Are You Feeling Lonely The Impact ofRelationship Characteri.docx

Are You Feeling Lonely? The Impact of
Relationship Characteristics and Online
Social Network Features on Loneliness
SABINE MATOOK, JEFF CUMMINGS, AND HILLOL BALA
SABINE MATOOK is a senior lecturer of information systems
at the UQ Business
School, University of Queensland, Australia. She received her
Ph.D. from the
Technische Universität Dresden, Germany. Her research
interests include the IT
artifact, social media, and agile IS development. Her work has
appeared in the
European Journal of Information Systems, Information and
Management, MISQ
Executive, Decision Support Systems, Journal of Strategic
Information Systems, and
other journals. She has served or is currently serving as an
associate editor and track
chair for major information systems conferences, including the
International
Conference on Information Systems and European Conference
on Information
Systems.
JEFF CUMMINGS is an assistant professor of information
systems and operations
management in the Cameron School of Business at University of
North Carolina,
Wilmington. He received his Ph.D. from Indiana University. His
research interests

include the impacts of social media on the organization, health-
care IT, and virtual
team collaboration. His work has been published or is
forthcoming in Business
Horizons and Journal of the American Society for Information
Science and
Technology.
HILLOL BALA is an assistant professor of information systems
and Whirlpool
Corporation Faculty Fellow in the Kelley School of Business at
Indiana
University, Bloomington. He received his Ph.D. from the
University of Arkansas.
His research interests include IT-enabled business process
change and management,
IT use, adaptation and impacts, and use of IT in health care. His
work has been
published or is forthcoming in Information Systems Research,
Journal of
Management Information Systems, MIS Quarterly, Management
Science,
Production and Operations Management, Decision Sciences,
Information Society,
Communications of the ACM, MISQ Executive, and other
journals. He has served or
currently serves on the editorial boards of Information Systems
Research and
Decision Sciences, and as a track chair, an associate editor, or a
program committee
member of major information systems conferences, such as the
International
Conference on Information Systems, the Pacific Asia
Conference on Information
Systems, and others.

ABSTRACT: In contemporary society, many people move away
from their personal
networks for extended periods to reach professional and/or
educational goals. This
separation can often lead to feelings of loneliness, which can be
stressful and
sometimes debilitating for the individual. We seek to
understand how a person’s
Journal of Management Information Systems / Spring 2015,
Vol. 31, No. 4, pp. 278–310.
Copyright © Taylor & Francis Group, LLC
ISSN 0742–1222 (print) / ISSN 1557–928X (online)
DOI: 10.1080/07421222.2014.1001282
use of online social networks (OSNs)—technology-enabled
tools that assist users
with creating and maintaining their relationships—might affect
their perceptions of
loneliness. Prior research has offered mixed results about how
OSNs affect lone-
liness—reporting both positive and negative effects. We argue
in this study that a
clearer perspective can be gained by taking a closer look at how
individuals
approach their relationship management in OSNs. Building on
theoretical works
on loneliness, we develop a model to explain the effects of
relationship character-
istics (i.e., relationship orientation, self-disclosure, and
networking ability) and OSN

features (i.e., active or passive) on perceived loneliness. Our
findings show that OSN
can be linked to both more and less perceived loneliness, that
is, individuals’
relationship orientation significantly affects their feelings of
loneliness, which are
further moderated by their degree of self-disclosure within the
OSN. Furthermore,
how users engage in the OSN (either actively or passively)
influences their percep-
tions of loneliness. Practical implications regarding perceived
loneliness include
recommendations for firms to encourage mobile workers to
utilize OSNs when
separated from others, for education providers to connect with
their new students
before they arrive, and for users to utilize OSNs as a social
bridge to others they feel
close with.
KEY WORDS AND PHRASES: social media, online social
networks, loneliness, relationship
management, communal orientation, social exchange theory,
self-disclosure,
networking ability.
In recent years, individuals have become increasingly mobile,
and in doing so travel
significant distances from their homes to reside at the target
destination for an
extended period of time [30]. Consequently, individuals are
separated from the
social environments that are both familiar and comforting to
them [58]. Prior
research in sociology has suggested that this separation leads to
negative social

outcomes, including feelings of loneliness [66, 82]. It is
assumed that lonely people
desire human attachment that can be achieved through creating
new or nurturing
existing relationships [7].
Prior research has produced mixed findings regarding the
impact of technology,
especially online social networks (OSNs), on feelings of
loneliness [78]. Whereas
some studies point to a negative association between OSN use
and loneliness [31],
others show the opposite [19]. When loneliness is reduced
because OSNs engage
users in relationships with their social network contacts [31],
the technology serves
as an outlet that captivates and helps users to buffer their social
separation [60]. In
contrast, other studies have shown that OSN use increases
loneliness. Some users
who only consume information that others share have developed
feelings of envy,
emotional withdrawal, and loneliness [50]. Indeed, studies
showed that individuals’
life satisfaction and well-being decreased when using Facebook
intensively because
they compared their lives with their impressions of others in the
OSN [52]. Because
of these mixed findings, it is not clear whether OSNs alleviate
or exacerbate
perceived loneliness, and a more comprehensive and
theoretically grounded account
is needed.
IMPACT OF ONLINE SOCIAL NETWORK FEATURES ON
LONELINESS 279

The current study seeks to address this gap by focusing on the
primary purpose of
OSNs as relationship tools. OSNs are uniquely suited for
managing relationships
because users can virtually replicate their social network of
relationships [34].
However, not everybody uses the OSN in the same way nor do
they have the
same approach to how they manage their relationships. We thus
propose that (1) a
user’s OSN feature use, (2) the user’s relationship orientation
moderated by their
degree of self-disclosure, and (3) a user’s networking ability
have an impact on their
loneliness. As the overarching theory for this study, we draw on
the loneliness
literature and insights from theories of social exchange,
communication, and poli-
tical skills to develop our model, and we test the model in the
context of students’
use of OSNs. While all three theories center around human
relationships and are
core parts of human interactions, social exchange theory from
the relationship
literature enables us to theorize about relationship norms and
how benefits between
relationship partners (e.g., OSN users) are given and received.
Communication
theories are used to explain different forms of communication—
either direct or via
broadcasting—whereas the management theory on personal
influences and political
skills illuminates the OSN user’s ability to create and maintain

relationships.
This paper contributes to information systems (IS) research by
enhancing our
understanding of OSN use and the impact it has on users’
perceived loneliness.
This research expands the relationship literature by examining a
user’s approach to
relationship management in OSNs and extends theory in social
psychology, in
particular the body of knowledge of loneliness. We further add
to the theoretical
understanding of on self-disclosure in virtual settings by
proposing it as a moderator
between relationship orientation and loneliness. Finally, we
contribute to manage-
ment research by demonstrating the importance of networking
for an OSN user to
ease feelings of loneliness. This research also provides practical
insights about how
organizations, such as firms and education providers, can help
alleviate loneliness
experienced by those they are responsible for.
Background
This section presents prior research on OSNs and perceived
social loneliness that is
relevant to our study.
Online Social Networks
An OSN, such as Facebook or Google+, is a web-based
technology that allows users
to exchange social information with others who may be near or
far, including

friends, family, colleagues, and teammates [56]. Kane et al. [46,
p. 279] summarized
the unique characteristics of OSNs by describing how they
enable users to “(1) have
a unique user profile that is constructed by them, members of
their network, and the
platform; (2) access digital content through and protect it from
various search
mechanisms provided by the platform; (3) articulate a list of
other users with
280 MATOOK, CUMMINGS, AND BALA
whom they share a connection; and (4) view and traverse their
connections and those
made by others within the system.”
An OSN’s relationship management capability is enabled
through features avail-
able in most OSNs, including the ability to list contacts, share
social information
(including photos, videos, text via microblogging [79]),
send/receive private mes-
sages, social search engines, and express support to others (e.g.,
“Likes” on
Facebook) [11]. Through these features, user-generated content
is created that facil-
itates both active engagement with one’s network and passive
consumption of
content [64]. Social search engines help in finding contacts as
well as filtering
user-generated content [28].
In OSNs, self-disclosure is a common behavior [69]. Self-
disclosure is defined as

“any message about the self that a person communicates to
another” [87, p. 338]. In
OSNs, disclosed information includes personal details such as
interests, preferences,
relationship status, and habits [85]. People self-disclose to
others they like and trust,
allowing for a relationship to become more intimate [59].
Reciprocal self-disclosure
leads to a “you tell me, I tell you” behavior whereby disclosed
information increases
in depth and breadth [54, p. 170]. Indeed, research on bloggers’
online behavior has
demonstrated the importance of reciprocity for knowledge
sharing [17]. Research
has shown that self-disclosure plays an important role in
relationship growth, leading
to a closer, high-quality relationship, especially for friendships
[15]. Consequently,
OSNs are apt for relationship creation (i.e., to form new and to
revive neglected
relationships) and relationship maintenance (i.e., to nurture and
foster existing
relationships) to impact perceived loneliness [70].
Perceived Social Loneliness
Loneliness results from a perceived absence of satisfying
relationships and a deficit
in an individual’s social network [39, 66]. According to the
belongingness hypoth-
esis, human beings have “a pervasive drive to form and
maintain at least a minimum
quantity of lasting, positive, and significant interpersonal
relationships” [7, p. 497].
The loneliness literature differentiates between two types of

loneliness: emotional
and social [86]. Emotional loneliness is associated with a lack
of intimate ties and a
deficit in intimate attachments, particularly in romantic
relationships. Conversely,
social loneliness “results from the lack of a network of social
relationships in which
the person is part of a group of friends who share common
interests and activities”
[74, p. 1314]. Given that we are interested in the perceived
loneliness of people who
are geographically separated from their familiar social network,
we focus on social
loneliness.
Social loneliness is thought to be the result of social isolation
because an indivi-
dual is not able to have repeated interactions with the same
contacts [7]. The
network of lonely individuals tends to be smaller, which gives
them the impression
that they do not belong to a group [39]. Research reports that
social loneliness comes
from infrequent interactions with friends [27], as well as less
supportive behaviors
LONELINESS 281
and unhelpfulness from one’s networks in times of need [80].
To compensate
for feelings of social loneliness, prior research stipulates active
relationship manage-

ment [86].
Hypothesis Development
This section presents hypotheses on the impacts of relationship
characteristics
(relationship orientation, self-disclosure and networking ability)
and OSN features
(active and passive features) on social loneliness. Our
theoretical foundation is based
on human relationships and its core focuses on human
interactions. We therefore
build on the relationship literature using social exchange theory
to theorize about
self-disclosure in relationships as well as norms and benefits
given and received
between relationship partners (e.g., OSN users). We use
communication theories to
explain different forms of communicating via OSN networks.
Furthermore, we use a
management theory on personal influences and political skills to
explain an OSN
user’s ability to proactively create and maintain interpersonal
relationships. The
research model is presented in Figure 1 at the end of the
section.
Relationship Orientation
Two social exchange theorists, Clark and Mills [21, 23], have
shown that different
norms govern a person’s behavior when creating and
maintaining a relationship.
These norms affect individuals’ orientations toward
relationships and their under-
standing of how benefits are given and received. In general,

individuals maintain
relationships only when the comparison between given and
received benefits is
perceived to be satisfactory, but individuals differ in how they
judge the extent of
Exchange Orientation
Relationship Orientation
Communal Orientation
Self-Disclosure
Social
Loneliness
Networking Ability
Passive Features
OSN Features
Active
Features
H 1a
H 4a
H 3
H 2bH 2a
H 1b

