Impacts of online word-of-mouth and personalities on intention to choose a destination

Computers in Human Behavior 116 (2021) 106656
Available online 15 December 2020
0747-5632/© 2020 Elsevier Ltd. All rights reserved.
Full length article
Impacts of online word-of-mouth and personalities on intention to choose
a destination
Tommi Tapanainen a
, Trung Kien Dao b,*
, Thi Thanh Hai Nguyen c
a
College of Economics and International Trade,Pusan National University, Busan, Republic of Korea
b
Faculty of Economics and Business, Phenikaa University, To Huu, Yen Nghia, Ha Dong, Ha Noi, Viet Nam
c
Faculty of Social Sciences, Business and Economics, Abo Akademi University, Vänrikinkatu 3 B, Turku, Finland
A R T I C L E I N F O
Keywords:
Electronic/online word-of-mouth
Social media
Personalities
Big five model
Information adoption model
Travel destination
A B S T R A C T
Electronic word of mouth (eWOM), enabled by social media, has been widely recognized as an influential
marketing tool. It has been suggested that personality influences how people accept information originating in
eWOM, but most research has not connected personalities with behavioral intention or information use. This
study aimed to assess the link between eWOM and personalities on intention to choose a tourism destination. The
research model was developed from the Big Five personality model and the information adoption model (IAM).
An analysis of 647 participants in Vietnam confirmed that IAM can predict behavioral intention for tourism
destinations, thereby adding to the literature, which so far has mainly utilized the technology adoption model.
The result also shows that personality (specifically the traits of agreeableness, extraversion, and imagination)
affects information adoption regarding tourism destinations. However, the impact of personality on behavioral
intention could only be partly explained by IAM, suggesting that other variables play a role. The findings suggest
avenues for future research linking personality and eWOM, as well as ways for companies to improve their
customer targeting and advertising content that can be involved in eWOM.
1. Introduction
Word-of-mouth communication (WOM) was defined as direct, non-
commercial face-to-face conversations between people who have expe
rienced and those who are considering a product or a service (Arndt,
1967). However, in the digital age, WOM communications were not just
direct conversations, but also became online or electronic word of
mouth (eWOM) through information channels such as social networks
(Erkan & Evans, 2016). Through social networks, people could form
virtual communities to share, exchange, and forward their posts or
comments about products and services to a wider audience (Chu & Kim,
2011; Jeong & Jang, 2011). With the increasing of using social net
works, the role of eWOM should not be ignored and the review by King
et al. (2014) supported this argument by concluding that eWOM had
stronger influence on consumers than other forms of marketing
communication.
Information technology (IT) in general and social media in particular
have significantly changed the tourism industry and created new pos
sibilities for tourism services provision (Cao & Yang, 2016; Kim et al.,
2011; Xiang et al., 2008). The literature review by Leung et al. (2013)
stated that social media played an important role in creating strategic
competitiveness for tourism firms. Xiang and Gretzel (2010) and Xiang
et al. (2015) also argued that social media was increasingly prevalent as
a tool in travel consumers’ planning, particularly so among young
people such as those of Generation Y, who have used digital technologies
and social networks all their lives and do not hesitate to engage with
their peers on electronic media (Bennett et al., 2008; Bolton et al., 2013;
Prensky, 2001; Wesner & Miller, 2008). Similarly, studies by Di Pietro
et al. (2012), Murphy et al. (2007) and Tan and Tang (2013) demon
strated the importance of eWOM in people’s destination selection
decision-making process. For example, Di Pietro et al. (2012) found that
eWOM affected users by influencing their attitudes toward social net
works in travel planning. Hence, eWOM is interesting for both in
dividuals and companies and a more detailed understanding of its effects
can facilitate a match between consumers’ and businesses’ needs in
promoting eWOM.
Previous studies have shown that eWOM influenced the selection of
products or services (Bickart & Schindler, 2001; Chan & Ngai, 2011;
Erkan & Evans, 2016; Park et al., 2007; See-To & Ho, 2014) and had an
impact on customer purchasing decisions (Erkan & Evans, 2016; Sin
* Corresponding author.
E-mail addresses: tojuta@gmail.com (T. Tapanainen), kien.daotrung@phenikaa-uni.edu.vn (T.K. Dao), tngu2407@yahoo.com (T.T.H. Nguyen).
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: http://www.elsevier.com/locate/comphumbeh
https://doi.org/10.1016/j.chb.2020.106656
Received 14 January 2020; Received in revised form 28 October 2020; Accepted 6 December 2020

2
et al., 2012). The influence of eWOM on intention to travel has also been
reported in prior research (Di Pietro et al., 2012; Jalilvand & Samiei,
2012; Murphy et al., 2007). Jalilvand and co-workers published studies
on eWOM and tourism for Islamic destinations and in their studies,
eWOM was found to have a powerful effect on destination image and
travel intention (Jalilvand & Heidari, 2016; Jalilvand & Samiei, 2012).
However, beyond the content transferred by eWOM, as eWOM conver
sations involve the subjective judgement of communicating individuals,
the influence of eWOM on the recipient may vary from person to person;
the same content can evoke differing notions among receivers (Cheung
et al., 2008). Different subjective factors can affect an individual’s
judgement; personality has recently been found as one of the most
important factors influencing behavior (Wehrli et al., 2008). Personality
traits such as introversion and extraversion were found to play a sig
nificant role in certain online communication experiences (Butt &
Phillips, 2008; Kraut et al., 2002). Most prior studies on eWOM have
focused on the properties of information and communication functions
(Bickart & Schindler, 2001; Erkan & Evans, 2016; King et al., 2014)
without regard to the personality of the receivers.
To address these research needs and gaps, a framework was built to
explain how eWOM and personality can influence travel destination
selection. The research question that guided this study was: how can
eWOM (information from social media) and personality influence the
destination selection intention of social network users? This research had
three objectives: 1) to investigate the antecedents of users’ travel
destination selection intention; 2) to verify the influence of eWOM and
personality on intention to select travel destination; and 3) to develop a
model that can provide insights into why some consumers adopt eWOM
in selecting travel destination. The research model was created by
integrating two recognized models in information management and
psychology: the information adoption model (IAM) and the big five
model (BFM) of personality theory. The results of this study can be used
for better targeting of customers and therefore marketing costs saving
from the company perspective, as well as the relevance of advertising
from the consumer perspective.
We selected Vietnam as the target country for this research due to
two reasons: (1) there are many tourism destinations within Vietnam
creating awareness of tourism and making it accessible for citizens, and
(2) the tourism industry is developing rapidly, creating more choice for
consumers and interest in choosing tourism destinations.
This paper was organized as follows. In the next sections, we present
the theoretical background, research variables, and the hypotheses that
linked them. In section 4, we operationalize the constructs and explain
our research method and sample. Then, based on the analysis presented
in section 5, we discuss the results in section 6. Finally, we describe
theoretical and practical implications learned from this research and
outline some directions for future research in the last section.
2. Literature review
2.1. Electronic word of mouth (WOM) in social media
eWOM information is defined as positive or negative comments in
present or past time about the product or service, available to many
people and organizations referencing the internet (Hennig-Thurau et al.,
2004). Among the different important platforms where eWOM may be
shared, social media is considered highly appropriate for eWOM (Erkan
& Evans, 2016). eWOM in social media can arise in several different
ways which makes social media very versatile as a platform for eWOM.
For example, users can post and share their comments not only via
written texts, but also pictures, videos or applications (Sohn, 2014).
Visually enriched contents of social media can also make eWOM more
enjoyable and appealing. For these reasons, consumers increasingly
resort to social media to obtain information about products and services
and businesses have long considered eWOM an influential marketing
instrument (Erkan & Evans, 2016).
