Knowledge transfer in a tourism destination: the effects of a network structure

TL;DR

This paper explores how network analysis and epidemic diffusion models can be used to understand and optimize knowledge sharing among tourism destination stakeholders to foster innovation and competitiveness.

Contribution

It introduces a novel approach combining epidemic diffusion models with network analysis to improve knowledge transfer in tourism destinations.

Findings

Network analysis can identify key nodes for knowledge dissemination.

Epidemic models help simulate and optimize knowledge transfer processes.

Practical application demonstrated with the Elba destination case.

Abstract

Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.