Managing customer knowledge through the use of big data analytics in tourism research

Centobelli, Piera; Ndou, Valentina

doi:10.1080/13683500.2018.1564739

Cited by 85 publications

(68 citation statements)

References 124 publications

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“…In a similar review of literature from the Web of Science, ScienceDirect, SAGE Journals Online, Emerald Insight, Wiley Online Library and Springer published between 2007 and 2016, the authors examined 144 journal papers and 21 conference papers [41]. In a more recent literature review of work in Scopus published between 1990 and 2017, the authors selected 109 papers for descriptive as well as content analyses [40].…”

Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

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Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.

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Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

“…gave rise to the link between their analysis and that of big data [34][35][36]. Therefore, much research has been devoted to social media analytics in general [37][38][39] and tourism analytics in particular [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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“…It was assessed using four items based on a seven-point Likert scale, with responses ranging from "completely disagree" (−3) to "completely agree" (+3) (Gregor et al, 2006;Ji-fan Ren et al, 2016). The second, strategic value, refers to the degree of perceived benefits for the organization at the strategic level, as enabling quicker response to change or improving customer relations (Centobelli and Ndou, 2019). It was assessed using three items based on a seven-point Likert scale, with responses ranging from "completely disagree" (−3) to "completely agree" (+3) (Gregor et al, 2006;Ji-fan Ren et al, 2016).…”

Section: Independent Variablementioning

confidence: 99%

Big data analytics business value and firm performance: linking with environmental context

Vitari

Raguseo

2019

International Journal of Production Research

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…In the tourism industry, the widespread availability of big data opportunities has changed the traditional research approach substantially. The enormous amount of available data has made the information extraction phase more complex, such that it now requires advanced analytics techniques to perform [18,19].…”

Section: Introductionmentioning

confidence: 99%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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Technological advances have led to numerous developments in data sources. Geo-tagged photo metadata has provided a new source of mass research data for tourism studies. A series of data processing methods centering on the various types of information contained in geo-tagged photo metadata have thus been proposed; as a result, the development of tourism studies based on such data has advanced. However, an in-depth study of the data processing methods designed to conduct tourist flow prediction based on geo-tagged photo metadata has not yet been conducted. In order to acquire accurate substitutive data regarding inbound flows in cities, this paper introduces and designs several methods, including data screening, text data similarity calculation, geographical location clustering, and time series data modelling, in order to realize a data preprocessing model for inbound tourist flows in cities based on geo-tagged photo metadata. Wherein, the entropy filtering method was introduced to aid in determining whether the data were posted by inbound tourists; whether the inbound persons’ activities were related to tourism was judged through the calculation of tag text similarity; an efficient clustering method based on geographic grid partition was designed for cases in which the tag values were empty; finally, the time series of the inbound tourist flows of a certain region and period were obtained through data statistics and normalization. For the empirical research, Beijing City in China was selected as the research case, after which the feasibility and accuracy of the methods proposed in this paper were verified through data correlation analysis between Flickr data and real statistical yearbook data, as well as analysis of the prediction results based on a machine learning algorithm. The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction. These methods can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.

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Managing customer knowledge through the use of big data analytics in tourism research

Cited by 85 publications

References 124 publications

Destination Image Analytics Through Traveller-Generated Content

Destination Image Analytics Through Traveller-Generated Content

Big data analytics business value and firm performance: linking with environmental context

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

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