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Title: Determinants of Users’ Continuance of SocialNetworking Sites: A Self-Regulation Perspective
Author: Hui Lin Weiguo Fan Patrick Y.K. Chau
PII: S0378-7206(14)00045-7DOI: http://dx.doi.org/doi:10.1016/j.im.2014.03.010Reference: INFMAN 2715
To appear in: INFMAN
Received date: 21-4-2011Revised date: 4-12-2013Accepted date: 11-3-2014
Please cite this article as: H. Lin, W. Fan, P.Y.K. Chau, Determinants of Users’Continuance of Social Networking Sites: A Self-Regulation Perspective, Information& Management (2014), http://dx.doi.org/10.1016/j.im.2014.03.010
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Determinants of Users’ Continuance of Social Networking Sites: A Self-Regulation Perspective
1. Introduction
The use of social media such as social networking sites (SNS) and blogs has become
ubiquitous. The number of SNS users has grown exponentially in recent years and individuals
are incorporating it as part of routine activities to socially interact with one another. Social media
has redefined the ways individuals connect with each other, disseminate information, express
themselves, and socialize with others. Through social media like SNS, people are finding new
ways to manage and expand their personal network more efficiently and effectively [26; 33].
Among different types of social media, SNS especially have gained tremendous
momentum and have revolutionized the way individuals build and maintain interpersonal
relationships [9] . A SNS allows individuals to construct a public or semi-public profile within a
bounded system, to communicate with other users with whom they share a connection, and to
view and traverse their list of connections and those made by others within the system [8].
Individuals worldwide have incorporated SNS into their lives and made using SNS a frequent
and sometimes daily activity. Many SNS aim at the general population while some may cater to
a specific audience or purpose. For example, LinkedIn is the world’s largest professional
network and MedicalMingle is a SNS created specifically for medical professionals. Among the
most popular SNS, Facebook has made a huge and enduring impact in the world. As of March
2012, Facebook reports that there are 901 million monthly active users and on average 526
million daily active users. Indeed, various sources of evidence show that users are spending a
great deal of time on SNS and that it has become a significant component of people’s daily lives
[13].
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Due to the undeniable popularity and diffusion of SNS, they have become increasingly
interesting and intriguing to researchers. Academics have examined SNS using a variety of
methodologies and theoretical underpinnings from multiple disciplines. Researchers have
examined topics such as the formation and maintenance of social relationships on SNS [11; 20]
and industry competition dynamics [12]. Our study contributes to the literature by focusing on
the determinants of SNS continuance. Recent statistics show that the growth of SNS has
decelerated and SNS usage has leveled off in recent periods [18]. The second tier networks, such
as Tagged and Hi5, are beginning to receive more attention from users as the competition
becomes fiercer among SNS providers. Industry analysts have noted that MyYearbook and
Tagged users spend more time on these sites than the average MySpace user. According to
analytics released by ComScore in 2011, Twitter has proven to be a major competitor of
Facebook, as the micro-blogging service has managed to increase its number of regular users by
more than 500% since 2009. Thus, the exploration of determinants of SNS continuance is both
relevant and timely as it would help reveal the factors that cause users to attach to a SNS. The
results can provide insight to SNS service providers regarding the factors that may retain active
and regular users and evolve infrequent users into committed ones.
Thus, the objective of this study was to examine the determinants that impact users’ SNS
continuance. We aimed to answer the following research question: what and how do
determinants collectively affect user satisfaction, sense of belonging, and SNS continuance
intention? Specifically, our focus of study centered on SNS established for social communication
and interaction in general. we used Bagozzi’s framework of self-regulation of attitudes, intention,
and behavior [4] as the overarching theoretical framework to establish relationships between the
determinants, attitudes, and continuance intention. We applied research findings from social
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presence and system continuance research to identify the factors in the research model. We
empirically tested the model by surveying users from one of the most popular SNS, Facebook.
The survey results provided support for the research model and offered insights on the social
implications of SNS and the various factors that impact SNS continuance.
The rest of the paper is structured as follows. In Section 2, we discuss the theoretical
background which is followed by the research model and hypotheses in Section 3. We then
describe the research methodology and present data analysis and results in Sections 4 and 5
respectively. Next, we discuss the research and managerial implications, research limitations,
and future research opportunities in Section 6. Finally, we conclude the research in Section 7.
2. Theoretical Background
To investigate the determinants of SNS continuance, we applied Bagozzi’s self-
regulation framework of attitudes, intentions, and behavior [4] as the overarching conceptual
framework to understand the process that users experience leading to SNS continued usage.
