Embed Size (px)
Citation preview
This article was downloaded by: [Universidad Autonoma de Barcelona]On: 15 October 2014, At: 06:56Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
Behaviour & Information TechnologyPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tbit20
Development of computer-supported collaborativesocial networks in a distributed learning communityH. Cho a , J.-S Lee b , M. Stefanone c & G. Gay da Communications and New Media Programme , National University of Singapore , Singaporeb School of Communication and Information , Nanyang Technological University , Singaporec HCI Group, Department of Communication , Cornell University , Ithaca, NY, USAd Department of Communication, Information Science HCI Group, Information ScienceProgram , Cornell University , Ithaca, NY, USAE-mail:Published online: 03 Feb 2007.
To cite this article: H. Cho , J.-S Lee , M. Stefanone & G. Gay (2005) Development of computer-supported collaborativesocial networks in a distributed learning community, Behaviour & Information Technology, 24:6, 435-447, DOI:10.1080/01449290500044049
To link to this article: http://dx.doi.org/10.1080/01449290500044049
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.
This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Development of computer-supported collaborative social networksin a distributed learning community
H. CHO{*, J.-S. LEE{, M. STEFANONE§ and G. GAY}
{Communications and New Media Programme, National University of Singapore, Singapore{School of Communication and Information, Nanyang Technological University, Singapore
§HCI Group, Department of Communication, Cornell University, Ithaca, NY, USA}Department of Communication, Information Science HCI Group, Information Science Program,
Cornell University, Ithaca, NY, USA
This study examines the formation and change of collaborative learning social networks
in a distributed learning community. A social network perspective is employed to
understand how collaborative networks evolved over time when 31 distributed learners
collaborated on a design project using a computer-mediated communication system
during two semesters. Special attention was paid to how pre-existing friendship networks
influenced the formation of macro-level collaborative learning networks and individual
level social capital. We discovered that pre-existing friendship networks significantly
influenced the formation of collaborative learning networks, but the effect was dependent
on the developmental phase of community. Also, pre-existing networks generally acted as
a social liability that constrained learners’ ability to enhance their social networks and
build social capital when they participated in a new learning environment. The results
suggest that, in order to fully understand how to build effective collaborative learning and
work environments, participants’ social network structures need to be considered.
1. Introduction
A perspective of community-based learning and knowing is
receiving growing attention by both researchers and
practitioners. Proliferation of popular terms such as
communities of practice (Brown and Duguid 1991, Lave
and Wenger 1991), knowledge-building communities (Scar-
damalia and Bereiter 1994), knowledge communities
(Erickson and Kellogg 2001), communities of knowing
(Boland and Tenkasi 1995), and online collaborative
learning communities (Alavi 1994) reflect the growing
theoretical concerns on, as well as practical interests in,
such new ways of learning and knowing. Increasingly, these
communities are moving beyond face-to-face exchanges, to
interact in Computer-Mediated Environments (CMEs)
such as email lists, online forums, and shared web spaces,
requiring us to reconfigure our conceptual as well as
physical boundaries of community forms and community-
based learning and knowing (Preece 2000, 2001, Woodruff
2002).
The purpose of this study is to gain a better under-
standing of this new form of social system—distributed
learning community (DLC)—by examining the building
process of community social infrastructure, i.e., Computer-
Supported Collaborative Social Network (CSCSN). A
social network perspective is employed to understand how
collaborative learning networks evolved over time when 31
distributed learners had collaborated on design projects
using computer-mediated communication (CMC) systems.