The research so far has found that the key factors that drive con
sumers to seek eWOM include: to reduce search and evaluation efforts in
both pre- and post-purchase evaluations (Brynjolfsson & Smith, 2000;
Bronner & de Hoog, 2010; Dabholkar, 2006; Goldsmith & Horowitz,
2006, Hennig-Thurau and Walsh, 2004) and to reduce risk (Kim et al.,
2011; Sweeney et al., 2008). These effort and risk reductions occurred
because ready access to information helped consumers better determine
which products from which vendors best met their needs and prefer
ences (Dellarocas, 2003). Previous studies have also shown that eWOM
had an impact on customer purchasing decisions (Bickart & Schindler,
2001; Chan & Ngai, 2011; Erkan & Evans, 2016; Park et al., 2007;
See-To & Ho, 2014; Sin et al., 2012). Positive reviews on the social
networks influenced customer perceptions and attitudes toward the
products and services of suppliers, promoting their adoption of infor
mation and purchase intentions (Lee & Shin, 2014; Park et al., 2007). In
terms of social media and tourism, the studies by Di Pietro et al., 2012;
Jalilvand & Samiei, 2012; Jalilvand & Heidari, 2016 showed that eWOM
had influence on travel destination image.
2.2. Information adoption model (IAM)
The Information Adoption Model (IAM) attempts to explain users’
knowledge transfer intention by measuring the likelihood that a person
will adopt transferred information (Fig. 1). IAM was the integration of
TAM (Davis, 1989) with dual process theories (Petty & Cacioppo, 1986).
With this integration, IAM aims to explain how people are affected by
the information on computer mediated communication platforms
(Sussman & Siegal, 2003). As shown in Fig. 1, the primary message of
IAM is that perceived usefulness is a mediating variable between influ
ence processes and information adoption. Hence, IAM addresses the
reason-based adoption motivation for information. As this model ex
plains the information on computer-mediated communication plat
forms, it is readilyapplicable to eWOM studies (Cheung et al., 2008,
2009; Shu & Scott, 2014). IAM has been used in studies related to eWOM
such as Erkan & Evans, 2016; Shu & Scott, 2014.
Although IAM has been applied in previous studies (Cheung et al.,
2008, 2009; Erkan & Evans, 2016; Shu & Scott, 2014) related to eWOM
adoption, determining destination attractiveness and behavior inten
tion, they only focused on the characteristics of information, mainly
quality, credibility, and usefulness. According to Knoll’s latest research
(2015), which reviews the recent eWOM studies undertaken in the social
media context, the influence of eWOM depended on both the informa
tion and the consumer. Consumer characteristics such as personal at
tributes (Casalo et al., 2011) or consumers’ regulatory mode orientation
(Lee & Koo, 2015) or users’ personality (Big Five Model) could influence
whether people seek eWOM-related information in the first place, and
how effective eWOM is. In addition, as some individuals are more
goal-oriented and risk averse than others, it was expected that such in
dividuals would utilize eWOM more since they could find social assur
ance/reassurance via eWOM (Bailey, 2005). Another reason for seeking
eWOM may be underlined for individuals who are heavily dependent on
others’ opinions and judgement since researchers of eWOM have found
that factors related to seeking eWOM include relevance and empathy
generation (Bickart & Schindler, 2001; Cheung et al., 2008), helpfulness
(Mudambi & Schuff, 2010) and information value (Weiss et al., 2008).
All these factors, which reflect the personality of online information
users, should be included in the study of the influence of eWOM on
users’ behaviors.
2.3. Big five model and social media use
One of the most popular and useful personality models is the Big Five
Model (Devaraj et al., 2008; Digman, 1990; Ewen, 1998) with five fac
tors: neuroticism, extraversion, agreeableness, conscientiousness, and
openness to experience (Barrick et al., 2001; Costa & McCrea, 1992;
Mouakket, 2018; Wehrli, 2008).
T. Tapanainen et al.

3
• “Neuroticism” describes the degree to which people are reactive to
stimuli in their environment. Neurotic people more easily become
unstable, worried, temperamental and sad. Sometimes, in place of
neuroticism, its polar opposite of emotional stability is used as the
term for this variable.
• “Extraversion” contrasts an outgoing character to a withdrawn na
ture. Those high in extraversion are adventurous, assertive, active,
sociable and talkative.
• “Openness” is a measure of depth, breadth and variability in a per
son’s imagination and urge for experiences. People with a high
openness to experience have broad interests, divergent thinking, are
liberal, have intellectual curiosity and like novelty.
• The “agreeableness” scale is associated with altruism, nurture, caring
and emotional support versus competitiveness, hostility, indiffer
ence, self-centeredness, spitefulness and jealousy.
• A “conscientious” individual is purposeful, strong willed, deter
mined, and goal oriented. The more conscientious a person is the
more competent, dutiful, orderly and responsible they become.
We conducted a literature review using the keywords: Big Five
Model, Social Media, and Technology Adoption, together with the
snowball method to find the relevant previous studies. The results of the
literature review are presented in Tables 1 and 2. As shown in Table 1,
previous studies attempted to integrate the Big Five Model with Tech
nology Acceptance Model (TAM) to investigate the impact of personality
on main technology acceptance variables, particularly perceived use
fulness. However, very few studies (only Koban et al., 2018 and
Svendsen et al., 2013) have studied the relationship between
personalities and behavioral intention and none of them used the In
formation Adoption Model.
Many recent papers have focused on the relationship between per
sonalities and social media use (Table 2). However, social media use in
these studies was defined as the amount of time spent on Facebook,
Facebook use frequency, the number of Facebook friends, and infor
mation posting. The link between personality and the structural facets of
Facebook (such as preferences for specific features of Facebook) has also
been investigated. However, this review showed that the mechanism by
which personalities impact on social media information usage has been
largely ignored in prior research. To fill this gap, we focus on the use of
information in social media and do so by applying the information
adoption model, which is particularly suited to problems of this type, but
has received less attention in the literature so far. In addition, we inte
grate this with the Big Five model to understand whether there is a link
between the personality traits of social media users and their informa
tion adoption behavior.
3. Research model and hypotheses development
3.1. Intention to choose a destination (DI)
Travel destinations are places that offer visitors the experience of
exploring landscapes, people, or cultural elements that meet their needs,
provide value, and can create valuable experiences (Agapito et al., 2013;
Räikkönen & Honkanen, 2013). The intention to choose a destination is
often defined as desire to visit a particular travel place (Chen et al.,
2014). The intention to visit a tourist site also implies a reasonable
calculation of the benefits and costs, or images created across different
sites through external sources of information including online infor
mation (Abubakar, 2016; Chen et al., 2014; Jalilvand & Heidari, 2017;
Jalilvand & Samiei, 2012), but is also influenced by individuals’ sub
jective personalities, preferences, and feelings (Casalo, 2011; Jani et al.,
2014; Jani, 2014; Leung & Law, 2010; Olga, 2015; Passafaro et al., 2015;
Fig. 1. Information adoption model.
Source: Adopted from Sussman & Siegal, 2003.
Table 1
Literature review between big five model and perceived usefulness (PU) and
behavior intention (BI).
Context Theories Sources
Social media The Big Five Model and the
confirmation-expectation model
Mouakket
(2018)
Social media The Big Five, The Dark Triad,
Impulsivity and sensation seeking
Koban et al.
(2018)
Social media The Big Five Model and Technology
Acceptance Model (TAM)
Rosen and
Kluemper (2008)
Social media Information seeking and Personality Sin and Kim
(2013)
Collaborative
Technologies
The Big Five and TAM Devaraj et al.