Bagozzi’s framework seeks to understand the interrelationship between cognitive, affective, and
conative variables. It posits that attitude generates desire, which then leads to individual
behavioral intentions. Specifically, individuals first appraise a situation and assess whether it will
enable them to achieve their goals. The appraisal process will lead to emotional reactions which
may be positive or negative. As a result, an individual will then form intentions to either
maintain the positive experience or cope with the situation by forming intentions to avoid or
change the negative consequences. Thus, the process consists of three major steps: appraisal,
emotional reactions, and coping responses. The tenor of Bagozzi’s framework lies in its
emphasis on the role of cognitive and emotional self-regulation mechanisms. To understand the
relationship between attitudes and behavioral intention, one needs to consider self-regulation
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processes of appraisal emotional reactions coping responses. Self-regulation is
accomplished by conative processes and emotional responses caused by outcome-desire
interactions. Outcomes are events such as SNS continuance. Desire refers to wanting to pursue or
avoid a given situation. If a past or present experience is pleasant, positive emotions (e.g.,
satisfaction, enjoyment) will develop which then leads to behavioral intentions to cope with the
result of this outcome-desire fulfillment [4].
Prior research in IS has used Bagozzi’s framework to examine the factors and processes
that lead to website usage [17], while various adaptations of Bagozzi’s framework have shed
light on consumer behavior and loyalty [e.g., 3]. In consumer behavior research, perceived
quality and perceived value are part of the appraisal process in Bagozzi’s framework. After
appraisal, a consumer will form emotional reactions such as satisfaction. These emotional
reactions accordingly affect consumers’ coping responses such as usage intentions and behaviors.
We posit that a user’s decision to continue using a SNS is similar to a consumer’s repurchase
decision. Specifically, after users visit a SNS and utilize the service for a period of time, they
will have a general perception about the perceived system quality of the SNS and the values they
gain from usage. Users will then likely form a desire to use the SNS if they experience pleasant
encounters and develop favorable emotional reactions. In other words, favorable cognitive
appraisals would trigger positive emotional reactions from using a SNS, which will then lead to
continuance intention.
In order to better adapt Bagozzi’s framework within the SNS context and develop the
appraisal and emotional reaction factors, we turned to relevant research in online community and
social presence theory [e.g., 5; 31] to further identify the factors in our research model of SNS
continuance. First, we included sense of belonging as an emotional reaction factor because it has
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been shown to have a significant impact on usage in virtual environments [23]. Users develop a
sense of belonging to SNS via managing personal relationships and developing feelings of
membership, identification, and shared socio-emotional ties as a result of continued exposure and
usage of the system. Sense of belonging is different from satisfaction in that the former refers to
the user’s identification with the SNS and its affiliated members, whereas the latter measures the
user’s overall contentment with a system. Moreover, sense of belonging is considered more
enduring and long-lasting (long-term emotional reaction) while satisfaction is more transient
(short-term emotional reaction). Thus, in our research model, we included both sense of
belonging and satisfaction as emotional reaction constructs affecting SNS continuance.
Second, we included both system quality and perceived value as the appraisal factors in
Bagozzi’s framework. In a SNS, system quality refers to the overall technical adequacy of the
website. Findings from online community research show that system quality positively
influences member satisfaction and sense of belonging to the community [22]. Perceived value
has been found to be an important construct affecting user satisfaction and repeated purchase
decision in consumer behavior research [19].
Third, to effectively contextualize perceived value in the SNS context, we used social
presence theory to define the benefits of SNS from the users’ standpoint. Social presence
research has increased in the recent years due to technical and social developments of computer-
mediated communication and social interactions that have evolved from face to face to entirely
virtual interactions [33]. Short et al. [31] defined social presence as the "degree of salience of the
other person in a mediated communication and the consequent salience of their interpersonal
interactions" (p. 65). Biocca and Harms [5] extended social presence theory by defining three
levels of social presence. Level one is the perceptual level which primarily deals with the
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detection and awareness of the co-presence of the other’s mediated body. Level two, the
subjective level, consists of the user’s awareness in addition to his or her ability to access others’
emotional state, comprehension, and behavioral interaction. Level three, the dynamic or inter-
subjective level, is comprised of the user’s sense of the other’s sense of social presence in
relation to the user. In this study, we apply the first two levels of social presence as perceived
values in a SNS where users achieve both awareness and connectedness. Awareness is the
understanding of the activities of others. Connectedness is an appraisal which is characterized by
a feeling of staying in touch within ongoing social relationships [15]. We exclude level three
because we are focusing more on the individual perceptions and inter-subjective perceptions are
beyond the scope of the research goal. In addition, we also include pleasure as a component of
perceived value because the use of SNS also provides hedonic values such as fun and enjoyment
to users [26]. Therefore, we posited part of the perceived value of SNS originates from fun and
delight experienced by users. Figure 1 depicts the mappings of the constructs in our research
model to the process outlined by Bagozzi’s framework.
Appraisal
System QualityAwarenessConnectednessPleasure
Emotional Reaction
SatisfactionSense of Belonging
Coping Response
Continuance Intention
Figure 1. Conceptual Construct Mappings to Bagozzi’s Self-Regulation Framework
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3. Research Model and Hypotheses
Figure 2 presents the research model and the hypothesized relationships based on
Bagozzi’s Self-Regulation Framework. The model considers system quality, awareness,
connectedness, and pleasure as the appraisal factors, user satisfaction and sense of belonging as
the emotional reaction factors, and users’ continuance intention as the coping response.
