Destination eWOM: A macro and meso network approach?

Authors: Williams, N.L., Inversini, A., Ferdinand, N. and Buhalis, D.

Journal: Annals of Tourism Research

Volume: 64

Pages: 87-101

ISSN: 0160-7383

DOI: 10.1016/j.annals.2017.02.007

Abstract:

The purpose of this paper is to develop a framework that describes the characteristics and the underlying drivers of publically shared electronic word-of-mouth (eWOM) for destinations. Tweets about a destination were collected while the destination hosted a hallmark event over a 5-year period (2011–2015). In each year, interactions on Twitter were analysed using macro and meso-level social network analysis to identify the network structure and hubs of eWOM activity. A K means clustering algorithm was then applied to create clusters of nodes with similar characteristics and eWOM content within each cluster was analysed using automated content analysis. The resulting model indicates that destination and event eWOM maintains a macro network structure in which a small number of accounts or hubs influence information sharing. Hub characteristics evolve over time, whereas eWOM content can fluctuate in response to emergent destination activities.

http://eprints.bournemouth.ac.uk/27848/

Source: Scopus

Destination eWOM: A macro and meso network approach?

Authors: Williams, N.L., Inversini, A., Ferdinand, N. and Buhalis, D.

Journal: ANNALS OF TOURISM RESEARCH

Volume: 64

Pages: 87-101

eISSN: 1873-7722

ISSN: 0160-7383

DOI: 10.1016/j.annals.2017.02.007

http://eprints.bournemouth.ac.uk/27848/

Source: Web of Science (Lite)

Destination EWOM: A Macro and Meso approach

Authors: Williams, N., Inversini, A., Ferdinand, N. and buhalis, D.

Journal: Annals of Tourism Research

ISSN: 1873-7722

http://eprints.bournemouth.ac.uk/27848/

Source: Manual

Destination eWOM: A macro and meso network approach?

Authors: Williams, N.L., Inversini, A., Ferdinand, N. and Buhalis, D.

Volume: 64

Issue: C

Pages: 87-101

Abstract:

The purpose of this paper is to develop a framework that describes the characteristics and the underlying drivers of publically shared electronic word-of-mouth (eWOM) for destinations. Tweets about a destination were collected while the destination hosted a hallmark event over a 5-year period (2011–2015). In each year, interactions on Twitter were analysed using macro and meso-level social network analysis to identify the network structure and hubs of eWOM activity. A K means clustering algorithm was then applied to create clusters of nodes with similar characteristics and eWOM content within each cluster was analysed using automated content analysis. The resulting model indicates that destination and event eWOM maintains a macro network structure in which a small number of accounts or hubs influence information sharing. Hub characteristics evolve over time, whereas eWOM content can fluctuate in response to emergent destination activities.

http://eprints.bournemouth.ac.uk/27848/

Source: RePEc