H 4b
Broadcasting
Direct
Communication
H 4c
Figure 1. Research Model
reciprocity required [26]. This leads to two different
orientations toward relation-
ships: an exchange orientation and a communal orientation [21].
According to Clark
and Mills, these two relationship types represent two distinct
concepts and not a
continuum that can vary in strength.
An individual with an exchange orientation is concerned with
equal reciprocity
and maintains a relationship with others only for instrumental
reasons [22]. An
exchange relationship orientation is characterized by giving
benefits “with the
expectation of receiving a comparable benefit in return or as
repayment for a benefit
received previously” [21, p. 684]. These individuals carefully
record obligations and
keep score of “give and take.” In contrast, a communal
relationship orientation is
characterized by giving benefits “in response to needs or to
demonstrate a general

concern for the other person” [21, p. 684]. A communal-
oriented individual gen-
erally has no expectations of immediate repayment of a supplied
benefit, but shows a
concern for the other’s welfare [22]. While these two types of
relationship orienta-
tion are general in nature, they can be translated into an OSN
environment. In such a
context, they manifest as different social-emotional benefits
that stem from the user-
generated content. The benefits one can give and receive in
OSNs include an initial
posting on a contact’s profile page, responding to a posting
(textual or via a “like it”
function), gift giving, sending private messages, and initiating
chats.
Communal relationship orientation: Within OSNs, we argue that
users with a
communal relationship orientation behave in such a way that
results in lower degrees
of perceived social loneliness. These users undertake OSN
activities to please others
and without expectation of immediate repay. For example, a
communal-oriented user
would post a comment on a contact’s OSN profile (i.e., give a
benefit) because he/
she sees the contact is in need or he/she cares about that contact
(e.g., when the
contact posted about a lost wallet or a canceled flight). As these
OSN postings are
undertaken without the expectation of receiving anything in
return, the communal-
oriented person might post on a contact’s profile multiple times
without receiving a
comment back. Posting continuously when a need is observed

(i.e., providing the
benefit), however, stimulates reciprocity because the receiver
may eventually return
the comment. In doing so, the interaction frequency between the
two OSN contacts
increases and the user feels more integrated in the social
network. Simply engaging
in the act of giving may also lead the communal-oriented user to
feel more
connected. For both reasons, perceptions of loneliness should
decrease, and we
thus expect that users who are more communal-oriented will
feel less lonely.
Hypothesis 1a: A user’s communal relationship orientation is
negatively asso-
ciated with perceived social loneliness.
Exchange relationship orientation: Within OSNs, we argue that
users with an
exchange-relationship orientation exhibit behaviors that result
in increased perceived
loneliness. Exchange oriented individuals expect their OSN
activity to be recipro-
cated equally and on a timely basis. For example, if the user
posts on a contact’s
profile, a returning post is soon expected. If the user gives a
virtual gift, then the
LONELINESS 283
expectation is that the other returns a gift. If the gift is not
returned, the exchange-

oriented user refrains from giving a gift to this contact again
[77]. Contacts who fail
to reciprocate may even be removed from the user’s OSN
network [9]. Because of
an exchange-oriented user’s “scorekeeping,” the user knows of
the failed reciprocity,
and would stop giving benefits to contacts who do not
reciprocate. In this case, the
number of contacts with whom the user interacts becomes
smaller, resulting in the
user being more disconnected from the social network. Instead
of feeling integrated,
an exchange-oriented individual may feel isolated [16] and thus
experience social
loneliness [83]. Consequently, we would expect an OSN user
who only gives
benefits in expectation of equal and timely reciprocity to
experience a higher degree
of perceived social loneliness.
Hypothesis 1b: A user’s exchange-relationship orientation is
positively asso-
ciated with perceived social loneliness.
Self-Disclosure as a Moderating Factor
Prior research has studied the relationship between self-
disclosure and loneliness,
with ambiguous results. Some studies report that self-disclosure
affects loneliness
[53, 73], whereas other studies suggest the reverse [3, 45].
Despite mixed findings,
these studies agree that self-disclosure is important because of
its role in facilitating
the deepening of relationships. We draw on this insight to argue
that self-disclosure

could be an important moderator in our study, in that it could
alter the way in which
a user’s relationship orientation affects loneliness.
For a communal-oriented OSN user, we expect that higher self-
disclosing behavior
will reduce perceived loneliness by strengthening the
relationship between commu-
nal orientation and loneliness. When a communal-oriented OSN
user increases
disclosure of social information, more opportunities for the
user’s contacts to
reciprocate emerge. As a result, the extent of the returning self-
disclosure also
increases [34], albeit not necessarily proportional to the giving.
Because the com-
munal-oriented user is not concerned with equal reciprocity, a
lack of it would
therefore not stop the user’s future self-disclosure, that is, the
communal-oriented
user would continue disclosing social information. Indeed, as
communal-oriented
individuals are more focused on giving than on receiving, and
thus, an increase in
giving would make the user feel close to his/her network,
especially as no “scores”
are kept on how frequently the network reciprocates. Moreover,
continuing disclo-
sure will most likely result in more reciprocated self-disclosure
that will in turn
diminish the communal-oriented user’s loneliness.
Consequently, in the presence of
increased self-disclosure, the negative relationship between
communal orientation
and perceived social loneliness is strengthened.

Hypothesis 2a: Self-disclosure moderates the relationship
between communal-
relationship orientation and perceived social loneliness such
that the relation-
ship becomes stronger when there are higher levels of self-
disclosure.
For an exchange-oriented OSN user, we expect that a higher
degree of self-
disclosure will strengthen the relationship between exchange
orientation and lone-
liness, that is, more self-disclosing behavior for this type of
user will result in more
feelings of loneliness. An exchange-oriented OSN user expects
higher self-disclo-
sure to receive, in return, disclosed information of the same
extent (e.g., based on
novelty or interestingness, media richness or message length).
However, recipients
of disclosed information may only be a small number of OSN
contacts—those who
have proved to reciprocate. Because these OSN contacts are
already having to
reciprocate the “normal” information (e.g., postings made or
photos sent), sending
them additional information (through self-disclosure) could
result in information
overload [35, 48]. To ease this overload, these contacts may
well ignore or hide
information, further reducing reciprocity [9]. The exchange-
oriented user is likely to
feel that his/her calls are falling on increasingly deaf ears. This

effect is likely to
increase feelings of disconnectedness (i.e., reduced
belongingness) that in turn
stimulate perceptions of social loneliness [83]. Consequently, in
the presence of
higher self-disclosure, the positive relationship between
exchange orientation and
perceived social loneliness is strengthened.
Hypothesis 2b: Self-disclosure moderates the relationship
between exchange-
relationship orientation and perceived social loneliness such
that the relation-
ship becomes stronger when there are higher levels of self-
disclosure.
Networking Ability
Networking is the proactive creation and maintenance of
interpersonal relationships
with the objective of leveraging these relationships at some
point [29]. Networking
ability is defined as an individual’s “capacity to identify and
develop a diverse group
of contacts” [55, p. 691]. Building on the theory of political
skill [36], networking is
a human skill leveraged for understanding and influencing
others in professional
settings to achieve personal and organizational objectives.
Individuals with a strong
networking ability find it easy to develop friendships, alliances,
and coalitions [36].
Furthermore, these individuals master, effortlessly, the creation
and maintenance of
large and diverse networks to take advantage of the
opportunities that emerge from

these relationships. Yet, networking ability is just a personal
trait; rather, it represents
a social resource and an informational asset that stems from
having access to the
social network [10].
Networking ability is also an important factor in OSNs because
it allows users to
create a large OSN network [34]. Users skilled in networking
can also employ the
OSN’s communication features to interact with their contacts
and thereby deepen
existing relationships [42]. These contacts in the OSN can
provide social support
(e.g., expressing concern and sharing news), and contribute to
feelings of belonging
and lower feelings of social loneliness. Individuals who feel
integrated within a
social network show reduced feelings of social loneliness
because their desire for
LONELINESS 285
attachment is satisfied [32]. When lacking networking ability,
the OSN user may
wait for others to initiate an OSN interaction instead of
proactively approaching
them. Missing out on interaction opportunities, a user with
lower networking ability
may feel disconnected, which is associated with feelings of
loneliness [72].
Consequently, OSN users who possess the ability to proactively
create and maintain

interpersonal relationships experience lower feelings of social
loneliness.
Hypothesis 3: Networking ability of an individual is negatively
associated with
perceived social loneliness.
Use of Active and Passive OSN Features
OSNs are used to create and consume user-generated content
[47]. Content is
produced through active engagement with a user’s contacts, for
example, in the
form of status updates or by sharing photos, videos, or links.
Communication
theorists differentiate active engagement as direct
communication (one-to-one) and
broadcasting (one-to-many) [12]. In addition, viewing user-
generated content is
referred to as “passive consumption,” also known as “social
surveillance” [45].
The rich architecture of OSNs provides features for performing
both active engage-
ment and passive consumption [11].
OSN users experience social isolation when they passively
consume user-gener-
ated content because of their lack of interaction [51].
Furthermore, passive con-
sumption restricts individuals in creating and managing
relationships [66]. The
literature has linked passive consumption to feelings of
disconnectedness and lone-
liness [4]. For instance, passive content consumption on
Facebook creates feelings of
envy that reduce a user’s life satisfaction through social

comparison between the
user and their contacts [50]. These researchers showed that
reading about the travel
and leisure experiences of OSN contacts led users to feel envy,
dissatisfaction, and
loneliness. Consequently, we propose that the use of OSN
features for observing
others (i.e., passive consumption) creates perceptions of social
loneliness because
users do not engage with their OSN network. By only using
passive OSN features,
the user misses out on interactions that could create a sense of
belonging.
Hypothesis 4a: The use of passive OSN features is positively
associated with
perceived social loneliness.
The creation of user-generated content and, as such, the active
engagement with
one’s OSN network, stimulates mutual content sharing among
OSN contacts [69].
Active engagement is achieved through the use of active OSN
features, where
repeated interactions strengthen a user’s social integration and
create a sense of
belonging [7]. The enhanced sense of social belonging is related
to feeling lower
degrees of social loneliness [31]. Active OSN features that use
either direct com-
munication or broadcasting “may have dramatically different
outcomes” on lone-
liness perceptions [12, p. 572] because of the number of
recipients, and thus the
number of potential interaction encounters.

