{"id":4282,"date":"2017-05-17T11:27:16","date_gmt":"2017-05-17T11:27:16","guid":{"rendered":"https:\/\/microsites.bournemouth.ac.uk\/tourism\/?p=4282"},"modified":"2017-05-13T11:29:04","modified_gmt":"2017-05-13T11:29:04","slug":"new-article-on-tourism-destination-word-of-mouth","status":"publish","type":"post","link":"https:\/\/microsites.bournemouth.ac.uk\/tourism\/2017\/05\/17\/new-article-on-tourism-destination-word-of-mouth\/","title":{"rendered":"New Article on Tourism Destination Word of Mouth"},"content":{"rendered":"

New Article on Tourism Destination Word of Mouth \u00a0:\u00a0<\/span>Williams, N., Inversini; A., Buhalis, D., Ferdinand, N., 2017 Destination eWOM drivers and characteristics, Annals of Tourism Research<\/a>\u00a064 (2017) 87\u2013101 \u00a0<\/span>DOWNLOAD FOR FREE\u00a0<\/span>http:\/\/doi.org\/10.1016\/j.annals.2017.02.007<\/a><\/span><\/p>\n

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Destination eWOM: A macro and meso netw<\/a>ork approach?<\/h1>\n

Nigel L. Williams<\/a>a<\/sup><\/a>,\u00a0<\/sup><\/a>,\u00a0<\/sup>,\u00a0Alessandro Inversini<\/a>b<\/sup><\/a>,\u00a0Nicole Ferdinand<\/a>c<\/sup><\/a>,\u00a0Dimitrios Buhalis<\/a>d<\/sup><\/a><\/p>\n

\u00a0https:\/\/doi.org\/10.1016\/j.annals.2017.02.007<\/a><\/div>\n
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\u00a0Highlights<\/div>\n<\/div>\n
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\u2022Destination eWOM network structure remains unchanged despite scale significant growth.<\/dt>\n
\u2022Meso (community) level hubs act as global information senders and receivers.<\/dt>\n
\u2022Hub roles evolve over time.<\/dt>\n
\u2022Discourse within destination eWOM networks can fluctuate from year to year.<\/dt>\n<\/dl>\n<\/div>\n
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Abstract<\/h2>\n
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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\u20132015). 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.<\/p>\n<\/div>\n<\/div>\n

Keywords<\/h2>\n