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Tourism Management 46 (2015) 274e282

Contents lists available at ScienceDirect

Tourism Management
journal homepage: www.elsevier.com/locate/tourman

Using social network analysis to explain communication
characteristics of travel-related electronic word-of-mouth on social
networking sites
Qiuju Luo a, b, *, Dixi Zhong a, b, 1
a
b

School of Tourism Management, Sun Yat-sen University, Building 329, 135 Xingangxi Road, Guangzhou 510275, PR China
Center for Tourism Planning and Research, Sun Yat-sen University, Building 329, 135 Xingangxi Road, Guangzhou 510275, PR China

h i g h l i g h t s
 We viewed eWOM communication on SNSs as a network based on social relationships.
 We examined social ties and network structure with social network analysis.
 Travel-related eWOM communication relies on strong, middling, or weak social ties.
 The communication is structured, loose-knit, flat, and of high centrality.
 Travel-related eWOM on SNSs tends to be dominated by travel interests.

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 26 February 2013
Accepted 7 July 2014

Available online 26 July 2014

Social networking sites (SNSs), which are platforms based on user interactions, currently play increasingly important roles in sharing electronic word-of-mouth (eWOM) among tourists. Viewing eWOM
communication on SNSs as a network based on the users' social relationships, this study applied social
network analysis to examine the communication characteristics of travel-related eWOM on SNSs from
the perspective of both ego and whole networks. Results show that travel-related eWOM communication
via SNSs relied on existing social relationships, ties of which can be categorized as strong, of middling
strength, or weak. Furthermore, the effect of transmitted information was stronger than that of influential decision-making. The communication network studied was found to be structured, loose-knit, flat,
and of high centrality. These results enrich current research on the effects of eWOM and provide a dynamic perspective for understanding how eWOM disseminates and influences users through
interactions.
© 2014 Elsevier Ltd. All rights reserved.

Keywords:
Travel-related electronic word-of-mouth
Communication characteristics
Social networking sites
Social network analysis
Ego network
Whole network

1. Introduction
A significant symbol of Web 2.0, the boom in social networking
sites (SNSs) has also aroused a worldwide upsurge in tourism
destination marketing. With SNSs, a great deal of tourists post and
share real-time feelings (Gretzel, 2006; Pan, MacLaurin, & Crotts,
2007), as well as travel reviews, opinions, and personal experiences while traveling (Xiang & Gretzel, 2010). In particular,

* Corresponding author. School of Tourism Management, Sun Yat-sen University,
Building 329, 135 Xingangxi Road, Guangzhou 510275, PR China. Tel.: þ86 20
84112735/13450357112.

E-mail addresses: (Q. Luo),
(D. Zhong).
1
Tel.: þ86 18011718710.
/>0261-5177/© 2014 Elsevier Ltd. All rights reserved.

individuals younger than 35 years old with at least a college degree
chiefly participate in sharing travel experiences and photos on SNSs
(Lo, McKercher, Lo, Cheung, & Law, 2011). Given the general
popularity of sharing photos on SNSs, photos depicting travel have
especially become a way of self-expression and self-image construction among younger generations (Lo et al., 2011). As mobile
Internet capabilities progress, users more often share travel information whenever and wherever possible, which makes sharing via
SNSs increasingly prevalent. In fact, travel information provided by
SNSs has quickly become commonplace in the day-to-day lives of
SNS users.
SNSs such as Facebook, Twitter, Myspace, and Microblog are
platforms with dynamic, multimodal features by which users can
post, share, and discuss interests with other interested users
(Jansen, Zhang, Sobel, & Chowdury, 2009). These features of SNSs


Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

expand users' social circles as well as increase the frequency of
interpersonal contact. Unlike traffic on other websites, users more
often form close-knit relationships with each other (Ding & Wang,
2010). Given the strength of these ties, SNSs have transformed
traditional information dissemination that relies on central mass
media (e.g., newspaper and television). With the popularity of SNSs
and general Internet use, a dual-core dumbbell structure of online

information dissemination has emerged that includes both mainstream forums and microblogs as well as mainstream portals,
which as the two core sources of influence have transformed how
peer-to-peer influence works (Li, 2011). In terms of consumption,
consumers are no longer passive recipients of information; instead,
they actively engage in peer-to-peer product recommendations and
electronic word-of-mouth (eWOM) (Chu & Kim, 2011). eWOM offers a useful perspective from which to study information
dissemination and its influence on users and followers. Since the
development of Web 2.0, traditional word-of-mouth (WOM) has
had to accommodate eWOM (Chatterjee, 2001; Hennig-Thurau,
Gwinner, Walsh, & Gremler, 2004), which by comparison is more
influential due to its speed, convenience, broadcast appeal, and lack
of the pressures of face-to-face interaction (Sun, Youn, Wu, &
Kuntaraporn, 2006). Another aspect of such influence is that any
communication and contact between communicators and receivers
might alter the recipient's attitude, especially regarding purchase
decisions (Cheung, Lee, & Thadani, 2009; Kiecker & Cowles, 2002;
Park & Kim, 2008; Park & Lee, 2008).
Likewise, travel-related eWOM on SNSs may significantly affect
the cognition and behavior of potential tourists. Tourism is an
experiential good; consumers cannot perceive the quality of
tourism products in advance. Therefore, interpersonal communications have become an important technique to reducing the risks
of travel (Murray, 1991). Litvin, Goldsmith, and Pan (2008) point out
that interpersonal influence and WOM were ranked the most
important sources of information for purchase decisions. Partly as a
result, Chu and Kim (2011) suggest that product-focused eWOM on
SNSs is a unique phenomenon with important social implications.
Therefore, the characteristics of communication via eWOM on SNSs
requires more sustained attention, particularly from the perspective of network structure and social relationships, which allows a
more thorough examination of how interpersonal influence can
spread among users and followers (Chu & Kim, 2011). From this

