Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

Abstract

Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions’ statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson’s correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node’s characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.