3.1 Sense of Belonging
Sense of belonging measures a user’s feeling of identification with or attachment to a
SNS. The positive relationship between sense of belonging and usage has been demonstrated in
online community research [24]. Joinson [16] investigated the gratifications and shared identities
users derive from Facebook usage. Facebook users express themselves through their profiles,
meeting “like-minded” people, and developing a shared identity with their contacts and friends.
Therefore, SNS bond members together through social ties and relationships [29]. When users
have a stronger sense of belonging to a SNS and feel attached to the site, it will motivate them to
continue using the SNS. Based on the Bagozzi’s framework, sense of belonging as an emotional
reaction factor will influence users’ coping response. Thus, we hypothesized:
H1: Sense of belonging positively influences users’ SNS continuance intention.
3.2 Satisfaction
In addition to sense of belonging, user satisfaction is also an emotional reaction factor in
our research model that impacts users’ continuance intention. User satisfaction is an emotional
response based on users’ cognitive appraisals, and user satisfaction will then influence users’
coping response. The positive relationship between satisfaction and continued system usage or
re-use intention has also been found to be significant in numerous IS studies [e.g., 32].
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Accordingly, we also hypothesized that user satisfaction will positively affect users’ continuance
intention in SNS.
Furthermore, as a more enduring emotional reaction, sense of belonging is developed and
strengthened from a user’s satisfactory experience with system usage over time. Although it is
possible for users to experience instances of dissatisfaction, sense of belonging and long-term
commitment will not develop through continued dissatisfaction. Previous online community
research confirms that attitudinal constructs such as content and satisfaction lead to stronger
connection with the community [6; 23]. Therefore, users’ overall satisfaction with the SNS will
enhance their sense of belonging to the SNS and encourage their prolonged usage [23; 24].
Hence, we proposed the following two hypotheses:
H2: User satisfaction positively influences users’ continuance intention. H3: User satisfaction positively influences sense of belonging.
3.3 System Quality
System quality has been extensively researched in IS and multiple measures have been
developed and empirically tested in different IS contexts. System quality has been found to
significantly affect member satisfaction and user’s perception of the overall success of an online
community [24; 25; 30]. We believe such a relationship also holds in SNS as adequate system
and technological functionalities will enhance users’ satisfaction. Poor site functionalities that
distract users may result in negative and unpleasant emotional reactions. For instance, if a SNS is
easy to navigate and reliable, it will help individuals conduct interactions more efficiently and
the system performance of the SNS will be perceived positively by users. Further, interactive
functions, such as private messaging and online chatting, enable users to locate contacts more
efficiently and communicate in real time. Thus, part of the cognitive appraisal process involves
users’ evaluation of the SNS system quality, and we posited the following:
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H4: System quality positively influences user satisfaction.
3.4 Awareness, Connectedness and Pleasure
Awareness refers to the degree to which a user stays informed and current with others’
activities through the use of SNS. Devoted SNS users often post updates on their individual
profiles about their recent activities. Alexander [2] reported that of the 845 million active users
as of 2012, an average Facebook user spent 20 minutes on the site per visit. Awareness
represents the first level of social presence [5] and has been found to be very important in
fostering connections and maintaining social friendship [15]. In a SNS, awareness provides the
informational value users gain from spending time on SNS. This level is objective as it is
achieved by users searching for desired information to be aware of updates and new information
in their social network. Past research has identified social searching and surveillance functions
as the most important uses of Facebook [16; 20]. If users gain awareness through information
sharing and gathering, it will likely enhance their usage experience by increasing their
satisfaction and sense of belonging to the SNS. In other words, in order for users to feel satisfied
and a sense of belonging to the SNS, they will likely appraise the SNS’s ability to keep them
aware and informed. Hence,
H5a: Awareness positively influences user satisfaction. H5b: Awareness positively influences sense of belonging. Level two of social presence, connectedness, refers to the degree to which a SNS helps
users stay connected and maintain social relationships and ties. This level of social presence is
considered subjective because it goes beyond information awareness and reaches a level of
emotional awareness and social bonding. SNS often provide functionalities that enable users to
not only share information but also communicate back and forth to maintain connections. The
mission of Facebook states that it gives people the power to share and make the world more open
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and connected. For example, Facebook users can post messages on each other’s “timelines” or
send private messages to one another. Users can comment on each other’s profile updates and
postings. Such communication has been found to be essential in maintaining social
connectedness in many computer-mediated communication settings [15]. Prior research has
examined the importance of social connection in SNS in that users often use SNS to connect with
friends and maintain relationships with people who are not physically close [16; 29]. As part of
the appraisal process, strong connectedness will likely help form positive emotional reactions
toward SNS by increasing user satisfaction and sense of belonging with the SNS. Hence, we
hypothesized the following relationships between connectedness and the emotional reaction
factors in our model.