(2008)
The university’s
bulletin board
system
The Big Five and IS continuance Lin and Ong
(2010)
Word Processing and
Internet
The Big Five and Computer Based
Assessment Acceptance Model
(CBAAM)
Terzis et al.,
2011
Tourism Information
Search
The Big Five Model Tan and Tang
(2013)
Smart Phone The Big Five and TAM Ozbek et al.
(2014)
A software tool The Big Five and TAM Svendsen et al.
(2013)
Table 2
Literature review: Big five model and social media usage.
Context Theories Source
Facebook usage: key usage factors
including the time spent online,
the use of Facebook-specific
communication functions (i.e., the
Wall, personal messages) and
Facebook group membership
The Big Five and
Competency (Computer
Mediated Communication
(CMC)
Ross et al.
(2009)
Social media The Big Five, Life
Satisfaction, and Socio-
demographics
Correa et al.
(2010)
Facebook usage or non-usage
including Facebook features and
usage frequency
The Big Five and Shyness,
narcissism
Ryan and
Xenos (2011)
Facebook usage including number of
friends, photos, wall postings and
level of regret for inappropriate
Facebook content.
The Big Five Moore and
McElroy
(2012)

4
Tan & Tang, 2013).
3.2. Relationship between eWOM and intention to choose a destination
3.2.1. Information quality (IQ) and information credibility (IC)
In social media websites, people can use their nickname or account to
freely exchange their opinions and experiences about products or ser
vices with friends or acquaintances. This reduced anonymity has the
potential to make eWOM information more trustworthy and reliable
(Chu & Choi, 2011; Wallace et al., 2009). The study by Abubakar, 2016
and Jalilvand & Heidari, 2017 found that recommendations by previous
visitors could be taken as the most reliable information sources for po
tential tourists. On the other hand, when most social network users
could easily create eWOM in the process of connecting or interacting
with others, the quality and credibility of eWOM information is
becoming increasingly important (Erkan & Evans, 2016). The quality
and credibility of information are the signals that help customers eval
uate the usefulness of information in selecting the product or service
(Erkan & Evans, 2016; Prendergast et al., 2010; Nabi & Hendriks, 2003;
Wathen & Burkell, 2002). Thus, we argue that the quality and credibility
of the information has a positive effect on the perception of its usefulness
and indirectly on both the adoption of the information and the moti
vation for choosing a travel destination. Therefore, we hypothesize:
H1. Information quality is positively associated with information
usefulness.
H2. Information credibility is positively associated with information
usefulness.
3.2.2. Information usefulness (IU)
Information usefulness refers to users’ perception of the degree to
which information help improve work efficiency (Bailey & Pearson,
1983; Cheung et al., 2008). Users tend to engage with information when
it is deemed useful, so the usefulness of information is viewed as a
predictor of information retrieval (Davis, 1989; Sussman & Siegal,
2003). In the internet era, users often receive large amounts of online
word-of-mouth information about products or services (Chu & Kim,
2011). This information may be useful and be used as references for their
activities or behaviors. Previous studies have found that information
usefulness was one of the factors which promoted people to use eWOM
(Cheung et al., 2008; Erkan & Evans, 2016; Shu & Scott, 2014). The
usefulness of eWOM-based information could also come from reducing
risk and efforts of search and evaluation (Bronner & de Hoog, 2010;
Dabholkar, 2006; Goldsmith & Horowitz, 2006; Kim et al., 2011;
Sweeney et al., 2008). Therefore, we argue that people have more in
tentions of adopting information when they find it more useful and we
propose the following hypothesis: H3: Information usefulness is posi
tively associated with information adoption.
3.2.3. Information adoption (IA)
Previous research evidence suggests that positive information
reception has a significant effect on consumer purchase intentions
(Erkan & Evans, 2016; See-To & Ho, 2014; Wang et al., 2012). The
purchasing behaviors of social media users are under the influence of the
significant amounts of eWOM to which they are both intentionally and
unintentionally exposed daily (See-To & Ho, 2014; Wang et al., 2012).
Indeed, since the conversations in social media frequently refer to
brands (Wolny & Mueller, 2013), they could naturally be influential in
consumers’ purchase intentions (Wang et al., 2012). According to Erkan
and Evans (2016), who based their argumentation on IAM, the impact of
eWOM on behavioral intentions was a process of perceiving information
attributes regarding usefulness, adoption of information, and ultimately
intention. The results of previous studies in the tourism industry (Abu
bakar, 2016; Chen et al., 2014; Jalilvand & Samiei, 2012) also indicate
that eWOM has a significant impact on intention to select the travel
destination. Therefore, we hypothesize:
H4. Information adoption is positively associated with intention to
choose a destination
3.3. Relationship between personalities and intention to choose a
destination
3.3.1. Extraversion (EX)
Extravert people were found to be actively using, sharing and
exchanging information (Al-Samarraie et al., 2017; Heinstrom, 2005;
Tidwell & Sias, 2005) while sharing is one of the main functions of social
networks. Chen (2013) argued that extravert individuals found social
networks suitable for meeting their social needs because they offer
various communication features such as texting and video sharing.
Similarly, Rosen and Kluemper (2008) and Mouakket, 2018 argued that
extravert individuals found social networking sites to be useful. In
addition, extravert people, as frequent information seekers, used most of
the available resources (both formal and informal) to search for infor
mation (Heinstrom, 2005). Thus, they could easily adopt and use eWOM
for their information need. Previous studies (Correa et al., 2010; Panda
& Jain, 2018; Ross et al., 2009) have also found that outgoing person
ality tends to have positive perceptions of social networking usage and
compulsive smartphone usage. In the study by Ross et al. (2009), par
ticipants who had higher levels of extraversion were members of more
groups on Facebook than those with lower extraversion levels. The
finding of Correa et al. (2010) and Ryan and Xenos (2011) confirmed
that extraversion is a common characteristic of Facebook users. There
fore, we speculate that extroverted personality can affect information
usefulness perception, information adoption, and intention to choose a
destination of visitors when obtaining information shared on the
network. The following hypotheses are proposed:
H5. Extraversion is positively associated with perceived Facebook in
formation usefulness.
H6. Extraversion is positively associated with information adoption.
H7. Extraversion is positively associated with intention to choose the
destination.
3.3.2. Agreeableness (AG)
People who have accepting and agreeable qualities and who are less
egocentric were found to be high on zeal for information search
(Al-Samarraie et al., 2017; Heinstrom, 2005) and tend to be more pos
itive towards new technologies (Devaraj et al., 2008; Tan & Tang, 2013).
Previous studies (Lin & Ong, 2010; Mouakket, 2018) have indicated that
agreeableness has a positive effect on perceived usefulness on a uni
versity bulletin board system and intention to continue use of Facebook.
Similarly, Rosen & Kluemper, 2008 argued that since Facebook could
facilitate people to enhance their personal relationships with others,
agreeable individuals would be more likely to consider Facebook useful.
In addition, agreeable people were found to be much more diverse in
their information search patterns and were more frequent users of the
information acquired (Tan & Tang, 2013). Due to their positive,
accepting nature they felt satisfied with their information seeking pat
terns, which could encourage them to adopt and use information for
their decision-making. Koban et al. (2018), who studied the link be
tween personalities and uncivil commenting intentions in public Face
book discussions, found that agreeable people had intentions to act
based on the information from social media. Consequently, we expect
that individuals who are agreeable would consider Facebook a useful
communication tool. This leads to the following hypotheses:
H8. Agreeableness is positively associated with the perceived useful
ness of eWOM.
H9. Agreeableness is positively associated with information adoption.
H10. Agreeableness is positively associated with intention to choose a
destination.

