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.
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
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
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.
Source: RePEc