H6a: Connectedness positively influences satisfaction. H6b: Connectedness positively influences sense of belonging. In addition to awareness and connectedness, we proposed to include pleasure as a
perceived value of SNS, and it is solely associated with the fun and enjoyment gained from
usage. Research findings have shown that hedonic feelings have significant impacts on user
satisfaction and commitment with services and websites [21]. For instance, perceived playfulness
has been found to be important in online retailing settings [1], and pleasure has been integrated
as an important factor of website success and has been found to impact both satisfaction and
users’ commitment [10].
Similarly, as part of the appraisal process, pleasure and enjoyment will likely form
positive emotional reactions toward SNS by increasing satisfaction and feeling of belonging with
the SNS. Indeed, prior research has shown that SNS users often reported gaining pleasure and
enjoyment from using SNS [26]. Thus, we asserted that fun and pleasure from SNS usage is
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another important aspect of perceived values in SNS usage. This leads to the following
hypotheses:
H7a: Pleasure positively influences satisfaction. H7b: Pleasure positively influences sense of belonging.
SystemQuality
Awareness
Continuance Intention
Sense of Belonging
Pleasure
Satisfaction
Connectedness
H1
H2
H3
H6b
H5a
Figure 2. Research Model
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4. Research Methodology
4.1 Sample and Data Collection
To empirically test the research model and hypotheses, we conducted a Web-based
survey of Facebook. Our data sample comes from college students from a state university located
in the southeastern United States. The survey was posted on a course webpage over a four-week
time frame. We followed a respondent-driven sampling approach [14] for data collection which
allows respondents to refer those they know, and these individuals then refer those they know,
and so on. We started with an initial subject pool of 40 students enrolled in an undergraduate
business course. Each initial subject was asked to complete the online survey and also recruit
three of their Facebook friends to participate in the study. Subsequently each of those new
subjects was asked to refer three of their local Facebook friends to participate in the survey. We
obtained 505 valid samples from this process. Due to practical constraints such as privacy
restrictions posed by Facebook and the university, we were not able to obtain a pure random
sample. No incentive was offered to the students for participating in the survey.
To further ensure the sample quality and address possible non-response bias, we collected
a second round of data using students enrolled in five business courses in the same university. In
this round we collected 237 responses. We then compared the 237 responses collected in the
second round with the previous 505 responses and found no statistical differences on
demographic variables. Thus, we merged the two data sets which generated a total of 742 valid
responses for model validation and hypotheses testing.
Out of the 742 survey respondents, 82% were between 20 and 23 years old. Fifty six
percent were males, and 44% were females. A majority of the respondents (77%) were in their
junior and senior years in college. Over 64% of the respondents identified themselves as frequent
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users (e.g., logging in daily, browsing content, and posting and commenting on friends’ profiles).
About 82% of the survey respondents spent about 15 minutes or less on Facebook with each visit.
4.2 Survey Development
We used previously validated measures from prior research where possible and modified
them slightly to suit the SNS context. We custom developed and pilot tested the measures for
awareness and connectedness. All the items were measured on 7-point Likert scales ranging from
“strongly disagree” to “strongly agree”.
After developing the initial survey, we further validated the measures by asking for
feedback from a group of 20 students to elicit their understanding on the constructs of interests.
We then pilot tested the instrument using a group of 80 students enrolled in an undergraduate
business course to further improve ambiguous or poorly worded items. Based on the results of
the pilot test, we made modifications to the wording of awareness and connectedness items and
also shortened the survey to motivate participation. The survey items and their original sources,
organized by construct, are shown in the Appendix.
5. Data Analysis and Results
5.1 Measurement Model We used the partial least squares (PLS) to analyze the data. PLS is a structural equation
modeling technique that simultaneously assesses the reliability and validity of the measures of
the theoretical constructs and estimates the relationships among these constructs. The first stage
of data analysis analyzed the measurement properties of constructs, which included the
estimation of internal consistency (reliability) and the convergent and discriminant validity of the
constructs. Table 1 presents results on the psychometric properties of the measurements and the
descriptive statistics.