In direct communication, the user communicates with one or
more recipients
whereas in broadcasting no specific recipient exists and the
content is shared with
a larger audience. This greater number of recipients provides
more opportunities for
social interactions than direct communication. Nevertheless,
actively engaging with
one’s network contacts creates a sense of belonging and it is
expected to result in
lower degrees of social loneliness.
Hypothesis 4b: The use of active OSN features that facilitate
broadcasting is
negatively associated with perceived social loneliness.
Hypothesis 4c: The use of active OSN features that facilitate
direct communica-
tion is negatively associated with perceived social loneliness.
Methodology
Participants
For the study, OSN users were recruited from a master’s
program in a business
school at a major Australian university. We invited students
from a large manage-
ment information systems (MIS) course in which 205 students
were enrolled. Prior
studies on social media have repeatedly used student samples to
test their hypothesis

[see, e.g., 49, 54]. Students are particularly appropriate sample
subjects for social
media research because they represent the typical OSN user
population based on age
and gender [56]. Our decision to use a student sample is further
supported by criteria
and recommendations put forward by Compeau et al. [24] who
posit that one can
generalize from student samples when the intended population
is clearly identified
and a rationale for the use of students is provided.
The sample of our study includes 61 percent females with an
average age of 25 years.
Participants had substantial experiences with OSNs and had an
OSN account for 5.5
years on average. Among the participants, 62 percent logged in
at least once a day and
64 percent spent at least 30 minutes a day in their OSN. On
average, participants spent
about 55 minutes per day at their OSN. The OSN sites varied
across participants, with
Facebook being the primary site used (32 percent), followed by
two Chinese OSNs,
RenRen (27 percent) and QQ (20 percent), and various other
OSNs (21 percent).
The participants were first-semester students, mainly
international students (92
percent Asian, 5 percent Australian, 1.5 percent European, and
1.5 percent South
American) who had arrived in Australia two to three weeks
prior to the data
collection. Thus, the majority of students had recently been
taken out of their
familiar environment and, therefore, the country and the

university were unfamiliar
to them. Hence, we assume that these students were
experiencing at least some level
of social loneliness. Our assumption was supported by a
research study reporting
that social loneliness is a concerning but common phenomenon
among university
students [27]. We also believe that the students used their OSN
for relationship
management and to maintain relationships with their familiar
network of people
from their home country. It should be noted that the majority of
students (66 percent)
LONELINESS 287
came from China where access to Facebook is blocked. This
number corresponds
roughly to the percentage of participants who indicated that
RenRen and QQ are
their main OSNs. This is not a problem for our study, however,
because we do not
study Facebook specifically, but rather the features available in
Facebook and other
OSNs. We only refer to Facebook to introduce OSN features
because it is a well-
known OSN.
Data Collection
Participants were provided with an online questionnaire at the
beginning of the study
that included instructions to answer the questions regarding past

OSN experience.
Participants were informed that the questions concerned their
OSN usage behavior.
Specifically, they were asked to evaluate the questions in the
context of how they
currently participate in OSN.
We undertook three rounds of data collection as illustrated in
Figure 2. To facilitate the
tracking of participant responses over time, each participant was
given a unique
identification number. Participation in the study was voluntary.
Responses varied
cross the different data collection waves. Of the 205 students,
185 (20 missing
responses), 178 (27 missing responses), and 169 (36 missing
responses) students
responded to the questionnaire at T1, T2, and T3, respectively.
Only one student
dropped the course and was removed from the sample. After
deleting responses of
those who did not participate all three times, we arrived at a
final sample of 166
participants (final response rate 81 percent).
Various measures were utilized to incentivize participation to
achieve a high
response rate and to minimize dropouts, including
prenotification of the data collec-
tion, endorsement of the research project by the lecturer,
participation reward
through nonfinancial tokens, and the assurance of privacy and
anonymity for all
participants. We kept the survey length reasonably short by
collecting different
constructs at different time periods. For example, communal and

exchange orienta-
tions were collected at T3 because these two relationship
orientations are relatively
stable individual traits [21].
Figure 2. Data Collection Procedure
In survey research, common method bias has the potential to
inflate the data
collected [68]. As outlined in Appendix B, we followed prior
research guidelines
for procedural and statistical remedies to mitigate threats of
common method biases
[68]. Procedural remedies to address these issues included
temporal, proximate, and
methodological separation of the measurements. A temporal
separation was
achieved by collecting the independent and dependent variables
at different time
points. Proximate separation was achieved by distributing the
questions on different
pages of the online survey. A methodological separation was
achieved by mixing
different scales (Likert scale or binary) throughout the
questionnaire [68].
Our statistical remedies relate to three different statistical
analyses, such as
Harman’s single factor test, a partial correlation procedure
(e.g., marker variable
technique), and controlling for effects of an unmeasured latent
method factor (i.e.,

single-common-factor method). All three tests, as detailed in
Appendix B, produced
results that suggest no major threat of common method bias. In
particular, our
Harman’s single factor test did not reveal a single factor with
more than 50 percent
variance explained. Further, the partial correlation approach did
not indicate any
significant correlations. Finally, the single-common-factor-
method results did not
significantly load on an unobserved method factor. Overall, the
results of the
procedural and statistical remedies suggested that common
method bias was not a
major concern.
Measures of Survey Constructs and Control Variables
For the survey, we adapted existing scales from the literature
wherever possible. All
constructs were multi-item measures with fixed answer
categories. Except for the
OSN feature-related constructs, which were operationalized as
formative constructs,
all the other constructs were operationalized as reflective
constructs (see Appendix
A). For the reflective constructs, shorter scales with fewer
indicators were used. One
of the important characteristics of reflective indicators is their
substitutability [67]. In
other words, an indicator may substitute another indicator of the
same construct, and
hence allow shorter scales. In addition, prior research has
suggested that it is often
practical and psychometrically viable to include a short version
of a scale to manage

the length of a survey [8]. In the IS literature, Venkatesh et al.
[84] used short scales
to operationalize the constructs. The important consideration is
construct validity
and reliability. If a reflective construct demonstrates
psychometrically acceptable
characteristics using a short scale, it is often useful to keep the
short scale to reduce
the length of the survey. In our study, we found strong
psychometrical properties
(e.g., reliability, factor loadings, convergent validity, and
discriminant validity) for
our constructs using a short scale during the pilot study. Hence,
consistent with prior
research, we kept the short scale for operationalizing the
constructs in our study.
Perceived social loneliness: This construct was measured as a
reflective construct
via seven items of the Revised UCLA Loneliness Scale [75],
which has become the
LONELINESS 289
standard scale to determine social loneliness for younger
populations [76]. The scale
ranged from 1 (never) to 5 (very often).
Relationship orientation: This was measured as two reflective,
independent con-
structs which is consistent with the way that communal and
exchange relationship
orientation are measured in the literature [21]. First, we

measured communal orien-
tation via a three-item scale based on the original communal
orientation scale [23].
Second, we measured exchange orientation via a three-item
scale based on the
revised versions of the exchange orientation scale [61]. The
scale for both subcon-
structs ranged from 1 (strongly disagree) to 5 (strongly agree).
Networking ability: This construct was measured as a reflective
construct via four
items of the political skill inventory [36]. The scale ranged
from 1 (strongly
disagree) to 7 (strongly agree).
OSN features: This variable was measured as three separate and
independent
constructs, which is consistent with the way that prior research
distinguishes these
different forms of communication. In particular, we
differentiate the degree to which
OSN users actively create and passively consume user-
generated content that man-
ifests as different forms of communication behavior. To
determine an OSN user’s
communication behavior, we listed a number of OSN activities.
The choice of
activities translates as either active or passive OSN feature use.
We measured the
use of active OSN in the form of broadcasting and direct
communication via three
items each based on [12, 13]. We measured the use of passive
OSN features via five
item that were also based on [12, 13]. Participants indicated
their frequency of each
activity; the scale ranged from 1 (never) to 5 (every time).

These three subconstructs
were measured as formative constructs.
Self-disclosure: This was measured as a reflective construct
with a five-item sub-
scale of the self-disclosure index [59]. On a scale from 1 (no
information) to 5 (very
detailed information), the degree of information disclosed on
their OSN was rated.
Control variables: Studies have shown that the degree to which
users feel competent
to use computers in diverse situations impacts their usage
behaviors [57]. It is reason-
able to assume that how competent users feel using OSNs may
impact their perceived
social loneliness. Thus, computer self-efficacy was introduced
as a control variable. It
was measured as a reflective three-item subset of a construct
developed by Compeau
and Higgins [25]. The scale ranged from 1 (strongly disagree) to
7 (strongly agree).
We also included age, gender, and home country as control
variables.
A preliminary survey was first pilot tested for
comprehensiveness, clarity, lan-
guage usage, and face validity with a small sample of 20 OSN
users and 3
experienced researchers in information systems, as
recommended by Churchill
[20]. The pilot test identified that some questions were difficult
to understand and
subsequently, wording was changed prior to launching the main
study. For example,
we had originally phrased all questions about Facebook only

assuming that this is
the major OSN for our target participants. However, this was
not the case and we
revised the questions to “on Facebook or the social networking
site that you use the
most.” Furthermore, we provided additional instructions in our
surveys to ensure that
participants knew to answer questions in the context of their
OSN use. We also
revised the scales for several constructs from the traditional
Likert scale (strongly
disagree to strongly agree) to other scales that were more
appropriate for those
constructs. For example, we used a five-point extent scale (no
information to very
detailed information) to measure self-disclosure. Finally, the
networking ability scale
had the qualifier “at work.” We removed this qualifier and
adapted the items to the
context of OSN (see Appendix A).
Data Analysis and Results
Partial least squares (PLS), a component-based structural
equation modeling (SEM)
technique, was used to analyze the data. PLS provides reliable
estimates for complex
structural models when the sample size is not large, by placing
less importance on
model fit and more importance on prediction [18, 37]. Further,
PLS is considered an

appropriate data analytic approach for a research model such as
ours that has
formative construct(s) and a moderator [67]. We used SmartPLS
Version 2 as our
statistical software application to test the various PLS models
[71].
Measurement Model
The measurement model for the reflective constructs was
assessed for both reliability
and validity [5]. In order to assess reliability and validity for
the reflective constructs
in our model, we followed the guidelines suggested by Fornell
and Larcker [37],
which include internal consistency reliability, convergent
validity, and discriminant
validity. Internal consistency reliabilities (ICRs) were evaluated
using Cronbach’s
alpha to ensure that model items reliably measured the proposed
constructs. ICRs
were greater than the recommended value of .70 for all
constructs at all time periods
[62] (see Table 1). From these results, we can assume
acceptable internal consistency
of our measures.
Convergent validity was assessed using indicator loadings and
average variance
explained (AVE) (see Table 1). As suggested by Hair et al. [40]
and Bagozzi [6],
indicator loadings were examined to confirm that factor
loadings were greater than
.70 on their intended constructs with minimal (less than .30)
cross-loading on other
constructs. Indicator loadings for our model were greater than

.70 for all constructs
at all time periods (except for LONL3 being slightly below .70),
with cross-loadings
being lower than .30, suggesting convergent validity of the
structural model. In
addition to indicator loadings, AVE was also examined to
ensure that the variance
explained by the construct is higher than variance from
measurement error [37].
AVE for all constructs either met or exceeded .50, further
suggesting convergent
validity for the current model. Given these results, convergent
validity can be
assumed for the proposed model.
We assessed discriminant validity by examining the square roots
of the shared
variance between the constructs and their measures (see Table
2). The diagonal
elements are the square root of the shared variance between the
constructs and their
LONELINESS 291
T
ab
le
1
.
Q
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al

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it
er
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tw
o
rk
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ty
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P
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ss
iv
e
O
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fe
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tu

re
s
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F
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ct
iv
e
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fe
a
tu
re
s:
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ro
a
d
c
a
st

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g
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lf-
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2
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L
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7

2
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6
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9
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**
C
o
m
p
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te
r
se
lf-
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ic
a
cy
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1
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2
1
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4
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2
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9
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4
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3
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1
1
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3
**
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S
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3
5
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1
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3

**
N
o
te
s:
F
ac
to
r
lo
ad
in
g
s
an
d
w
ei
g
h
ts
ar
e
fr
o
m
th
e
P
L
S
an
al

y
si
s
(*
*
p
<
.0
1
;
^
th
es
e
v
al
u
es
re
p
re
se
n
t
it
em
w
ei
g
h
ts
);
IC
R

:
in
te
rn
al
co
n
si
st
en
cy
re
li
ab
il
it
y
(ρ
c
=
(Σ
λ i
)2
/
[(
Σ
λ i
)2
+
Σ
iv

ar
(ε
i)
],
w
h
er
e
λ i
is
th
e
co
m
p
o
n
en
t
lo
ad
in
g
to
an
in
d
ic
at
o
r
an
d
v

ar
(ε
i)
=
1
-λ
i2
).
LONELINESS 293
T
ab
le
2
.
D
es
cr
ip
ti
v
e
S
ta
ti
st
ic
s
an
d