5
3.3.3. Conscientiousness (CO)
Given the strong motivation of conscientious individuals to improve
their level of performance, we believed that they were likely to consider
the usefulness of information from social networks. The study by
Mouakket (2018) found that conscientious individuals had a positive
influence on the perceived usefulness of Facebook users. Hence, we
postulate that conscientious individuals would find eWOM to be useful
as it allowed them to be connected with people in their network faster
and perform their duties more effectively. However, dedicated people
who are highly disciplined may be less likely to be influenced by outside
factors such as social networks (Chittaranjan et al., 2013; Landers &
Lounsbury, 2006). Thus, they may be quite conservative about adopting
information on social networks. This kind of personality can be more
deliberate about receiving information and more hesitant when making
decisions based on information from social networks. In addition, people
with high scores on conscientiousness traits may try to focus on their
duties and then view media usage as a distraction. This may contribute
to reducing their social media usage. The finding of Ryan & Xenos, 2011
supported this argument by showing the significant negative link be
tween conscientiousness Facebook users and their time spent on Face
book per day. Thus, the following are hypothesized within the context of
eWOM on social media:
H11. Conscientiousness is positively associated with the perceived
usefulness of eWOM.
H12. Consciousness is negatively associated with impact on informa
tion adoption.
H13. Consciousness is negatively associated with intention to choose a
destination.
3.3.4. Neuroticism (NE)
“Neuroticism” as an of personality trait was found to be negatively
correlated with all the dimensions of information seeking behaviors
including motivation for search, information use, and resources utilized
(Heinstrom, 2005). Previous studies (Devaraj et al., 2008; Mouakket,
2018; Ozbek et al., 2014; Sin & Kim, 2013; Terzis et al., 2011) have also
argued that neurotic users likely view working with information systems
as threatening and stressful tasks; thus, they tend to have negative
feelings towards its perceived usefulness. In addition, these people
preferred accessing only a few familiar sources because of their inse
curity about new sources and information (Heinstrom, 2005). Due to
their doubts about their own abilities, anxiety, self-consciousness, and
impulse, they also tended to keep aloof from sharing or transferring the
information acquired (Heinstrom, 2005). Students with high neuroti
cism in the study by Panda and Jain (2018) were found to limit their
smart phone usage because of their anxiety. Therefore, we postulate that
a neurotic individual would not find eWOM to be useful, and moreover,
that they would not adopt information and use it for selecting travel
destinations as in the following hypotheses:
H14. Neuroticism is negatively associated with the perceived useful
ness of eWOM.
H15. Neuroticism is negatively associated with information adoption
from eWOM.
H16. Neuroticism is negatively associated with intention to choose a
destination.
3.3.5. Imagination (IM)
Imaginative people are often willing to try new things and look for
different experiences. Therefore, they tend to be innovative pioneers or
among the early adopters of new technologies and services (Constantiou
et al., 2006; Tuten & Bosnjak, 2001). The study by Svendsen et al., 2013
showed that innovation interest of imaginative users promoted their
intentions to use new technologies. Imagination or openness was an
important factor in predicting internet usage (McElroy et al., 2007) and
social media use (Correa et al., 2010). Ross et al. (2009) state that
imaginative traits may help to utilize new media tools such as social
networks more eaily than other group traits. Tan and Tang (2013) also
found that the higher individuals were in openness to experience, the
more willing they were to provide trip feedback to their friends via so
cial networks. Furthermore, in the study by Maican et al. (2019) open
ness was the only Big Five trait which had a direct effect on the use of
online technologies for work. People exhibiting openness were also
found to be positively correlated with diversity in information seeking
judgment and acquisition (Heinstrom, 2005). Therefore, we anticipate
that openness or imagination might have a positive impact on the
perceived usefulness of eWOM information, information adoption, and
intention to choose a destination through online information as in
following hypotheses:
H17. Imagination is positively associated with the perceived useful
ness of eWOM.
H18. Imagination is positively associated with information adoption.
H19. Imagination is positively associated with intention to choose a
destination.
3.4. Research model
Based on the literature review and hypotheses, the research model is
presented in Fig. 2 below:
4. Research methodology
4.1. Development of measurement tools
We used structured questions to investigate the intention to choose a
destination of the customer through surveys by handing out question
naires directly to each potential individual. Items of each construct in
the research model were adapted from previous studies (Bailey &
Pearson, 1983; Coyle & Thorson, 2001; Erkan & Evans, 2016; Park et al.,
2007; Prendergast et al., 2010). Particularly, the information quality
construct (IQ) was measured by three items from the study by Park et al.
(2007) and Erkan and Evans (2016). The information credibility
construct (IC) consisted of four items adapted from Prendergast et al.
(2010) and Erkan and Evans (2016). Three scales were adapted from
Bailey and Pearson (1983) and Erkan and Evans (2016) to measure in
formation usefulness (IU). Intention to choose a destination construct
(DI) (four items) was based on Coyle and Thorson (2001) and Erkan and
Evans (2016). The Big Five model short questionnaire version created by
Donnellan et al., 2006 was used to evaluate the personality traits of
participants in the survey. The questionnaire was translated from En
glish to Vietnamese and the back translation method was used to ensure
the equivalence of meaning in all items. Before conducting the official
survey, we conducted a pilot interview with 30 people, who were under
30 years old and were employees in enterprises (15 people), students in
a Foreign Trade University (10 students) and freelancers (5 people) to
check the meaning and understandability of the questionnaire. After
receiving comments from the pilot test, we reviewed these and made
adjustments to achieve a final questionnaire with 37 items (Appendix
A). The scales used in the study are 5-point Likert scales with 1 - totally
disagree and 5 - totally agree. The survey was conducted during the
three months from March to June 2017.
4.2. Sample collection
Potential participants were identified as individuals wishing to travel
or planning to travel in the coming year and using social networking
sites (i.e. Facebook and Instagram) which were the most popular social
networks in Vietnam. According to the report by the Ministry of Infor
mation and Communications in 2018, Vietnam had more than 60

6
million internet users (accounted for 70% of population) and of whom
more than 80% used social networks. Xiang and Gretzel (2010) and
Xiang et al. (2015) argued that social media is increasingly prevalent as
a tool in travel planning for consumers, particularly so among young
people such as those of Generation Y, who were also termed “digital
navies” (Prensky, 2001). While older generations also use social net
works, members of Generation Y very often participate in interactions
on social media networks that result in electronic word-of-mouth
(Bennett et al., 2008; Wesner & Miller, 2008). Therefore, as in previ
ous studies (Correa et al., 2010), the target group of this study was
young social media users (from 18 to 30 years old). The survey was
conducted in five different areas including Hanoi and Ho Chi Minh City;
the largest universities in Vietnam are located in these two cities. The
other three sites were tourist destinations: Lao Cai and Nam Dinh are
tourist areas in the northern part of the country and locations for the
famous Tran Temple and Sa Pa natural resort; Dak Lak is in the southern
mountainous area of the country, where the locally popular coffee
festival was ongoing during data collection. We delivered the ques
tionnaires to eligible potential participants. First, people were asked
about their background information including internet and social
network usage, and their travel experience and plans for the coming
year. If they had traveled or had a travel plan for the coming year, they
were invited to participate in the survey. With a 5% error rate (Saunders
et al., 2007), the sample size for the study should be at least 500.
Therefore, we distributed 800 and received 647 valid questionnaires,
which surpassed the abovementioned criteria. In terms of the observa
tions per indicator, which is typically required to be at least 10 (Bartlett
et al., 2010; Schreiber et al., 2006), our respondent number is sufficient,
resulting in a ratio of 17.49 observations per indicator (Nguyen, 2020).
The sample characteristics was listed in Table 3.
