Who are more responding to marketing on social media? A motivation-based segmentation of Facebook users in Vietnam
Khai Trieu Tran, Quyen Phu Thi Phan/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Who are More Responding to Marketing on Social Media?
A Motivation-based Segmentation of Facebook Users in
Vietnam
Khai Trieu Trana*, Quyen Phu Thi Phanb
a, bFaculty of Marketing, University of Economics – The University of Danang, Danang City, Vietnam
bPhD Student, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic
A B S T R A C T
This study (1) explores users’ motivations to use social media in Vietnam market, (2) identifies different user
segments based on these motivations, and (3) examines relationships between the user segments and their
responses to marketing activities on social media. Facebook is selected as the social media platform of choice
due to its popularity. We conduct a survey on a sample of 429 young Facebook users in Vietnam. Findings
show that the motives for Facebook use can be identified as helping, social connection, social creation,
entertainment, working, information seeking, learning, and status seeking. Using cluster analysis, we identify
five Vietnamese Facebook user segments, namely Maximizers, Socializers, Middle of the road, Information
seekers, and Laggards. Moreover, users with different motivations for Facebook use have varying levels of
responses to Facebook marketing. These findings provide firms with important implications for designing and
implementing their marketing strategies on social media environments.
Keywords: motivation; segmentation; social media marketing; Facebook; Vietnam.
1. Introduction
Social networking sites (SNSs) have become one of the most noticeable marketing tools for firms due to
their marketing potential. Of several SNSs, Facebook is now with over one billion registered users globally
(Statistic, 2018). In online retailing sector, Facebook accounts for 64% of total social revenue (Business Insider
2015). According to Facebook (Q4, 2017), 42% of marketers report that Facebook is critical to their business. It
is undoubted that Facebook is one of the most prominent marketing tools in the heart of social commerce. From
consumers’ perspective, consumers are spending significant amount of their time to present on Facebook
(Nielsen, 2011). To know about product offerings and get updates on preferred brands, consumers do not
necessarily visit a company’s websites or stores, but through its Facebook pages. Therefore, companies are
harnessing this technology by integrating social commerce into their operations and sales (Turban et al., 2017).
Despite much investment on Facebook, 55% of users support a company’s Facebook page, and they do not want
to see posts from companies or to visit the company’s page again (Lee & Bae, 2016). In fact, companies are
getting more difficulty in keeping in touch with customers and engaging them for a long term (Lee & Bae,
2016). Understanding how consumers interact with and respond to marketing on SNSs presents a challenge.
There is little empirical research to understand Facebook users and their responses to marketing efforts on
Facebook (Lee et al., 2011). We address this by conducting a Facebook user segmentation study.
* Corresponding author. E-mail address: trantrieukhai@yahoo.com
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Market segmentation or classification is an important step in understanding characteristics of social media
users (Dibb, 2005). Segmentation in general and segmentation of SNS users in particular offers various
advantages for businesses (Shao et al., 2015). For instance, companies can know what types of people respond to
messages they post on their Facebook pages, and how these people respond to different types of messages.
Additionally, segmentation can help companies optimize their messages so that they resonate with each audience
group’s characteristics (Daniel Kushner, 2015). Previous research has employed various segmentation
approaches to classify SNS users, such as segmenting based on user usage, user motivations to participate (Shao
et al., 2015; Foster et al., 2011), user activities on SNSs (Lorenzo-Romero & Alarcón-del-Amo, 2012), or user
reactions to social media marketing (Campbell et al., 2014). These studies have successfully identified distinct
user typologies primarily in western contexts. However, limited attention has been paid to connect the resulting
segments and their reactions to marketing efforts. Recent studies reveal that experiences with SNSs affect user
responses to online shopping and advertising (Lee & Bea, 2016; Lee et al., 2011; Shao et al., 2015).
Furthermore, SNS users’ motivations are likely to stimulate users to engage in viral advertising and marketing,
and electronic word of mouth (Libai et al., 2010; Campell et al., 2014). Antoniadis et al. (2017) suggest that
further research on SNS user segmentation should focus on how segments vary in their behaviors and their
patterns of SNSs usage.
Regarding the research context, previous studies have predominantly segmented SNS users or consumers
among samples from Western countries. In Western cultures, people are often characterized by individualism
that focuses on personal values such as independence and personal goals (Hofstede, 1980). On the contrary,
Triandis et al. (1990) argue that individuals are interdependent to each other, and group identity is more critical
than personal identity. Vietnamese people represent users with strong collectivist and social values in which
being socially informed and social connections are more concerned. Thus, this study extends SNSs literature by
identifying and characterizing segments of Facebook users in Vietnam. In addition, the study aims to examine
differences in responses to marketing efforts on Facebook between different segments.