C
o
rr
el
at
io
n
s
M
ea
n
S
D
1
2
3
4
5
6
7
8
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1
.
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o
m
m

u
n
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l
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n
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tio
n
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0
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.
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xc
h
a
n
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e
o
ri
e
n

ta
tio
n
2
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5
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2
–
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4
*
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4
3
.
N
e
tw
o
rk
in
g
a
b
ili
ty
4
.4
7

1
.1
1
.0
7
–
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*
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1
4
.
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e
lf-
d
is
cl
o
su
re
2
.5
1
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–
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**
.7
8
5
.
P
e
rc
e
iv
e
d
so
ci
a
l
lo
n
e
lin
e
ss

2
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2
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–
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3
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*
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3
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–
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–
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*
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.
C
o
m

p
u
te
r
se
lf-
e
ff
ic
a
cy
5
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1
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3
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–
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2
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–
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.
P
a
ss
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e
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fe
a
tu
re
s
2
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1
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–
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.0
9
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2
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–
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–
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–
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8
.
B
ro
a
d
ca
st

in
g
2
.5
1
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6
–
.1
4
*
.0
6
.2
4
**
.1
9
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–
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8
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–
.0
8
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9
**
*
–
–
9
.
D
ir
e
ct
co
m
m
u
n
ic
a
tio
n
2
.8
0
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3
.1
7
**
–

.1
0
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7
**
.3
0
**
*
–
.0
8
–
.1
1
.1
7
**
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4
**
*
–
–
1
0
.
G

e
n
d
e
r
.6
1
.4
9
.0
9
.0
3
–
.2
0
**
–
.1
4
*
.0
3
.0
9
–
.0

4
.0
9
.0
9
1
1
.
A
g
e
2
4
.9
1
3
.5
6
.1
7
**
–
.1
1
.0
1
.0

3
–
.0
5
–
.0
7
–
.0
4
–
.0
9
–
.1
5
1
2
.
C
o
u
n
tr
y
.7
1
.4

5
–
.0
8
.1
4
*
–
.0
8
.1
2
*
.1
7
**
.0
2
.0
4
.1
8
**
.1
0
*

p
<
0
.0
5
,
*
*
p
<
0
.0
1
,
*
*
*
p
<
0
.0
0
1
.
N
o
te
:S
q
u
a
re
ro
o

t
o
f
th
e
av
er
ag
e
v
ar
ia
n
ce
ex
tr
ac
te
d
(A
V
E
)
is
o
n
d
ia
g
o
n
al
el
em

en
ts
.
measures; off-diagonal elements are correlations between
constructs. For discrimi-
nant validity, diagonal elements should be larger than off-
diagonal elements [37]. We
found all diagonal elements to be higher than the correlations
across constructs,
hence supporting discriminant validity.
To determine the quality of the formative construct of OSN
features, we inspected
the item weights and their multicollinearity (see Table 1). For
all items, the weights
are significant (p < .01). We examined multicollinearity because
it can destabilize
the model [67]. The variance inflation factor (VIF) statistic was
used to determine
whether the formative measures were too highly correlated. We
did not find any
major multicollinearity issues with all VIFs below the strict
threshold of 3.3.
Structural Model: Hypotheses Testing
Following the examination of the measurement model, we then
tested the structural
model to assess the significance of the proposed hypotheses
using a bootstrap

procedure of 1,000 resamples [38]. As presented in Table 3, we
ran three structural
models to test our hypotheses: Model 1 included only the
control variable, Model 2
Table 3. Structural Model Results for Perceived Social
Loneliness
Predictors Model 1 Model 2 Model 3
Control variables
Gender (female = 1) –.12* –.03 –.06
Age .04 .06 .09
Country of origin (China = 1, other countries = 0) .19** –.11
.04
Computer self-efficacy [T1] –.17** –.09 –.09
Direct/indirect effects
Communal orientation [T3] –.34*** –.21**
Exchange orientation [T3] .22** .16**
Networking Ability [T1] –.32*** –.28***
Passive OSN features [T2] .24** .23**
Active OSN features: Broadcasting [T2] –.14* –.12*
Active OSN features: Direct communication [T2] –.05 –.04
Moderator
Self-disclosure [T1] –.09 –.03
Moderating effectsa
Communal orientation [T3] × Self-disclosure [T1] –.33***
Exchange orientation [T3] × Self-disclosure [T1] .29***
R2 .07 .44 .56
ΔR2 .37*** .12**
* p < 0.05, ** p < 0.01, *** p < 0.001.

aWe included other second-order interaction terms in Model 3.
Given that these were nonsignificant
and did not change the overall model estimates substantially, we
excluded these terms from this
table for brevity and parsimony.
LONELINESS 295
included the main effects (H1a/b, H3, H4a/b/c),and Model 3
(H2a/b) included both
main and interaction effects. To assess the incremental variance
explained by the
interaction terms, a comparison of the R2 between these models
was conducted using
the guidelines suggested in the literature when testing for
interaction effects [2, 18,
43]. Following these guidelines, variables at the indicator level
were mean-centered
prior to creating the interaction terms.
We hypothesized that communal orientation would have a
negative influence
(i.e., decrease) on perceived social loneliness (H1a) and
exchange orientation
would have a positive influence (i.e., increase) on perceived
social loneliness
(H1b). As per Table 3, communal orientation had a negative
influence on per-
ceived loneliness (Model 3: β = –.21, p < .01) and exchange
orientation had a
positive influence on perceived loneliness (Model 3: β = .16, p
< .01), thus
supporting H1a and H1b.

Self-disclosure was hypothesized to moderate the relationships
between communal
and exchange orientation on perceived social loneliness such
that the relation would
be stronger when self-disclosure is high (H2a and H2b). We
found support for both
hypotheses. Perusal of Figure 3 indicates that the relationship
between communal
orientation and loneliness was found to be moderated by self-
disclosure such that for
OSN users with high self-disclosure, communal orientation had
a stronger negative
effect on perceived social loneliness (Model 3: β = –.33, p <
.01). In other words,
self-disclosure and communal orientation will work in tandem
such that when both
are high, individuals will feel less lonely. Alternatively, as
shown in Figure 4,
exchange-oriented individuals displayed greater loneliness when
they exercised
high levels of self-disclosure (Model 3: β = .29, p < .01). In
other words, if an
individual with high self-disclosure is exchange-oriented, he or
she will feel even
lonelier. In the presence of both, loneliness will increase. Table
3 shows that the
addition of the interaction terms increases R2 significantly.
Overall, the interaction
model explained 56 percent of the variance in perceived social
loneliness compared
to 44 percent without the interaction terms, which is
significantly different.
Networking ability was hypothesized to negatively influence (or
decrease) per-

ceived social loneliness (H3). As per Table 3, network ability
had a significant
negative influence on perceived social loneliness in both models
(Model 3: β = –.28,
2
2.5
3
3.5
Low Communal Orientation High Communal Orientation
P
er
ce
iv
ed
S
oc
ia
l
L
on
el
in
es

s
Low Self-
disclosure
High Self-
disclosure
Figure 3. Moderating Effect of Self-Disclosure on Communal
Orientation and Loneliness
p < .001), suggesting that individuals who had a high degree of
proclivity to connect
with others are less likely to be lonely. Thus, H3 was supported.
OSN feature use was also hypothesized to impact perceived
social loneliness. We
hypothesized that the use of passive features would positively
influence (or increase)
loneliness (H4a). Results of Table 3 indicate that passive
features used had a
significant positive influence on feelings of loneliness (Model
3: β = .23, p < .01),
supporting H4a. The use of active features in the form of
broadcasting was hypothe-
sized to negatively influence (or decrease) loneliness (H4b). We
found that broad-
casting had a strong negative influence on loneliness (Model 3:
β = –.12, p < .05). In
addition, direct communication was hypothesized to negatively
influence (or
decrease) loneliness (H4c). However, we found no support for

active direct com-
munication on perceived social loneliness (Model 3: β = –.04,
ns.), rejecting H4c.
Table 4 summarizes the hypothesized relationships including
the path coefficients
for those relationships.
2
2.5
3
3.5
Low Exchange Orientation High Exchange Orientation
P
er
ce
iv
ed
S
oc
ia
l
L
on
el
in

es
s
Low Self-
disclosure
High Self-
disclosure
Figure 4. Moderating Effect of Self-Disclosure on Exchange
Orientation and Loneliness
Table 4. Summary of Results as per Model 3 from Table 3
Path
Path
coefficient
Supported/
rejected
H1a Communal orientation → Perceived social loneliness –
.21** Supported
H1b Exchange orientation → Perceived social loneliness .16**
Supported
H2a Self-disclosure × Communal orientation → Perceived
social loneliness
–.33*** Supported
H2b Self-disclosure × Exchange orientation → Perceived
social loneliness
.29*** Supported

H3 Network ability → Perceived social loneliness –.28***
Supported
H4a Passive OSN features → Perceived social loneliness .23**
Supported
H4b Active OSN features: Broadcasting → Perceived social
loneliness
–.12* Supported
H4c Active OSN features: Direct communication → Perceived
social loneliness
–.04 Rejected
* p < 0.05, ** p < 0.01, *** p < 0.001.
LONELINESS 297
Discussion
The research aimed to determine how using an OSN can impact
feelings of lone-
liness. To this end, we used the literature on loneliness as our
overarching theory to
examine the influence of relationship characteristics (i.e.,
relationship orientation
moderated by self-disclosure and networking ability) and active
versus passive OSN
features on perceived loneliness. The study shows how OSNs
can be associated with
both more and less perceived loneliness. Specifically, loneliness
increased for
individuals who were involved in passive feature use and for

those who had
exchange-relationship orientation and high degrees of self-
disclosure. However,
loneliness was reduced when a user had increased networking
ability and used
active OSN features for broadcasting and when a user with a
communal relationship
orientation had high degrees of self-disclosure. Yet, active OSN
features via direct
communication was not associated with feelings of social
loneliness. We elaborate in
the following on the theoretical and practical implications of the
study.
Theoretical Implications and Contributions
This research contributes to the literature in several ways. First,
the study contributes
to IS research on OSN use regarding an individual’s social
loneliness when sepa-
rated. Prior research has produced contradicting results on the
relationship between
OSN use and loneliness, specifically, there is disagreement
whether OSN use
reduces or increases loneliness feelings [31, 52, 78]. This study
suggests that the
relationship between OSN and loneliness depends how OSNs
are used. Many prior
works treated OSNs as monolithic without much consideration
that these platforms
offer diverse features and functionalities [63]. We extended
prior research by
examining OSNs at a feature level and differentiated between
active and passive
features of an OSN. Our findings demonstrate that loneliness is
impacted by the use

of both active and passive OSN features. Loneliness is reduced
when the active
features related to broadcasting are used, but the use of only
passive features leads to
an increased level of loneliness.
Using OSNs for broadcasting to all contacts facilitates sharing
information with
the entire network, which in turn attracts reciprocity. As such,
our study shows that
features to support mass communication within OSNs can lower
social loneliness by
creating feelings of belonging. Broadcasting is a time-efficient
approach to distribute
social information, especially when the user is busy, something
not uncommon in
today’s fast-paced society.
In contrast, our findings also show that passive content
consumption expressed
through the use of certain OSN features (e.g., reading postings)
increases loneliness.
The fact that such passive behavior is labeled “social
surveillance” strongly suggests
isolation because the user takes on an observer role, hence
monitoring others from a
distance but carefully avoiding interaction.
Finally, for direct communications, we did not find a significant
impact on lone-
liness. One explanation for the insignificant results could be the
misalignment of the