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Table 1. Psychometric Properties of Measurements and Descriptive Statistics
Construct Item Mean St. Dev. Loading t-value St. Error
System Quality CR = 0.885 SysQual1 5.888 1.177 0.759 27.522 0.028 AVE = 0.659 SysQual2 5.771 1.238 0.819 39.360 0.021 SysQual3 5.567 1.312 0.820 48.299 0.017 SysQual4 5.319 1.393 0.846 62.505 0.014 Awareness CR = 0.942 Awareness 1 4.681 1.573 0.941 122.888 0.008 AVE = 0.891 Awareness 2 4.640 1.565 0.947 182.242 0.005 Connectedness CR = 0.933 Connectedness 1 4.860 1.613 0.912 109.234 0.008 AVE = 0.822 Connectedness 2 4.612 1.644 0.918 113.287 0.008 Connectedness 3 4.465 1.646 0.890 81.749 0.011 Pleasure CR = 0.945 Pleasure 1 4.567 1.470 0.928 140.009 0.007 AVE = 0.851 Pleasure 2 4.433 1.439 0.923 119.525 0.008 Pleasure 3 4.691 1.458 0.916 104.540 0.009 Satisfaction CR = 0.958 Satisfaction 1 5.015 1.381 0.959 200.584 0.005 AVE = 0.920 Satisfaction 2 4.937 1.396 0.959 183.547 0.005 Sense of Belonging CR = 0.968 Sense of Belonging 1 4.168 1.742 0.960 259.655 0.004 AVE = 0.910 Sense of Belonging 2 4.182 1.724 0.945 165.342 0.006 Sense of Belonging 3 4.294 1.713 0.957 189.323 0.005 Continuance Intention CR = 0.897 Intention1 5.403 1.440 0.895 82.900 0.011 AVE =0.814 Intention2 5.018 1.502 0.910 109.507 0.008
As can be seen from Table 1, each average variance extracted (AVE) value is well above
0.50, which indicates good convergent validity of the instrument. All the values of composite
reliability and AVEs are considered adequate, with composite reliability all above 0.85 and
AVEs all above 0.65. The AVE from each construct is greater than the variance shared between
that construct and the other constructs in the model which demonstrates satisfactory discriminant
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validity. Table 2 lists the correlation matrix for all first-order constructs. Diagonals are the square
root of AVEs. In all cases, the square root of AVE for each construct is larger than the
correlation of that construct with all the other constructs in the model, which indicates
satisfactory discriminant validity. We further tested for potential multicollinearity due to the high
correlation between awareness and connectedness. We ran VIF tests and the VIF values ranged from
1.60 to 2.11 for all the independent variables which indicate no serious concern for multicollinearity.
Table 2. Correlations of Latent Constructs (Bolded diagonal values are square roots of AVE)
Aware Sense of
Belonging Connect Pleasure Satisfaction System
Quality Intention
Awareness 0.944 Sense of Belonging 0.526 0.954 Connectedness 0.650 0.567 0.907 Pleasure
0.538 0.605 0.575 0.922 Satisfaction 0.392 0.533 0.466 0.609 0.959 System Quality 0.403 0.391 0.414 0.449 0.567 0.812 Continuance Intention 0.478 0.599 0.537 0.674 0.747 0.564 0.902
Discriminant and convergent validity are further confirmed when individual items load
above 0.50 on their associated factors and when the loadings within construct are higher than
those across constructs as shown in Table 3. All items loaded on their constructs as expected.
Furthermore, all items loaded more highly on their construct than they loaded on any other
construct, and in all cases, the differences were greater than 0.25. Overall, the results of the
measurement model testing collectively indicate that the constructs demonstrate satisfactory
measurement properties.
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Table 3. Cross Loadings Aware Connect Pleasure Satisfaction Sense of
Belonging System Quality
Intention
Aware1 0.941 0.600 0.496 0.356 0.483 0.378 0.443 Aware2 0.947 0.627 0.519 0.383 0.510 0.383 0.459 Connect1 0.594 0.912 0.525 0.444 0.520 0.402 0.517 Connect2 0.595 0.918 0.540 0.439 0.533 0.387 0.497 Connect3 0.581 0.890 0.496 0.382 0.488 0.335 0.443 Pleasure1 0.506 0.511 0.928 0.546 0.551 0.402 0.620 Pleasure2 0.516 0.536 0.923 0.545 0.557 0.397 0.608Pleasure3 0.467 0.542 0.916 0.594 0.566 0.442 0.637Satisfaction1 0.390 0.453 0.576 0.959 0.505 0.562 0.719 Satisfaction2 0.361 0.441 0.593 0.959 0.518 0.526 0.713 Belonging1 0.495 0.524 0.569 0.507 0.960 0.373 0.573 Belonging2 0.510 0.566 0.593 0.502 0.945 0.382 0.566 Belonging3 0.500 0.534 0.571 0.517 0.957 0.363 0.574 SysQual1 0.371 0.388 0.354 0.421 0.352 0.759 0.457 SysQual2 0.285 0.303 0.321 0.413 0.297 0.819 0.436 SysQual3 0.308 0.341 0.368 0.483 0.290 0.820 0.465 SysQual4 0.346 0.319 0.407 0.513 0.332 0.846 0.473 Intention1 0.374 0.445 0.558 0.651 0.514 0.493 0.895 Intention2 0.485 0.521 0.656 0.695 0.565 0.525 0.910 5.2 Common Method Bias
We performed Harman’s single factor test to check for the possible effects of common
methods bias after data collection. Harman’s single factor test is arguably the most widely
known approach for assessing common method variance in a single-method research design [28].
The basic assumption is that if a single factor emerges from the factor analysis that explains a
significant amount of the variance in the data, there is strong evidence of common method bias.
Following this approach, all the variables from the research model were loaded into an
exploratory factor analysis and the unrotated factor solution was examined to determine the
number of factors that were necessary to account for the variance in the variables. The results did
not yield a single factor that accounted for the majority of the variance.
