THE DEPENDENT VARIABLE IN SOCIAL MEDIA USE
Helana Scheepers
Swinburne University of Technology
Hawthorn, Victoria 3122 Australia
Rosemary Stockdale
Swinburne University of Technology
Hawthorn, Victoria 3122 Australia
RENS Scheepers
Deakin University
Burwood, Victoria 3125 Australia
Nurdin Nurdin
Swinburne University of Technology
Hawthorn, Victoria 3122 Australia
Abstract
What is the dependent variable in social media use? From a
research perspective, this is a pertinent question to help explain and
understand the behaviors that underpin the widespread adoption
and use of social media throughout society. From a practical
perspective, the question is relevant for social media technology
providers, for businesses that use social media, and community
organizations that turn towards social media to reach out to their
constituents. We propose the construct ‘sense of community’ as
the dependent variable, which is reflected in four sub-constructs
related to the behaviors of social media users. These behaviors are
information seeking, hedonic activities, sustaining of strong ties
and extending weak ties. Empirical evidence for these constructs
comes from a survey of social media use by 18-25 year-olds in
Indonesia, a country with exceptionally high utilization of social
media. We outline practical implications of the findings and areas
for further theoretical development.
Keywords: Social Media, Use, Community, Dependent
Variable.
The aim of this research is to identify the dependent variable in
individuals’ use of social media. This will contribute to theoretical
underpinnings in what is, as yet, a sparse body of empirically
validated research. Identifying the dependent variable is the key
to Information Systems research [14] and understanding it will
enable researchers to identify and operationalize the independent
variables (op. cit). In return researchers will be able to provide
advice for practice [14]. Hence, we ask: What is the dependent
variable of social media use?
The paper develops hypotheses that address the activities
individuals undertake when a sense of community exists in a
social media environment. The hypotheses are articulated (using
the vocabulary of structural equation modeling) to address
the very complex structures of community and to explore four
sub-constructs that would indicate the existence of a sense of
community in social media.
This paper is structured as follows. First, we examine existing
literature on the use of social media. Second, we detail a survey
of 18 – 25 year olds and an analysis via PLS. The findings are
then discussed along with implications of the results. The paper
concludes with suggestions for future research and limitations.
INTRODUCTION
WHAT IS SOCIAL MEDIA?
The use of social media has considerable implications for the
way people interact both personally and within organizations.
For individuals, social media are easy to use and offer enhanced
capabilities of communication. In organizations, the focus
of information systems research increasingly includes social
computing [46]. This shift is driven by user-centered, technological
developments that increase accessibility and use in a way that
empowers the individual user. These changes have led to a highly
dynamic and unstructured environment where the economic and
technical are giving way to an emphasis on the social [34].
Social media use is grassroots and community driven and is a
key contributor in the shifting role of individuals in networks [34,
36]. Day [12] identifies changes in established social structures
where networks center on the individual. This reflects Wellman’s
[68] identification of communities evolving “from being a social
network of households to a social network of individuals” (p.
55). The emphasis on the individual relates to the ability of
people to move beyond geographic and spatial communities
to form or choose their own online communities based around
personal interests and relationship groups. This highly dynamic
environment allows for transient membership of a multitude of
communities, each offering both rich content and high scalability
[15, 46]. This requires some exploration as the ubiquity of social
media grows and communities that are built around a social
media platform, such as Facebook, become a common
phenomenon [2, 9].
Winter 2014
Developments in communication tools have led to what
Castells [6, p.389] calls the “privatization of sociability”. This is
seen as the rebuilding of social connections around the individual
rather than around a physical space. Such personal communities
are defined by Wellman and Gulia [66] as social networks. There
is a confusing array of terms, such as social networking, social
media, Enterprise 2.0 and social networks, that have arisen to
reflect the fast paced growth of Web 2.0 applications and the
creation of platforms such as Facebook and LinkedIn. While
distinctions can be made, this study uses social media in the
broadest sense to describe Web 2.0 applications that facilitate
social interchange, with the important feature that they allow
for “the creation and exchange of User Generated Content” [34,
p.61]. User generated content as defined by the OECD requires
that the content has an element of creativity, is published openly
(or at least is widely available) online and is created outside a
professional environment [45]. That is, user generated content
facilitates the ability to connect, to form relationships and to
create personal or decentralized communities [46, 66].