OSN design goals compared to its use. When directly
communicating, OSN users
commit their full attention to one person; however, the
approached OSN contact may
not be willing or able to reply, leaving the sent messages
unanswered. Given that
OSNs are designed to support a network approach, with a
broadcasting functionality
in mind, in which messages spread through the entire social
network, the private
one-to-one communication does not align with this design
purpose and may lead to
ineffective use of the technology [14]. Other systems, such as e-
mail and chat rooms,
or even the old-fashioned phone, might be better suited for one-
to-one communica-
tion. Further, directly communicating requires more effort to
reach the same number
of people compared to broadcasting, and users might feel that
the benefits of direct
communication do not outweigh the costs. Based on this
imbalance, Thibaut and
Kelley’s [81] exchange theory suggests that a user would refrain
from directly
communicating via OSNs and, as such, may not be able to ease
perceived social
loneliness. In sum, this study contributes to IS by suggesting
that theorizing about
OSNs and how they can affect social outcomes needs to
investigate this relationship
at the feature level because the different technology features
allow for different
outcomes.
Second, we contribute to the relationship literature and
especially to research on

interpersonal relationships. Prior research has indicated that
people have different
understandings of the degree of reciprocity in creating and
maintaining relationships
[21]. Our study now shows that users benefit differently from
OSNs, depending on
their relationship orientation. More importantly, a communal
orientation is a bene-
ficial characteristic because these users will naturally perform
OSN-related activities
that are associated with lower degrees of perceived social
loneliness, if any. Hence,
users with a communal orientation (e.g., where benefits are
given in response to
needs or to show general concern for a user’s OSN contacts) are
able to create a
sense of belonging that makes them feel less lonely when
compared to exchange-
oriented users. Although an exchange orientation can be
successful in professional
environments, our findings illustrate that this tit-for-tat
behavior in OSNs leads to
increased feelings of loneliness.
The third contribution relates to the identification of self-
disclosure as a moderat-
ing factor. Extensive research has examined the direct influence
of self-disclosure in
social media [54, 69]. In contrast, this study demonstrates the
interaction effects of
self-disclosure on the relationship between a user’s relationship
orientation and
loneliness. The impact on social loneliness for both exchange-
oriented and commu-
nal-oriented users becomes stronger as their self-disclosure

increases. Sharing social
information increases liking and leads to closer relationships
[49, 69], and our
findings support this for communal-oriented users. When they
increase self-disclo-
sure, improved social outcomes (such as reduced loneliness) are
the result. However,
for exchange-oriented users, increased self-disclosure does not
have such positive
effects. Indeed, for these users, their disclosing behavior may
be perceived as
excessive, and recipients experience information overload. As
hypothesized, our
findings may be an example showing that increased levels of
self-disclosure burden
the relationship between exchange orientation and loneliness.
Thus, exchange-
LONELINESS 299
oriented users might fare better by not increasing their
disclosure because their
contacts may not be able to keep up with reciprocating. Yet, if
they fail to return
the information, the user may discard the contact for future
interactions. This means
that for exchange-oriented users, prior research findings that
higher self-disclosure
leads to reduced loneliness do not apply. We find a similar
theoretical association of
“less is more” in prior research on electronic word of mouth. A
study on the
optimal number of online product recommendations has shown

that after three
recommendations, wear-out effects manifest, causing future
recommendations to
be ignored, even if the recommendations are still valuable [1].
Consequently,
OSNs provide various possibilities for self-disclosure, but
increased levels of sharing
user-generated content can impose, even indirectly, detrimental
effects, namely, that
users feel more lonely.
Fourth, we contribute to the management literature on personal
influences and
political skills via networking abilities. Our results demonstrate
that within OSNs the
ability to establish interpersonal relationships is a crucial factor
as to why people feel
and likely remain lonely. In the literature on workplace
influences networking ability
was deemed as a key aspect to improving social capital that
resides in a relationship
[36]. Our study illustrates that networking abilities are also
valuable for an OSN
user because these relationships can be leveraged for creating
and maintaining
relationships resulting in feelings of belonging thereby reducing
social loneliness
perceptions.
Fifth, this research contributes to theory in social psychology,
in particular to the
body of knowledge of loneliness and the factors impacting these
feelings. Loneliness
has been extensively researched in psychology in offline
settings and exemplary in
an online context, however, this study is the first we know of

that shows which
factors affect perceived social loneliness in the technology-
mediated environment of
OSNs. Most important, our research explains loneliness from a
relationship point of
view whereas prior research to date has examined individual
characteristics (e.g.,
personality or self-esteem), which are only loosely related to
relationships. However,
the literature on loneliness stresses the qualitative and
quantitative deficits of
relationships as a key reason for a person to be in a lonely state
[66]. Hence, this
study highlights the theoretical importance of relationship
management factors to
understand social loneliness perceptions of OSN users.
Implications for Practice
A number of practical implications arise from this research for
firms and OSN users.
For firms, our findings help to highlight how they can utilize
OSNs to support their
workers. Corporate managers should be mindful of how heavy
travel demands can
disrupt workers’ personal relationships and how they can use
OSNs to reduce
feelings of loneliness. In modern workplaces, travel is often
unavoidable and
many individuals work in remote locations or in foreign
countries for an extended
period of time. Thus, firms should actively seek ways to address
the negative

implications of perceived social loneliness. Allowing and
endorsing the use of an
OSN can be a promising strategy. Prior research has shown that
firms often use
technology internally for networking, collaboration, and
knowledge sharing [33].
Our study points to the potential benefits a firm can gain
through the “private” use of
OSN by their employees during work times. This research
suggests that firms focus
on usage policies that encourage positive outcomes for the
employee and the firm
rather than prohibiting OSN use at work [41].
Our study participants were university students, which makes
our findings directly
relevant to education providers, many of which have large
international student
cohorts. This research suggests that a university should actively
approach its stu-
dents through an OSN while the students are still in their home
country. The
objective of the university should be to establish personal ties
with the students
and to connect with them before their arrival at the university.
In this way, the OSN
can be actively used prior to any development of social
loneliness in an unfamiliar
environment, and can be used further to help education
providers to develop
relationships once the student has arrived in the foreign
country.
And finally, the findings are also valuable for individuals who
are the focus of the

study—hose who are physically separated and away from home.
Parents, family
members, couples, and close friends need to understand OSNs
as a social tool that
allows them to comfort and communicate with meaningful
people in their lives. If
OSNs can ease feelings of loneliness, these technologies should
be used in a way
that allows for bridging temporal and spatial separation and
making the contact
person feel connected, for example, by uploading photos,
posting related comments,
and using the “like it” option more often.
Limitations and Future Research
A number of study limitations should be discussed. First, there
are limitations in
regard to sampling and data collection. Data collected from
university students might
raise issues of generalizability to other populations that are
separated from their
familiar social environments. The majority of the students in
this study had just
moved to Australia, and thus the participants were away from
home in an unfamiliar
environment and we assumed that they experienced social
loneliness. We believe
that our findings could be generalized to other student or
organizational settings, but
further empirical work would be necessary. We suggest two
settings that are
particularly interesting and valuable to study: industries that
utilize “fly-in-fly-out”
workers (such as the mining industry) and industries in which

staff (and often their
immediate families) regularly move from base to base (e.g., a
country’s armed
forces). It would be valuable in such studies to control for age
and life stage, the
use intensity of OSNs, and whether and how social loneliness
may be experienced
differently.
A second limitation refers to the evolving nature of OSN. The
active and passive
features OSN users can employ today will most likely change in
the future as
LONELINESS 301
technology develops. Yet we believe the nature of such features
will not change
because there will always be features that support more active
use or more passive
use. Furthermore, we did not restrict our research to one
particular OSN (i.e.,
participants responded to questions in relation to their main
OSN site), and hence,
we focused on features available across popular OSNs (e.g.,
Facebook, RenRen).
Another study might want to explore variations in features
across different OSNs
and what impact the variations have on OSN use and loneliness.
In the same light, one might want to develop a taxonomy of
OSN features to
classify different OSNs. In doing so, OSN features can be

differentiated at a more
fine-grained level than our active–passive distinction. In
addition, future research
can examine the extent to which active and passive features
impact other factors, for
example, interpersonal influence (word of mouth) and the
benevolence of users.
Furthermore, we have exclusively focused on perceived social
loneliness and dif-
ferent antecedents in the virtual world. A future research study
might undertake a
comparison between the virtual world and the physical world to
determine whether
people would react differently and to what extent relationship
orientation, self-
disclosure, and networking ability affect perceived social
loneliness. Finally, prior
research has highlighted the importance of privacy in relation to
OSN users’ self-
disclosure [54]. Thus, future research might want to examine
the extent to which
privacy concerns affect a user’s relationship management,
because these concerns
could restrict self-disclosure or active use of OSN features to an
extent that it may
increase perceived loneliness.
Conclusion
This research aimed at explaining the impact of relationship
characteristics and OSN
features on feelings of social loneliness within an OSN. We
have drawn on the
literature of loneliness and integrated theories of social
exchange, communication,
and political skills to study the phenomenon. We find that an

OSN user’s communal-
relationship orientation moderated by self-disclosure, use of
active OSN features,
and networking ability are negatively associated with perceived
social loneliness,
whereas an exchange orientation and use of passive OSN
features are positively
associated with these feelings. Our study highlights how and
why creating and
maintaining interpersonal relationships in the OSN influences
perceived social lone-
liness. The findings enhance our understanding of OSNs and
their ability to bridge
distances of time and place and thus, enhance users’ sense of
well-being, connect-
edness, and human attachment.
REFERENCES
1. Abendroth, L.J., and Heyman, J.E. Honesty is the best policy:
The effects of disclosure
in word-of-mouth marketing. Journal of Maketing
Communication, 19, 4 (2012), 245–257.
2. Aiken, L.S., and West, S.G. Multiple Regression: Testing and
Interpreting Interactions.
Thousand Oaks, CA: Sage, 1991.
3. Al-Saggaf, Y., and Nielsen, S. Self-disclosure on Facebook
among female users and its
relationship to feelings of loneliness. Computers in Human
Behavior, 36 (2014), 460–468.

