A social network analysis of tourism cooperation in the Yangtze River Delta: A supply and demand perspective

Abstract

This study explores the spatial structure of regional tourism cooperation networks among 27 cities in the Yangtze River Delta from the perspective of supply and demand. Data from the supply network were collected from official news released by the Chinese government and quotations for tour routes published by travel agencies. Travel notes published on tourists’ blog community platforms about their travel experiences were used as source data for the demand network. The degree of cooperation between the cities was analyzed based on the frequency of occurrence and co-occurrence of information on tourist attractions or cities in the Yangtze River Delta region in tourist notes, tourist route quotes, and official news. This study divides 27 cities in the Yangtze River Delta region into three categories: those where supply matches demand (e.g., Shanghai and Nanjing), nine cities where there is a demand lag (e.g., Zhenjiang), and 16 cities where there is a supply lag (e.g., Wuxi). Investigating the differences between the supply and demand networks is helpful to understand the effectiveness of regional tourism cooperation mechanisms and government policies, which is crucial for the sustainability and good governance of regional tourism.

Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective

“…The tourism flow of the Yangtze River Delta urban agglomeration was studied by referring to the existing research results [3,8,10,11,22], using SNA and GIS methods, and the QAP. In network analysis, the cohesion of the tourist destination and its network structure characteristics are usually tested by density and centrality.…”

“…The network/Core and Periphery/Categorical analysis module of UCINET software can be used to analyze the tourism flow relationship matrix. In addition, the core of each city (destination) can be measured using a continuous core-periphery association model (network/Core and Periphery/Continual in UCINET) [8].…”

“…The degree of node i represents the sum of the number of nodes connected to it, and is a centralized reflection of the importance and influence of nodes. If a node is directly related to multiple nodes in the network, then the node is in the center of the network [3,8]. The calculation Formula (3) of degree centrality is as follows:…”

“…Furthermore, this study considers the tourism flow network as a network composed of tourist destinations and tourism flows among them. The tourists' multi-destination travel behavior based on their diversity preference, high grade preference, and pursuit of low unit cost makes the interaction of destination space increasingly frequent and multi-directional, resulting in a more complex tourism flow network [8].…”

“…Wang et al (2021) believe that the spatial movement of tourists from source to destination, and between destinations, contributes to a regional tourism cooperation network, and they used SNA to analyze the structure of tourism scenic spot cooperation networks [3]. Further, Wang et al (2022) made a comparative analysis of tourism cooperation networks by using SNA from the perspective of both supply and demand [8].…”