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In addition, we used the common-method bias method proposed by Lindell and Whitney
[27] which uses a theoretically unrelated construct (marker variable) to adjust the correlations
among the principal constructs. We used perceived risks users may perceive related to the
protection of privacy as the marker variable. Any high correlation among any of the items of the
study's principal constructs and perceived risks would suggest common method bias. The results
show that the average correlation among perceived risk and the principal constructs was r =.099
which does not indicate substantial evidence for common method bias.
5.3 Structural Model
With a satisfactory measurement model, bootstrap re-sampling method (500 sub-samples)
was used to determine the significance of the path coefficients and indicator loadings of the
structural model. This approach is consistent with recommended practices on model testing using
PLS in previous IS studies [32]. Results of the structural model analysis are shown in Figure 3.
All path coefficients are statistically significant (with p values less than 0.05) except for the link
between awareness and satisfaction (H5a). Interestingly, the relationship between awareness and
sense of belonging is significant. One plausible explanation for the results on awareness is that
users associate awareness more with long-term commitment to SNS than satisfaction. Since
awareness is considered more objective than the feeling of connectedness, it is likely that
connecting with others and enjoyment in SNS will result in higher levels of satisfaction than
browsing and keeping up-to-date with others’ activities.
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SystemQuality
Awareness
Continuance Intention
R2 = 0.614
Sense of BelongingR2 = 0.479
Pleasure
SatisfactionR2 = 0.486
Connectedness
0.206**
0.209**
0.044 n.s.
p<0.01, ** p<0.001, n.s. = not significant
Figure 3. Structural Model Results
The results from Figure 3 indicate that the model explained 61.4% of the variance in
continuance intention, 47.9% of the variance in sense of belonging, and 48.6% of the variance in
user satisfaction. The magnitude and significance of most path coefficients provide strong
support of our research model. Table 4 summarizes the path coefficients and their significance.
Results demonstrate that both sense of belonging and satisfaction are strong predictors for SNS
continuance intention.
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Table 4. Path Coefficients and Significance Level
Sense of Belonging Satisfaction Continuance Intention
Awareness 0.164** H5b supported
0.044 n.s. H5a not supported
Sense of Belonging 0.280** H1 supported
Connectedness 0.209** H6b supported
0.113* H6a supported
System Quality 0.354** H4 supported
Pleasure 0.272** H7b supported
0.409** H7a supported
Satisfaction 0.206** H3 supported
0.598** H2 supported
* p<0.01, ** p<0.001, n.s. = not significant We also examined the total effects and mediating effects satisfaction and sense of
belonging have on the other variables in the research model. As can be seen from Table 5, user
satisfaction has the most significant impact on continuance intention, followed by sense of
belonging, pleasure, system quality, connectedness, and awareness.
Table 5. Total Effects Table
Satisfaction Sense of Belonging Continuance Intention
System Quality 0.353** 0.001 n.s. 0.136** Awareness 0.044 n.s. 0.164** 0.027 n.s. Connectedness 0.113* 0.209** 0.06 n.s. Pleasure 0.409** 0.347** 0.225** Satisfaction 0.206** 0.598** Sense of Belonging 0.280** * p<0.01, ** p<0.001, n.s. = not significant
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6. Discussion The objective of this research was to examine the determinants of users’ SNS
continuance intention. We applied Bagozzi’s framework [4] as the overarching theoretical
foundation and utilized prior research in IS continuance and social presence to identify the
factors and develop the research model. We followed the process of appraisal emotional
reactions coping responses to theorize the relationships in the research model. We found that
both satisfaction and sense of belonging are strong determinants of continuance intention. Our
results showed a stronger relationship between pleasure, connectedness, system quality and
satisfaction than between awareness and satisfaction. All of the appraisal factors positively
influence users’ sense of belonging toward SNS. This finding confirmed that users assess the
system quality and perceived values provided by SNS and that these assessments of the appraisal
factors contribute to the development of a sense of belonging to the SNS. Ultimately, satisfaction
and sense of belonging significantly impact users’ SNS continuance intention.
6.1 Implications for Research and Practice
Our research findings offer several implications for SNS research. First, our study
contributes to the SNS usage literature and provides insights regarding why individuals continue
to use SNS. To the best of our knowledge, this research is the first study to apply the Bagozzi’s
framework of self-regulation to examine users’ SNS continuance intention. Previous studies in
this area approached this topic more from a user gratification perspective and focused on
motivational factors [26; 33]. Our application of Bagozzi’s self-regulation framework to study
SNS continuance proves to be a viable theoretical foundation based on the empirical results. The
research model follows the appraisal emotional reactions coping response process, and we
were able to obtain strong explanatory power for SNS continuance intention, the dependent
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variable. This model’s strong explanatory power confirms its applicability in the SNS context.
This framework offers an alternative theoretical framework that can potentially benefit future IS
continuance research. The framework is parsimonious and yet richly provides researchers with
ample flexibility to incorporate additional variables in the context of study in order to examine
what drives user continuance of different information systems or services.