perspective, studying the communication characteristics of travelrelated eWOM on SNSs can expand the present understanding of
eWOM's influence, especially as it pertains to tourists and the
tourism industry.
Currently, SNS regarding tourism has received scant scholarly
attention. Most research exploring the function of SNSs for locating
tourism information, as well as users' motivations and behavior,
has neglected to investigate communication among users. By
contrast, eWOM communication and how it affects consumers'
purchase decisions has gradually attracted the attention of researchers (Jansen et al., 2009; Lee & Youn, 2009; Riegner, 2007).
Current research is conducted from three perspectivesdnamely,
those of the communicator, the receiver, and the communication
process. Although studies on the communication process are well
outnumbered by those on communicators and receivers, recent
research has begun to study the social characteristics of eWOM
communication. Nevertheless, most studies thus far have considered consumers to be independent individuals and have thus
emphasized the effects of eWOM on online purchase decisionmaking, while research on eWOM via SNSs remains in its infancy.
In the meantime, eWOM communication in those studies is static,
for few have conducted their research from a dynamic perspective
and considered communication as a dynamic dissemination process. Therefore, this study focuses on the communication of travel-

275

related eWOM on SNSs to underscore its practical and academic
significance.
To these ends, this study performed social network analysis
(SNA) to examine the communication characteristics of travelrelated eWOM on SNSs from the perspective of social ties and
network structure. Its results not only enrich the existing theoretical
research, but also provide further inspiration for conducting effective word-of-mouth marketing on SNSs in the tourism industry.
2. Literature review
2.1. SNS research in tourism

Most research on SNSs has been published since 2008 and primarily emphasized user motives and behaviors. Among SNS
research, the few travel-related studies can be grouped into two
categories. On the one hand, most studies have considered SNSs to
be one kind of social media in terms of their use for travel-related
information searches. Using Google as a search engine, Xiang and
Gretzel (2010) investigated the role of social media in online
searches for travel-related information. The results showed that
SNSs were not yet the main sources for users seeking travel-related
information. Meanwhile, other research has suggested that user
trust of travel websites varies significantly; the three types
considered most trustworthy were official websites of tourism
bureaus, websites of travel agencies, and third-party websites
(Burgess, Sellitto, Cox, & Buultjens, 2011; Yoo, Lee, & Gretzel, 2009).
Though trust of SNSs was lower than expected and SNSs are far
from the most popular way to gather travel-related information,
the reasons for both conditions have gone unaddressed in these
studies. Furthermore, rapid changes that occur as mobile Internet
become popularized may have altered the conditions in recent
years.
On the other hand, tourism studies have also focused on the use
of SNSs in terms of user characteristics and motivations for sharing.
Current studies in this category remain in the descriptive stage. Lo
et al. (2011) found that most people sharing travel photos were
young and well-educated, as well as had substantial incomes, rich
travel experiences, and a willingness to involve themselves in the
destination. Huang, Basu, and Hsu (2010) identified three functional motives for sharing travel-related information via
SNSsdnamely, obtaining travel information, disseminating information, and documenting personal experiencesdand that of these
motives, obtaining travel information was the most important. Both
studies described nevertheless failed to present the characteristics
of the social networkdnamely, the effect of social features on

tourists.
In sum, research of SNSs in tourism remains in its infancy.
Though earlier studies explored the function of SNSs for locating
travel information, most neglected to investigate the communication process, for few conducted their research from a dynamic
perspective. If substantial characteristics of SNSs have been overlooked, such oversight precludes further understanding of the
acquisition and impact of travel information. At the same time,
since few studies viewed online travel-related information as
eWOM, we have viewed travel-related information as such and,
moreover, sought to provide a dynamic perspective for understanding how eWOM disseminates information and influences
users.
2.2. Communication research on eWOM
Current research on eWOM is conducted from three perspectivesdnamely, those of the communicator, the receiver, and the
communication process.


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Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

Research conducted from the perspective of the communicator
emphasizes the motives for eWOM communication. In this strain of
the literature, Hennig-Thurau et al. (2004) identified eight motives
for sharing product reviews, including social benefits and the need
for advice. These results also suggest that social relationships
among users cannot be ignored in eWOM studies.
The perspective of the receiver has drawn the most scholarly
attention. In this strain, relevant research has primarily discussed
the effect of eWOM on the receiver in two aspects: the receiver's
attitude and the receiver's willingness to purchase.
Regarding the effect of eWOM on the receiver's attitude, various