2. Literature Review
2.1 Market segmentation
Market segmentation is a traditional marketing tool which groups potential consumers into distinctive
categories (LaRose & Eastin, 2004). It assists marketers to create rich portfolios of consumer information that
can be used in designing a targeted marketing strategy (Wedel & Kamakura, 2012). In the online setting,
consumers’ demographic, psychographic and behavioral characteristics are increasingly diversified (Lee & Ma,
2012). In order to understand different characteristics of online consumers, research attention has concentrated
on segmenting online consumer market. Identifying user segments on online environment means classifying
users with diversified characteristics into meaningful categories. In the SNS context, users form a heterogenic
consumer group in terms of their demographic characteristics, motivations, interest, responses to marketing and
purchasing behavior (Perrin, 2015). Consumer segmentation in SNS environment has been explored by using a
number of segmentation bases such as consumers’ motivations, demographics (Park & Yoon, 2009),
preferences, attitudes, beliefs, activities (Shao et al., 2015), and shopping behavior (Campbell et al., 2014).
However, with respect to Facebook, there is tendency to consider users as a single segment rather than
understanding different user segments on this SNS platform (Shao et al., 2015). This research attempts to
segment Facebook users based on their motivations and demonstrates how consumers in these segments can
respond to marketing activities.
2.2 Consumers’ motives for engaging in social media
Facebook is built based on computer-mediated SNSs. Previous studies on motivations for engaging virtual
world can be extended to motivations to use Facebook. Motivation of SNS users are explored from a number of
different perspectives. One approach is to use the uses and gratifications theory (Blumler, 1979), which explains
why an individual makes a media selection and the satisfaction of needs, interests and objectives that he/she
achieves from this selection. Based on this theory, Park and Yoon (2009) identifies four primary motivations for
Facebook participation including: socializing, self-status seeking, information seeking and entertainment value.
In particular, Facebook provides users with social benefits to communicate with others, such as the ability to
keep in touch with friends, make new friends and locate old friends. Entertainment dimension of Facebook is
applicable to virtual communities through playing with interactive tools such as online games. Self-status also
becomes a strong motivating force for social media participation because it is able to enhance user self-status or
peer admiration. Meanwhile, consumers can seek and exchange information with other individuals participating
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in the same virtual communities in SNSs. Other studies consider motivations of SNS users with regard to two
dimensions: utilitarian and hedonic motivation (Campbell et al., 2014). Utilitarian motivations relate to the
retrieving information, while hedonic motivations are defined as consumers’ enjoyment and escape (Korgaonkar
&Wolin, 1999). Online users motivated by utilitarian values seek the convenience of saving time or the ease of
accessing information. For users motivated by hedonic values, they have a desire for entertainment and escapism
(Anderson et al., 2014).
Another approach looks at user motivations through their activities on social media. A few studies have used
an unifying theory “4Cs” (connect, create, consume a control) to explain in part why so people spend their time
using social media and why social media are so popular (Hoffman & Novak, 2011). Users can use SNS
platforms to share their information with their friends thus connecting people together. Users can create (i.e.
post, upload, comment) and consume (i.e. read, watch, listen to) content through applications of social media.
Finally, users can actively control the application like page layout, tagging, or online setting such as profile or
privacy functions. Then, Hoffman & Novak (2012) tested the reasons people use social media by using the 4Cs
theory. They found four social media goal pairs (connect-consume, connect-create, control-consume, and
control-create) and this shows that motivations differentially drive social media goas and users with different
primary social media goals differ in perceptions of well-being. Additionally, Chung et al. (2016) adopted the
3Cs – connecting with others, creating content, and control over the user experience in order to distinguish four
segments of consumers who support social ventures.
2.3 Consumers’ responses toward marketing activities on social media
Social media on which consumers are spending majority of their time (Nielsen, 2011) provide a new
landscape for marketing practices (Campbell et al., 2014). This opportunity brings about “social media
marketing”, which refers to the practices that marketers use social media platforms to promote their products or
services (Felix et al., 2016). Social media marketing is becoming more popular for both practitioners and
researchers (Shaltoni, 2016). It is a powerful way for businesses to reach customers through connections
between brands and consumers. Particularly, social media tools provide opportunity for companies to engage
and interact with potential and current consumers, enhance a sense of intimacy with them, and thus strengthen
important and meaningful relationships with the consumers, especially in competitive business environments
(Davis Mersey et al., 2010). Additionally, it allows consumers to share information about products or services to
others, and co-create values with companies online, which can result in offline benefits.
