Network science: A Review Focused on Tourism

By Rodolfo Baggio, N. Scott, Chris Cooper. 2010

367 citations recorded by the source corpus.

This paper presents a review of the methods of the science of networks with an application to the field of tourism studies. The basic definitions and computational techniques are described and a case study (Elba, Italy) used to illustrate the effect of network typology on information diffusion. A static structural characterization of the network formed by destination stakeholders is derived from stakeholder interviews and website link analysis. This is followed by a dynamic analysis of the information diffusion process within the case destination demonstrating that stakeholder cohesion and adaptive capacity have a positive effect on information diffusion. The outcomes and the implications of this analysis for improving destination management are discussed. © 2010 Elsevier Ltd.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3b608293b23b34b3aa3f4ef64b8f8d85a9b91568

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In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, background): “…network for the city of Beijing for tourist route prediction and bus route design – the opportunities offered by the complex networks approach (see Baggio, Scott, & Cooper, 2010 for a review of network methods in the field of tourism) for the study of tourism flows have thus far mostly been…”
Improving tourism destination governance: a complexity science approach (2010, methodology): “In the case of Elba, Q has been measured in this way and compared with the value obtained after having used a stochastic algorithm (Baggio et al., 2010; da Fontoura Costa and Baggio, 2009).”
Tourism networks unravelled; a review of the literature on networks in tourism management studies (2015, background): “In other studies on network configuration, sparse and weak network structures were found to negatively influence collaboration and information sharing (Baggio et al., 2010; Baggio, 2011), although the presence of a network connection that bridges structural holes might link local tourism networks to”
The structural changes of a local tourism network: comparison of before and after COVID-19 (2021, background): “They have subsequently been used to determine the collaboration and partnership of stakeholders regarding tourism destinations, supply chains, and policies (Baggio, 2011; Baggio et al., 2010), and extended research on sustainable tourism (Albrecht, 2013; Timur & Getz, 2008; Wang & Xiang, 2007).”