Special attention was paid to how pre-existing face-to-face
networks influenced the over-time change and formation of
CSCSN, and whether they constrained or facilitated the way
the individual learners developed their social capital in this
emergent social structure. In other words, we asked:
*Corresponding author. Email: [email protected]
Behaviour & Information Technology, Vol. 24, No. 6, November-December 2005, 435 – 447
Behaviour & Information TechnologyISSN 0144-929X print/ISSN 1362-3001 online ª 2005 Taylor & Francis
http://www.tandf.co.uk/journalsDOI: 10.1080/01449290500044049
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
1. how does a community social infrastructure emerge
in a computer-mediated learning environment
2. whether this emerging structure is influenced by a
pre-existing social structure, and
3. if so, what is the nature of its effect on the
development of macro-level social structure and
individual-level social capital?
2. Review of literature
2.1 Communities of practice, social networks, and social
capital
From the social network perspective, knowledge is a social
and collective outcome and always embedded in a social
context—both created and sustained through ongoing
social relationships. Many researchers argue that learning
is fundamentally a social process and the purpose of a
knowledge community is to create and sustain knowledge,
culture and social infrastructures (i.e. social networks) that
foster seamless conversations and networks of connections
and relations among members (Lave and Wenger 1991,
Haythornwaite 1998).
Assuming that learning activity is fundamentally situated
in networks of multiple interactions and shared practices,
many scholars have called for more research to examine
knowledge creation and learning processes within the
broader context of how communities are structured, and
how individuals are situated in the larger social structure of
the communities (Brown and Duguid 1991, Lave and
Wenger 1991, Wenger 1998). For instance, Nahaphiet and
Ghoshal (1998) argue that, in order to understand how
individuals attain and build knowledge, it is necessary to
analyse how they are situated in networks of social relations,
resource exchange and social support. In a similar vein,
Nardi et al. (2002) also suggest that the most fundamental
unit of analysis for computer-supported cooperative/colla-
borative work and learning (CSCW/CSCL) should not be
the group level, but at the collective social network level.
While many have emphasised the theoretical importance of
social and structural elements of communities of practice
(see Brown and Duguid 1991 for review), there is surpris-
ingly little empirical work to directly examine how such
social and communicative structures evolve over time in
knowledge communities, and what social and technological
elements influence such process (Woodruff 2002).
Studies have shown that social networks in a community
serve as a conduit for information and resource exchanges
within and across the larger social system (Wellman et al.
1996, Haythornwaite 1998). For individuals, this provides a
basis of social capital, the networks of crosscutting
personal relationship that provide their members with
cooperation, trust, opportunity and access to a set of
resources ‘collectively owned’ by network members (Naha-
phiet and Ghoshal 1998). For instance, Baldwin et al.
(1997) found that centrality in a network was positively
correlated with satisfaction with a team-based learning
program. With regard to information exchange among
peers, proximity and the strength of ties between peers led
to the exchange of more kinds of information and the use
of more media (Haythornwaite and Wellman 1998).
Haythornthwaite (1998) found that a learner’s network
centrality was positively associated with a sense of
belongingness in a learning community.
Previous studies show that social networks are an impor-
tant antecedent for successful collaboration and socialisat-
ion in knowledge communities. However, studies examining
the building process of community social infrastructure are
surprisingly rare. Process-oriented research is imperative in
that the community perspective holds strong assumptions
about the ongoing nature of learning and knowing. The
concept of legitimate peripheral participation in situated
learning theory (Lave and Wenger 1991), for instance,
emphasises that the learner gradually moves from peripheral
participation towards full participation in the community of
practice as they engage in over-time interactions and shared
practices with multiple actors in a community.
Using a longitudinal analysis, we examine change and
formation of a large collaborative learning network and
individual social capital. As noted above, we pay special
attention to the effect of a pre-existing social structure on
this emergent pattern of knowledge community. In other
words, we look at how a pre-existing social network
develops into a CSCSN. In addition to this descriptive
analysis, we also examine whether, to what extent, and how
a pre-existing social network influences the formation of
macro-level social structure and individual level social
capital. In the subsequent sections, we specify the rationale
for this research focus, review previous studies and
summarise implications for this study.
