Big data or small data? a methodological review of sustainable tourism

Xu, Feifei, Nash, Nicholas ORCID: https://orcid.org/0000-0003-4421-6041 and Whitmarsh, Lorraine ORCID: https://orcid.org/0000-0002-9054-1040 2020. Big data or small data? a methodological review of sustainable tourism. Journal of Sustainable Tourism 28 (2) , pp. 144-163. 10.1080/09669582.2019.1631318

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Abstract

Research in the field of sustainable tourism is increasingly important due to significant growth in tourism industries and the unsustainable impacts incurred. Innovation in sustainable tourism studies is required to meet a number of challenges including socio-ecological impacts; the critical turn in tourism research; and the growth of ICTs, mobile technologies and big data analytics. These shifts in particular are transforming the field and creating new research opportunities. This article seeks to identify potentially new methodological areas of application to sustainable tourism studies for both quantitative and qualitative methods. A range of methods are reviewed, focusing on big data (e.g. mobile device signaling, GPS, social media and search engine data) that elucidates wider patterns of tourist movement, as applied to forecasting travel demands and sustainable management of a destination. Three novel “small data” methods are also discussed, comprising visual methods, autoethnography and qualitative GIS, that provide deeper, contextual insights into the drivers, dynamics and impacts of sustainable tourism. We consider how expansive qualitative methodologies might yield potentially important insights concealed by existing methodologies. Furthermore, we argue that combined big data and small data approaches can address methodological imbalance and generate mutually reinforcing insights at a number of levels.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	Taylor & Francis (Routledge)
ISSN:	0966-9582
Date of First Compliant Deposit:	12 August 2019
Date of Acceptance:	10 June 2019
Last Modified:	23 Nov 2024 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/124876

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