Having consumers engaged in social media marketing requires an understanding of individuals’ motivations
and connecting these motivations to users’ usage and adoption. Previous studies have discussed the relationship
between motivations and consumer willingness to join a Facebook brand page community (Shao et al., 2015).
There is evidence that different motivations of users are associated with different attitudes and behaviors, for
instance, socializing and information seeking motive were found to be important for joining or posting on
Facebook brand page community (Shao et al., 2015). Cho et al. (2014) also found that Facebook users with high
motivation for status seeking tended to have positive intention to share information about products or services
they have used. Given that individuals’ motivations to use Facebook are diverse, they constitute an effective
basis for segmentation activities. However, few studies have linked motivation-based segmentation to users’
responses to marketing activities. Shao et al. (2015) suggests that future research should examine the effect of
motivational segments on consumer interaction with Facebook brand communities. For instance, Foster et al.
(2011) investigated social media user segmentation and how it influences on brand management in SNSs
context. Consumer responses to social media marketing are also shown to be heterogeneous (Campbell et al.,
2014). However, this perspective ignores the motivations that prompt consumers to engage in social media, as
well as the contextual effects of social networking on consumers’ perception and responses. Thus, this study
explores the relationship between motivation-based segments of social media users and their responses to social
media marketing. According to Campbell et al. (2014), consumers’ reactions to social network marketing
encompass brand engagement, word of mouth referral behavior and purchase intention. Drawing on Campbell et
al.’s (2014) approach, this study measures consumers’ responses to marketing activities based on these
constructs.
3. Methods
3.1 Samples
We used a convenience sampling technique to recruit respondents due to the time and resource constraints.
The sample included 429 Vietnamese respondents, who had used at least a Facebook account at the time. The
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sample was characterized by: most females (70%), single (95%), low income (82%), university students (86%),
and young age (mean = 20.9 years, SD = 3.5). Although the sample is highly bias, it generally reflects
characteristics of the target market of Facebook (i.e., young and active people with high level of education and
technological savviness).
3.2 Procedure
This study adopted a quantitative methodology. Data were collected by means of a survey. Participants were
asked to complete a self-administered survey in the form of either online or paper-and-pen questionnaire. The
questionnaire contained several items asking about (1) motivations for using Facebook, (2) consumers’ reactions
to marketing activities on Facebook, and (3) demographics (age, gender, marriage status, education and income)
and their Facebook usage (experience with Facebook, frequency of Facebook use, average time on Facebook
daily, average time on Facebook per access, number of Facebook accounts, devices to access Facebook). A pilot
test was conducted with a small group of Facebook users to check for clarity and comprehensiveness of the
survey items.
3.3 Measures
The Facebook use motivations were measured by 34 items compiled from several previous studies (Chung et
al., 2016; Hoffman and Novak, 2012; Heinonen, 2011; Foster, West & Francescucci, 2011; Park et al., 2009;
Shao et al., 2015). Consumers’ reactions to marketing activities on Facebook were examined in terms of
purchase intention, eWOM intention, and brand engagement according to Campbell et al. (2014). These user
response subscales (i.e., brand engagement, eWOM referral intention and purchase intention) were adapted from
Campbell et al.’s (2014) study. All items measuring motivations and reactions to Facebook marketing were
measured on five-point Likert scale, anchored by 1 = strongly disagree. 5 = strongly agree.
3.4 Data analysis
Exploratory factor analysis and reliability analysis (Cronbach alpha) were employed to adjust and validate
the motivations and responses to marketing on Facebook scales. Subsequently, the cluster analysis was used to
classify social media users into meaningful sub-groups based on their motivations of using Facebook. Cluster
analysis relates to a group of multivariate techniques whose primary purpose is to group objects into relatively
homogeneous groups, so-called clusters, based on the characteristics they possess (Hair et al., 2010). Finally,
ANOVA was used to determine if there were significant differences in how segments/clusters response (i.e.,
engage in, intend to purchase, and intend to make referrals) to marketing on Facebook.