factors influence receivers' adoption of eWOM, including both the
features of eWOM and of receivers. The features of eWOM include
quantity of eWOM (Doh & Hwang, 2009), source characteristics
(e.g., its reliability, objectivity, and expertise), information characteristics (e.g., its general allure, completeness, accuracy, and timeliness) (Chen & Zhang, 2008), relevance, and completeness
(Cheung, Lee, & Rabjohn, 2008). In particular, the usefulness of
information is a mediator that influences eWOM adoption (Cheung
et al., 2008). At the same time, the features of the receiver include
involvement and prior knowledge, both of which variously affect
the recipient's attitude (Doh & Hwang, 2009).
By comparison, studies on the effect of eWOM in purchase decisions are more diverse, for scholars have conducted research on a
variety of influential factors. Park and Kim (2008) concluded that
benefit-centric eWOM has a greater influence on the willingness to
purchase for consumers who lack expertise, while attribute-centric
eWOM exerts a greater influence on consumers with professional
knowledge of the product. Poyry, Parvinen, Salo, and Blakaj (2012)
showed that, compared to utilitarian information searches, hedonic
information searches significantly improve the consumers'
perception of eWOM's usefulness and shortens the decision-topurchase time. Though it is clear that SNSs prefer hedonic information searches, whether there is any perceptible influence on
tourists' decision-making requires further exploration. De Bruyn
and Lilien (2008) developed a multi-stage model to identify the
role of eWOM plays during each stage of recipients' decisionmaking process.
Compared to the perspectives of the communicator and
receiver, the perspective of the communication process has
received scant scholarly attention, though recent research has
begun to study the social characteristics of eWOM communication.
On one hand, social relationships between consumers come into
notice. Among studies of eWOM, Chu and Choi (2011) have evaluated the effects of social relationships between consumers' purchase decisions on SNSs. Their results suggest that Chinese users
communicate most and most trustfully with users with whom they
have strong social relationships, thus the social capital of a preexisting social relationship plays a significant role in Chinese
users' eWOM communication. By contrast, Americans interact

more with extended social circles or with other users with whom
they have no social relationship. Chu and Kim (2011) developed and
tested a conceptual framework that identifies tie strength, homophily, trust, normative and informational interpersonal influence as
an important antecedent to eWOM behavior in SNSs, and tie
strength is positively associated with eWOM behavior. As might be
expected, several researchers have suggested that WOM communication has relied on social relationships and that consumers were
inclined to trust acquaintances and people with whom they
maintained strong social ties (Brown & Reingen, 1987), family
members, and friends (Jansen et al., 2009). In the era of Web 2.0,
social interaction on SNSs determined by social relationships continues to merit in-depth investigation.
On the other hand, some researchers studying the features of
eWOM communication networks have produced results indicating

that eWOM communication networks are structured instead of
random. Among these researchers, Vilpponen, Winter, and
Sundqvist (2006) conducted a case study of communication on
personal websites that used a downloadable banner to show
resistance to a proposed copyright law in Finland. Using SNA,
Vilpponen et al. (2006) concluded that eWOM communication can
be characterized as a loose-knit network of high centralization and
cliques. In another study, by modeling an eWOM communication
network on multi-agent simulation, Jiang (2009) found that the
structure of any eWOM communication network influences both
the scale and efficiency of communication.
Altogether, current research on eWOM regarding the communication process remains insufficient. On the one hand, since researchers viewed users as independent individuals, most research
failed to consider the relationships among users and the pathways
of communication most taken by users seeking to share and exchange information. On the other hand, few studies address eWOM
communication on SNSs. Certain features of SNSsdstrong interactivity and timeliness, to name twodare likely to distinguish SNSs
from general websites. Our study has thus aimed to provide indepth research on travel-related eWOM communication via SNSs
from the aspect of user interaction.

3. Research design
In all social communication processes, at least two individuals
are needed to form an information-sharing relationship in order to
share information symbols (Schramm & Porter, 2010). In this sense,
the process of information exchange should not be viewed as a
specific behavior (i.e., A acts upon B) but as information sharing that
leads to a common understanding (Schramm & Porter, 2010).
SNA views the social structure as an interpersonal network that
emphasizes interpersonal relationships, the content of the relationships, and the interpretation of social phenomena within the
structure of a social network (Luo, 2010). A social network is a
collection of social actors and the relationships among them (Liu,
2009). Consequently, each node in the network represents one
actor, which can be a social unit or entity, and each link represents
the relationship between the actors. SNA emphasizes three
network levels: ego, partial, and whole. During the past 30 years,
SNA has been applied to many studies in sociology, organizational
behavior, and social relationships. More recently, SNA has been
increasingly applied to social media-based communication
research.
This study applied SNA to answer research questions from the
perspective of each single relationship and then extended that
perspective to the whole network. In short, this study concerns two
aspects, one is the features of social ties of each communication
pathway, and the other is the structural features of a travel-related
eWOM communication network. To do so, we applied ego-network
analysis to examine social ties as well as whole-network analysis to
measure the structure of travel-related eWOM communication
network (Fig. 1).
The interaction between users on SNSs can be silent (i.e., not
directly observable) or visible (Pempek, Yermolayeva, & Calvert,

2009). Since silent contact is difficult to assess, the present analysis was conducted based on visible contact. Visible interactive
behavior between users, including their comments and forwarding
comments,
represents
the
completion
of
information
dissemination.
3.1. Ego-network analysis
From the perspective of social ties, ego-network analysis was
used to analyze the strength of social ties between the


Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

277

Fig. 1. Research framework.

communicator and the receiver in travel-related eWOM communication. An ego network refers to any network consisting of an
individual and other users directly connected to him or her; it is
used to interpret the relationship features between the communicator and receiver in travel-related eWOM.
In this study, data were collected by questionnaire. A nomination method was used to identify five users who had made travelrelated eWOM communication with respondents during a period of
six months (October 2011eApril 2012). After the ego network was
constructed, a nomination interpretation method was used to
describe and measure each communication relationship. The
questionnaire design was based on the standard ego-network
questionnaire exhibiting high reliability used in general social
surveys in the U.S. formulated by Burt (1984), which uses three