4. Amichai-Hamburger, Y., and Ben-Artzi, E. Loneliness and
Internet use. Computers in
Human Behavior, 19, 1 (2003), 71–80.
5. Bagozzi, R.P. The role of measurement in theory construction
and hypothesis testing:
Toward a holistic model. In C. Fornell (ed.), A Second
Generation of Multivariate Analysis.
New York: Praeger, 1982, pp. 5–23.
6. Bagozzi, R.P. On the evaluation of structural equation
models. Journal of the Academy
of Marketing Science, 16, 1 (1988), 74–94.
7. Baumeister, R.F., and Leary, M.R. The need to belong:
Desire for interpersonal attach-
ments as a fundamental human motivation. Psychological
Bulletin, 117, 3 (1995), 497–529.
8. Bergkvist, L., and Rossiter, J.R. The predictive validity of
multiple-item versus single-
item measures of the same constructs. Journal of Marketing
Research, 44, 2 (2007), 175–184.
9. Bevan, J.L.; Pfyl, J.; and Barclay, B. Negative emotional and
cognitive responses to
being unfriended on Facebook: An exploratory study.
Computers in Human Behavior, 28, 4
(2012), 1458–1464.
10. Blass, F.R., and Ferris, G.R. Leader reputation: The role of
mentoring, political skill,
contextual learning, and adaptation. Human Resource
Management, 46, 1 (2007), 5–19.
11. Boyd, D., and Ellison, N. Social network sites: Definition,

history, and scholarship.
Journal of Computer-Mediated Communication, 13, 1 (2007),
210–230.
12. Burke, M.; Kraut, R.; and Marlow, C. Social capital on
Facebook: Differentiating uses
and users. In D. Tab (ed.), CHI ‘11: Annual Conference on
Human Factors in Computing
Systems. Vancouver: ACM, 2011, pp. 571–580.
13. Burke, M.; Marlow, C.; and Lento, T. Social network
activity and social well-being. In
E. Mynatt (ed.), CHI ‘10: Annual Conference on Human Factors
in Computing Systems.
Atlanta: ACM, 2010, pp. 1909–1912.
14. Burton-Jones, A., and Grange, C. From use to effective use:
A representation theory
perspective. Information Systems Research, 24, 3 (2013), 632–
658.
15. Butler, B.S., and Matook, S. Social media and relationships.
In The International
Encyclopedia of Digital Communication and Society. London:
Wiley-Blackwell, 2015, pp.
1–20.
16. Buunk, B.P., and Prins, K.S. Loneliness, exchange
orientation, and reciprocity in
friendships. Personal Relationships, 5, 1 (1998), 1–14.
17. Chai, S.; Das, S.; and Rao, H.R. Factors affecting bloggers’
knowledge sharing: An
investigation across gender. Journal of Management Information
Systems, 28, 3 (2011),

309–342.
18. Chin, W.W.; Marcolin, B.L.; and Newsted, P.R. A partial
least squares latent variable
modeling approach for measuring interaction effects: Results
from a Monte Carlo simulation
study and an electronic-mail emotion/adoption study.
Information Systems Research, 14, 2
(2003), 189–217.
19. Chou, H.-T.G., and Edge, N. “They are happier and having
better lives than I am”: The
impact of using Facebook on perceptions of others’ lives.
Cyberpsychology, Behavior, and
Social Networking, 15, 2 (2012), 117–121.
20. Churchill, G.A. A paradigm for developing better measures
of marketing constructs.
Journal of Marketing Research, 16, 1 (1979), 64–73.
21. Clark, M., and Mills, J. The difference between communal
and exchange relationships:
What it is and is not. Personality and Social Psychology
Bulletin, 19, 6 (1993), 684–691.
22. Clark, M.S. Record keeping in two types of relationships.
Journal of Personality and
Social Psychology, 47, 3 (1984), 549–557.
23. Clark, M.S.; Ouellette, R.; Powell, M.C.; and Milberg, S.
Recipient’s mood, relationship
type, and helping. Journal of Personality and Social Psychology,
53, 1 (1987), 94–103.
24. Compeau, D.; Marcolin, B.; Kelley, H.; and Higgins, C.
Research Commentary:

Generalizability of information systems research using student
subjects—A reflection on our
practices and recommendations for future research. Information
Systems Research, 23, 4
(2012), 1093–1109.
LONELINESS 303
25. Compeau, D.R., and Higgins, C.A. Computer self-efficacy:
Development of a measure
and initial test. MIS Quarterly, 19, 2 (1995), 189–211.
26. Cropanzano, R., and Mitchell, M. Social exchange theory:
An interdisciplinary review.
Journal of Management, 31, 6 (2005), 874–900.
27. Cutrona, C.E. Transition to college: Loneliness and the
process of social adjustment. In
L.A. Peplau and D. Perlman (eds.), Loneliness: A Sourcebook
of Current Theory, Research,
and Therapy. New York: Wiley-Interscience, 1982, pp. 291–
309.
28. Dang, Y.; Zhang, Y.; Chen, H.; Brown, S.A.; Hu, P.J.H.;
and Nunamaker, J.F. Theory-
informed design and evaluation of an advanced search and
knowledge mapping system in
nanotechnology. Journal of Management Information Systems,
28, 4 (2012), 99–127.
29. de Janasz, S.C., and Forret, M.L. Learning the art of
networking: A critical skill for
enhancing social capital and career success. Journal of

Management Education, 32, 5 (2008),
629–650.
30. de Silva, H.; Johnson, L.; and Wade, K. Long distance
commuters in Australia: A
socio-economic and demographic profile. Thirty-Fourth
Australasian Transport Research
Forum, 2011 Adelaide, Australia.
31. Deters, F.G., and Mehl, M.R. Does posting Facebook status
updates increase or
decrease loneliness? An online social networking experiment.
Social Psychological and
Personality Science, 4, 5 (2013), 579–586.
32. DiTommaso, E., and Spinner, B. Social and emotional
loneliness: A re-examination of
Weiss’ typology of loneliness. Personality and Individual
Differences, 22, 3 (1997), 417–427.
33. Durcikova, A., and Gray, P. How knowledge validation
processes affect knowledge
contribution. Journal of Management Information Systems, 25,
4 (2009), 81–107.
34. Ellison, N.B.; Steinfeld, C.; and Lampe, C. The benefits of
Facebook “friends”: Social
capital and college students’ use of online social network sites.
Journal of Computer-Mediated
Communication, 12, 4 (2007), 1143–1168.
35. Eppler, M.J., and Mengis, J. The concept of information
overload: A review of literature
from organization science, accounting, marketing, MIS, and
related disciplines. Information
Society, 20, 5 (2004), 325–344.

36. Ferris, G.R.; Treadway, D.C.; Kolodinsky, R.W.;
Hochwarter, W.A.; Kacmar, C.J.;
Douglas, C.; and Frink, D.D. Development and validation of the
political skill inventory.
Journal of Management, 31, 1 (2005), 126–152.
37. Fornell, C., and Larcker, D.F. Evaluating structural equation
models with unobservable
variables and measurement error. Journal of Marketing
Research, 18, 1 (1981), 39–59.
38. Gil-Garcia, J.R. Using partial least squares in digital
government research. In G.D.
Garson and M. Khosrov-Pour (eds.), Handbook of Research in
Public Information
Technology. Hershey, PA: IGI Global, 2008, pp. 239–253.
39. Green, L.R.; Richardson, D.S.; Lago, T.; and Schatten-
Jones, E.C. Network correlates of
social and emotional loneliness in young and older adults.
Personality and Social Psychology
Bulletin, 27, 3 (2001), 281–288.
40. Hair, J.; Black, W.; Babin, B.; and Anderson, R.
Multivariate Data Analysis. Upper
Saddle River, NJ: Prentice Hall, 2010.
41. Huang, Y.; Singh, P.; and Ghose, A. A structural model of
employee behavioral
dynamics in enterprise social media. Management Science.
Forthcoming (2015).
42. Huffaker, D.A., and Calvert, S.L. Gender, identity, and
language use in teenage blogs.
Journal of Computer Mediated Communication, 10, 2 (2005), 1–

12.
43. Jaccard, J., and Turrisi, R. Interaction Effects in Multiple
Regression. Thousand Oaks,
CA: Sage, 2003.
44. Joinson, A.N. “Looking at,” “Looking up” or “Keeping up
with” people? Motives and
uses of Facebook. In D. Tan (ed.), Twenty-Sixth Annual
SIGCHI Conference on Human
Factors in Computing Systems. Florence: ACM, 2008, pp.
1027–1036.
45. Jones, W.H.; Freemon, J.; and Goswick, R.A. The
persistence of loneliness: Self and
other determinants. Journal of Personality, 49, 1 (1981), 27–48.
46. Kane, G.C.; Alavi, M.; Labianca, G.J.; and Borgatti, S.
What’s different about social
media networks? A framework and research agenda. MIS
Quarterly, 38, 1 (2014), 274–304.
47. Kaplan, A.M., and Haenlein, M. Users of the world, unite!
The challenges and
opportunities of social media. Business Horizons, 53, 1 (2010),
59–68.
48. Koroleva, K.; Krasnova, H.; and Günther, O. “STOP
SPAMMING ME!” Exploring
information overload on Facebook. In D.E. Leidner and J.J.
Elam (eds.), Americas Conference
on Information Systems. Lima, Peru: Association for

Information Systems, 2010.
49. Krasnova, H.; Spiekermann, S.; Koroleva, K.; and
Hildebrand, T. Online social net-
works: Why we disclose. Journal of Information Technology,
25, 2 (2010), 109–125.
50. Krasnova, H.; Wenninger, H.; Widjaja, T.; and Buxmann, P.
Envy on Facebook: A
hidden threat to users’ life satisfaction? In: Proceedings of 11th
Annual Conference on
Wirtschaftsinformatik, Alt, R., and Franczyk, B., (eds.), (2013),
Feb 27 – Mar 01, pp.
1477–1491, Leipzig, Germany.
51. Kraut, R.; Patterson, M.; Lundmark, V.; Kiesler, S.;
Mukophadhyay, T.; and Scherlis, W.
Internet paradox: A social technology that reduces social
involvement and psychological well-
being? American psychologist, 53, 9 (1998), 1017–1031.
52. Kross, E.; Verduyn, P.; Demiralp, E.; Park, J.; Lee, D.; Lin,
N.; Shablack, H.; Jonides,
J.; and Ybarra, O. Facebook use predicts declines in subjective
well-being in young adults.
PLoS ONE, 8, 8 (2013).
53. Leung, L. Loneliness, self-disclosure, and ICQ (“I Seek
You”) use. CyberPsychology
and Behavior, 5, 3 (2002), 241–251.
54. Lowry, P.B.; Cao, J.; and Everard, A. Privacy concerns
versus desire for interpersonal
awareness in driving the use of self-disclosure technologies:
The case of instant messaging in
two cultures. Journal of Management Information Systems, 27,

4 (2011), 163–200.
55. Magnusen, M.J.; Mondello, M.; Kim, Y.K.; and Ferris, G.R.
Roles of recruiter political
skill, influence strategy, and organization reputation in
recruitment effectiveness in college
sports. Thunderbird International Business Review, 53, 6
(2011), 687–700.
56. Manago, A.M.; Taylor, T.; and Greenfield, P.M. Me and my
400 friends: The anatomy
of college students’ Facebook networks, their communication
patterns, and well-being.
Developmental Psychology, 48, 2 (2012), 369–380.
57. Marakas, G.M.; Mun, Y.Y.; and Johnson, R.D. The
multilevel and multifaceted char-
acter of computer self-efficacy: Toward clarification of the
construct and an integrative
framework for research. Information Systems Research, 9, 2
(1998), 126–163.
58. Marshall, G.W.; Michaels, C.E.; and Mulki, J.P. Workplace
Isolation: Exploring the
Construct and Its Measurements. Psychology & Marketing, 24,
3 (2007), 195–223.
59. Miller, L.C.; Berg, J.H.; and Archer, R.L. Openers:
Individuals who elicit intimate self-
disclosure. Journal of Personality and Social Psychology, 44, 6
(1983), 1234–1244.
60. Morahan-Martin, J., and Schumacher, P. Loneliness and
social uses of the Internet.
Computers in Human Behavior, 19, 6 (2003), 659–671.
61. Murstein, B.I.; Wadlin, R.; and Bond, C.F. The revised

exchange-orientation scale.
Small Group Research, 18, 2 (1987), 212–223.
62. Nunnally, J.C. Psychometric Theory. New York: McGraw-
Hill, 1978.
63. Orlikowski, W.J., and Iacono, C.S. Research commentary:
Desperately seeking the “IT”
in IT research. Information Systems Research, 12, 2 (2001),
121–134.
64. Pagani, M., Hofacker, C.F., and Goldsmith, R.E. The
influence of personality on active
and passive use of social networking sites. Psychology and
Marketing, 28, 5 (2011), 441–456.
65. Pavlou, P.; Liang, H.; and Xue, Y. Understanding and
mitigating uncertainty in online
exchange relationships: A principal-agent perspective. MIS
Quarterly, 31, 1 (2007), 105–136.
66. Peplau, L.A., and Perlman, D. Perspectives on loneliness. In
L.A. Peplau and D.
Perlman (eds.), Loneliness: A Sourcebook of Current Theory,
Research, and Therapy. New
York: Wiley-Interscience, 1982, pp. 1–20.
67. Petter, S.; Straub, D.W.; and Rai, A. Specifying formative
constructs in information
systems research. MIS Quarterly, 31, 4 (2007), 623–656.
68. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; and
Podsakoff, N.P. Common method
biases in behavioral research: A critical review of the literature
and recommended remedies.
Journal of Applied Psychology, 88, 5 (2003), 879–903.