In this study, female participants outnumbered males (68.3%
compared to 317%). This reflects the fact that females are more likely to
participate in social networks (Ahn, 2011) and have been found to spend
more time on these sites (Raacke & Bonds-Raacke, 2008). Regarding
age, young users (from 18 to 30 years old) accounted for more than 85%
of the sample. Social networking site users were regular Internet users;
more than one third spent more than 2 h per day on Internet. Around
80% of participants had a bachelor’s or higher degree. As a result, more
than 75% of participants were students or office staff.
4.3. Data analysis methods
In this study, we used the multivariate data analysis method to
analyze and test the proposed hypotheses. Survey data was cleaned
before conducting the data analysis. The missing data values were
checked and processed by the multiple imputations method (Curley
et al., 2019) using the “mice” package in R software. There were 314
missing values out of 23,939 targeted values (i.e. 647 observations
multiple 37 items). Thus, the missing value rate is 1.3% and the missing
value rate per each item ranged from 0% to 5%. If the means of the
observed values were three times greater than the standard deviation,
these were considered as outliers, and such observations were excluded
from the dataset. In this study, we adopted scales from previous studies;
thus we used confirmatory factor analysis (CFA) with maximum likeli
hood (ML) to assess the overall fit indexes, the reliability and validity of
the factors in the model.
The popular criteria that are used to assess the overall fit indexes
include Chi-square/df, CFI, TLI, IFI, and RMSEA (Hair et al., 2010;
Hooper et al., 2008; Hu & Bentler, 1999; Kline, 2011; Schreiber et al.,
2006; Schumacker & Lomax, 2015). The cutoff of model fit indexes
differs between authors. Some scholars suggest that the Chi-square/df
should be less than 2 or less than 3 (Hair et al., 2010; Kline, 2011),
and even less than 5 in a large sample (West et al., 2012). Cutoff criteria
of CFI, TLI, and IFI are traditionally required to be above 0.9 (Hair et al.,
2010; Hooper et al., 2008; Kline, 2011; Schumacker & Lomax, 2015),
though other scholars suggest a more stringent requirement of 0.95 (Hu
& Bentler, 1999; Schreiber et al., 2006; West et al., 2012). Similarly,
with regard to the RMSEA value, some scholars propose a criterion of
Fig. 2. Research model.
Table 3
Sample classification.
Classification criteria Number of
participants
Percentage
(%)
Gender Male 205 31.7
Female 442 68.3
Age <18 10 1.5
18–24 469 72.5
24–30 90 13.9
>30 78 12.1
Education No schooling 4 .6
Primary school 1 .2
Lower Secondary
school
7 1.1
Senior High school 45 7.0
Junior College 73 11.3
University 491 75.9
Postgraduate 26 4.0
Occupation Student 223 34.5
Office staff 280 43.3
Freelancer 128 19.8
Business 8 1.2
Retired 6 .9
Homemaker 2 .3
Internet usage per
day
<2 h 130 20.1
2–4 h 253 39.1
4–8 h 181 28.0
>8 h 83 12.8

7
less than 0.05 (Browne & Cudeck, 1993; Steiger, 1990), whereas others
suggesting 0.08 (Hair et al., 2010; Hu & Bentler, 1999; Schumacker &
Lomax, 2015).
In this study, because the survey sample size was quite large (N >
500) and had the high observation-indicator ratio of 17.49 (mentioned
above), we chose the criteria for overall fit indexes between the rigorous
criteria and more relaxed criteria. Specifically, we judged Chi-square/df
to be sufficient with a value of less than 3 (Hair et al., 2010; Kline, 2011),
required CFI, TLI, IFI to be greater than 0.9 (Hair et al., 2010; Hooper
et al., 2008; Kline, 2011), and RMSEA to be less than 0.08 (Hair et al.,
2010; Hu & Bentler, 1999; Schumacker & Lomax, 2015).
If the factor loadings on items in the constructs are larger than 0.5,
the constructs in the model achieve convergence validity (Hair et al.,
2010). Regarding discriminant validity, we used the 95% confidence
intervals of the correlation coefficients between constructs in the model.
If the 95% confidence intervals of the correlation coefficients do not
contain one value, the constructs in the model reach discriminant val
idity (Anderson & Gerbing, 1988; Torkzadeh et al., 2003). We used
Cronbach’s Alpha, composite reliability coefficients to evaluate the
reliability of each construct. If these are greater than 0.6, the constructs
are reliable (Hair et al., 2010). Finally, structural equation modeling was
used to test proposed hypotheses with statistically significant at the 5%
level.
5. Data analysis and results
5.1. Measurement model
Before testing the hypothesized relationships, we accessed the reli
ability and validity of the measurement scales. After eliminating vari
ables with load factors less than 0.5 (i.e. IQ3, NE2 in Appendix A), the
analysis of data from 647 individuals indicated that the model was
compatible with the actual data (when Chi-square/df = 2.217 < 3; CFI
= 0.916; TLI = 0.902; IFI = 0.917 were all greater than 0.9; RMSEA =
0.043 < 0.08), and all the factor loading factors were greater than 0.5,
which indicated that the factors in the model were convergent. The
Cronbach’s Alpha and composite reliability coefficients were greater
than 0.7, which shows that the constructs in the model are reliable
(Table 4).
Analysis of the 95% confidence intervals with the correlation co
efficients showed that the maximum correlation coefficient with the two
IU - IA concepts was 0.883 (95% confidence intervals was 0.828–0.933),
which indicated that discriminant validity had been achieved (Table 5).
5.2. Structural model
The result of the structural equation modeling (SEM) showed that the
model was compatible with the actual data (Chi-square/df = 2.794, CFI
= 0.920, TLI = 0.908, IFI = 0.921, RMSEA = 0.053). Table 6 presents
the SEM analysis result.
In the information adoption model part, the estimation indicated
that Information Quality (IQ), and Information Credibility (IC) had a
positive impact on Information Usefulness (IU) (p-value < 0.05). In
addition, there was a clear impact of perceived usefulness on informa
tion adoption (β = 0.858, p < 0.05), and the adoption of information on
intention to choose a destination (β = 0.522, p < 0.05). In short, the
analysis result supports all the hypotheses related to information
adoption, namely H1, 2, 3, and 4.
Regarding the five constructs in the Big Five model, only three
constructs had an impact on Information Usefulness (IU), Information
Adoption (IA), and Intention to choose destination (DI). Specifically,
Agreeableness (AG) had a positive impact on Information Usefulness
(IU), and Intention to choose destination (DI) (p-value < 0.05). Extra
version (EX) had a positive on Information Adoption (IA) and Intention
to choose destination (DI) (p-value < 0.05), and Imagination (IM) had a
positive impact on Intention to choose destination (DI) (p-value < 0.05).
The other relationships in the model did not support the analysis result
(p-value > 0.05) (Table 5). In short, the analysis results support Hy
potheses H6, 7, 8, 10, and 19 but not H5, 9, 11, 12, 13, 14, 15, 16, 17 and
18. The relationships in the research model are described in Fig. 3
below.
5.3. Direct, indirect, and total effects
The intention to choose a destination in addition to the direct impact
of information adoption and personality is indirectly influenced by other
factors. Table 7 below shows the direct, indirect, and composite impacts
of the seven factors in the model on the intention to choose a destination.
The most influential factor is information adoption, following by in
formation usefulness and agreeableness, which is also the most impor
tant factor in the Big Five group, while the lowest impact comes from the
imagine factor.
6. Discussion
Electronic word of mouth (eWOM) is not expected to lose its
attraction to consumers in a growing information economy (King et al.,
2014). Recognizing that tourism is one area where consumers seek to
benefit from eWOM, this study investigated the relationship between
user personality, eWOM information content, and eWOM-informed
Table 4
Reliability analysis results.