2.2 A pre-existing network in a DLC
Social systems do not originate in a social vacuum (Cohen
and Prusak 2001). As structuralists often argue, develop-
ment of any social system is dependent upon and
constrained by previous social structures, histories and ties
(see Emirbayer and Goodwin 1994 for review). That is,
social networks ‘exhibit aspects of both emergence, being
called into existence to accomplish some particular work,
and history, drawing on known relationships and shared
experience’ (Nardi et al. 2002, p. 207). Therefore it is
critical to examine whether and how pre-existing social
networks and social ties influence the emerging pattern of
collaborative social networks in DLCs.
In theory, a pre-existing network such as a friendship
network is a relational foundation, providing a base for
quick formation of multiple communication and social
436 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
support networks in a DLC. Electronic communities often
can be characterised by a lack of social bonds (especially in
their initial phase), because the communication channels
between members from distant locations are restricted to
CMC, which may not be suitable for building strong
relationships (for counter arguments see Walther 1992,
Walther et al. 2001). In this sense, pre-existing friendship
networks are the building blocks for emerging social
infrastructures, as people develop new social ties from
‘friends of a friend’ and ‘friends of a friend of a friend’, etc.
However, a pre-existing social network may also simul-
taneously act as a constraint on communication by
restricting the evolution of network ties and structures to
predefined, existing social circles (Granovetter 1973).
Because long-term relations tend to be strong, symmetrical
(i.e. reciprocated) ties, they tend to cluster in dense,
interconnected groups (Krackhardt 1992). That is, strong
ties tend to bond similar people together, such that they are
all mutually connected and share similar or redundant
resources (Granovetter 1973). Such tightly bound groups
become insulated from outside information and resources.
These types of groups are better suited for social support
than for instrumental ends such as access to unique
information and resources (Granovetter 1973). Considering
that one of the main objectives of building a DLC is to
produce an efficient flow of ideas, approaches, information
and knowledge across different cultures, locations and
social boundaries, an emergent network strongly rooted in a
pre-existing friendship network is somewhat problematic as
it functions to promote closed, rather than open, commu-
nication and resource exchanges (Robertson et al. 1996).
The above discussion indicates that pre-existing friend-
ship networks could be expected to have both positive and
negative influences on the formation of emerging CSCSN
in a DLC. Few previous studies have empirically examined
whether or how a pre-existing network influences the
development of collaborative social networks in a dis-
tributed learning environment. Therefore, we ask the
following research questions:
RQ 1: How does an existing social network develop into
a CSCSN over time?
RQ 2: To what extent does an existing social network
influence the formation of collaborative learning net-
works in a DLC?
RQ 3: Do existing social networks function to constrain
or facilitate emergence of new collaborative networks
and social capital?
2.3 The effect of a pre-existing network on CSCSN
With regard to the second research question, previous
CMC literature suggests somewhat contradictory predic-
tions on this question as follows. On one hand, early CMC
studies generally held that CMC would liberate people
from traditional social and physical constraints, fostering
the formation of new, heterogeneous social relationships in
CMEs. Researchers predicted that the use of CMC would
radically reconfigure social relationships and structures,
because anonymity, reduced social and contextual cues,
and increased connectivity through CMC would make it
easier for people to find social links and ties across
hierarchical, social and organisational boundaries (Sproull
and Kiesler 1986 and 1991, Jones 1994). CMC researchers
also predicted that the advent of computer networks and
applications would dramatically expand the geographical
and temporal boundaries of interpersonal or small group
communication, which would lead more frequent and
heterogeneous information and resource exchange between
weak social ties (Constant et al. 1996) and increase diversity
of strong ties across organisations (Lievrouw et al. 1987).
The above discussion emphasises the role of CMC
technology as an active agent helping members of DLCs
form increasingly diverse and heterogeneous social and
work relationships. Accordingly, pre-existing networks will
minimally influence the formation of collaborative learning
networks, as participants quickly and easily develop new
‘network capital’ due to the liberating effect of CMC
technology (Wellman et al. 2001).