LONELINESS 305
69. Posey, C.; Lowry, P.B.; Roberts, T.L.; and Ellis, T.S.
Proposing the online community
self-disclosure model: the case of working professionals in
France and the UK who use online
communities. European Journal of Information Systems, 19, 2
(2010), 181–195.
70. Reis, H.T.; Collins, W.A.; and Berscheid, E. The
relationship context of human behavior
and development. Psychological Bulletin, 126, 6 (2000), 844–
872.
71. Ringle, C.M.; Wende, S.; and Will, A. SmartPLS 2.0 (M3)
beta. Hamburg, Germany:
Retrieved 04 June 2013, from http://www.smartpls.de, 2005.
72. Rook, K.S. Research on social support, loneliness, and
social isolation: Toward an
integration. Review of Personality and Social Psychology, 1984,
pp. 239–264.
73. Rook, K.S., and Peplau, L.A. Perspectives on helping the
lonely. In L.A. Peplau and D.
Perlman (eds.), Loneliness: A Sourcebook of Current Theory,
Research, and Therapy. New
York: Wiley-Interscience, 1982, pp. 351–378.
74. Russell, D.; Cutrona, C.E.; Rose, J.; and Yurko, K. Social
and emotional loneliness: An
examination of Weiss’s typology of loneliness. Journal of

personality and social psychology,
46, 6 (1984), 1313–1321.
75. Russell, D.; Peplau, L.A.; and Cutrona, C.E. The revised
UCLA Loneliness Scale:
Concurrent and discriminant validity evidence. Journal of
Personality and Social Psychology,
39, 3 (1980), 472–480.
76. Russell, D.W. UCLA Loneliness Scale (Version 3):
Reliability, validity, and factor
structure. Journal of Personality Assessment, 66, 1 (1996), 20–
40.
77. Skågeby, J. Gift-giving as a conceptual framework: Framing
social behavior in online
networks. Journal of Information Technology, 25, 2 (2010),
170–177.
78. Song, H.; Zmyslinski-Seelig, A.; Kim, J.; Drent, A.; Victor,
A.; Omori, K.; and Allen,
M. Does Facebook make you lonely? A meta analysis.
Computers in Human Behavior, 36,
(2014), 446–452.
79. Stieglitz, S., and Dang-Xuan, L. Emotions and information
diffusion in social media:
Sentiment of microblogs and sharing behavior. Journal of
Management Information Systems,
29, 4 (2013), 217–248.
80. Suh, A.; Shin, K.-S.; Ahuja, M.; and Kim, M.S. The
influence of virtuality on social
networks within and across work groups: A multilevel approach.
Journal of Management
Information Systems, 28, 1 (2011), 351–386.

81. Thibaut, J.W., and Kelley, H.H. The Social Psychology of
Groups. New York: Wiley, 1959.
82. Torkington, A.M.; Larkins, S.; and Gupta, T.S. The
psychosocial impacts of fly-in fly-
out and drive-in drive-out mining on mining employees: A
qualitative study. Australian
Journal of Rural Health, 19, 3 (2011), 135–141.
83. Townsend, K.C., and McWhirter, B.T. Connectedness: A
review of the literature with
implications for counseling, assessment, and research. Journal
of Counseling and
Development, 83, 2 (2005), 191–201.
84. Venkatesh, V.; Morris, M.; Davis, G.; and Davis, F. User
acceptance of information
technology: Toward a unified view. MIS Quarterly, 27, 3
(2003), 425–478.
85. Walther, J.B.; Heide, B.V.D.; Kim, S.-Y.; Westerman, D.;
and Tong, S.T. The role of
friends’ appearance and behavior on evaluations of individuals
on Facebook: Are we known
by the company we keep? Human Communication Research, 34,
1 (2008), 28–49.
86. Weiss, R.S. Loneliness: The experience of emotional and
social isolation. Cambridge,
MA: MIT Press, 1973.
87. Wheeless, L., and Grotz, J. Conceptualization and
measurement of reported self-dis-
closure. Human Communication Research, 2, 4 (1976), 338–
346.

APPENDIX A: Constructs and Measurement Items
Instructions were given to participants to answer the
questionnaire in the context of
their OSN experience, we stated especially on the title page that
the goal of the
survey is to find out more about participants’ online social
networks and how they
use it or have used it in the past.
http://stephanieskolmoski.com/blog/product/a-paper-hug-book/
C
o
n
st
ru
ct
It
em
w
o
rd
in
g
B
as
ed
o

n
an
d
d
ev
el
o
p
ed
fr
o
m
C
o
m
m
u
n
a
l
o
ri
e
n
ta
tio
n
(s
tr
o
n
g
ly

d
is
a
g
re
e
—
st
ro
n
g
ly
a
g
re
e
)
C
O
M
M
1
I’m
n
o
t
th
e
so
rt
o

f
p
e
rs
o
n
w
h
o
o
ft
e
n
co
m
e
s
to
th
e
a
id
o
f
o
th
e
rs
.
(R
)
C

la
rk
e
t
a
l.
[2
3
]
C
O
M
M
2
W
h
e
n
p
e
o
p
le
g
e
t
e
m
o
tio
n

a
lly
u
p
se
t,
I
te
n
d
to
a
vo
id
th
e
m
.
(R
)
C
O
M
M
3
P
e
o
p
le

sh
o
u
ld
ke
e
p
th
e
ir
tr
o
u
b
le
s
to
th
e
m
se
lv
e
s.
(R
)
E
xc
h

a
n
g
e
o
ri
e
n
ta
tio
n
(s
tr
o
n
g
ly
d
is
a
g
re
e
—
st
ro
n
g
ly
a
g
re

e
)
E
X
C
H
1
If
I
te
ll
so
m
e
o
n
e
a
b
o
u
t
m
y
p
ri
va
te
a
ff
a
ir

s
(b
u
si
n
e
ss
,
fa
m
ily
,
lo
ve
e
xp
e
ri
e
n
ce
s)
,
I
e
xp
e
ct
th

e
m
to
te
ll
m
e
so
m
e
th
in
g
a
b
o
u
t
th
e
ir
s.
M
u
rs
te
in
e
t
a
l.
[6

1
]
E
X
C
H
2
If
I
ta
ke
a
fr
ie
n
d
o
u
t
fo
r
d
in
n
e
r,
I
e
xp
e
ct

h
im
o
r
h
e
r
to
d
o
th
e
sa
m
e
fo
r
m
e
so
m
e
tim
e
.
E
X
C
H
3

It
d
o
e
s
n
o
t
m
a
tt
e
r
if
p
e
o
p
le
I
lik
e
d
o
le
ss
fo
r
m
e
th
a
n

I
d
o
fo
r
th
e
m
.
(R
)
N
e
tw
o
rk
in
g
a
b
ili
ty
(s
tr
o
n
g
ly
d
is
a
g

re
e
—
st
ro
n
g
ly
a
g
re
e
)
N
E
T
A
1
I
sp
e
n
d
a
lo
t
o
f
tim
e

d
e
ve
lo
p
in
g
co
n
n
e
ct
io
n
s
w
ith
o
th
e
rs
.
F
e
rr
is
e
t
a
l.
[3

6
]
N
E
T
A
2
I
kn
o
w
a
lo
t
o
f
p
e
o
p
le
a
n
d
a
m
w
e
ll
co

n
n
e
ct
e
d
.
N
E
T
A
3
I
h
a
ve
d
e
ve
lo
p
e
d
re
la
tio
n
sh
ip

s
w
ith
a
lo
t
o
f
p
e
o
p
le
w
h
o
m
I
ca
n
a
sk
fo
r
su
p
p
o
rt
.

N
E
T
A
4
I
a
m
g
o
o
d
a
t
b
u
ild
in
g
re
la
tio
n
sh
ip
s
w
ith
p
e

o
p
le
.
P
a
ss
iv
e
O
S
N
fe
a
tu
re
s
(n
e
ve
r—
e
ve
ry
tim
e
)
P
A

S
F
1
R
e
a
d
o
th
e
r
p
e
o
p
le
’s
st
a
tu
s
u
p
d
a
te
s
B
u
rk
e
e
t

a
l.
[1
2
,
1
3
]
P
A
S
F
2
L
o
o
k
fo
r
fr
ie
n
d
s
P
A
S
F
3
A
d
d

g
ro
u
p
s
a
n
d
fa
n
p
a
g
e
s
P
A
S
F
4
U
se
a
p
p
lic
a
tio
n
s
P

A
S
F
5
C
h
e
ck
e
ve
n
ts
A
ct
iv
e
O
S
N
fe
a
tu
re
s
(n
e
ve
r—
e
ve

ry
tim
e
)
B
ro
a
d
c
a
st
in
g
A
C
T
F
1
U
p
d
a
te
m
y
st
a
tu
s

B
u
rk
e
e
t
a
l.
[1
2
,
1
3
]
A
C
T
F
2
U
p
lo
a
d
p
h
o
to
s
a
n
d
vi
d

e
o
s
A
C
T
F
3
T
a
g
p
e
o
p
le
in
p
h
o
to
s
a
n
d
vi
d
e
o
s
(c
o

n
ti
n
u
es
)
LONELINESS 307
C
o
n
ti
n
u
ed
C
o
n
st
ru
ct
It
em
w
o
rd
in
g

B
as
ed
o
n
an
d
d
ev
el
o
p
ed
fr
o
m
D
ir
e
c
t
c
o
m
m
u
n
ic
a
tio
n
A

C
T
F
4
W
ri
te
o
n
o
th
e
r
p
e
o
p
le
’s
w
a
lls
A
C
T
F
5
C
h
a
t

w
ith
fr
ie
n
d
s
A
C
T
F
6
S
e
n
d
p
e
rs
o
n
a
l
m
e
ss
a
g
e
s
to

fr
ie
n
d
s
S
e
lf-
d
is
cl
o
su
re
(n
o
in
fo
rm
a
tio
n
—
ve
ry
d
e
ta
ile
d
in