4. Results
4.1 Motivations to use Facebook
Exploratory factor analysis (with the extraction method of Principal Component Analysis and the rotation
method of Varimax) was conducted on a pool of the 34 motivation items to identify the underlying motivation
dimensions. In the final solution (KMO = 0.846; χ2 (df = 378) = 4267.288; p < 0.001), 28 out of the original 34
items were retained, resulting in eight motivation factors which accounted for 63.8% of variance in the data. The
first factor, helping included two items focused on helping others and getting support from others. Five items
comprised the second factor, which described aspects of social connection, which is, using Facebook to
maintain current relationship. Three items formed a scale assessing social creation: socialize with anonymous
people, meet new people, interact with peoples who are the same hobbies. Entertainment consisted of four items
focused on entertaining, escape, funning/interesting on Facebook. Additionally, four items for work, three items
for information seeking, three items for learning, and four items for status seeking. Table 1 shows that all factors
met criteria for convergent validity (all items’ factor loadings were above 0.4), discriminant validity (no
significant cross-loading between the factors), and reliability (Cronbach’s alpha values were above 0.6, and
item-total correlations were above 0.3).
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Table 1: EFA and reliability analysis for the motivation measurement model
Item-
Total
Cor.
Factor
loading values
Eigen-
Cronbach
Alpha
Motivation constructs
Helping
MOT1
MOT2
Gaining a sense of helpfulness to others
Being helpful to others
0.863
0.865
1.005
2.352
0.621
0.760
0.727
0.621
Social connection
MOT4
MOT5
MOT6
To stay in touch with people I know
Understanding about friends and acquaintances
0.693
0.748
0.503
0.552
0.426
Creating and managing social network of friends and 0.584
acquaintances
MOT7
MOT8
Connecting with people I can’t meet directly
Reconnecting with people I’ve lost touch with
0.547
0.594
0.480
0.501
Social Creation
MOT9
Sharing and experiencing with others
0.661
0.775
0.693
0.498
0.563
0.409
0.673
0.743
MOT10 Meeting new people
1.311
1.764
MOT11 Interacting with groups that share my interests
Entertainment
MOT14 Seeking/ Providing emotional support
MOT25 It is entertaining
0.534
0.684
0.782
0.732
0.493
0.559
0.497
0.633
MOT26 Escaping the real world for a while
MOT27 It is funny and exciting
Work
MOT16 To connect or build relationship about my business
MOT17 To develop my career
MOT18 Promote myself or my business
MOT33 Network for business/professional purposes
Information Seeking
0.482
0.776
0.775
0.801
0.464
0.664
0.624
0.572
2.088
0.775
MOT19 Updating information about events what is happening 0.720
0.475
0.434
0.429
MOT20 Seeking/providing advice
MOT21 To get useful information about product/ service
Learning
0.652
0.524
1.132
6.710
0.636
0.894
MOT22 Learning about unknown things
MOT23 Learning about useful things
MOT24 Exploring about new things
Status seeking
0.860
0.864
0.786
0.795
0.818
0.761
MOT28 I feel peer pressure to participate
MOT29 It makes myself look cool
MOT30 I am invited
0.652
0.766
0.752
0.642
0.390
0.585
0.477
0.450
1.495
0.690
MOT31 It is a new trend
4.2 Motivation-based segmentation of Facebook users
A two-stage cluster analysis was applied on the basis of eight motivation dimensions identified by the
preceding factor analysis (i.e., helping, connection, creation, entertainment, work, information seeking, learning,
and status seeking). The values of these clustering variables were obtained by averaging the values of their
corresponding items. First, a hierarchical cluster analysis using Ward’s method was conducted to determine the
number of clusters (using agglomeration coefficients) and cluster’s centroids. The results suggested a three to
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six cluster solution. In the next step, we conducted four non-hierarchical (k-means) cluster analyses for these
four possible solutions to select the best one. Mean values of each cluster from the hierarchical cluster analysis
were used as initial seeds of the non-hierarchical cluster analysis. Consequently, the comparison between the
four analyses favored for the five cluster solution based on the relationships between cluster solutions and the
background variables, as well as existing literature. ANOVA showed that the differences in the clustering
variables (motivation factors) between the five clusters were statistically significant (p < 0.001).