On the other hand, recent CMC studies suggest that a
pre-existing network should have moderate or perhaps
strong effects on the formation of emerging CSCSNs. Evi-
dence suggests ‘electronic links primarily enhance existing
interaction patterns rather than creating new ones’ (Bikson
et al. 1989, p. 102). Child and Loveridge (1990) find that
CMC is designed precisely to support ongoing hierarchical
relations. More recently, Wellman et al. (2001) found that
people’s interaction online supported pre-existing social
capital by supplementing face-to-face contacts.
To summarise, previous studies on CMC have shown
that it is unclear whether and how the effects of pre-existing
social network manifest in a DLC setting. Some studies
predict that the emergence of CSCSNs in a DLC is free
from history as CMC liberates members from previous
social, hierarchical, and relational boundaries and con-
straints. Others suggest that a pre-existing friendship
network should have very strong influence on the forma-
tion of CSCSN. Hence we ask:
RQ 2a: How do CSCSNs evolve in a CME? Do existing
social networks significantly influence the emergent
patterns in CSCSNs?
2.4 Pre-existing social network and development of social
capital
In addressing the third research question, we examine
whether a pre-existing social network and network ties
Development of CSCSN 437
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
constrain or facilitate individuals’ ability to maintain,
refresh and activate their social capital in a new learning
and work environment. A review of literature suggests
contradictory predictions.
Studies on social capital generally assume that individuals
with diverse social networks are capable of building greater
social capital over time by utilising their existing network
relationships. Social network studies also stress that an actor
occupying central positions in a given social structure obtains
a number of advantages including efficient access to new
relational partners (Gulati, 1995). For instance, friendship
network ties in organisations help people form new partner-
ships by providing access to information on the availability,
competencies and reliability of potential partners, thus
lowering searching costs and alleviating the risk of opportu-
nism (Jehn and Shah 1997, Dirks et al. 2001). These studies
suggest actors occupying positions with high centrality in a
pre-existing network should realise potential benefits of a
DLC earlier than others, because they have greater accessi-
bility and the flexibility to access different social groups than
actors structurally located on the periphery of a network. A
pre-existing friendship network thus is an enabler that helps
people quickly form new links and ties, moving deep into
emerging structures of aDLC. In this case pre-existing social
ties are valuable relational assets for effective information
exchange and knowledge gain in a learning community.
Conversely, researchers have found that strong network
ties can function as a social liability, especially in dynamic
environmental conditions like DLCs (Leenders and Gabbay
1999). In such cases, pre-existing social ties constrain one’s
ability to rejuvenate their network composition, which
increasestheabilitytoadapttochangingconditions.Gargiulo
and Benassi (1999) suggest that these negative effects operate
in two ways: limited resources and relational inertia.
First, actors holding many social relationships are less apt
to develop new relations because maintaining such pre-
existing ties takesupasignificantportionof their limited time,
energy and emotion. Investment in social capital is substan-
tially more complex than investments in human capital
(Coleman 1990). While a person can typically acquire new
skillswithout having to discard previous ones, the same is not
true for social capital. Since people have limited resources,
pressures to maintain pre-existing relationships may hinder
the ability to cultivate other relationships necessary to refresh
their social capital. That is, unbalanced investment or
overinvestment in pre-existing social capital can transform
potentially productive assets into constraints and liabilities
(Garguilo and Bernassi 1999, Leenders and Gabbay 1999).
Obligations and expectations for strong, long-lasting rela-
tionships may prevent a person from realising greater
economic opportunities by constraining the search for, and
development of, new trading partners (Granovetter 1985).
The secondmechanism is relational inertia. Peopleget used
to dealing with their long-term partners. Individuals tend to
keep strong ties with the same group of people (Quinn
et al. 1983), and take similar positions and roles even when
they belong to multiple social networks (Rice 1994).
