Author:
Micera Roberto,Crispino Raffaele
Abstract
Purpose
Grounded in destination web reputation, the purpose of this paper is to propose a methodological framework for the analysis of the destination image-building process, where the sentiment analysis (SA) is integrated with social network analysis (SNA) tools and social media analytics (SMA), making them accessible and functional for destination management choices. The outlined framework was successively tested through an empirical analysis carried out on the image and web reputation of Naples as a tourism destination.
Design/methodology/approach
Critical analysis of literature on web reputation has allowed the building of the conceptual framework applied to analyse the online reputation of the case study of the city destination of Naples. The empirical research was based on the use of a platform to listen to and analyse social media conversations that allowed one to collect the data on both the destination image and the online reputation of the city.
Findings
The paper conceptualises the theme of destination web reputation, proposing an online discourses classification, presenting and ranking destination dimensions, which might influence prospective tourist decision-making processes. Furthermore, this study can also be apt for all destination managers who need to monitor their destination online reputation and brand, and better manage their online presence.
Originality/value
From a theoretical perspective, through the integration of destination management principles with SA, SNA and SMA, this paper provides an original point of view for the analysis. The web reputation analysis of a destination is one of the instruments which – if systematically used – allows us to develop an effective policy to promote a destination in a tourist-centric way, in which the local offer system will be increasingly co-created and thus tailored to customers.
Subject
Tourism, Leisure and Hospitality Management,Geography, Planning and Development,Management, Monitoring, Policy and Law
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