Analyzing the structure of tourism destination network based on digital footprints: taking Guilin, China as a case
| DOI | https://doi.org/10.1108/DTA-09-2021-0240 |
| Published date | 23 May 2022 |
| Date | 23 May 2022 |
| Pages | 56-83 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Caihua Yu,Tonghui Lian,Hongbao Geng,Sixin Li |
Analyzing the structure of tourism
destination network based on
digital footprints: taking Guilin,
China as a case
Caihua Yu
Nanjing University of Information Science and Technology, Nanjing, China, and
Tonghui Lian, Hongbao Geng and Sixin Li
Nanjing University of Finance and Economics, Nanjing, China
Abstract
Purpose –This paper gathers tourism digital footprint from online travel platforms, choosing social network
analysis method to learn the structure of destination networks and to probe into the features of tourist flow
network structure and flow characteristics in Guilin of China.
Design/methodology/approach–The digital footprint of tourists can be applied to study the behaviors and
laws of digital footprint. This research contributes to improving the understanding of demand-driven network
relationships among tourist attractions in a destination.
Findings –(1) Yulong River, Yangshuo West Street, Longji Terraced Fields, Silver Rock and Four Lakes are
the divergent and agglomerative centers of tourist flow, which are the top tourist attractions for transiting
tourists. (2) The core-periphery structure of thenetwork is clearly stratified. More specifically, the core nodes in
the network are prominent and the core area of the network has weak interaction with the peripheral area.
(3) There are eight cohesive subgroups in the network structure, which contains certain differences in the
radiation effects.
Originality/value –This research aims at exploring the spatial network structure characteristics of tourism
flows in Guilin by analyzing the online footprints of tourists. It takes a good try to analyze the application of
network footprint with the research of tourism flow characteristics, and also provides a theoretical reference for
the design of tourist routes and the cooperative marketing among various attractions.
Keywords China, Digital footprint, Social network analysis, Tourist destination, Guilin, Tourist flow
Paper type Research paper
1. Introduction
Tourist flow is a key factor in tourism and plays an important role in understanding
tourist behavior (Asakura and Iryo, 2007;Xia et al., 2011). The tourist flow reflects tourist
consumption styles (Connell and McManus, 2019;Lew and Mckercher, 2006); it has an
effect on local economy and society to a certain degree (Montanari and Muscar
a, 1995;
Ruiz and Gonz
alez, 2012). For instance, tourist flow has an impact on the rationalization
of doing business and relocating hotels (Joksimovi
cet al., 2014), playing a guiding role to
the construction of transportation in tourist destinations (Duval, 2007).Researchonthe
characteristics of tourist flow is the basis for tourist destinations in planning and
marketing (Cenamor et al., 2017;Uysal, 2008). It is closely connected with the
characteristics of tourist flow whether in the aspects of the formulation of tourism
development plans, tour routes or the decision of opening up tourism products and its
marketing strategies (Liu et al., 2017,2021). As an important piece of information, the time
fluctuation of tourism flow has not been fully understood (Cantis et al., 2011;Jang, 2004).
At present, there are few studies on tourism flow fluctuation in the field of tourism
DTA
57,1
56
Funding: This work was supported by the National Social Science Found of China (No: 20BGL162).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 9 September 2021
Revised 20 January 2022
1 March 2022
Accepted 28 March 2022
Data Technologies and
Applications
Vol. 57 No. 1, 2023
pp. 56-83
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-09-2021-0240
demand. The regularity of tourism flow fluctuation is important to forecast the trend of
tourism demand in the future. In tourism research, it has become a trend to use machine
learning methods to obtain data and analyze online tourism information (Cenamor et al.,
2017;Lian and Yu, 2017). The analysis of tourism flow data is mainly collected through the
internet, which will be more objective and accurate.
The rapid development of information and communication technologies, especially the
high-speed development of the internet, has changed the development patterns of the tourism
industry (Buhalis and Law, 2008;Ho and Lee, 2007;Law, 2014;Law et al., 2018;Lian and Yu,
2019). A multitude of information containing the accurate visited geographical positions, the
access time and the identification of tourists is regaled by sightseers on online platforms after
traveling (Cvelbar et al., 2018). The emergence of social media has had a huge impact on
tourism. It has motivated the participation and interaction of tourists, provided a platform for
tourists to share information on tourism experiences (Chung et al., 2015). As a result, a large
amount of information on tourism experiences has been exchanged effectively in social media
and it has become a significant source of information for tourists to acquire information and
make travel plans (Choi, 2013). The digital footprint is the information trace that people
associate with this kind of behavior on the internet, and this information trace can reflect
certain people’s behaviors and laws (Ahas et al., 2010,2015;Fisher, 2007;Girardin et al., 2008).
Tourism digital footprint as a digital footprint related to tourism activities that is shaped
through tourist activities with geographical tags or location information (Ahas et al., 2007;
Gartner et al., 2007), and can clearly reflect the travel time and space trajectory of tourists
(Chen et al., 2017b). Using tourism digital footprint cannot only study the characteristics of
tourist behaviors, but also provide a scientific reference for tourist destination management
and marketing (Chen et al., 2017b;Walden-Schreiner et al., 2018).
Thus, in order to better comprehend the networks of tourist attractions in a destination
in consideration of tourism movements and behaviors, a new method from tourism digital
footprint is needed. In order to revealing the underlying mechanisms and complexities of
tourism networks formation, social network is used in the examination and intended to
explore the relationships behind the network relationships that may offer a fuller
explanation of why the networks are formed as they are. In order to achieve this goal,
social network analysis (SNA) is used to analyze the spatial movement of tourists after
tourism. SNA) can be used to analyze the relationship between tourist attractions (Mou
et al., 2020;Sun et al., 2020). It helps to explore the potential flow patterns of tourists in
tourist destinations (Dietz et al.,2020), discover the flow networks formed under different
modes of transportation, evaluate the transition ability of tourists (Liu et al., 2021), and
thus promote the cooperation between scenic spots. In addition, the SNA method provides
a basis for the formulation of tourist routes and the development of tourist
destination space.
This paper takes Guilin of China, as an example, fully obtains the tourism network data
of tourists and uses the network analysis to evaluate the tourism social network of Guilin.
By analyzing the online footprints of tourists, this research aims at exploring the spatial
network structure characteristics of tourism flows in Guilin as a case. This paper is a
beneficial attempt to analyze the application of network footprint with the research of
tourism flow characteristics, and also provides a theoretical reference for the design of
tourist routes and the cooperative marketing among various scenic spots. Attractive
operators find this knowledge useful in planning, as it facilitates cooperation between
tourist attractions. Moreover, the research results are of great value to the managers of
tourism destinations, because they not only help the government to formulate policies for
the development of tourism space scientifically, but also provide a scientific basis for the
development and optimization of tourism space, tourism traffic management and tourism
service facilities construction in specific regions.
Destination
network
structure
analysis
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