constructs to interpret questionnaire data: contact duration, contact frequency, and intimacy (Granovetter, 1973). The questionnaire
used in this study was revised based on local research conducted by
Luo and Xie (2008), added a construct (i.e., “relationship between
close circles of friends”), and adjusted the range of years of each
item for the construct of contact duration, as well as the measures
and contents of the items in the construct of intimacy. Additionally,
we added travel-related questions to explore respondents' tourism
preferences, common travel experiences, and reciprocal behaviors
in travel-related eWOM. Five undergraduate students from
different universities were recruited to take the pilot test, after
which the questionnaire was adjusted accordingly.
The questionnaire was distributed from April 1e8, 2012 to highfrequency users of SNSs in Guangdong, China. Users were mostly
office workers and college studentsdsome foreign exchange studentsdselected based on three considerations. First, the sample
originated from the primary group of SNS users in China, which is
representative of China. Second, the questionnaire design was new
and informative and thus required respondents with adept
comprehension skills and patience. Third, active SNS users (i.e.,
those who log in more than two or three times per week) may have
various travel-related eWOM communication behaviors. To ensure
a high reliability of questionnaire results, we distributed the
questionnaires to each participant one at a time.
In this study, an ego network consisted of each respondent and
up to five of his or her nominated contacts of travel-related eWOM.
Each network was a sample set, in which the respondent served as
the core, while each communication relationship directly connected to the respondent formed an independent sample. In total,
64 questionnaires were collected; those of participants whose SNS
use frequency was less than two or three times per week or who
could not provide a complete dataset of at least one nominated
contact were excluded. Altogether, 61 questionnaires (95.3%) were
deemed valid. A total of 303 (97.7%) independent samples was

obtained, of which 289 were deemed valid; 14 samples with
missing values were excluded.
The profile of respondents is shown in Table 1. The sample sets
generally represented the typical SNS user.
3.2. Whole-network analysis
Whole-network analysis examines the network structure of
eWOM communication. For this study, a representative microblog
was selected as a sample. This study only focused on the network
characteristics of travel-related eWOM in a single circle of microblogging relationships instead of multiple circles. With wholenetwork analysis, the study aimed to develop a directional adjacency matrix to analyze travel-related eWOM communication. The
whole network refers to all relationships among all group members

Table 1
Profile of respondents (n ¼ 61).
Demographic characteristic
Gender
Male
Female
Age
<18 years
19e30 years
31e40 years
41e50 years
>50 years
Highest level of education achieved
Junior high school
High school or technical secondary school
University or college
Postgraduate
Travel frequency within a year

More than twice
Once
None
Occupation
Governmental agencies or institutions
Corporations or enterprises in the service industry
Individual industrialists and businessman
Researchers and teachers
Retired
Housewives
Students
Other
SNS use frequency
Everyday
Two or three times weekly
Weekly
Monthly
Rarely

n

Percent

26
35

42.6
57.4

0

59
2
0
0

0.0
96.7
3.3
0.0
0.0

0
4
55
2

0.0
6.6
90.2
3.3

47
12
2

77.0
19.7
3.3

4

6
0
0
0
0
51
0

6.6
9.8
0.0
0.0
0.0
0.0
83.6
0.0

53
8
0
0
0

86.9
13.1
0.0
0.0
0.0

278

Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

(Liu, 2009). Network density, graph centralization, centrality, and
subgroup analysis were four important measurements used to
explore the network cohesion, integration, role and position, and
composition and structure of the travel-related eWOM communication network.
The sample microblog originated on Sina Microblog, the most
popular microblogging platform in China. Users on Sina Microblog
can be classified into two types: authenticated and ordinary users.
An authenticated user must be a well-known figure in a particular
field with an authentication icon highlighted. Since there are no
identity constraints on ordinary users, they form the major group
and account for a larger percentage of users. To understand the
communication structure of ordinary users was therefore more
remarkable for destination marketing regarding SNSs. In this study
ordinary bloggers were used as subjects for whole-network analysis; the microblog account of user Gaoli_Ivy provided data. Factors
such as the quantity of travel-related microblogs and the degree of
interaction were considered during the sample selection to ensure
a sufficient amount of data. The selected blogger loved traveling
and on average traveled threeefive times per year, including one or
two long-distance journeys. After registering as a user in
September 2010, from September to December 2011 she posted a
total of 92 travel-related messages, each of which addressed topics
such as travel experiences, air travel, accommodation deals, and
recommendations for travel destinations. The blogger followed a
total of 120 blogging friends and was followed by 227 users (March
26th, 2012).
Data from September to December 2011 were collected, which

consisted of all travel-related microblogging contacts followed by
the blogger and her followers. The research dates included two
public holidays in Chinadnamely, the Mid-Autumn Festival and
National Daydto ensure data adequacy. The blogger was asked to
review her use history in order to organize her travel microblogs
and microblog accounts she had commented on from September to
December 2011. During the same period, we read travel-related
microblogs in order to construct a travel-related eWOM communication pathway for other users.
A case-by-case adjacency matrix was constructed for wholenetwork analysis, for which each node represented an independent microblog user. The communication relationships between
the nodes indicating travel-related eWOM communication behaviors, which included forwarding or commenting on travel-related
microblogs, were represented by directional links. The data were
screened, and inactive users with fewer than 50 followers were
excluded. A 155 Â 155 adjacency matrix was eventually constructed
for data analysis.
UCINET 6 statistical analysis software was used for wholenetwork analysis.
4. Results
4.1. Ego networks
4.1.1. Results of tourist behavior
Regarding travel as a hobby, 75.6% of contacts enjoyed travel,
21.9% neither particularly enjoyed nor disliked travel, and 2.5% did
not enjoy travel. In terms of common travel experience, 39.4% of
contacts had traveled with respondents in the previous year.
Because the frequency of travel was less than the frequency of daily
entertainment activities, 39.4% can be considered a large percentage. Finally, regarding the sharing of travel information, 53.0% of
contacts shared travel information with respondents. Citing other
users or sending private messages were two ways they had shared
travel-related eWOM and can thus be viewed as individual-toindividual eWOM communication. Information sharing was a