fo
rm
a
tio
n
)
S
E
L
D
1
W
h
a
t
I
lik
e
a
n
d
d
is
lik
e
a
b
o
u
t
m
ys

e
lf
M
ill
e
r
e
t
a
l.
[5
9
]
S
E
L
D
2
W
h
a
t
is
im
p
o
rt
a
n
t
to

m
e
in
lif
e
S
E
L
D
3
W
h
a
t
m
a
ke
s
m
e
th
e
p
e
rs
o
n
I
a
m

S
E
L
D
4
M
y
w
o
rs
t
fe
a
rs
S
E
L
D
5
T
h
in
g
s
I
h
a
ve
d
o
n
e

th
a
t
I
a
m
p
ro
u
d
o
f.
P
e
rc
e
iv
e
d
so
ci
a
l
lo
n
e
lin
e
ss
(n
e
ve

r—
ve
ry
o
ft
e
n
)
L
O
N
L
1
I
la
ck
co
m
p
a
n
io
n
sh
ip
.
R
u
ss

e
ll
e
t
a
l.
[7
5
]
L
O
N
L
2
T
h
e
re
is
n
o
o
n
e
I
ca
n
tu
rn
to

.
L
O
N
L
3
I
d
o
n
o
t
fe
e
l
a
lo
n
e
.
[R
].
L
O
N
L
4
I
a
m
n

o
t
cl
o
se
to
a
n
yo
n
e
.
L
O
N
L
5
M
y
in
te
re
st
s
a
n
d
id
e
a
s
a

re
n
o
t
sh
a
re
d
b
y
th
o
se
a
ro
u
n
d
m
e
.
L
O
N
L
6
T
h
e
re

a
re
p
e
o
p
le
I
fe
e
l
cl
o
se
to
.
[R
]
L
O
N
L
7
I
fe
e
l
le
ft
o
u

t.
C
o
m
p
u
te
r
se
lf-
e
ff
ic
a
cy
(s
tr
o
n
g
ly
d
is
a
g
re
e
—
st
ro
n
g

ly
a
g
re
e
)
C
o
m
p
e
a
u
a
n
d
H
ig
g
in
s
[2
5
]
I
a
m
co
n
fid
e

n
t
th
a
t
I
co
u
ld
fin
is
h
a
ta
sk
u
si
n
g
a
co
m
p
u
te
r
…
C
O
S
E

1
if
I
h
a
d
se
e
n
so
m
e
o
n
e
e
ls
e
u
si
n
g
it
b
e
fo
re
tr
yi
n
g
it

m
ys
e
lf.
C
O
S
E
2
if
I
ju
st
h
a
d
th
e
b
u
ilt
-i
n
h
e
lp
fa
ci
lit
y
fo

r
a
ss
is
ta
n
ce
.
C
O
S
E
3
if
so
m
e
o
n
e
sh
o
w
e
d
m
e
h
o
w

to
d
o
it
fir
st
.
A
P
P
E
N
D
IX
B
:
C
o
n
tr
o
ll
in
g
fo
r
C
o
m
m

o
n
M
et
h
o
d
B
ia
se
s
T
ec
h
n
iq
u
es
A
ct
io
n
s
ta
k
en
P
ro
c
e
d
u
ra

l
re
m
e
d
ie
s
T
e
m
p
o
ra
l,
p
ro
xi
m
a
te
,
p
sy
ch
o
lo
g
ic
a
l,
o
r
m
e

th
o
d
o
lo
g
ic
a
l
se
p
a
ra
tio
n
o
f
m
e
a
su
re
m
e
n
t
T
e
m
p
o
ra

ls
e
p
a
ra
tio
n
:
W
e
m
e
a
su
re
d
th
e
ke
y
d
e
p
e
n
d
e
n
t
va
ri
a

b
le
(i
.e
.,
p
e
rc
e
iv
e
d
so
ci
a
l
lo
n
e
lin
e
ss
)
se
p
a
ra
te
ly
fr

o
m
th
e
in
d
e
p
e
n
d
e
n
t
va
ri
a
b
le
s
a
t
d
iff
e
re
n
t
p
o
in
ts
in

tim
e
.
F
o
r
in
st
a
n
ce
,
p
e
rc
e
iv
e
d
so
ci
a
l
lo
n
e
lin
e
ss
w
a

s
m
e
a
su
re
d
a
t
T
3
;
n
e
tw
o
rk
in
g
a
b
ili
ty
,
se
lf-
d
is
cl
o
su
re
,

a
n
d
o
th
e
r
co
n
tr
o
lv
a
ri
a
b
le
w
e
re
m
e
a
su
re
d
a
t
T
1
;
a

n
d
O
S
N
fe
a
tu
re
s
w
e
re
m
e
a
su
re
d
a
t
T
2
.
P
ro
xi
m
a
te
se
p

a
ra
tio
n
:
W
e
d
is
tr
ib
u
te
d
th
e
q
u
e
st
io
n
s
in
th
e
q
u
e
st
io
n
n
a

ir
e
a
cr
o
ss
d
iff
e
re
n
t
w
e
b
p
a
g
e
s.
M
e
th
o
d
o
lo
g
ic
a
l
se
p

a
ra
tio
n
:
W
e
u
se
d
d
iff
e
re
n
t
sc
a
le
s
(L
ik
e
rt
sc
a
le
,
e
xt
e
n

t
sc
a
le
,
b
in
a
ry
sc
a
le
)
fo
r
o
u
r
q
u
e
st
io
n
s
to
d
im
in
is
h

a
n
y
re
ca
ll
o
f
in
fo
rm
a
tio
n
fr
o
m
sh
o
rt
-t
e
rm
m
e
m
o
ry
.
P

ro
te
ct
in
g
re
sp
o
n
d
e
n
t
a
n
o
n
ym
ity
a
n
d
re
d
u
ci
n
g
e
va
lu
a
tio

n
a
p
p
re
h
e
n
si
o
n
.
W
e
in
fo
rm
e
d
th
e
p
a
rt
ic
ip
a
n
ts
th
a
t

th
e
ir
re
sp
o
n
se
s
w
o
u
ld
b
e
a
n
o
n
ym
o
u
s,
a
ss
u
re
d
th
e

m
th
a
t
th
e
re
is
n
o
ri
g
h
t
o
r
w
ro
n
g
a
n
sw
e
r,
a
n
d
re
q
u
e
st

e
d
th
a
t
th
e
y
a
n
sw
e
r
q
u
e
st
io
n
s
a
s
h
o
n
e
st
ly
a
s
p
o
ss

ib
le
.
C
o
u
n
te
rb
a
la
n
ci
n
g
q
u
e
st
io
n
o
rd
e
r
W
e
co
u
n
te
rb

a
la
n
ce
d
th
e
ite
m
s
b
y
ra
n
d
o
m
iz
in
g
th
e
m
w
ith
in
e
a
ch
su
rv

e
y
b
lo
ck
a
n
d
b
y
ra
n
d
o
m
iz
in
g
th
e
su
rv
e
y
b
lo
ck
s
re
p
re
se

n
tin
g
d
iff
e
re
n
t
co
n
st
ru
ct
s.
Im
p
ro
vi
n
g
sc
a
le
ite
m
s
W
e

u
se
d
p
re
va
lid
a
te
d
re
lia
b
le
ite
m
s
(s
e
e
d
is
cu
ss
io
n
o
f
m
e
a
su

re
m
e
n
t)
a
n
d
p
ro
vi
d
e
d
d
e
fin
iti
o
n
s
a
n
d
e
xa
m
p
le
s
fo
r

p
o
te
n
tia
lly
u
n
fa
m
ili
a
r
te
rm
s.
S
ta
tis
tic
a
l
re
m
e
d
ie
s
H
a
rm

a
n
’s
si
n
g
le
fa
ct
o
r
te
st
T
h
e
H
a
rm
a
n
’s
si
n
g
le
fa
ct
o
r

te
st
in
d
ic
a
te
d
th
a
t
th
e
re
w
a
s
n
o
si
n
g
le
fa
ct
o
r
th
a
t
e
xp

la
in
e
d
m
o
st
o
f
th
e
va
ri
a
n
ce
.
T
h
e
fir
st
fa
ct
o
r
e
xp
la
in

e
d
o
n
ly
4
6
p
e
rc
e
n
t
o
f
va
ri
a
n
ce
.
P
ri
o
r
re
se
a
rc
h
h

a
s
su
g
g
e
st
e
d
se
ve
ra
l
lim
ita
tio
n
s
o
f
H
a
rm
a
n
’s
si
n
g
le

fa
ct
o
r
te
st
.
T
h
e
re
fo
re
,
w
e
co
n
d
u
ct
e
d
tw
o
a
d
d
iti
o
n
a
ls

ta
tis
tic
a
la
n
a
ly
se
s
th
a
t
w
e
d
e
sc
ri
b
e
b
e
lo
w
.
(c
o
n
ti
n

u
es
)
LONELINESS 309
A
P
P
E
N
D
IX
B
:
C
o
n
ti
n
u
ed
T
ec
h
n
iq
u
es
A

ct
io
n
s
ta
k
en
P
a
rt
ia
l
co
rr
e
la
tio
n
p
ro
ce
d
u
re
(e
.g
.,
m
a
rk
e

r
va
ri
a
b
le
te
ch
n
iq
u
e
)
G
iv
e
n
th
a
t
w
e
d
id
n
o
t
in
cl
u
d

e
a
n
y
co
n
st
ru
ct
s
th
a
t
w
e
re
co
m
p
le
te
ly
th
e
o
re
tic
a
lly
u
n
re

la
te
d
to
o
n
e
o
r
m
o
re
co
n
st
ru
ct
s
in
o
u
r
re
se
a
rc
h
m
o
d
e
l

to
re
d
u
ce
th
e
su
rv
e
y
le
n
g
th
,
w
e
,
fo
llo
w
in
g
P
a
vl
o
u
e
t
a

l.
[6
5
],
u
se
d
a
co
n
st
ru
ct
th
a
t
w
a
s
n
o
t
p
a
rt
o
f
o
u
r
re
se

a
rc
h
m
o
d
e
l
a
n
d
w
a
s
w
e
a
kl
y
re
la
te
d
to
o
th
e
r
co
n
st
ru
ct

s
in
th
e
re
se
a
rc
h
m
o
d
e
l.
W
e
co
m
p
a
re
d
th
e
co
rr
e
la
tio
n

b
e
tw
e
e
n
le
n
g
th
o
f
co
m
p
u
te
r
u
se
a
n
d
o
th
e
r
co
n
st
ru
ct

s
in
th
e
st
u
d
y
a
n
d
d
id
n
o
t
fin
d
a
n
y
m
a
jo
r
si
g
n
ifi
ca
n
t

co
rr
e
la
tio
n
s
(i
.e
.,
th
e
a
ve
ra
g
e
co
rr
e
la
tio
n
w
a
s
0
.1
1
),
in

d
ic
a
tin
g
th
a
t
th
e
re
w
a
s
n
o
e
vi
d
e
n
ce
o
f
m
a
jo
r
co
m
m
o

n
m
e
th
o
d
b
ia
s.
C
o
n
tr
o
lli
n
g
fo
r
th
e
e
ff
e
ct
s
o
f
a
n
u
n
m
e

a
su
re
d
la
te
n
t
m
e
th
o
d
s
fa
ct
o
r
(i
.e
.,
si
n
g
le
-c
o
m
m
o
n
-m
e

th
o
d
-f
a
ct
o
r
a
p
p
ro
a
ch
)
W
e
d
id
n
o
t
fin
d
a
g
o
o
d
fit
(i
.e

.,
ite
m
lo
a
d
in
g
s)
fo
r
th
e
m
o
d
e
l
w
h
e
n
w
e
u
se
d
th
e
si
n
g
le

-
co
m
m
o
n
-m
e
th
o
d
-f
a
ct
o
r
a
p
p
ro
a
ch
.
In
p
a
rt
ic
u
la
r,
w

e
cr
e
a
te
d
a
co
m
m
o
n
fa
ct
o
r
in
P
L
S
a
n
d
a
d
d
e
d
a
ll
th
e
ite

m
s
a
s
in
d
ic
a
to
rs
o
f
th
is
u
n
m
e
a
su
re
d
la
te
n
t
m
e
th
o
d
fa

ct
o
r.
T
h
e
ite
m
lo
a
d
in
g
s
ra
n
g
e
d
fr
o
m
–
.4
0
to
.6
2
.
W
e
fo

u
n
d
th
a
t
a
ll
th
e
ite
m
s
h
a
d
su
b
st
a
n
tia
lly
h
ig
h
e
r
lo
a
d
in
g

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