Big data: A new challenge for tourism

Chareyron, Gaël; Da-Rugna, Jérôme; Raimbault, Thomas

doi:10.1109/bigdata.2014.7004475

Cited by 30 publications

(16 citation statements)

References 4 publications

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“…BD analytics are data processing techniques used to obtain useful information from the data (Mariani et al, 2018) through users' activities while online (e.g. browsing and purchasing) and relevant Big data empowered agility interactions (feedback, perceptions and sentiments about goods and services) (Alaei et al, 2019;Chareyron et al, 2014). BD are becoming instrumental in their contribution to decision-making, when it is used for mining information for business' decisions (Mariani et al, 2018;Moro et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector

Stylos

Zwiegelaar

Buhalis

2021

IJCHM

136

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Purpose Dynamic, volatile, and time-sensitive industries, such as tourism, travel and hospitality require agility and market intelligence to create value and achieve competitive advantage. The aim of the current study is to examine the influence of big data (BD) on the performance of service organizations and to probe for a deeper understanding of implementing BD, based on available technologies. Design/methodology/approach An ethnographic study was conducted following an abductive approach. A primary qualitative research scheme was used with 35 information technology and database professionals participating in five online focus groups of seven participants each. Analytical themes were developed simultaneously with the literature being revisited throughout the study to ultimately create sets of common themes and dimensions. Findings BD can help organizations build agility, especially within dynamic industries, to better predict customer behavioral patterns and make tailor-made propositions from the BD. An integrated BD-specific framework is proposed to address value according to the dimensions of need, value, time and utility. Research limitations/implications Little research exists on the key drivers of BD use for dynamic, real-time and agile businesses. This research adds to the developing literature on BD applications to support organizational decision-making and business performance in the tourism industry. Originality/value This study responds to scholars’ recent calls for more empirical research with contextual understanding of the use of BD to add value in marketing intelligence within business ecosystems. It delineates factors contributing to BD value creation and explores the impacts on the respective service encounters.

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Section: Literature Reviewmentioning

confidence: 99%

Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector

Stylos

Zwiegelaar

Buhalis

2021

IJCHM

136

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“…The subjective perceptions of tourists about the destinations are expressed through social platforms, which has certain influences on the choice of potential tourists, directly affecting the decision-making process of tourism [32]. Thus, it changes the cognition of the original objective spatial structure of tourists, and results in the formation of a new subjective one [22,33,34]. With the constant changes in people's travel behaviors, traditional tourism space can no longer meet the needs of tourists.…”

Section: Research Ideasmentioning

confidence: 99%

From Subjective and Objective Perspective to Reconstruct the High-Quality Tourism Spatial Structure―Taking Gannan Prefecture in China as an Example

Ma¹,

Li²,

Bo³

et al. 2020

Sustainability

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Spatial relationship is the basic perspective of understanding regions. Tourism spatial structure is the spatial projection of tourism activities, reflecting the spatial attributes and interrelationships of tourism activities. In this paper, taking Gannan Tibetan Autonomous Prefecture as an example, we identified the objective and subjective tourism spatial structure of Gannan Prefecture based on the GIS spatial analysis function and using objective and subjective tourist attractions as the spatial object element. Then, the tourism spatial network was reconstructed. Results are as follows. (1) Both objective and subjective tourist attractions in Gannan Prefecture exhibit aggregated distribution. Among them, the spatial distribution of objective tourist attractions has a significant trend of contiguous aggregation, showing a relatively higher density in the northeastern and southeastern regions, and a lower density in the central and southwestern regions. This is opposite to that of the subjective tourist attractions. (2) The connectivity and accessibility between objective and subjective tourist attractions in Gannan Prefecture are poor, and only a few tourist attractions form a traffic connection with neighboring ones. (3) The objective tourism spatial network of Gannan Prefecture is layered with aggregation, and presents a significant cohesive development trend. This is opposite to the subjective one. (4) Based on the identification results of objective and subjective tourism spatial structures, the objective and subjective core tourism resources as well as tourist attractions should be integrated, and the road transportation system should be constructed and improved. Then, a high-quality tourism spatial network with ‘three poles, three axes and four groups’ was constructed. This study provides a scientific basis for the tourism spatial development, tourist route organization, the layout of tourism service facilities and product, and tourism spatial optimization in specific regions.

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“…Chareyron, Da-Rugna, and Raimbault [1] propose to use geographic visualizations to detect atypical tourist behaviors, using as a source geopositioning information from social network sites (Flickr, Instagram, and Panoramio). The authors also propose the use of node-link graphs to characterize and figure out review sites from users' communities, such as Tripadvisor.…”

Section: Contextualizationmentioning

confidence: 99%

Visualization Design Dimensions for Data Science in Tourism and Transport

Sánchez

Hassan-Montero

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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A taxonomic proposal based on dimensions and items that reflects the preferences in Data Science visualizations for domain users in tourism and road transport is presented. The needs transmitted by domain users are grouped into dimensions esthetics, ecosystem dominance, and interaction. For each item, the bibliographic review necessary to align user and ICT's (Information and Communications Technology) concerns is carried out. In this proposal, the main conclusion focuses on the need for the visualization environments in Data Sciences to be designed and integrated for each domain, allowing users to interact transparently by guiding their tasks.

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Big data: A new challenge for tourism

Cited by 30 publications

References 4 publications

Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector

Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector

From Subjective and Objective Perspective to Reconstruct the High-Quality Tourism Spatial Structure―Taking Gannan Prefecture in China as an Example

Visualization Design Dimensions for Data Science in Tourism and Transport

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