The second implication our research suggests is that sense of belonging is a strong
emotional reaction predictor for SNS continued usage. Previous research in virtual community
has demonstrated the importance sense of belonging has in impacting intention and usage
behavior [e.g., 7; 23]. Our result, consistent with previous findings, indicates the importance of
users’ feelings of cohesion to SNS and its strong link with continuance. Thus, the results showed
that user satisfaction alone does not entirely capture the emotional reaction of users since SNS
offers individuals a platform to interact and connect as a collective. Indeed, the empirical results
showed that satisfaction alone can only explain a fraction of variance of continuance intention
and that sense of belonging, on the other hand, plays a crucial role in SNS usage. Sense of
belonging proved to be a strong determinant of usage, perhaps because it is a more enduring
attitudinal construct than satisfaction and develops over time. This is a unique finding as much of
the prior research has primarily focused on satisfaction when applying Bagozzi’s self-regulation
framework [17].
Third, we found that the dimensions of perceived value (awareness, connectedness, and
pleasure) provide much insight in conveying user perceived benefits from SNS usage. Our study
differs from related research by not using perceived usefulness as a perceived benefit factor to
explain continuance [26], rather we defined perceived value factors by applying two levels of
social presence: awareness and connectedness. To the best of our knowledge, this research is the
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one of the first studies to apply social presence theory to examine SNS usage. Xu et al. [33]
defined social presence as a sense of human contact via SNS, whereas our perspective on social
presence focused more on the consequences of the interpersonal interactions on SNS.
Fourth, our research included pleasure, a pure hedonic factor, as a value component of
SNS continuance. The result demonstrated that users recognize the value of the hedonic aspect of
SNS usage. Unlike previous research [26; 33], which showed that enjoyment and leisure
significantly affected users’ SNS continuance intention, our study tested how pleasure affected
satisfaction and sense of belonging, which ultimately contributed to usage. The results are
consistent with previous findings that pleasure plays an important role in hedonic systems. In
addition, our appraisal factors also included system quality, and results are consistent with
previous research that system quality continues to show significance in affecting user system
satisfaction [30].
As the number and member size in SNS continue to grow, SNS have to deal with the
issue of building user loyalty and maintaining users’ continued usage. One of the biggest
challenges for SNS thus far has been finding sustainable ways to keep their massive audiences
engaged. Friendster, for example, burst onto the Internet in 2003 and soon attracted over 20
million visitors. However, its user base quickly dropped to below a million after MySpace and
other SNS with more and better functionalities lured users away. Moreover, it appears that the
novelty effect of SNS may be wearing off. A recent user engagement metric released by
ComScore in 2011 revealed that the average time spent on some of the most popular SNS (e.g.,
Facebook, MySpace) has been showing a steady decline.
Given the volatility of the SNS market and fierce competition among SNS providers, our
research suggests that users rely heavily on the values they gain from using SNS. Pleasure
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appears to be more valuable than awareness and connectedness. Users seem to achieve
gratification more from the fun and enjoyment they gain from using SNS. This finding provides
SNS purveyors clues about how to target the underlying form of value most likely to encourage
and engage users. When users perceive using SNS as pleasurable, they are more likely to be
satisfied and develop a sense of belonging that enriches their SNS usage experience. For
example, Facebook users can interact with others by playing games online. Service providers
should also focus on how to enable users to develop connectedness and awareness from users’
social interactions. This can be achieved by constantly evaluating their existing services,
obtaining feedback from users, keeping pace with technologies, and offering new features to
maintain user attachment levels.
The significant finding of sense of belonging provides practical implications to SNS
purveyors as well. The development of sense of belonging and identification can be critical for
users to remain loyal to SNS. While it is easier to develop a positive attitude toward using a SNS,
it requires more in-depth and continued usage to maintain a sense of belonging. In addition to
attracting new users, SNS purveyors should be mindful of users’ commitments and long-term
emotional reactions. When SNS users identify themselves as part of a collective, it will promote
stronger attachments and thus make them more likely to continue using SNS.
6.2 Limitations
The results of this study should be interpreted in light of its limitations. First, we tested
our research model using one SNS. Although Facebook is one of the most widely used SNS
worldwide, the results from this study may not directly extend to other SNS or other types of
social media. For example, some of the fastest growing second-tier SNS (e.g., MyYearbook,
Bebo) present interesting research cases as well. SNS such as LinkedIn which focus more on
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professionals and networking may require identifying additional appraisal factors to examine
determinants of usage. Second, we employed a respondent-driven sampling approach to collect
data which may have reduced the randomness of the sample due to the administration of the data
collection process and may in turn limit the external validity of our research model. Moreover,
our sample of college students may not be representative of SNS users in general since 82% of
the respondents were between 21 and 23 in age. Third, we acknowledge that our research model
did not contain a comprehensive or exhaustive list of determinants of SNS continuance, nor was
it our intention to do so. Rather we built upon social presence and IS continuance research to
identify the important perceived value and quality factors that may affect users’ emotional
reaction and coping response.
6.3 Suggestions for Future Research
We believe our research makes a timely contribution to the literature as SNS has begun to
draw immense attention from IS researchers. We hope our research model provides a basis that
can spark further research that will extend and enrich the current findings to continue
investigating usage and user interactions in various SNS settings.