THEORETICAL BACKGROUND AND HYPOTHESES
While there is ample anecdotal literature on the uses and
benefits of social media, there is a lack of theory development
in this area [46, 54]. Established theories of technology adoption
Journal of Computer Information Systems
25
and use tend to emphasize the organizational perspective. Social
theories, such as that of social capital, examine the relational
interactions of trust, norms and reciprocity [e.g., 17] between
participants.
A popular dependent variable that has been extensively
studied in the adoption of work-related information systems
is use. This is reflected in theories such as TAM that examines
the link between perceptions of ease of use and usefulness as
indicators of adoption [14, 15]. However, these constructs were
not found to be meaningful indicators of social media use [60,
71]. Instead our study explores social perspectives relating to the
how and why of social media use by individuals, using constructs
drawn from the literature on social interaction. These perspectives
include hedonic, and information seeking behaviors, as well as
the maintaining and forming of established and new ties [38,
43, 48, 56]. A further theme that runs extensively through social
media reporting is that of community and a sense of belonging.
The formation of communities based on social media platforms
such as Facebook is now a common phenomenon [34, 35, 42].
The concept of community is multi-layered and multi-faceted
and difficult to operationalize. A sense of community is central
to a community's existence, and therefore a useful theoretical
construct. A sense of community is reflected in and promoted by
individuals' interactions and behaviors.
Communities and Social Media
The concept of community is an abstract one with little
agreement on what is actually meant by the term [42], despite the
formalization of the study of ‘community’ from the 19th century
[6]. The village, or gemeinschaft model of community, based on
geographic and demographic boundaries [18], has shifted in the
online environment. Online communities transcend spatial or
temporal restrictions and encourage interaction between members
based on a vast range of interests [6, 68]. What remains is the
sense of community and depth of affiliation for the individual.
This arises from the perceived level of influence, integration and
shared emotional connection [37, 53, 66]. Rheingold [55] argues
that online communities draw on a commonality of interests
where communication of ideas is the important focus. He argues
that, contrary to the case in physical communities, friendships
may arise from the interaction rather than precede it.
Social media have extended opportunities for interaction
between people to new levels with the offer of easy-to-use
communication tools [46]. These tools are highly dynamic,
interoperable and confer a high locus of control on the user. They
also allow for rich content and high scalability and increased
opportunities for highly dynamic and decentralized communities
[46]. Such communities are very unpredictable and fluid as the
speed of their formation, activities and dispersal is often reflected
in the current needs of the individual members [33]. In terms of
social media, research suggests that a sense of belonging and
identity with a group promotes increased use [49].
Social media platforms such as Facebook allow for multiple
memberships to encompass community creation around family
and friends (maintain existing and recreating past connections).
These platforms also allow the creations of new connections based
on common interests or lifestyle [17, 48]. Social media tools offer
richer relationship opportunities than found in earlier forms of
online communities. These seem to engender deeper levels of
trust, although there are differences in how these tools can be used
to build, enhance and maintain these relationships [33, 35]. For
26
example, Facebook as a platform supports connections between
a vast range of strong and weak ties, while LinkedIn supports the
formation of weaker ties within a work-based context.
There is accumulating evidence that the concept of community
is inextricably linked to, and influenced by, the development of
social media. The apparent need for individuals to communicate
and relate to others within a community is met by the facility of
social media tools to offer new levels of interactivity [34, 35].
This has shifted the emphasis of networks to those that center
on the individual rather than more traditional geographic and
demographic concepts [6, 12]. This paper contends that the main
driver for participating in social media activities is to engender a
‘sense of community’.