Similarly, research has shown that information exchange
is heavily influenced by pre-existing friendships and
personal contacts. People tend to be motivated to share
information, and provide each other with early, frequent
access to resources available within their initial social circle
(Granovetter 1973, Krackhardt 1992). In other words,
people seek information that is the most easily accessed
(such as asking co-workers), rather than searching for the
best information (O’Reilly 1982).
Taken together, the above discussion suggests that
people holding strong ties in a friendship network might
be less motivated or able to explore new links and ties, as
they might be satisfied with existing social networks or
constrained by social liabilities. This is especially detri-
mental for knowledge building and learning in DLCs
because positive outcomes are expected to emanate when
heterogeneous actors share their diverse resources by
engaging in shared practices.
In summary, previous literature suggests that pre-
existing networks should operate as either a relational
asset or social liability, facilitating or constraining the
formation of new collaborative learning networks. Ques-
tions remain regarding how pre-existing social networks
influence individuals’ embeddedness in DLCs. Hence:
RQ 2b: Will a pre-existing social network constrain or
enable the way individuals create, maintain and activate
their new social capital?
To answer this question, we measured individuals’ initial
position in a pre-existing social network and tested whether
these initial structural proprieties enable or constrain their
ability to renew or expand social capital as they join in a
collaborative learning environment. Because of the con-
flicted findings from previous literature, we do not specify
predictions about the direction of association between a
pre-existing social network and emerging CSCSN, but we
do propose and test the following hypothesis regarding the
magnitude of association in quality.
H1: Learners’ initial structural positions will be sig-
nificantly associated with the degree to which they
develop new social ties and move into different social
groups in an emerging CSCSN.
3. Methods
3.1 Study site and data collection
The data for this study were collected from a multi-year
CSCL/CSCW research project. The goal of the project was
438 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
to develop the capability for individuals at distributed
geographic locations to interact effectively on development
of future aerospace systems. A distributed engineering
design class was co-hosted by engineering schools located
at two universities. The two universities are separated by
about 55 miles distance.1 Thirty-one senior and graduate
level students from two universities enrolled in a year-long
design course (14 from University A and 17 from
University B). Among the students, 23 were male (Univ.
A=9, Univ. B=14) and eight were female (Univ. A=4,
Univ. B=4).
The course emphasised distributed teamwork and
collaboration at the group level, as well as collective
learning and knowledge construction at the learning
community level. As a means of achieving this goal, a
web-based collaboration and communication system called
the Advanced Interactive Discovery Environment (AIDE)
was developed. The AIDE is a web-based portal providing
a suite of integrated tools including simulation, application
sharing, communication, networking, information retrie-
val, custom information storage, as well as instructor-
provided material. The communication tools include
realtime audio/video (AV) conferencing, chat and instant
messaging (IM), email and discussion boards.
One key feature of the project was that distributed
teams, consisting of students from two distributed
locations, had to work closely together to design a future
aerospace system. The group task focused on the design of
the structural subsystem for the next-generation space
shuttle, called a reusable launch vehicle (RLV). As
members of the collaborative distance design team,
students focused on materials and structure issues, as well
as on thermal control and thermal protection. To create a
full multidisciplinary experience, NASA engineers inter-
acted with the class in teams addressing such disciplines as
propulsion systems, hydraulics, aerodynamics, human
factors and cost analysis. The task was highly interde-
pendent, cooperative and multidisciplinary in nature. To
create effective designs, students had to be aware of
the overall system engineering but at the same time needed
to cooperative and collaborate with other as each group
member had to specialise in one area. In the first semester,
the students considered alternative designs for elements
and systems of the RLV. In the second semester, a detailed
design was made, with virtual manufacturing, construc-
tion and testing. The course ended with a presentation to
NASA.
The AIDE provided public social and knowledge space
through which distributed students freely exchanged ideas
and suggestions via email, IM and online discussion
boards. Team level achievements were frequently posted
on the shared web space so experiences, ideas and knowl-
edge could be exchanged across the boundaries of design
teams, classes and universities.