One avenue for future research is to apply and expand the model from this study to other
social networking settings such as LinkedIn to examine how professionals use SNS to expand
their professional network. To professionals, SNS may offer more utilitarian values than hedonic
values. It is possible that the quality of the information contained on the SNS may be perceived
as more valuable or important to professional users than college students, and pleasure may play
a less significant role. Moreover, given the global user base of SNS, future research can also
examine how cultural characteristics and differences may moderate the effect of the determinants
on SNS continuance. Future research can also incorporate and investigate the effects of
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individual factors (e.g., personality, propensity to share) and the role of trust and social norms on
SNS continuance.
SNS have become massive marketing tools utilized by companies to attract customers.
SNS represent an important media channel for reaching diverse demographic groups, and many
companies have realized the power of SNS in reaching customers and promoting their products.
In addition, consumers are beginning to respond less to traditional advertising and are relying
more on consumer-to-consumer communication such as blogging, mobile messaging, and word-
of-mouth marketing. The power of marketing on SNS presents intriguing research opportunities
for business academics. Future research can begin to explore how marketing on SNS improves
consumer understanding and satisfaction, how to better promote products and services, and how
to facilitate knowledge sharing among consumers.
Further, SNS have secured their place in organizations as well. Organizations (such as
GE and HP) have adopted the concept of social networking systems to help forecast sales,
generate new business ideas, maintain customer relationships, and predict emerging technologies.
Employee participation and usage of these social networking systems is essential to their success.
It is likely that Bagozzi’s framework of self-regulation applies in organizational SNS contexts as
well. In these SNS, utilitarian value obtained from usage may play a more important role in
usage than the hedonic values realized from using Facebook. Recent research has focused on the
utilitarian values of SNS [33], and future research can explore the role of the utilitarian values in
organizational SNS as the appraisal factors and how they affect users’ emotional reaction and
usage.
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7. Conclusion
This study examines the determinants of SNS continuance using Bagozzi’s framework of
self-regulation. Specifically, we discovered that individuals continue to use SNS because they
gain satisfaction and a sense of belonging from the usage experience. Further, appraisal factors
(pleasure, connectedness, and system quality) are strong determinants of users’ emotional
reaction (user satisfaction and sense of belonging). SNS are constantly evolving and becoming
ubiquitous; our research contributes to theory and practice by applying and extending prior social
presence and IS continuance research to SNS, an emerging area of IS context that has begun to
receive immense attention from IS researchers. Our findings and those of future research studies
will continue to enrich the SNS literature and help SNS purveyors design their systems more
effectively and improve user experiences.
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Appendix – Survey Items
Construct Item Sources
It is easy to navigate between pages on Facebook Facebook operates reliably The pages on Facebook are well-formatted
System Quality
Facebook provides various features (e.g., groups, wall) for me to interact with others
[22; 23]
Facebook keeps me informed of my friends' activities Awareness Using Facebook keeps me aware of my friends' activities
Self-developed
Using Facebook helps me stay connected with my friends Using Facebook helps me maintain social ties with my friends
Connectedness
Using Facebook helps me maintain friendships with others
Self-developed
Reading and posting to people's profiles on Facebook gives me pleasure Reading and posting information to peoples' profiles on Facebook makes me feel good
Pleasure
Interacting with people (e.g., posting message on walls) on Facebook makes me happy
[10]
I feel as if I belong to the Facebook community I feel as if I am socially connected to the Facebook community
Sense of Belonging
I feel as if I am a part of the Facebook community
[23]
I am satisfied with Facebook Satisfaction I feel pleased with Facebook
[23]
I intend to continue using Facebook. Continuance Intention I will recommend Facebook to my friends.
[26]
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Determinants of Users’ Continuance of Social Networking Sites: A Self-Regulation Perspective
Hui Lin1 School of Accountancy and MIS
DePaul University Telephone: (312) 362-8782 E-mail: [email protected]
Weiguo Fan R.B. Pamplin College of Business
Virginia Polytechnic Institute and State University Blacksburg, VA 24061
Telephone: (540) 231-6588 Email: [email protected]
Patrick Y.K. Chau School of Business
The University of Hong Kong Telephone: (852) 3917-1025
Email: [email protected]
Abstract
Social networking sites (SNS) have transformed how individuals interact, build and maintain social relationships. We proposed a research model on the determinants of user continuance using Bagozzi’s framework of self-regulation as the theoretical foundation. Following the process of appraisal emotional reactions coping responses, we developed the model by leveraging findings from social presence and IS continuance research. Based on survey data from Facebook users, we found that appraisal factors (pleasure, awareness, connectedness, and system quality) were strong determinants of emotional reaction (user satisfaction and sense of belonging). User satisfaction and sense of belonging together positively influenced continuance intention.
Keywords: awareness, connectedness, pleasure, self-regulation framework, sense of belonging, social networking sites (SNS), social presence theory
1 Corresponding Author