Community Membership
One of the key advantages of social media tools is the ability
to interact with others and share information [2, 34]. Wikipedia
is an exceptional example of the capacity for contributing and
sharing information [11]. User generated content is a key feature
of social media and individuals contribute a wide range of content
in a multitude of formats, such as photos, videos, blogs, wikis
and news articles, to their online contacts [45]. They also seek
information using a variety of social media tools, most notably
wikis and blogs. In the more established field of web-based online
communities, the most frequently cited reason for joining an
online community is information seeking [56] where the content
offered is from member or user-generated contributions. The
more content that is created, the more membership levels will
increase [25] although content must be compelling to sustain the
community [Sreevisanan in 56].
Hiltz [30] found that the communication of relevant and
current information that meets individuals’ needs is a key
element of ongoing membership; an attribute of interaction that
is particularly well served through the use of social media [36].
The exchange of information is an aspect of social behavior that
enhances a sense of community. The giving of such information,
for no apparent personal gain, has been noted in a wide variety
of virtual communities. Parameswaran and Whinston [46] note
gifting behavior in players of online games where the anonymity
of users precludes gain from reputation effects beyond that of
personal self-esteem. Such pro-social behavior, or contribution to
the public good, has also been noted by Wasko and Faraj [65] in
professional communities of practice. Contributing information,
they argue, can be seen as benefitting self-esteem and enhancing
the online reputation of the contributors. The contributors will
in turn seek information when they have a need, hence the
importance of the concept of reciprocity in such communities.
In their study of students’ use of Facebook, Park et al. [47]
found that information seeking was a key need for younger people
accessing Facebook, with students accessing information about
their friends’ activities, and campus and social events. Highlevel users tend to expand their information seeking activities
into broader communities such as civic and political interests.
Therefore, in the context of social media use we hypothesize
that:
H1: A sense of community is reflected in information
seeking behavior by network participants
Another key feature of social media use is that of enjoyment
and socializing [3, 48]. IT applications that bring a sense of
Journal of Computer Information Systems
Winter 2014
enjoyment are often not productivity-orientated, but aimed
at entertainment and games. Individuals’ use of social media
applications may be deemed hedonic where it brings selffulfilling value, meets personal rather than external objectives
and is used predominately for fun and entertainment in a nonwork context [63]. The interactivity that is inherent in social
media contributes to hedonic use in supporting the making,
re-establishing and maintenance of friendships [48, 70]. This
echoes research findings into why people are attracted to webbased online communities. Ridings and Gefen [56] identify
friendship and social support as key factors alongside information seeking. Their, albeit tentative, conclusions suggest that
social aspects of membership are very important for constructing and maintaining community. The degree to which participants’ social needs can be gratified is a driver of ongoing
membership. These social needs are related to an individual’s
desire to gain a sense of belonging, affiliation, encouragement
[31, 56] shared history [53, 62] emotional support, and
companionship [59, 66]. The meeting of such needs, along with
identity and expression, are seen as fulfilling a sense of basic
psychological benefits that are powerful forces in a community [4].
Psychological well-being contributes to the sense of enjoyment or
satisfaction in participation.
The sense of enjoyment is also reflected in recreational
activities and the sharing of common interest. This can be seen as
the hedonic aspects of online community membership that may
extend beyond a personal environment. Hiltz’s [30] study of an
electronic information exchange system (EIES) identified the
tendency for users to “exchange gossip and pleasantries, support
and comfort one another at times of personal crisis” (p. 106).
