Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China

Yuzhen Li, Guofang Gong, Fengtai Zhang, Lei Gao, Yuedong Xiao, Xingyu Yang and Pengzhen Yu
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Yuzhen Li: School of Management, Chongqing University of Technology, Chongqing 400054, China
Guofang Gong: School of Management, Chongqing University of Technology, Chongqing 400054, China
Fengtai Zhang: School of Management, Chongqing University of Technology, Chongqing 400054, China
Lei Gao: CSIRO, Waite Campus, Urrbrae, Mitcham, SA 5064, Australia
Yuedong Xiao: School of Management, Chongqing University of Technology, Chongqing 400054, China
Xingyu Yang: School of Management, Chongqing University of Technology, Chongqing 400054, China
Pengzhen Yu: School of Management, Chongqing University of Technology, Chongqing 400054, China

Sustainability, 2022, vol. 14, issue 15, 1-23

Abstract: Exploring the spatial network structure of tourism flow and its influencing factors is of great significance to the transmission of characteristic culture and the sustainable development of tourism in tourist destinations, especially in backward rural areas. Taking Qiandongnan Miao and Dong Autonomous Prefecture (hereinafter referred to as Qiandongnan Prefecture) as an example, this paper adopts social network analysis and Quadratic Assignment Procedure regression analysis to study the network structural characteristics and influencing factors of tourism flow using online travel blog data. The results show that: (1) There are seasonal changes in tourism flow, but the attractions that tourists pay attention to do not change with the seasons. (2) The tightness of the tourism flow network structure is poor. The core nodes are unevenly distributed, and there are obvious structural holes. (3) The density of the tourism flow network is low. There is a clear core–periphery structure in the network, and the core area has a weak driving effect on the periphery area. There are more cohesive subgroups in the network, but the degree of connectedness between the subgroups varies greatly. (4) Geographical adjacency, transportation accessibility, and tourism resource endowment influence tourism flow network structure. The study found that the influencing factors of tourism flow in rural areas are different from those in urban areas. These results provide useful information for the marketing and development of tourism management departments in rural areas.

Keywords: tourism flow; network structure; social network analysis; QAP regression analysis; rural areas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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