form of reciprocal behavior between the respondents and contacts.
These results suggest that users who enjoyed travel or had common

travel experience with communicators were more likely to have
visible contact and more inclined to travel-related eWOM
communication (Table 2).
4.1.2. Results of social ties
Three constructsdnamely, contact duration, contact frequency,
and intimacydwere used to measure the strength of travel-related
eWOM communication ties on SNSs. To distinguish communication
relationships by strength of social ties, a k-means analysis was
conducted for 289 pairs of contact relationships. We conducted
two-, three-, and four-category clustering analyses to better interpret and categorize the samples and finally selected a threecategory analysis since it best explained the differences.
Variance analysis of the cluster results showed that all indicators
from the three clusters were significantly different, which was
consistent with the required statistical significance. Results of
cluster analysis are shown in Table 3. The cluster characteristics of
travel-related eWOM communication relationships included the
following:
Category I: Strong social ties. A total of 90 relationships (31.1%)
fell into this category, the most significant characteristic of
which was a high average of the five indicators. Average contact
frequency in these relationships occurred more than two or
three times weekly, while the average contact duration was
threeeten years. The topics and behaviors of communication
were intimate and diverse, and on average, a small group of
familiar and common friends was shared among the respondents and their contacts. Of the contacts, 77.8% shared
travel-related eWOM by citing other users and sending private
messages to and from the respondents with strong reciprocity.
Category II: Social ties of middling strength. Compared to Category
I, slightly fewer relationships (n ¼ 78, 27.0%) were considered to
have ties of middling strength. Average contact frequency for
this category was once or twice per week; contact duration was

slightly shorter than that of the contacts with strong social ties;
the topics and behaviors of communication were more general;
and the degree of overlap between the circles of close friends
among contacts was slightly lower than of that of contacts with
strong social ties. In this category, 51.3% of contacts were
reciprocal subjects of travel-related eWOM from the respondents. Based on the indicators, members of this category of
communication relationship were determined to have social ties
of middling strength.
Category III: Weak social ties. Given their low scores for each
item, most relationships (n ¼ 121, 41.9%) belonged to the category encompassing contacts with weak social ties. Contact frequency within the sample was as little as oneethree times
monthly, while the average contact duration was from one to
three years and the intimacy of topics and behaviors was
extremely low. Furthermore, the degree of overlap between

Table 2
Analytical results of travel behaviors of travel-related eWOM receivers.
Analysis of travel behavior
Contact enjoys travel

Contact traveled with respondent
in the previous year
Would share online travel information
with respondent

Yes
Neutral
No
Yes
No
Yes

No

n

Percent

211
61
7
113
174
151
134

75.6
21.9
2.5
39.4
60.6
53.0
47.0


Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

279

Table 3
Clustering analysis results.

Contact frequency
Contact duration
Intimacy of communication
Intimacy of behavior
Relationship between close circles of friends
Mutual confiding
Percentage

Category I: Strong social ties

Category II: Social ties of middling strength

Category III: Weak social ties

M

SD

M

SD

M

4.01
3.98
14.42
26.93
3.21
77.8%

31.1%

1.13
0.80
5.16
3.03
0.63

3.38
3.86
11.10
15.08
3.06
51.3%
27.0%

1.30
0.80
4.35
4.48
0.67

2.63
3.11
3.23
2.63
2.61
35.3%
41.9%

SD
1.34
1.02
3.22
2.84
1.06

Note. Score for indicators ranged from 1 to 5 points for contact frequency, 0 to 5 points for contact duration, 1 to 22 points for intimacy of communication, 1 to 31 points for
intimacy of behavior, and 1 to 4 points for a close circle of friends; SD ¼ standard deviation.

close circles of friends was low; most individuals in these relationships did not have contacts in common. Above all, since
social contact in this category was sporadic, these relationships
were determined to have weak social ties. The social tie strength
was comparable to that of general colleagues and classmates,
neither of whom share much contact.
In an empirical study, Marsden and Campbell (1984) showed
that the degree of intimacy is the best indicator to measure the
strength of social relationships. In this study, the degree of intimacy
(i.e., the intimacy of topics and behavior indicators) was the primary index for distinguishing social tie strength. More recently,
 (2010) categorized virtual social rePetroczi, Nepusz, and Bazso
lationships, which guided the definition of the strong social ties
category in this paper, which exhibited intimate friendship since
contacts were familiar with and supported each another (e.g., by
offering suggestions, sympathizing, and giving spiritual support).
By contrast, the categories of middling and weak social ties were
characterized the relationships of acquaintance, in which contacts
only knew one another in passing or were friends. The contacts
with middling and weak social ties might share interests, hobbies,
and exchanged messages in private.
4.2. The whole network