She reports that play activity, which extended to an ‘electronic
soap opera’, was an important contributor to social cohesion and
enjoyable use. Another study within a work environment found
that the community offered entertainment value, and membership
was seen as fun and enjoyable [64]. In more recreation centered
communities, such as MUDs, fun is the reason and primary
motivation for membership [61]. In common interest communities,
membership is seen to lead to friendship with participants 'hanging
out together' [55, 56]. Hedonic behavior is well reflected in the
growing literature on social media [38] where studies show that
older teens use social media for communication, for entertainment
and as ‘a way of passing time’ [1]. Facebook use is ‘about having
fun and killing time’ [44 p.85]. Along with self-status seeking and
information seeking, socializing and entertainment are key needs
for participation in Facebook groups [47]. A possible change from
earlier web-based community behavior is in the context of use,
where the hedonic element of social media use appears to be more
pronounced. For example, Twitter was designed primarily as an
information tool to disseminate information and not to support
intense social interaction [40], but users have created a hedonic
element where communities have evolved and Twitter has been
adapted in ways beyond its intended design [24]. We therefore
hypothesize that:
H2: A sense of community is reflected in hedonic behavior
by the network participants
Social Ties
A significant aspect of social networks is the strength of
dyadic ties. Granovetter [23] identifies the importance of weak
ties, which he sees as indispensable to individuals for integration
Winter 2014
into communities. In contrast, he argues strong ties reflect
connections where robust social circles exist for reasons related to
family or close interests shared with friends. Strong ties can breed
local cohesion, and can lead to fragmentation of community by
setting barriers to those who do not belong. Weak ties bridge
networks giving individuals access to circles of people to whom
they are not directly connected. Individuals can therefore access
information that is not part of their own circles, leading to
enhanced connections and a flow-through of ideas that promotes
a sense of community.
Haythornthwaite [28] asserts that for personal interaction
“online exchanges are as real in terms of their impact on the tie as
offline exchanges” [p.388]. She also finds that weak ties are more
at risk form changes of platform. Where ties are strong, individuals
will use several forms of communication media, including face to
face, to maintain the relationship. The Internet and, more recently,
social media appear to offer opportunities that support this mix
of communication media for strong ties. Ellison et al. [17] find
that while social network sites support both existing ties and
the formation of new connections, there is evidence individuals
tend to use the online space for supporting offline connections.
They found individuals overwhelmingly use Facebook to keep in
touch with old friends, to maintain/intensify relationships
of offline connections (such as old school friends) and to reestablish lost offline connections. This echoes Wellman’s [67]
earlier finding that email is used to maintain long distance friendships rather than as a substitute for geographical proximity.
There is also emerging evidence that users of social media engage with a wide variety of tools to maintain high levels of contact
with family and friends. A recent US study reports that current
college students feel closer to their families than older siblings as
a result of this interaction [5].
Such ties can be maintained through easy-to-use social media
tools at a relatively low cost. Facebook lowers barriers and
encourages participation that might otherwise not take place [22].
This allows for latent ties, identified as those ties that are possible
but not activated socially [29], to be more easily converted into
weak ties.
The Internet is particularly suited as a medium for developing
multiple weak ties. The online environment allows for a more
egalitarian view of individuals, where social characteristics are
less visible and judgments are based on virtual interaction [6].
It easily and cheaply allows for a large number of overlapping
networks, which do not affect the strength of weak ties [22].
Indeed, weak ties create bridging social capital from the
interactions of a wide range of people, offering benefits such as
increased information, work and social opportunities [17]. The
facility for individuals to easily ‘friend’ others on Facebook
provides a capacity for a number of connections that may be
latent or weak ties [17]. A similar capacity to encourage weak ties
is seen in Twitter. Asynchronous tweeting means that newcomers can easily be integrated into the interaction on a particular
topic [24].
What is emerging is the role that social media plays in blurring
the roles between personal and business use [5]. Where people
engage in social media use they will form new relationships
regardless of the intended purpose of the application they are using
[24, 26]. In a study of a language learning site, it was concluded
that the making of new social networks via the application was a
necessary component of the learning [30]. Despite the argument
that social sites are primarily used to maintain existing ties [3],
there is growing evidence that users are using social media to
Journal of Computer Information Systems
27
create new ties through extensive networking [35]. While
platforms such as Facebook are used to ‘friend’ a very broad
range of connections, more business-orientated sites such as
LinkedIn are used to network across a range of new connections
that may be useful in a work context. The potential to develop
a sense of community, across social media sites, appears to
Table 1: Construct definitions
Construct
encourage individuals to network with a range of established and
new connections.