Construct/
Items
Factor
loadings
(CFA)
Error
loadings
Composite
Reliability
Cronbach’s
Alpha
Information credibility (Mean = 3.122, SD = 0.716)
IC1 0.710 0.46 0.828 0.813
IC2 0.768 0.31
IC3 0.795 0.32
IC4 0.682 0.42
Information quality (Mean = 3.522, SD = 0.857)
IQ2 0.779 0.28 0.647 0.613
IQ1 0.600 0.48
Information usefulness (Mean = 3.822, SD = 0.721)
IU1 0.734 0.32 0.805 0.803
IU2 0.792 0.27
IU3 0.757 0.33
Information adoption (Mean = 3.746, SD = 0.665)
IA1 0.737 0.34 0.816 0.811
IA2 0.799 0.24
IA3 0.740 0.30
IA4 0.617 0.44
Destination intention (Mean = 3.466, SD = 0.684)
DI4 0.545 0.38 0.617 0.674
DI2 0.609 0.37
DI1 0.619 0.30
Extraversion (Mean = 0.532, SD = 0.857)
EX4 0.611 0.44 0.817 0.813
EX3 0.747 0.36
EX2 0.788 0.45
EX1 0.754 0.37
Consciousness (Mean = 3.459, SD = 0.792)
CO3 0.632 0.40 0.662 0.661
CO2 0.641 0.41
CO1 0.613 0.37
Imagination (Mean = 3.427, SD = 0.832)
IM4 0.633 0.41 0.825 0.823
IM3 0.844 0.42
IM2 0.856 0.43
Neuroticism (Mean = 3.270, SD = 0.892)
NE4 0.712 0.51 0.725 0.77
NE3 0.648 0.42
NE1 0.692 0.48
Agreeableness (Mean = 3.615, SD = 0.702)
AG3 0.641 0.62 0.685 0.716
AG2 0.650 0.33
AG1 0.656 0.31

8
selection of tourism destination. To this end, we applied the information
adoption and Big Five models.
6.1. Information adoption model (IAM)
The results of our research supported the relevance of information
adoption model in studying the role of eWOM in choice of travel
destination. This result was consistent with the conclusions of previous
studies on the Information Adoption Model (IAM) that the intention
behaviors of social media users depended on the process of receiving
information and information attributes including information quality
and credibility (Cheung et al, 2008, 2009; Erkan & Evans, 2016; Lee &
Koo, 2015; See-To & Ho, 2014; Shu & Scott, 2014; Wang et al., 2012).
Specifically, the study found that perception of information usefulness
could affect both adoption directly and motivation of the customer’s
destination selection indirectly.
We also noted that information attributes of eWOM including in
formation quality and information credibility directly affected the in
formation’s perceived usefulness, indirectly information adoption and
the intention to choose a tourist destination. This was consistent with
previous studies demonstrating that quality and credibility of informa
tion influenced perceived usefulness, information adoption and
purchase intention (Erkan & Evans, 2016; Park et al., 2007; Sussman &
Siegal, 2003). Quality and reliability were required for any shared in
formation to be perceived as useful, then accepted and conveyed in use.
This study affirmed that the characteristics of eWOM played an impor
tant role in the information adoption process and the user behaviors.
6.2. The big five model
This study aimed to identify the personality characteristics associ
ated with adopting and using information from social media for decision
making, particularly in considering travel destinations. We also inves
tigated the mechanism or process by which these characteristics could
impact the way people adopt and use eWOM. The results showed that
five personal traits had different levels of influence on individuals’
behavior when exposed to eWOM. Particularly, Facebook users evincing
extraversion, agreeableness and imagination, were more likely to use
eWOM in their decision making while conscientious and neurotic in
dividuals were not influenced by eWOM. In addition, extraversion,
agreeableness and imagination were significantly associated with dif
ferential stages of information adoption process.
In terms of specific hypotheses, the predictions that extraverts would
adopt and apply eWOM in their decisions were supported. These results
were consistent with previous studies such as Correa et al., 2010; Panda
& Jain, 2018; Ryan & Xenos, 2011. Correa et al. (2010) found that ex
traversion was the most important predictor of social media use while
Ross et al. (2009) concluded that individuals high on the trait of ex
traversion were found to belong to significantly more Facebook groups
and related positively to the use of the communicative features of
Facebook, such as the Wall and Chat. In the study by Sin and Kim (2013),
extroverts were found to use SNS more frequently than did introverts for
everyday life information seeking. However, in this study, we did not
find a positive relationship between extraversion and perceived infor
mation usefulness. This interesting finding could be explained by the
fact that extraverts were diverse and wider in their information searches
(Heinstrom, 2005). For extraverts, social media was convenient and
effective communication tools but they could also use other information
sources, apart from eWOM, as references to support their decisions. In
addition, Terzis et al., 2011 found that extroversion was positively
related to perceived importance and then indirectly to behavioral
intention toward computer based assessment (CBA). Therefore, other
reasons rather than information usefulness may lead extraverted social
media users to adopt eWOM for their decision making.
Second, the agreeableness factor was found not only to have a strong
and effective influence on perceived usefulness but also to be positively
Table 5
The result of discriminant validity test.
IC IQ IU IA PI NE EX AG CO IM
IC 1
IQ .660 (.564-
.757)
1
IU .517 (.446-
.591)
.515 (.395–643) 1
IA .527 (.446-
.597)
.504 (.382-
.613)
.883 (.828-
.933)
1
PI .563 (.452-
.665)
.556 (.430-
.672)
.694 (.601-
.777)
.736 (.654-
.814)
1
NE .264 (.155-
.372)
.198 (.059-
.319)
.166 (.053-
.277)
.241 (.132-
.349)
.343 (.206-
.467)
1
EX .344 (.253-
.440)
.324 (.205-
.429)
.340 (.246-
.431)
.400 (.303-
.488)
.636 (.548-
.719)
.375 (.251-
.493)
1
AG .275 (.160-
.347)
.378 (.260-
.487)
.383 (.274-
.481)
.364 (.251-
.469)
.643 (.552-
.736)
.264 (.130-
.395)
.513 (.397-
.642)
1
CO .321 (.208-
.421)
.207 (.077-
.325)
.293 (.175-
.398)
.337 (.217-
.440)
.433 (.302-
.551)
.567 (.421-
.703)
.464 (.341-
.571)
.429 (.304-
.548)
1
IM .257 (.160-
.347)
.207 (.092-
.322)
.205 (.097-
.306)
.217 (.115-
.317)
.475 (.369-
.573)
.346 (.211-
.464)
.275 (.166-
.388)
.444 (.312-
.551)
.350 (.207-
.457)
1
Note: The values in parentheses are 95% confidence intervals.
Table 6
Results of structural model and hypotheses tests.
Hypotheses Relationships Std.
