Predicting Tourist Demand Using Big Data

Song, Haiyan; Liu, Han

doi:10.1007/978-3-319-44263-1_2

Cited by 88 publications

(58 citation statements)

References 52 publications

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“…The use of big data in tourism forecasting has only recently gained academic attention. For example, Pantano et al () investigate the extent to which open data analyses can predict tourists' responses to a certain destination and Song and Liu () propose a framework for tourism forecasting with big data.…”

Section: Discussion and Collaborative Tsc Forecasting Frameworkmentioning

confidence: 99%

“…Although there is no unified definition, big data are often used to describe massive, complex, and real‐time data that require special management, analytical, and processing techniques to extract value from the information (Beyer & Laney, ). There exists a widespread belief that big data can help improve tourism demand forecasts provided that tourism forecasters can analyse and discover hidden patterns (Blal & Sturman, ; Song & Liu, ). Choi and Varian () use search engine data (i.e., Google Trends) to forecast visitor arrivals in Hong Kong.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such information affects other travellers' decision‐making processes, such as their selection of a tourist destination, accommodations, and attractions to visit (Filieri, Alguezaui, & McLeay, ; Hudson, ; Pantano & Di Pietro, ; Pantano et al, ; Xiang, Magnini, & Fesenmaier, ). Recognizing the benefits and challenges of big data analytics to tourism demand forecasters, Song and Liu () propose a tourism demand forecasting framework using big data. They suggest that the potential growth in application of big data in the tourism and hospitality industry is huge.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2017) investigate the extent to which open data analyses can predict tourists' responses to a certain destination and Song and Liu (2017) propose a framework for tourism forecasting with big data.…”

Section: Forecasting With Big Datamentioning

confidence: 99%

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An integrative framework for collaborative forecasting in tourism supply chains

Zhang

Song

2017

Journal of Tourism Research

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Tourism practitioners must often rely on each other in a tourism supply chain (TSC). Demand forecasting plays a key role in shaping the activities of TSC practitioners. In the past 4 decades, researchers have developed many techniques for advanced tourism demand forecasting, but practitioners have had little interest in them. To bridge this gap, we examine the nature of the forecasting tasks of TSC practitioners in Hong Kong and propose a collaborative TSC forecasting framework that not only integrates tourism demand forecasting methods with practitioners' knowledge, but also facilitates information sharing between TSC practitioners to increase industry collaboration and improve forecasting performance.

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Section: Discussion and Collaborative Tsc Forecasting Frameworkmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Forecasting With Big Datamentioning

confidence: 99%

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An integrative framework for collaborative forecasting in tourism supply chains

Zhang

Song

2017

Journal of Tourism Research

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“…According to Martínez et al (2016) the rapid growth of information generated by tourists is part of the Big Data revolution. Song and Liu (2017) refer that analytics are needed in order to make sense of the information within the large amount of data stored.…”

Section: Literature Reviewmentioning

confidence: 99%

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

Dinis

Costa

Pacheco

2017

TMS

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Over the last years, we observed an exponential growth in the number of tourism consumers that use the Internet as a source of information during a destination selection process. Google Trends is a tool that displays data, almost in real time, on the interest of people in a particular topic based on search trends. This paper demonstrates that Google Trends is a tool that can provide useful and relevant information about the interests of individuals in relation to domestic tourism destinations at national and regional levels. Our findings indicate that overnights spent in hotel establishments by the residents in Portugal are strongly correlated with the Google index, mainly in mainland Portugal, Alentejo and Algarve regions, and that the results improve when more municipalities names and the national or the regional tourism brands are included as search terms.Keywords: Google Trends, domestic tourism, search data, Portugal, big data. ResumoAo longo dos últimos anos, tem-se verificado um crescimento exponencial no número de consumidores de turismo que utiliza a Internet como fonte de informação no processo de seleção do destino turístico. O Google Trends é uma ferramenta que disponibilizada dados, quase em tempo real, sobre o interesse das pessoas num determinado tema com base nas tendências de pesquisa. Este artigo demonstra que o Google Trends é uma ferramenta que disponibiliza informação útil e relevante sobre os interesses dos indivíduos em relação aos destinos de turismo doméstico ao nível nacional e regional. Os resultados indicam que as dormidas dos residentes em Portugal nos estabelecimentos hoteleiros estão fortemente correlacionadas com o índice do Google, principalmente em Portugal continental, na região do Alentejo e do Algarve, e que os resultados melhoram quando são incluídos como termos de pesquisa mais nomes de municípios e a marca de turismo nacional ou das regiões de turismo. Palavras-chave:Google Trends, turismo doméstico, dados de pesquisa, Portugal, big data.

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