We therefore hypothesize:
H3: A sense of community is reflected in sustaining
interaction with strong ties by the network
participants
H4: A sense of community is reflected in extending weak
ties by the network participants
Research Model
Definition
Information seeking Information seeking behavior
behavior (ISB)includes accessing of information
via Internet-based technologies
for entertainment, professional or
personal interest reasons [56]
Hedonic behavior (HB)The use of social media
applications for activities that
bring self-fulfilling value in a
personal context, predominantly
for fun and entertainment [63]
Sustain Strong Ties Using social technologies to
(SST)maintain robust social circles
related to family and nurture close
communication with peer groups
[1]
Extend Weak Ties Interaction with a wide range of
(EWT)people who offer increased
access to information, work
and social opportunities and
where newcomers can easily be
integrated into the communication
[17, 24]
Sense of community A sense of affiliation and
(SC)emotional connection, interaction
and identification with a group of
people [44, 53, 62]
We model that a sense of community is reflected in four types
of behaviors: information seeking, hedonic, sustaining strong
ties and extending weak ties. ‘Sense of community’ is a complex construct and the proposed theoretical model, in
Figure 1, highlights one piece of the puzzle. Clearly a particular individual will be part of multiple communities, but we
consider singular communities and investigate the behavior
that the activities that an individual engages in where a sense of
community exists.
METHODOLOGY
Data Collection
Empirical data were collected via a survey of students at two
Indonesian Universities during 2011. Indonesia was specifically
chosen for this survey as it has a very high level of social media
use [16, 51] particularly in the 18-25 age group. Social media use
derives from the very high adoption of mobile devices resulting
in Indonesians being amongst the most prolific users of Facebook
outside the United States. University students were surveyed as
young graduates are the main diffusers of social innovation [7].
We argue that the sample is representative [58] of users that utilize
social media extensively.
Students from STMIK Bina Mulia Palu, and STAIN
Datokarama Palu Universities were invited to participate and
184 out of a potential 215 students took part in the survey. Of
the 184 surveys received 12 were incomplete and discarded
giving a response rate of 80%. Of the 31 students that did not
participate, the majority did not have access to social media.
The process suggested by De Vaus [13] was used to develop
the survey. As a first step an extensive list of items that measure the concepts outlined in the research model were developed. Additional measures were also included that did not
necessarily relate to the five constructs (see Table 1), due to the
exploratory nature of the study. Examples of these measures
included: duration of use of social media and use of the
Internet, in general. The logical relationships between questions and the flow of the questions were then considered.
The survey instrument was further refined and finalized during
a pilot test conducted with a small number (14) of students.
Based on the feedback further changes were made to the
questionnaire.
Measures
Figure 1: Research model for Social media use
28
A definition of the five constructs is summarized
in Table 1 and the items included in the survey are shown in
Appendix A. All items were measured on a 7 point Likert
scale.
Journal of Computer Information Systems
Winter 2014
Sample characteristics
DATA ANALYSIS AND RESULTS
Sample characteristics are given in Table 2 and show that 56%
of the respondents were male and most of the participants were
in the age group 18-21. All of the respondents used at least one
social media site with 36% using more than one. In addition 61%
of the respondents also used the Internet for 1 to 3 hours per day.