Table 2. Results of Cluster Analysis
Seg. 1 Seg. 2 Seg. 3 Seg.4 Seg.5 Total
N=123 N=141 N=70 N=67 N=28 N=429
F
Motivations
(4,
p
424)
Helping
3.8
4.3
3.9
3.8
3.8
4.2
4.4
2.6
2.5
4.0
3.9
3.7
3.4
3.8
3.9
2.7
3.4
3.8
3.4
3.0
2.8
3.4
3.2
2.0
2.7
3.8
2.8
3.2
3.2
4.0
4.3
2.0
2.1
2.9
2.5
2.4
2.0
2.5
2.7
2.1
3.0
3.9
3.5
3.5
3.3
3.8
3.9
2.4
92.2 0.000
43.9 0.000
76.9 0.000
53.6 0.000
62.2 0.000
78.3 0.000
80.4 0.000
27.9 0.000
Connection
Creation
Entertainment
Work
Information seeking
Learning
Status seeking
Experience with Facebook
<2 years
7%
12%
58%
30%
4%
13%
54%
33%
18%
57%
25%
10%
60%
30%
2-4 years
63%
31%
69%
27%
>4 years
Fb use frequency
<= 1 per week
9%
17%
83%
10%
90%
16%
84%
29%
71%
14%
86%
Many times per day
91%
Average time on Facebook daily
<1 hour
9%
13%
57%
30%
13%
54%
33%
24%
51%
25%
21%
64%
14%
14%
55%
31%
1-3 hours
53%
38%
>3 hours
Average time on Facebook per access
<10 minutes
14%
46%
41%
18%
48%
35%
26%
37%
37%
19%
39%
42%
29%
39%
32%
19%
43%
38%
10-30 minutes
>30 mins
Number of friends on Facebook
<100
11%
48%
41%
11%
50%
38%
4%
10%
60%
30%
21%
54%
25%
11%
51%
38%
100-500
47%
49%
>500
Devices to access Facebook
Mobile phones
91%
20%
71%
91%
12%
66%
90%
17%
67%
76%
13%
70%
89%
21%
75%
88%
16%
69%
Tablets
PCs
Number of Facebook accounts
1
71%
29%
72%
28%
73%
27%
69%
31%
75%
25%
72%
28%
>1
The final five cluster solution is presented in Table 2. Each cluster was labelled as follows. The cluster 1 was
named as Maximizers (n = 123, 29%). This cluster had the highest score on the seven of eight motivation
constructs, for example, learning (M=4.4), connection (M=4.3), information seeking (M = 4.2). They explored
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to be maximizers of Facebook functionality and benefits. Maximizers were the only segment whose scores were
greater the point of the total scores on all eight motivation constructs. Of all the identified segments, maximizers
were the most positive about Facebook use. Specifically, the majority of users spent more than 3 hours
Facebook usage per day and 91% of users use Facebook many times per day.
The cluster 2 was named as Socializers (n = 141, 33%) who has scores lower than those of Cluster 1, but
higher than those of other cluster with regard to all types of motivations. On the status seeking dimension, their
scores (M=2.7) was higher than the average for the whole sample (M=2.4), and it had the highest score on the
rest of segments.
The third cluster, Middle of the road (n = 70, 16%) is no significant differences comparing with the other
segments. The majority of respondents had average scores for helping, connection; develop new relationship,
entertainment, or information seeking. They adopted a sensible, middle of the road for opinions leadership.
The fourth cluster was labeled Information seekers (n = 67, 16%). This segment was distinguished from the
other Facebook users mainly because the information seeking (M=4.0) and learning (M=4.3) motivations were
more important for this group than social creation (M=2.8) and status seeking (M=2.0). In the other hand, status
seeking and socialize were not motivators for members of this segment. Learning and information seeking were
the strongest motivation for this segment.
Cluster 5 had the lowest scores with regard to all Facebook usage motivations (n = 28, 7%). The scores for
this group were much lower than the average for the whole sample. Users in this cluster indicated that they are
not active users who develop relationship, learning or information seeking. For this reason, respondents in this
cluster are labeled Laggards.
4.3 Differences in response to marketing on Facebook between segments
An exploratory factor analysis revealed three factor, engagement (α=0.863), WOM (α=0.889), and purchase
intention (α=833), which explained 60.854% of the variance (Table 3).
Table 3: EFA and reliability analysis for the social marketing response measurement model
Item-
Total
Cor.
Factor
loading values
Eigen-
Cronbach
Alpha
Social Marketing Response Constructs
Engagement
ENG3
ENG1
ENG6
ENG7
ENG2
ENG5
I would be interested in receiving communications from 0.763
a brand/organization via social networking sites
0.645
I like to talk about brands/organizations that are 0.752
advertised on social networking sites
0.669
0.682
0.632
0.660
.587
I like to browse through social networking related 0.75
to brands/organizations
Compared to other people, I closely follow news 0.738
about brands/organization
6.951
0.863
I
am always interested in learning more about 0.737
brands/organizations that are present online
I am proud to have others know which brands/ 0.647
organizations I affiliate with via social networking
sites
ENG4
I
am accepting of communications from brands/ 0.593
0.565
organizations providing they seek my permission
Word of mouth
WOM5 I would share a social networking advertisement with 0.806
others if I see an advertisement that focuses on the
positive benefits of a product or service
0.766
0.671
2.199
0.889
WOM6 I would share a social networking advertisement with 0.791
others if I see an advertisement that focuses on how to
better deal with a specific problem or issue
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Item-
Total
Cor.