The 155 Â 155 directional multi-value matrix constructed from
the data of 155 users was screened and coded. From the subsequent
analysis we excluded 100 users who had no visible communication
of travel-related content with any other users in their social circles
from September to December 2011 in order to examine the characteristics of travel-related eWOM communication without the
interference of the data of users who had not participated in relevant communication. A 55 Â 55 matrix of travel-related eWOM
communication was constructed, in which each node was coded in
sequential order (AA, AB, …; BA, BB, …; CA, CB, …).
Fig. 2 shows the general pathways and active members in a
travel-related eWOM communication process. The contacts between users represented by the following nodes in the red circle
were more frequent and dense: AJ, BH, BM, CD, CF, CM, CO, CQ, CR,
CW, and DJ, as well as pairs AA and AI, DJ and BI, and CQ and CO.
4.2.1. Network density
Network density is a measure of network cohesion (Webster &
Morrison, 2004). In this sense, density signifies the ratio of the
actual number of links versus the maximum number of links
possible in the network (0e1).
To measure network density, the multi-value matrix was converted into a two-value matrix using UCINET 6. Nodes with information dissemination behavior between them were assigned
values of one whether the information dissemination went both

ways or not. By processing the 55 Â 55 directional two-value matrix
in UCINET 6, sample network had a total of 105 ties of eWOM
communication, which meant that network density was 0.0354.
When density was measured among the groups with frequent
contacts (represented by the red circle in Fig. 2), density increased
to 0.1703, which was nevertheless quite low.
The results above show that in most instances, the travel-related
eWOM contact network on an SNS was loose-knit instead of
densely connected. This result was significantly affected by the fact
that not all contacts in eWOM communication had a connection

with one another.
4.2.2. Graph centralization
Graph centralization measures the overall cohesion or integration of a network and describes the extent to which such cohesion
was organized around particular nodes (Scott, 2007). Regarding the
degree of centrality of graph centralization, the outdegree
centralization of the sample network was 73.73%, while the indegree was 20.92%. The betweenness centrality of the graph
centralization was low (24.41%).
A high degree of centrality of outward communication indicates
that the level of information integration was high. Any node with
high centrality, which indicates that the person has more travelrelated interaction with others, had a large effect in the network.
By contrast, as the degree centrality of the eWOM-receiving
network diminished, the receiving pathways became more
diverse. Low betweenness centrality of the graph centralization
suggests a low level of distortion, which indicates fast and effective
eWOM communication.
4.2.3. Centrality analysis
Centrality is an indicator of an individual's structural position
that assesses the importance of the individual in the network (Luo,

Fig. 2. The diagram of the sample networks.


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Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

Table 4
The output of the centrality.

Table 5

The output of betweeness centrality.

N

SOC

SIC

N

SOC

SIC

N

SOC

SIC

N

SOC

SIC

N

BC

SBC

N

BC

SBC

N

BC

SBC

N

BC

SBC

AA
AJ
CQ
CP
DJ
DL
BI
BP
DO
CR

DM
DE
CB
AK

75.93
24.07
18.52
12.96
9.26
7.41
5.56
5.56
5.56
3.70
3.70
3.70
1.85
1.85

24.07
5.56
9.26
1.85
5.56
1.85
5.56
1.85
1.85
1.85

1.85
0
1.85
1.85

CF
BK
CW
CM
CV
AC
DP
AI
BH
AT
BN
AN
AH
AB

1.85
1.85
1.85
1.85
1.85
1.85
1.85
1.85
0
0

0
0
0
0

9.26
0
1.85
1.85
3.70
3.70
0
5.56
3.70
5.56
1.85
1.85
1.85
3.70

BX
AF
AE
CD
BZ
CG
BM
CK
AL
CN

AG
AO
AR
AS

0
0
0
0
0
0
0
0
0
0
0
0
0
0

3.70
1.85
5.56
7.41
1.85
3.70
3.70
1.85
1.85
1.85

5.56
1.85
1.85
1.85

CU
AV
BA
DB
DD
CH
DF
DI
BO
CO
BQ
BV
BW

0
0
0
0
0
0
0
0
0
0
0

0
0

3.70
1.85
1.85
1.85
1.85
5.56
1.85
1.85
1.85
9.26
1.85
3.70
3.70

AA
CQ
AJ

704.17
125.83
77.67

24.60
4.40
2.71

DJ

BP
AI

38.00
17.00
16.50

1.33
0.60
0.58

BI
AC
CF

9.00
8.33
5

0.31
0.29
0.18

DO
CP
DM

1.50
0.50
0.50

0.05
0.02
0.02

Note. N ¼ nodes; SOC ¼ standard outdegree centrality; SIC ¼ standard indegree
centrality.

2010). This indicator is used to reflect the core-margin position of
actors in the diagram by focusing on each node in the network.
There are three centrality indexes: degree, closeness, and
betweenness (Scott, 2007). The higher the degree centrality index,
the more actors the user has contact within the network, thus the
more unofficial power and greater effect the individual exerts in the
network. By contrast, users with high betweenness centrality
occupy the central position of contact between two members in the
network. The more opportunities this user has to guide resources,
the more critical the position he or she occupies in the flow of
resources.
Regarding outdegree centrality, AA was the main core in the
network, with a standard outdegree centrality of 75.93, followed by
AJ (24.07) and CQ (18.52). The standard outdegree centrality of
more than 33 actors was zero. The results suggest that the sample
network had a structure dominated by one core surrounded by
several secondary cores. Because the sample network was the
extended microblog network of AA, the attributes of AA did not
have a reference value. Among the 10 individuals with the highest
outdegree centrality (excluding AA), AJ, CQ, DJ, BI, and CR were all
travel lovers,2 whereas DL, DO, and DM were the official microblogs
of AirAsia, Asiago, and Qyer.com, respectively. CP and BP traveled

frequently on business (i.e., approximately twice every six months).
Regarding indegree centrality, the standard indegree centrality
of three nodes was high; AA was the highest (24.07). The standard
indegree centrality of CQ, CF, and CO was 9.26 and then <8 for the
remaining nodes. The receivers of eWOM were relatively evenly
distributed and not centralized, which suggests that travel-related
eWOM attracted attention from a relatively large number of users,
not only a few actors. The sample network formed a network
structure of dispersed receiving (Table 4).
The betweenness centrality of only 12 nodes in the network was
greater than zero. AA occupied the central position, with a standard
betweenness centrality of 24.60, followed by CQ and AJ. AA occupied the center of the network and had a great effect on the
thoughts of the other actors in the online social circles. Except for
these three nodes, the betweenness centrality of the remaining
tourists was low, and many tourists were marginal (Table 5).
4.2.4. Subgroup analysis
Subgroup analysis examines the group characteristics of cohesion in the network by analyzing the substructures of the whole
network. Generally, a subgroup refers to a coalition of many

2
According to an interview with blogger AA. Authors listed users who had
contacts with blogger AA, and asked AA to select travel lovers.