Table 2: Sample Characteristics
Response
percentage
Response
count
Gender
Male
Female
97
75
56%
44%
Age
<18
18-21
22 – 25
26 – 28
29 – 30
>30
9
121
38
2
1
1
5%
71%
22%
1%
0.5%
0.5%
Hours per day using social media
Less than 1 hour
1 – 3 hours
4 – 6 hours
>7 hours
30
139
2
1
17.5%
81%
1%
0.5%
Number of Social media
sites used
One
More than one
110
62
64%
36%
Hours per day on
the Internet
Less than one hour
1 – 3
4 – 6
>7 hours
56
105
8
3
33%
61%
5%
1%
Table 3: Convergent validity
Constructs
Items†
Composite
Reliability
AVE
Information ISB1
0.793
0.565
seeking ISB2
behaviour
ISB3
0.842
0.634
0.763
Hedonic behaviour
HB1
0.880
0.710
HB2
HB3
0.833
0.874
0.820
Sustain Strong Ties
SST1
0.811
0.590
SST2
SST3
0.846
0.676
0.772
Extend Weak Ties
EWT1 0.797
0.567
EWT2
EWT3
0.781
0.752
0.725
Sense of community
SC1
0.871
0.533
SC2
SC3
SC4
SC5
SC6
0.624
0.781
0.529
0.794
0.785
0.771
† Refer Appendix A for item descriptions
* All item loadings were significant at p < 0.001
Factor
Loadings*
Winter 2014
The data analysis strategy followed the two-step process
described by James et al. [32]. The Partial Least Squares (PLS)
procedure described by Gefen and Straub [21] was followed to
perform the data analysis. The measurement model was studied
through Confirmatory Factor Analysis (CFA) and the hypotheses
was empirically tested through structural model analysis. The
structural model was analyzed using SmartPLS [57].
Measurement Model
A key condition for theory development is construct validity
[50], which was tested in this study through convergent validity
and discriminate validity.
Convergent validity
Convergent validity is the extent to which indicators are
related to a theoretical construct [8]. The three indices used to
evaluate convergent validity were composite reliability, Average
Variance Extracted (AVE) and factor loading. A composite
reliability of 0.7 and AVE of 0.5 are acceptable indices [18, 19].
In addition factor loadings must be greater than 0.6 for convergent
validity [27].
Convergent validity was achieved as seen in the factor loadings
for items that are above 0.6 at a significance level of 0.001 (See
Table 3) as well as the composite reliability and AVE.
Discriminant validity
Two criteria should be satisfied to achieve discriminant validity
[8]. Indicators should load more strongly on their assigned
construct than on other constructs in the model. Furthermore, the
square root of the AVE should be larger than the correlation with
other constructs. Table 4 and Table 5 indicate that discriminant
validity was achieved.
The results of the convergent and discriminant validity provide support for the reliability and validity of the measures in
the model.
Structural Model Analysis
Figure 2 represents the results of the study including the
path coefficients and their significant levels. Results show that
H1 (Sense of community g Information Seeking behavior),
H2 (Sense of community g Hedonic activities), H3 (Sense of
community g Sustain strong ties) and H4 (Sense of community g Extend Weak ties) are supported. The R2 values for
all the constructs in our model exceed the weak to moderate
threshold levels suggested in the PLS literature [8], and all
fall in the range deemed “acceptable in social science research” [20]. This indicates reasonable support for the structural
model.
Podsakoff et al. [52] advise that common method bias should
be evaluated when the dependent and independent variables
were not collected from different sources. The Harman’s singlefactor test [52] was used as varied for PLS by Liang et al.
[39]. The method requires a latent method factor to be added
to the structural model and each indicator changed to a
single indicator construct. The method factor loadings were
Journal of Computer Information Systems
29
not significant and the indicator variance was significantly
higher than the method factor variance. According to Williams
et al. [69] this indicates common method bias is not likely to
be a concern.
DISCUSSION
Social media use is of intense interest to researchers and
organizations alike due to the very high adoption rates. Researchers are endeavoring to understand this phenomenon and orgaTable 4: Result of factor analysis
ISB
HB
SST
EWT
SC
ISB1
0.842
0.277
0.506
0.390
0.497
ISB2
0.634
0.239
0.307
0.299
0.351
ISB3
0.763
0.220
0.356
0.230
0.423
HB1
0.215
0.833
0.185
0.397
0.341
HB2
0.230
0.874
0.162
0.328
0.439
HB3
0.363
0.820
0.303
0.335
0.456
SST1
0.444
0.252
0.846
0.492
0.494
SST2
0.358
0.295
0.676
0.341
0.290
SST3
0.413
0.060
0.772
0.307
0.349
EWT1
0.326
0.261
0.457
0.781
0.354
EWT2
0.225
0.265
0.300
0.752
0.287
EWT3
0.358
0.397
0.375
0.725
0.371
SC1
0.335
0.274
0.280
0.315
0.684
SC2
0.540
0.389
0.463
0.347
0.781
SC3
0.220
0.363
0.063
0.336
0.529
SC4
0.443
0.424
0.405
0.317
0.794
SC5
0.472
0.415
0.391
0.378
0.785
SC6
0.407
0.311
0.509
0.316
0.771
ISB = Information seeking behavior; HB = Hedonic behavior;
SST = Sustain Strong Ties; EWT = Extend Weak Ties;
SC = Sense of community
nizations see the potential of social media for both internal
and external uses. Determining the dependent variable contributes to the development of a body of research that will support extensive and rigorous research into this important and
growing area.