Beta
Critical
ratio
p-value Accepted or
not
H1 IQ → IU 0.230 3.057 0.002 Yes
H2 IC → IU 0.331 4.890 <0.001 Yes
H3 IU → IA 0.858 15.262 <0.001 Yes
H4 IA → DI 0.522 9.380 <0.001 Yes
H5 EX → IU 0.089 1.471 0.141 No
H6 EX → IA 0.119 3.372 <0.001 Yes
H7 EX → DI 0.248 4.379 <0.001 Yes
H8 AG → IU 0.201 3.949 <0.001 Yes
H9 AG → IA − 0.054 − 0.958 0.338 No
H10 AG → DI 0.239 3.514 <0.001 Yes
H12 CO → IA 0.026 0.431 0.667 No
H11 CO → IU 0.105 1.370 0.171 No
H13 CO → DI − 0.058 − 0.750 0.453 No
H14 NE → IU − 0.081 − 1.200 0.230 No
H15 NE → IA 0.067 1.229 0.219 No
H16 NE → DI 0.016 0.235 0.814 No
H17 IM → IU 0.000 − 0.003 0.998 No
H18 IM → IA − 0.003 − 0.065 0.948 No
H19 IM → DI 0.183 3.646 <0.001 Yes

9
related to behavioral intention. These results were consistent with the
previous findings of Devaraj et al. (2008); Landers and Lounsbury
(2006); Lin and Ong (2010); Mouakket (2018); Soltani et al. (2013). For
example, in the study by Devaraj et al. (2008) and Soltani et al. (2013)
agreeableness was related to accepting new technologies since agreeable
users thought that the new technologies were important and useful in
accomplishing their daily work. Landers and Lounsbury (2006) also
concluded that people with high tolerance found social networks more
useful than people with low tolerance. In the study by Moore and
McElroy (2012) and Koban et al. (2018) agreeable individuals were
actively making postings about themselves or responses to provocative
statements on Facebook. In this study, agreeable trait was found to be a
positive predictor for using eWOM in selecting travel destination se
lection. More agreeable people were usually more easy-going and ac
commodating so they were more likely to be persuaded that an
information source has the desired information than were less agreeable
people (Tan & Tang, 2013). The agreeable people could also make new
friends more easily to create larger social networks which could create
Fig. 3. Results of the structure model.
Table 7
Direct, indirect, and total effects of the construct on intention to choose a destination.
Dependent variable Effect IM AG EX IC IQ IU IA
IU Direct 0.000 0.201 0.000 0.331 0.230 0.000 0.000
Indirect 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Total 0.000 0.201 0.000 0.331 0.230 0.000 0.000
IA Direct 0.000 0.000 0.119 0.000 0.000 0.858 0.000
Indirect 0.000 0.173 0.000 0.284 0.197 0.000 0.000
Total 0.000 0.173 0.119 0.284 0.197 0.858 0.000
DI Direct 0.183 0.239 0.248 0.000 0.000 0.000 0.522
Indirect 0.000 0.090 0.062 0.148 0.103 0.447 0.000
Total 0.183 0.329 0.31 0.148 0.103 0.447 0.522

10
more useful information for them. However, we found no association
between agreeableness and information adoption. Xu et al. (2016) and
Ross et al. (2009) also found that agreeableness did not influence the
adoption of mobile social applications or Facebook use. In other words,
for agreeable people, eWOM adoption was not the mediating variable in
their eWOM adoption process. Their information usefulness perception
could be strong enough to direct their behavior without adopting in
formation. In addition, the growing popularity of technologies and so
cial apps such as ‘Facebook’ and ‘Instagram’ could diminish the impact
of agreeable trait on adoption (Xu et al., 2016).
Regarding imagination, given the novel nature of information on
social media networks and as an alternative method of communication,
it was not surprising to find that imaginative people were willing to
build up the intention to adopt and use eWOM. There have been quite a
few studies linking the characteristics of imagination and technology
adoption in the literature. For example, the relationship between
openness and intention to use or actual use was positive in the studies by
Jani et al. (2014); Maican et al. (2019); Koban et al. (2018). Similarly,
Jani et al. (2014) generalized that the higher the openness to experience,
the more and diverse travel information was sought through the
Internet. However, despite intentions to select a destination based on
eWOM, people with imagination did not adopt and perceive eWOM as a
useful information source. Previous studies likewise found no evidence
to support the link between openness and perceived usefulness. For
example, Sin and Kim (2013) found that even though international
students valued social media, students with imagination did not think
that information from these sources was useful. Similarly, Devaraj et al.
(2008); Rosen and Kluemper (2008); Terzis et al. (2011) found that
imagination trait had no influence on perceived usefulness among uni
versity students using Facebook or computer-based assessment (CBA)
system or collaborative technology. This result could be explained by the
interest that people high in imagination have in information perceived
as new. However, even in spite of this interest, they will not necessarily
be moved to adopt information for their decision-making, as prescribed
by IAM, necessitating the addition of other theories to better explain
their behavior.
Finally, none of hypotheses regarding conscientiousness and
neuroticism being connected with information usefulness, information
adoption and destination intention was supported. Most previous
studies have likewise found no relationship between conscientiousness
and perceived usefulness and behavior intention (Koban et al., 2018;
Moore & McElroy, 2012; Ozbek et al., 2014; Ross et al., 2009; Terzis
et al., 2011; Xu et al., 2016). On the other hand, the relationship be
tween neuroticism and perceived usefulness was inconsistent in previ
ous studies. Some of them (e.g. Koban, 2018; Sin & Kim, 2013; Xu et al.,
2016) did not find the evidence supported while others (e.g. Devaraj
et al., 2008; Correa et al., 2010; Jani et al., 2014; Mouakket, 2018;
Ozbek et al., 2014; Terzis et al., 2011) found that neuroticism had an
impact (either negative or positive) on perceived usefulness and social
media use. Some other studies have reported influence of neuroticism on
intention to use and actual use via mediating variables such as work
engagement in communication and collaboration applications (Maican
et al., 2019) and self-presentation in Facebook use (Nadkarni & Hof
mann, 2012).
7. Implications and conclusion
From a theoretical point of view, our research confirmed the appli
cability of the Information Adoption Model (IAM) in eWOM with regard
to tourist destinations. Prior research has relied mainly on the technol
ogy acceptance model, and while IAM is partly a derivation of TAM, our
study represents an added level of sophistication to the literature. Our
results proved that the Information Adoption Model (IAM) proposed by
Sussman and Siegal (2003) was applicable to eWOM studies. This result
was also confirmed in previous eWOM studies such as Cheung et al.
(2008) (2009); Shu and Scott (2014); however, in this study, we
integrated IAM with personality theories (i.e. Big Five Model), and
added social media users’ intentions to choose destinations as a
dependent variable. The result showed the importance of information
attributes when people adopted information from social networks. In
formation quality and credibility had a positive impact on information
usefulness, adoption, and intention to use. Information usefulness and
adoption were the main factors influencing intention to select travel
destinations. These results confirmed that eWOM enabled social media
users to obtain useful information.
We also exposed the association of individual personality traits to the
process of adopting information and influencing the individual’s in
tentions with regard to choosing a tourism destination. In other words,
our study revealed that the personality of social media users affects
whether they view eWOM as being able to provide the useful informa
tion and whether they should adopt eWOM and use it in their decision
making. Previous studies, while considering personality in eWOM
adoption, do not account for the linkage of personality and behavioral
intention (including adoption behavior). The model developed in this
study therefore extended IAM through considering the personalities of
eWOM users regarding information adoption and behavioral intention.
The results of this study proved that the influence of eWOM on behav
ioral intention not only depends on the characteristics of eWOM infor
mation but is also affected by eWOM users’ personalities.
To be specific, three personality traits, agreeableness, extraversion,
and imagination, affect the destination intention by differentiating the
information adoption process. In particular, extraverts adopted tourism
information from social media sources and used this in their destination
selection. Agreeableness had a positive influence on the perception that
eWOM-related sources contained useful tourism information, while
openness to experience had a positive influence on destination intention
without information usefulness perception and information adoption.
Two personality traits, neuroticism and conscientiousness, stand out
from other personality traits and have no effect on any main variables in
the information adoption process. In the context of tourism, the results
added to the research of Tan and Tang (2013) and Jani et al. (2014), and
showed that it is worthwhile to explore the influence of personality traits
on tourism information sources and feedback channels.