Factor
loading values
Eigen-
Cronbach
Alpha
Social Marketing Response Constructs
WOM4 I would share a social networking advertisement with 0.765
others if I see an advertisement that focuses on how easy
a product is to use.
0.643
WOM7 I would share a social networking advertisement with 0.754
others if I see an advertisement that mentions how other
people are getting good results from a product
0.717
0.714
0.663
0.621
WOM2 I would share a social networking advertisement with 0.736
others if I see an advertisement about a product that I
think would be useful to someone you know
WOM3 I would share a social networking advertisement with 0.656
others if I see an advertisement that focuses on how easy
a product is to use
WOM1 I would share a social networking advertisement with 0.609
others if an advertisement offers a discount or coupon for
a particular product.
Purchase Intention
INT1
I am likely to buy products that I see on social 0.885
networking sites if the price is appealing
0.745
0.749
INT2
I am likely to buy products that I see on social 0.834
networking sites if the delivery period is
satisfactory
1.804
0.833
INT4
INT3
I am likely to buy products that I see on social 0.730
networking sites if it is a new and exciting product
0.587
0.577
I am likely to buy products that I see on social 0.637
networking sites if it is a brand I know and trust
Additionally, ANOVA was used to test whether there were differences in how five Facebook user segments
response to marketing efforts on Facebook. As a result, the five segments showed differential responses to
marketing attempts in all three aspects: engagement (F(4, 424) = 10.7, p = 0.000), eWOM (F(4, 424) = 9.7, p =
0.000) and purchase intention (F(4, 424) = 5.0, p = 0.001). Overall, the engagement response was found to be
strongest in segment Maximizers (M=3.1) and weakest in segment Middle of the Road and Laggards (M=2.5).
Results showed that the segment Maximizers display the highest motivation on WOM referrals and purchase
intention (M=3.5). While the Middle of the Road (M=2.9) was found to have the lowest eWOM intention
compared to the other segments, the Laggards (M=2.8) showed the lowest purchase intention compared to the
others. Results also revealed that the Socializers and Information seekers have similar response to marketing
activities: brand engagement (M = 2.8), WOM referrals intention (M=3.3 and M =3.2, respectively), and
purchase intention (M=3.3). The response scores of the both groups were similar to the average scores of the
whole sample.
Table 4. Social marketing response on Facebook by segments
Maximi Socializ Middle of Information
Laggards Total
F
zers
ers
the Road
seekers
(n=123) (n=141) (n=70)
(n=67)
(n=28)
(n=429) (4,
424)
10.7
P
Engagement
WOM
3.1
3.5
3.5
2.8
3.3
3.3
2.5
2.9
3.2
2.8
3.2
3.3
2.5
3.1
2.8
2.8
3.3
3.3
0.000
0.000
0.001
9.7
5.0
Intention
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5. Discussion
The current study explores reasons for using Facebook of Vietnamese Facebook users. Findings from this
study back up the study by Shao et al. (2015), in which respondents reported that they use Facebook for
socializing, entertainment, information seeking, and status seeking. Moreover, this study extends previous
research on Facebook use motivations by elaborating the motivation dimensions. For example, “socializing”
motivations were examined in more detail. The EFA of the motivation scale suggested that Vietnamese users
conceptualized “socializing” motivations in three distinct aspects: (1) to keep in touch with friends/
acquaintances; (2) to establish new relationships, and (3) to help others. It is important to distinguish between
established and new friendships on Facebook because the maintenance of existing relationships may be different
from its establishment (Hoffman & Novak, 2012). Also, helping others and getting help from others give
individuals a sense of belonging and socialization to a community. Therefore, with regard to socializing,
Facebook can be thought to serve for three different purposes: social connection, social creation, and helping.
Meanwhile, the entertainment, information seeking, and status seeking motivations have been similarly found in
previous studies (Hoffman & Novak, 2012; Park et al., 2009; Shao et al., 2015). These findings offered an
evidence of the generalizability of some motivation dimensions of Facebook use across cultures, with four
motivations appearing consistently in Vietnam (in the present study) and in Western countries (in the previous
studies).