In this study we propose an abstract model drawn from
the literature. Analysis of empirical data indicates support
for the dependent variable of social media and the reflective
sub-constructs discussed in this paper. The identification
of a sense of community as the dependent variable is, at first
sight, potentially problematic. Community is an imprecise
term that evokes a range of different meanings for different
people and is used widely and loosely in many contexts.
Nevertheless, recognizing the central role of community
within social media will enable the use and evolution of these
platforms.
Due to the exploratory nature of the research, we first
attempted to relate ‘a sense of community’ to hours of social
media use and hours of Internet use as the dependent variable,
both individually and in combination. However, these turned
out not to be appropriate dependent variables. Our observation in
this regard is consistent with recent studies on TAM and social
media use [for example: 60, 71]. In addition, we performed a
factor analysis to reduce the number of items associated with the
model.
We found that individuals’ behavior in engaging with social
media is driven by a sense of community. Individuals form
their own, multiple, overlapping communities in the social
media space to seek information, to find enjoyment and to
connect with close family and friends as well as to connect
with new people. A sense of community allows individuals
to extend their interactions with strong ties and increases their
opportunities to form weak ties. It encourages and supports
information seeking behavior and reflects the sense of enjoyment
that individuals gain from connecting with others via a range
of social media tools.
The four reflective constructs identified in this study were
found to be statistically significant. To our knowledge this is
the first attempt at deconstructing the amorphous concept of
social media. Some attempts have been made to understand
the intentions of social media use through TAM although few
studies have found it to be applicable to this research domain
[10, 71]. Our identification of the four reflective constructs
provides an alternative view of social media that will enable
researchers to operationalize the constructs or alternatively to
identify additional constructs.
Table 5: Correlation between constructs
Figure 2: Result of path analysis
30
HB
ISB
SST
EWT
HB
0.843
ISB
0.326
0.752
SC
SST
0.262
0.529
0.768
EWT
0.415
0.411
0.508
0.753
SC
0.496
0.570
0.509
0.454
0.730
The bold numbers on the leading diagonal are the square
root of the correlation shared between the constructs and the
measure. While the off diagonal elements are the correlations
with the construct.
Journal of Computer Information Systems
Winter 2014
An important contribution of our identification of a dependent
variable in social media is that it enables researchers to use the
dependent construct (sense of community) to conceptualize
other constructs for specific environments such as organizations. Castells [7] argues that the overwhelming proportion
of computer-mediated communication (CMC) takes place
in work or work-related situations and that the separation of
work and the personal has become more problematic today.
This has implications for organizations when individual networks, enabled by social media, span organizational boundaries [46, 9, 33, 46]. In other words, the separation of the
personal and the professional is becoming blurred as societal
shifts and social technologies are rapidly developed and deployed. This leads us to argue that individual use of social
media can and should be used to inform adoption and use by
organizations; effectively examining the grassroots to develop
frameworks that will enable organizations to leverage the
potential benefits of social media. This is new territory for many
organizations where the reach of individuals is made global
by social media and the lack of control over user-generated
content produces unease for managers [9, 34, 41]. There is a
need to develop appropriate strategies and governance, based
on informed decisions, to control use and minimize harm.
