodations from January 2008 to December 2018. The results of the analysis show that the hotel-accommodation component of the tourism demand appears to be more vulnerable than extra-hotel accommodation component to the fluctuations of the overall tourism demand and this feature is more evident for the arrivals than for nights spent. Moreover, the dependence on high quantiles suggests strategy of diversification or market segmentation to avoid overtourism phenomena and/or carrying capacity problems. Conversely, dependence on low quantiles suggests the use of push strategies to stimulate tourism demand. Finally, the results suggest that it could be very useful if the stakeholders of the tourism sector in Campania focused their attention on the collaboration theory.

Artificial Intelligence Applications in Tourism

2011 ◽  
pp. 241-247 ◽  
Author(s):  
Carey Goh ◽  
Henry M.K. Mok ◽  
Rob Law
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Investment Planning ◽  
Data Mining Techniques ◽  
Travel And Tourism ◽  
The Future ◽  
Multiplier Effects

The tourism industry has become one of the fastest growing industries in the world, with international tourism flows in year 2006 more than doubled since 1980. In terms of direct economic benefits, United Nations World Tourism Organization (UNWTO, 2007) estimated that the industry has generated US $735 billion through tourism in the year of 2006. Through multiplier effects, World Travel and Tourism Council (WTTC, 2007) estimated that tourism will generate economic activities worth of approximately US $5,390 billion in year 2007 (10.4% of world GDP). Owing to the important economic contribution by the tourism industry, researchers, policy makers, planners, and industrial practitioners have been trying to analyze and forecast tourism demand. The perishable nature of tourism products and services, the information-intensive nature of the tourism industry, and the long lead-time investment planning of equipment and infrastructures all render accurate forecasting of tourism demand necessary (Law, Mok, & Goh, 2007). Past studies have predominantly applied the well-developed econometric techniques to measure and predict the future market performance in terms of the number of tourist arrivals in a specific destination. In this chapter, we aim to present an overview of studies that have adopted artificial intelligence (AI) data-mining techniques in studying tourism demand forecasting. Our objective is to review and trace the evolution of such techniques employed in tourism demand studies since 1999, and based on our observations from the review, a discussion on the future direction of tourism research techniques and methods is then provided. Although the adoption of data mining techniques in tourism demand forecasting is still at its infancy stage, from the review, we identify certain research gaps, draw certain key observations, and discuss possible future research directions.

Shifting tourism flows in a changing climate: policy implications for the Caribbean

2014 ◽  
Vol 6 (2) ◽  
pp. 118-126 ◽  
Author(s):  
Kwame Emmanuel
Keyword(s):  
Climate Change ◽  
Rapid Assessment ◽  
Tourism Industry ◽  
Policy Implications ◽  
Tourism Demand ◽  
Content Type ◽  
Changing Climate ◽  
The Future ◽  
The Caribbean ◽  
Tourism Flows

Purpose – Population growth, climate change, shortages of oil and other resources will have dramatic implication on where, when and how tourists travel in the future. This will also reshape the tourism industry for the future. Knowing what will happen in the future has always fascinated mankind from time immemorial. However, forecasting and predictions require not only a systematic approach to development but also an imagination and the ability to think and see beyond the ordinary. As a result, the purpose of this paper is to underscore the projected northward shift in tourism demand due to the global impacts of climate change and the lack of policy attention. Design/methodology/approach – A rapid assessment of the literature was conducted to explore tourism flows to the Caribbean in a changing climate and recommendations for adaptation. Findings – Tourism demand from major markets such as Europe and North America may be reduced significantly as tourists travel to other destinations, which are closer to home and have a more favourable climate. Regulation of carbon emissions from long haul flights will also influence demand substitution. Despite this projection, current policies in the Caribbean promote further development of the climate sensitive 3S model without anticipating a possible decrease in demand in the future. Research limitations/implications – Research implications include a recalibration of tourism policy and diversification of Caribbean tourism and economies. Originality/value – Recommendations are outlined for a critical issue that is not on the policy agenda.

scholarly journals Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 454 ◽  
Author(s):  
Diyana Izyan Amir Hamzah ◽  
Maria Elena Nor ◽  
Sabariah Saharan ◽  
Noor Fariza Mohd Hamdan ◽  
Nurul Asmaa Izzati Nohamad
Keyword(s):  
Demand Forecasting ◽  
Information Criterion ◽  
Research Paper ◽  
Tourism Industry ◽  
Percentage Error ◽  
Tourism Demand ◽  
Absolute Deviation ◽  
Correct Judgment ◽  
Accurate Forecast ◽  
Future Strategy

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.

scholarly journals Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

2020 ◽  
Vol 11 (21) ◽  
pp. 55-70
Author(s):  
Murat Cuhadar
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Percentage Error ◽  
Accurate Estimation ◽  
Tourism Demand ◽  
Tourist Flows ◽  
Ann Models ◽  
Optimal Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

scholarly journals A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

2019 ◽  
Vol 5 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Iman Ghalehkhondabi ◽  
Ehsan Ardjmand ◽  
William A. Young ◽  
Gary R. Weckman
Keyword(s):  
Design Methodology ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
Transportation Industry ◽  
Content Type ◽  
Forecasting Models ◽  
Passenger Transportation ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Combined Forecasting

Purpose The purpose of this paper is to review the current literature in the field of tourism demand forecasting. Design/methodology/approach Published papers in the high quality journals are studied and categorized based their used forecasting method. Findings There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods. Originality/value This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

Forecasting annual foreign tourist arrivals to China by incorporating firefly algorithm into fractional non-homogenous discrete Grey model

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaozhong Tang ◽  
Naiming Xie ◽  
Aqin Hu
Keyword(s):  
Firefly Algorithm ◽  
Demand Forecasting ◽  
Development Trend ◽  
Grey Model ◽  
Tourism Demand ◽  
Content Type ◽  
Foreign Tourist ◽  
Model Combining ◽  
Proposed Model ◽  
Inbound Tourism

Purpose Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism.

scholarly journals Tourism demand nowcasting using a LASSO-MIDAS model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Han Liu ◽  
Ying Liu ◽  
Gang Li ◽  
Long Wen
Keyword(s):  
Search Engine ◽  
Design Methodology ◽  
Statistical Data ◽  
Demand Forecasting ◽  
High Dimensional ◽  
Tourism Demand ◽  
Frequency Data ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Mixed Frequency Data

Purpose This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available. Design/methodology/approach This study is the first attempt to use the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data. Findings The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are used. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that such penalty-based MIDAS model is a useful option when using high-dimensional mixed-frequency data. Originality/value This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. This study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.

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