This study further identified two motives for using Facebook in Vietnam that have rarely examined in
previous studies, namely working and learning motivations. Using Facebook for working purposes is an
important aspect to Vietnamese users. It is no surprise given that nearly half of Vietnamese population has at
least of one Facebook account (Internet World Stats, 2017). We speculate that Facebook provides users who run
their own small businesses with a free platform for keeping in touch with consumers, attracting new customers
and increasing sales.
After identifying motivations of Facebook use, this study found a motivation-based segmentation through
clustering methods. This study showed that Facebook users fall into five distinct segments: Maximizers,
Socializers, Middle of the Road, Information Seekers, and Laggards. The five Facebook user segments
identified in this study resembles findings from the study of Lee et al. (2015), who also discovered five similar
segments of Facebook users between 18 and 24 years of age in xxx, such as Maximizers, Information seekers,
Middle of the Road, Interactors and Laggards.
Across all identified segments, this study found that consumer responses to social media marketing were
significantly related to Facebook user segments. The Maximizer segment exposed high levels of all behavioral
outcomes: brand engagement, purchase intention and referral intention. This segment consists of the most active
Facebook users, thus they may be willing to interact with brands on Facebook, most likely to make a purchase
decision, and spread positive words about products or services they used on Facebook. In contrast, the Laggards
who had the least motivations to use Facebook had the lowest levels of their responses to marketing activities on
Facebook. However, of the responses examined, they had higher tendency to engage in referral on Facebook.
This can be explained by the finding that social connection is the highest motivation of Laggards to use
Facebook. We also found that the Socializers and Information seekers have the same responses to social media
marketing. This suggests that Facebook users with high motivations for socializing and seeking information will
have positive intention to share information, make a purchase decision and brand engagement. They are able to
expand their social network, as well as gathering information from engaging in marketing activities. Finally, the
Middle of the road mainly focused on purchase intention, which might be due to their high motivation in helping
others. They also tend to put forth less effort on sharing information of a product/service.
6. Conclusions
The current study contributes to the social media marketing literature by investigating motivations for
Facebook use in more depth, and linking the motivations to consumers’ responses to marketing on Facebook.
Although there have been a number of studies investigating motivations of Facebook use, the current study
provided a better insight into the underlying motivations. Our findings offered empirical support to confirm
previous research results (Park & Yoo, 2009; Shao et al., 2015) from a sample of non-Western respondents.
Moreover, we explored two new motivations of Facebook users in the context of Vietnam, such as: working and
learning. Additionally, the findings confirmed that motivation is a valuable variable for segmenting SNS users.
Finally, it is worth to gain an understanding of how motivation-based segments respond to social media
marketing, particularly in Facebook context.
This study provides practical implications for both SNSs and business participants. Marketers should be
aware of the fact that a market segmentation strategy for SNSs might not be optimal at the platform level, unless
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the effectiveness of social media marketing are consistent with. Large segments of consumers on Facebook,
such as Maximizers, Socializers and Information seekers have high motivation for information and socializing;
therefore, marketers should complement information about their products or service with other valuable
information that satisfies the users’ information-seeking motivation. Meanwhile, marketers should combine
various tools for users to respond to posted information such as Like, Comment, or Share. It could stimulate
users to connect with friends and express themselves. The Laggards are perhaps least responsive to marketing
on Facebook, thus marketers need to concentrate on adopting listening platforms. Following what people in this
group are saying will get them motivated to start participating in marketing activities.
This research, like all, is subject to certain limitations. The current study focuses only on one type of social
media, Facebook, and one geographic area, Vietnam. The respondents were chosen based on a convenience
sampling and bias to young female students. Responses to Facebook marketing examined were limited to only
three reactions (engagement, purchase intention and referral intention) which do not necessarily reflect the
ultimate objectives of marketers (actual purchase behavior). Further research can extend to other social media
contexts like Twitter, and Instagram. It is also encouraged to carry out similar studies in other cultural contexts.
Future research should also pay attention to employment of more rigorous research design in terms of sampling
to enhance the generalizability of findings. At the heart of marketers’ concern, linking motivation-based
segmentation to consumers’ actual buying behavior deserves more research effort in the future.
References
[1] Anderson, K. C., Knight, D. K., Pookulangara, S., & Josiam, B. (2014). Influence of hedonic and
utilitarian motivations on retailer loyalty and purchase intention: a facebook perspective. Journal of
Retailing and Consumer Services, 21(5), 773-779.
[2] Antoniadis, I., Koukoulis, I., & Serdaris, P. (2017). Social networking sites’ usage in a period of crisis. A
segmentation analysis of greek college students. In Strategic Innovative Marketing (pp. 73-79). Springer,
Cham.