Note. N ¼ nodes; BC ¼ betweeness centrality; SBC ¼ standard betweeness centrality.

contacts who share a goal and have many stable contacts with each
another. From the perspective of social psychology, an individual is
an actor in the group and subjected to the concepts, influences,
norms, and values of the group (Liu, 2009). Therefore, it was
important to investigate whether there was a subgroup in the

travel-related eWOM communication network in order to understand how eWOM further affects receivers.
A component analysis was conducted on the 55 Â 55 directional
two-value matrix and revealed a strong component composed of 14
nodes (AA, AI, AJ, BI, CB, CM, CP, CQ, CR, CW, DJ, DL, DM, and DO). A
strong component refers to a component in which the connection
direction is considered.
In the subgroup of 14 nodes, AI, AJ, BI, CM, CQ, CR, CW, and DJ
were travel lovers, while DL, DM, and DO were the official microblogs of three travel websites. The subgroup members shared an
interest in travel and were therefore more likely to form a close-knit
subgroup in the travel-related eWOM communication network.
A k-core collapse sequence analysis was also conducted to
analyze whether the sample network on SNSs was structured. The
index analyzes the similarities in relationships and structure between the component and other nodes of the sample. Results show
that the core collapse sequence was 0, 0, 0.44, 0.71, and 0.89. The
core collapse sequence is thus gradual as k increases from zero,
which suggests that the communication and contact in the travelrelated eWOM network was not random but structured (Table 6).
5. Discussion and conclusions
As an early empirical attempt to understand the characteristics
of travel-related eWOM communication on SNSs, this study
examined the social ties and social network structure with SNA. It
thus offered a new perspective for better understanding how
eWOM disseminates and influences within user interactions.
In ego-network analysis, we examined the social relationship
variables among users in travel-related eWOM communication on
SNSs. We specifically examined tie strength as a potential predictor
of interpersonal influence in eWOM communication. In wholenetwork analysis, we also examined the network structure of
travel-related eWOM communication.
Our results first show that travel-related eWOM communication
via SNSs relied on existing social relationships, which can be
categorized into three groups: having strong social ties, social ties

of middling strength, or weak social ties. Only 0.7% relationships
were newly established between respondents and contacts. 1.7%
relationships had been established for six monthseone year. SNSs
stood apart from other social media in encouraging their users with
existing social relationship to interact online, which underscores its
academic significance. Relationships with weak social ties formed
Table 6
The output of the k-core collapse sequence analysis.
k

k-remainder

k-remainder percentage

0
1
2
3
4

0
0
24
39
49

0
0
0.44
0.71

0.89


Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

the largest category, while the other two categories were similar in
size. Contacts in relationships with strong, middling, or weak social
ties played different roles in communications with information
receivers (Granovetter, 1973); effects on the promotion and optimization of social learning therefore also differed. This conclusion
confirms the results of Chu and Choi (2011), who reported that
Chinese users were more likely to contact familiar users on SNSs
instead of expanding their existing social relationships. In the
present study, we further segmented tie strength to better understand the characteristics of eWOM communication and its effects.
Secondly, concerning its effect of communication, results reveal
that eWOM can transmit information and influence decisionmaking, though the effect of the former was stronger than that of
the latter. Travel-related eWOM on SNSs could overcome spatiotemporal limitations and spread to all corners of the social network.
Furthermore, eWOM also affected the attitudes and decisionmaking of contacts with strong social ties, since strong social ties
were conducive to influencing others and building trust, whereas
weak social ties were conducive to transferring knowledge and
information. The fact that relationships with strong social ties
occupied the smallest category suggests that travel-related eWOM
plays a more important role in knowledge and information
dissemination. Moreover, network density was low for travelrelated eWOM communication; the travel microblog did not
inspire frequent contact among tourists, which weakened eWOM's
influence. Characteristics of social relationships have been indexed
to reflect how eWOM works among tourists. In this study, the effects of eWOM were divided into two kinds: eWOM transmission
and influence. Compared to studies of trust of eWOM on SNSs
(Burgess et al., 2011; Yoo et al., 2009), the present study offers a
new perspective for examining the extent of eWOM's influence,
though this perspective nevertheless requires further study.

Thirdly, we found the communication of travel-related eWOM on
SNSs to be dominated by travel interests, while information and
influence were evenly disseminated among active travel-interested
users. Individuals who loved to travel and had common travel
experience were more likely to follow travel-related content in
relationship circles on SNSs, make visible contact, be reciprocated,
and have subsequent contact concerning travel-related eWOM.
According to centrality analysis, the effect of these individuals
cannot be ignored, given their significant centrality and impact. In a
travel-related eWOM communication network, these people would
be more likely to take important positions and from there influence
other users in the network.
Fourthly, we discovered that the communication network structure of travel-related eWOM on SNSs bears three characteristics. One,
the communication of travel-related eWOM was not random but
structured. The communication network of travel-related eWOM
could be divided into subgroups. In the present study, the sample was
dominated by a single subgroup with a close-knit network. The
structure of the overall network and components was consistent, and
there were no conditions in which dense areas were surrounded by
marginalized nodes. Two, the communication of travel-related eWOM
on SNSs was loose-knit based on social relations. The strength of most
social ties was middling or weak and its density low. Three, the degree
of centrality was high, while the degree of betweenness centrality was
low in the sample network. Network structure therefore exhibits high
centrality. To communicate travel-related eWOM, actors in the
network would bypass redundant relationships, which implies that
eWOM at important nodes (i.e., hubs) would influence other nodes
and thereby flatten communication. The above results thus also
essentially confirmed the conclusions of Jiang (2009) and Vilpponen
et al. (2006). Similar to information dissemination via personal websites, travel-related eWOM communication on SNSs was loose-knit