At the same time organizations need to accumulate knowledge of the different platforms and different behaviors that
will allow for the realization of benefits. Recognizing the sense
of community at the core of social media use establishes
the basis on which to go forward. The development of online
communities for internal and external use by organizations is
not new [25], but the shift in emphasis to the social and to the
individual within a community requires new ways of thinking
about them.
Organizations can utilize social media tools on various
fronts: externally for customers and internally for employees.
A sense of community can be developed in these contexts.
Organizations can determine whether they are successfully
creating and maintaining a sense of community for customers
by evaluating whether their social media presences address the
four identified constructs. In other words, are they providing
information about their products, are there some fun activities
related to the organization, does it create the ability for customers to closely relate to the organization and to others, and
finally does it provide opportunities for customers to link up
with others to create weak ties? Similarly, from an internal
perspective, organizations can use the four proposed constructs
to evaluate whether social media tools are used effectively
by employees. Is information about work activities available,
does the social media tool support hedonic activities, does it
Winter 2014
allow for and support the creation of close ties with colleagues
and does it create a network of weak ties based on certain
knowledge areas?
CONCLUSIONS AND IMPLICATIONS
We find the dependent variable to be a sense of community,
which is reflected in four sub-constructs that identify the behaviors of social media users. These behaviors are information seeking, hedonic activities, sustaining of strong ties and
extending weak ties. While there are many anecdotal reports
into the uses of social media, there is little published empirical
research into the underlying constructs that contribute to a
greater understanding of this fast developing domain. This
study invites exploration of further constructs and motivation
in this area. It also provides practitioners with a way of assessing how they might achieve their social media objectives
through offering guidance on the need for, and behaviors that
create, a sense of community. The four identified sub-constructs
provide measures for an organization to evaluate whether
they have actually created the necessary environment to support the formation of a community. It also enables organizations to track the behaviors that contribute to the sense
of community either internally or externally and thereby ascertain whether that sense of community is being maintained.
The collection of data from a student cohort has been addressed
in the study. This demographic is not only representative of the
most prolific users of social media, but also of the graduates
that are currently moving into industry. Organizations are now
faced with a young workforce that use social media intuitively
and ubiquitously, and who are expecting their organization to
support such behaviors. This creates an urgent need for greater
understanding of how to garner the benefits of social media and
how to provide the most appropriate environment for its effective
use.
The research model was developed based on a survey of
students in Indonesia. Although we believe that we have developed
a sound statistical model we acknowledge that further work is
necessary to validate the research in other environments. We
identify a number of limitations: the research should be repeated
for other demographic groups such as older social media users,
and young people in developed countries. Additional theoretical
constructs should also be considered eg. altuism and trust. A
potential bias is that most respondents used a specific social
media tool (Facebook).
Appendix and References on next page.
Journal of Computer Information Systems
31
APPENDIX A: SURVEY ITEMS
Construct
Code
Item
Information ISB1
I use these sites to see what
seeking
information / links / thoughts
behavior
other people share.
ISB2I use social media to be kept informed
of people / websites who can provide
me with useful information / links.
ISB3I use social media to be kept informed
of what is happening at conferences /
events I am attending.
Hedonic HB1
I use these sites to find out about
behavior
celebrities I like.
HB2I use these sites to find out about TV
shows and films I like.
HB3
I use these sites to find out about
products I like.
Sustain SST1
I use these sites to share my
Strong Ties
opinions with my friends.
SST2I use these sites to communicate with
my friends.
SST3I use these sites to share information
with my friends.
Extend EWT1 I use these sites to share my
Weak Ties
opinion and ideals with
like-minded people.
EWT2I use these sites to find people with
the same opinion and ideals.
EWT3I use social media to tell people what
I am doing.
Sense of SC1
I joined social media to find likecommunity
minded people to socialise with.
SC2I joined social media because it is
the best place to find out information
about people, events and things I am
interested in.
SC3I joined social media because my
friends have joined.
SC4I joined social media because it is the
place where many events and things
I am interested in are organized.
SC5I joined social media to establish my
online identity.
SC6I joined social media to find others
who share my views and opinions.
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