[3] Blumler, J. G. (1979). The role of theory in uses and gratifications studies. Communication research, 6(1),
9-36.
[4] Campbell, C., Ferraro, C. and Sands, S. (2014), “Segmenting consumer reactions to social network
[5] Cho, I., Park, H., & Kim, J. K. (2015). The relationship between motivation and information sharing about
products and services on Facebook. Behaviour & Information Technology, 34(9), 858-868.
[6] Chung, T. L. D., Anaza, N. A., Park, J., & Hall-Phillips, A. (2016). Who's behind the screen? Segmenting
social venture consumers through social media usage. Journal of Retailing and Consumer Services, 28,
288-295.
[7] Daniel Kushner (2015).
Why
segmentation
important
social
media
measurement.
[8] Davis Mersey, R., Malthouse, E. C., & Calder, B. J. (2010). Engagement with online media. Journal of
Media Business Studies, 7(2), 39-56.
[9] experience”, Computers in Human Behavior, Vol. 28 No. 2, pp. 331-339.
[10]Felix, R., Rauschnabel, P.A.; Hinsch, C. (2016). "Elements of Strategic Social Media Marketing: A
[11]Foster, M., West, B., & Francescucci, A. (2011). Exploring social media user segmentation and online
brand profiles. Journal of Brand Management, 19(1), 4-17.
[12]Foster, M., West, B., & Francescucci, A. (2011). Exploring social media user segmentation and online
brand profiles. Journal of Brand Management, 19(1), 4-17.
[13]Hair, J. F. (2010). Black, WC, Babin, BJ, & Anderson, RE (2010). Multivariate data analysis, 7
[14]Heinonen, K. (2011). Consumer activity in social media: Managerial approaches to consumers' social
media behavior. Journal of Consumer Behaviour, 10(6), 356-364.
[15]Hoffman, D., & Novak, T. (2012). Why do people use social media? Empirical findings and a new
theoretical framework for social media goal pursuit.
[16]Hofstede, G. (1980). Culture and organizations. International Studies of Management
Organization, 10(4), 15-41.
&
[17]Korgaonkar, P. K., & Wolin, L. D. (1999). A multivariate analysis of web usage. Journal of advertising
research, 39, 53-68.
[18]LaRose, R., & Eastin, M. S. (2004). A social cognitive theory of Internet uses and gratifications: Toward a
new model of media attendance. Journal of Broadcasting & Electronic Media, 48(3), 358-377.
[19]Lee, C., Jarvinen, V., & Sutherland, J. (2011, January). Profiling social network site users: who is the most
responsive to marketing attempts?. In American Academy of Advertising. Conference. Proceedings
(Online) (p. 59). American Academy of Advertising.
45
Khai Trieu Tran, Quyen Phu Thi Phan/ MICA 2018 Proceedings
[20]Lee, C.S. and Ma, L. (2012), “News sharing in social media: the effect of gratifications and prior
[21]Lee, H., & Bae, I. (2016). A Study on Understanding Social Media users by Segmentation Analysis Focus
on South Korea. Indian Journal of Science and Technology, 9(43).
[22]Libai, B., Bolton, R., Bügel, M. S., De Ruyter, K., Götz, O., Risselada, H., & Stephen, A. T. (2010).
Customer-to-customer interactions: broadening the scope of word of mouth research. Journal of service
research, 13(3), 267-282.
[23]Lorenzo-Romero, C., & Alarcón-del-Amo, M. D. C. (2012). Segmentation of users of social networking
websites. Social Behavior and Personality: an international journal, 40(3), 401-414.
[24]Park, D. B., & Yoon, Y. S. (2009). Segmentation by motivation in rural tourism: A Korean case
study. Tourism management, 30(1), 99-108.
[25]Perrin, A. (2015). Social media usage. Pew research center.
[26]Shaltoni, A. M. (2016). E-marketing education in transition: An analysis of international courses and
programs. The International Journal of Management Education, 14(2), 212-218.
[27]Shao, W., Ross, M., & Grace, D. (2015). Developing a motivation-based segmentation typology of
Facebook users. Marketing Intelligence & Planning, 33(7), 1071-1086.
[28]Triandis, H. C. (1990). Cross-cultural studies of individualism and collectivism
[29]Turban, E., Outland, J., King, D., Lee, J. K., Liang, T. P., & Turban, D. C. (2017). Electronic Commerce
2018: A Managerial and Social Networks Perspective. Springer.
[30]Wedel, M., & Kamakura, W. A. (2012). Market segmentation: Conceptual and methodological
foundations (Vol. 8). Springer Science & Business Media.
46
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