and occurred among small groups. The degree of connections

281

between nodes was not evenly distributed; a few nodes had many
connections in the network, whereas the majority of nodes had only a
few.
Fifth and above all, this study's findings illuminate the process of
travel-related eWOM communication on SNSs. To begin, the
network structure of travel-related eWOM outflows was dominated by a core and closed by several secondary cores. Travelrelated eWOM revealed a high degree of centrality and integration. It was disseminated from the nodes of high centrality to those
of low centrality with a high degree of information integration.
Users who loved to travel were more likely to take an important
role in travel-related eWOM communication. Moreover, the
network structure of inflows was dispersed. The degree centrality
of eWOM inflow was low; the pathways were diverse and exhibited
a dispersed reception network structure. The receiving nodes of
travel-related eWOM were relatively even with low centralization;
travel-related eWOM could therefore attract the attention of many
users, and accordingly, there were no instances in which travelrelated eWOM was concentrated to a few actors.
By comparison, Jiang (2009) and Vilpponen et al. (2006)
investigated the communication network structure on a macro
level, while Chu and Kim (2011) and Litvin et al. (2008) both provided conceptual frameworks of eWOM dissemination that judged
interpersonal influence to be an important variable. This empirical
study examined the communication process from the perspective
of social network, from where it was viewed as a dynamic, interactive process. Our findings gave the inspirations on how eWOM
disseminating its influence via social relationships on SNSs.
Perhaps above all, this study was based on social interaction, which
is the core feature of SNSs.
Altogether, our findings emphasize the importance of social relationships and social networks upon eWOM communication on SNSs
by making the following contributions. First, travel information on

SNSs was considered eWOM, which provided a new angle for studying
emergent media respecting tourism. Second, this study focused on
social interactions, whereas previous eWOM research primarily
emphasized the perspective of individuals and viewed the communicator and receiver as independent individuals with no connections.
However, any SNS is an interactive platform for users to establish
contacts in social circles. The features of communication, such as social
ties and communication network structure, likely influence the effect
of eWOM significantly; therefore, neglecting to examine communication contacts and relationships between communicators and receivers will yield a distorted understanding of eWOM's effects.
Furthermore, since travel-related eWOM communication was viewed
as a network based on the user's social relationships, current eWOM
studies have been enriched by this study's implication that eWOM
dissemination and influence can be better understood given user interactions from a dynamic perspective. Third, though both Chu and
Kim (2011) and Litvin et al. (2008) had proposed a conceptual
framework of eWOM communication that considered interpersonal
influence, empirical studies remain insufficient. By contrast, this study
was conducted by using SNA, which constitutes an empirical study of
travel-related eWOM on SNSs. To a great extent, this study therefore
provides a practical way to study eWOM communication: focusing on
interpersonal influence.
SNSs act as amplifiers of travel information. Tourists intuitively
comment on destinations and tourism experiences both while
traveling and in retrospect. By reaching a wide audience in social
circles and by depending on the strength of social ties in their relationships, users' perceptions of destinations and tourism products are greatly affected by eWOM on SNSs. The advantages and
disadvantages of destinations and the travel experiences as evident
in comments can be strengthened and amplified when provided by
people close to the potential tourists. These tourists' contributions


282

Q. Luo, D. Zhong / Tourism Management 46 (2015) 274e282

constitute an important force given the communication's influence
bolstered by strong social ties and the communication's power with
weak social ties. Since SNSs have become important marketing
tools in tourism, the phenomenon of travel-related eWOM on SNSs
promises to become a topic increasingly visited by scholars and
industry players alike. For these reasons, eWOM on SNSs requires
further research, especially regarding its dynamic and interactive
communication processes.
Acknowledgments
The research contained in the paper has been financially supported by a grant from National Natural Science Foundation of
China (to Luo Qiuju) (No.40971041). The authors express their
gratitude to Miss Gao, all respondents and proofreading editors
who have offered help in this study.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi:10.1016/j.tourman.2014.07.007.
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Qiuju Luo (1968), Ph.D., female, professor, deputy dean of
School of Tourism Management, Sun Yat-sen University
( G u a n g z h o u , G u a n g d o n g , C h i n a 510 27 5 ; e - m a i l :
). Prof. Luo, researching in the
area of exhibition, convention, mega event and event
tourism, has published more than 40 academic articles in
major journal home and abroad. With a series of influential
researches, she has earned a good reputation in event industry and tourism industry in China. As the popularization of new media in tourism industry, Prof. Luo devotes
herself to conducting innovative research on social media
and its influence in tourism domain.

Dixi Zhong (1989), female, master student of School of
Tourism Management, Sun Yat-sen University (Guangzhou,
Guangdong, China 510275; e-mail: dreamy_cecilia@
foxmail.com). As mobile Internet popularizes, Dixi Zhong
develops her interests in e-tourism as well as social media.
With an acute insight and rigorous academic attitude, she
begins to conduct research on new tourism phenomena.
The electronic referrals on social networking sites are one
of the topics she's probing into.