On the practical side, the results can help prevent wastage of time
and resources for both firms and customers, and timely interventions by
the firms for streaming the motivation for using eWOM among the social
media users. As this study found that consumers’ selection of tourism
destinations is influenced by eWOM, it underlines the need for tourist
firms to build tourism brands through social networks. Even in a fast-
changing tourism market such as Vietnam, consumers seek informa
tion for their travel planning; hence, firms need to (i) create word-of-
mouth channels such as fan pages, forums, social network groups
introducing and sharing information about tourist destinations; (ii)
focus on providing useful, accurate, complete and timely information
through social network channels to visitors; and (iii) personalize visi
tors, thereby suggesting a list of appropriate travel locations and services
to reduce the search time for travelers.
Furthermore, the results show the importance of information attri
butes when shared through social networks since the information
quality and credibility had a positive impact on information usefulness,
adoption, and intention to use. Therefore, tourism firms should ensure
the quality, credibility, and timeliness of information on their social
media. It is critical to provide basic information that is high in quality
and credible. In addition, customers usually search for reputable fan
pages or groups on a social network that are objective and not directly
endorsed by companies. Therefore, companies should ensure that their
services are also visible on social media that are built “by customers for
customers”, and not only their own web sites, where information may be
viewed as “promotion”, and hence less credible.
Third, this study found that individuals’ personality traits play a role
in the use of information from interactive social media in decision-
making. Therefore, tourism firms, organizations, and designers should

11
pay more attention to the individual characteristics of customers when
designing their information channels. In addition, our study found that
some dimensions of personality tare better predictors of eWOM usage
than others. Particularly, this study found that individuals’ personality
traits – extraversion, agreeableness, and openness to experiences – play
a role in the adoption and use of information on interactive social media,
which implies that marketing departments could use eWOM to approach
these customers. The other two personality traits, namely neuroticism
and conscientiousness, were not linked to eWOM, suggesting that social
media campaigns related to travel may be of less use in influencing the
behavior of customers with these traits. Hence, marketing departments
of tourism companies should consider other ways to reach such cus
tomers. For example, the official information channels, information
websites of tourist organizations or information centers could be more
useful than eWOM in these cases.
While our research probed the personality link to eWOM-related
information adoption, it appears that the IAM model is insufficient to
fully explain what role personalities play in this relationship. Personality
is a very fundamental human feature which influences our behavior in
complicated ways. Thus, we would suggest future research to involve
more variables that can complement the IAM model in explaining in
formation adoption from eWOM. In the case of extraversion and
agreeableness, which are connected to social relationships, the investi
gation of subjective norms and brand image might help in explaining the
adoption behavior of consumers with such personalities. For the trait of
imagination, understanding the subjective perceptions of information
novelty could help future study designs. Personality could also play a
moderating role in the relationship between technology usefulness and
intention to use and between subjective norms and intention to use.
Therefore, future studies should focus on developing more comprehen
sive information on personality-linked variables linked to information
use on social media.
While our data sample is Vietnamese, and there is some skewness in
the data toward female and highly educated respondents, this should not
affect the results as Big Five personality traits are seen to be universal,
irrespective of culture, gender, and education. An individual’s person
ality is not assumed to change on reaching adulthood. However, because
personality is a basic building block of our behavior, and it may be
expressed differently in different contexts, future research should try to
verify our results using different populations.
Credit author statement
Tommi Tapanainen: Conceptualization, Methodology, Writing-
Reviewing and Editing. Hai Nguyen Thi Thanh: Conceptualization,
Methodology, Writing – original draft preparation. Trung Kien Dao:
Conceptualization, Investigation, Formal analysis, Methodology,
Writing – original draft preparation.
Acknowledgements
This work was supported by a 2-Year Research Grant of Pusan Na
tional University.
Appendix A
Factor Question References
1. Information Adoption Model
Information Quality (IQ)
Information about travel destinations which are shared by my friends in social media, I think they are
IQ1: generally understandable to everyone
Park et al. (2007); Erkan and Evans (2016)
IQ2: clear
IQ3: updated regularly*.
Information Credibility (IC)
Information about travel destinations which are shared by my friends in social media, I think they are
IC1: persuasive
Prendergast et al. (2010); Erkan and Evans (2016)
IC2: reliable
IC3: accurate
IC4: complete and detailed
Information Usefulness (IU)
IU1: I think information on social networks makes it easier for visitors to select the destinations. Bailey and Pearson (1983); Erkan and Evans (2016)
IU2: I think information on social networks increases the effectiveness of destination choices.
IU3: I think information on social networks brings benefits such as updated information and different options for visitors.
Information Adoption (IA)
IA1: Information on social networks makes me more confident in my travel destination choice Cheung et al. (2009); Erkan and Evans (2016)
IA2: Information on social networks enhances my effectiveness in selecting the travel destination.
IA3: Information on social networks makes the destination choice easier
IA4: Information on social networks promotes me to select the travel destination.
Intention to choose a destination (DI)
After considering information about travel destinations which are shared by my friends in social media …
DI1: I often have the intention to travel.
Coyle and Thorson (2001); Erkan and Evans (2016)
DI2: I quickly decide on a travel destination.
DI3: I will select the recommended destination next time I want to travel.
DI4: I will recommend the travel destination shared on social media to my other friends.
2. Five Big Factor Model
Extraversion
EX1: Like participating in parties Donnellan et al. (2006)
EX2: Like communicating with others in parties
EX3: Be enthusiastic
EX4: Not a closed person
Agreeableness
AG1: Easily empathize with the feelings of others Donnellan et al. (2006)
AG2: Pay attention to the problems of people around
AG3: Easily understand the emotions of others
AG4: Have a tendency to be interested in the people around
(continued on next page)

12
(continued)
Factor Question References
Consciousness
CO1: Immediately do work after receiving it Donnellan et al. (2006)
CO2: Always remember and leave the widgets in their old location after use
CO3: Live in principle, discipline
Neuroticism (psychological stabilization)
NE1: Constant and stable mood Donnellan et al. (2006)
NE2: Always feel comfortable with yourself*
NE3: Not easily angered
NE4: Not easily bored
Imagination
IM1: Have a vivid imagination Donnellan et al. (2006)
IM2: Always interested in abstract things
IM3: Feel comfortable and easy to understand complicated, abstract issues
IM4: Good at imagining
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Tommi Tapanainen: Tommi Tapanainen is assistant professor at the Department of Global
Studies of Pusan National University, Republic of Korea. He has published in the Inter
national Journal of Healthcare Information Systems and Informatics, the International
Journal of Healthcare Technology and Management, and the Electronic Journal of Infor
mation Systems in Developing Countries. His research interests are in the fields of IS
agility, incident response, adoption and e-Health. He is a member of the Association of
Information Systems and the ISACA.
Hai Nguyen Thi Thanh: Dr. Hai Nguyen holds Bachelor from National Economics Uni
versity of Hanoi, Master of Commerce from Sydney University, Australia, and Ph.D. from
Waseda University, Japan. Her research focuses on information system adoption, dynamic
capabilities and strategies and e-Health. Together with her colleague, her work has
appeared in the Information Processing and Management, International Journal of Med
ical Informatics, Journal of Information Systems for Developing Countries.
Trung Kien Dao: Trung Kien Dao is a lecturer at Faculty of Economics and Business,
Phenikaa University, Vietnam. He holds a Master’s degree in Business from Hanoi Uni
versity of Science and Technology (HUST), Vietnam. Currently, he is also a Ph.D student at
HUST. He works as a statistician on many projects at Phenikaa University and HUST. His
research interests include entrepreneurial intentions of student, consumer behavior, and
intention to use e –services, innovation and dynamic capabilities in firms. He has been
published in some journals such as Electronic Journal of Information System in Developing
Countries, International Journal of Innovation and Learning, Journal of Information Sys
tem, International Journal of Business and Globalisation and conferences such as Americas
Conference on Information Systems (AMCIS), and Asia Pacific Management Research
Conference.

Impacts of online word-of-mouth and personalities on intention to choose a destination

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