3.2 Measures
3.2.1 Social networks. We examined how community social
infrastructure (CSCSN) emerged in this collaborative
learning and work environment (RQ1) and the degree to
which this emergent CSCSN was influenced by a pre-
existing face-to-face (FtF) social network (RQ2). Social
network data were gathered three times. The first survey
was administered in the second week of the first semester
measuring a pre-existing network. Students were asked to
look carefully at the class roster and indicate up to five
persons they most frequently communicated with, and how
often during a typical month. Considering this network was
measured before they participated in group aspects of the
design project, and students belonged to the same depart-
ments or schools for years, it is assumed that the reported
relationships were pre-existing friendships rather than any
other type of instrumental relation.
In a subsequent data collection (at the end of the first and
second semesters), students were asked to report names of
people they talked to for two specific functions—informa-
tion exchange and social support. Information exchange
refers to communication about class, coursework, or design
projects; social support refers to non-instrumental commu-
nication, or interactions that primarily provide social
support and socialisation (Ibarra and Steven 1993). Two
different types of networks are distinguished, considering
multiple types of interaction networks coexist within the
same organisation or community (Ibarra and Steven 1993,
Nardi et al. 2002).
3.2.2 Initial network positions. For RQ3 and H1, we
examined whether pre-existing ties facilitated or con-
strained the way individual learners developed new social
capital when they engaged in a distributed learning
community. To test this hypothesis, an individual’s initial
network positions in a pre-existing network were measured
in order to predict subsequent individual level social capital
development (see below for the measures). Individual
network positions were measured by two network central-
ity measures. Betweenessmeasures the frequency with which
an actor falls between other pairs of actors on the shortest or
geodesic paths connecting them (Freeman 1979). The higher
the betweeness score of an actor, the greater the likelihood
that actor serves as a structural conduit, connecting others
in the network. Degree centrality refers to the number of
social network ties that an actor holds in a given social
network (Freeman 1979). High degree actors are the most
1The two universities are relatively close, but a post-hoc interview revealed
that students from the two universities seldom met with each other face-to-
face. Only five students indicated that they had met their remote partner
once or twice for the entire study year for their group projects.
Development of CSCSN 439
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
active and strategically advantageous in the sense that they
have the most ties to other actors in the network (Wasser-
man and Faust 1994). These network variables represent an
actor’s structural advantage (connections to other people)
and disadvantage (liabilities) in terms of developing new
social capital in a DLC.
3.2.3 Social/network capital. Two variables were developed
to measure the extent individuals develop their social
(network) capital in the emergent CSCSN. First, change
propensity measures the degree to which an individual
added new contacts in his/her ego network. An ego
network is an individual level social network consisting of
network partners directly connected to a given individual.
For instance, if person ‘x’ initially reported a, b, c, d as her/
his interaction partners in phase I (pre-existing network)
and then reported b, c, f, g, t in phase II (information
exchange network), then the change propensity for this
particular individual in phase II is 0.6 (3/5). This variable
measures how actively an individual acquired new rela-
tional resources and assets in his/her social circle.
Second, we counted how many cliques an individual
belonged to in the final phase of network development.
Clique membership represents how actively an actor
accessed diverse social circles. A clique is defined as any
group of at least three actors for which all pairs are
adjacent to one another. Using the clique algorithm in
UCINET V (Version 1.0; Borgatti et al. 1999), we identified
19 cliques in this collaborative learning community. On
average, each student belonged to 2.34 cliques (mini-
mum=1, maximum=5). It is assumed that those
belonging to a number of different cliques (or in other
words, those who are holding relationships spanning across
multiple social circles) tend to have structural advantages
in that they maintain diverse information resources other-
wise unavailable to out-group members. As such, clique
membership measures the extent to which an individual
explored and moved around different subgroups emerging
in a DLC. To distinguish clique membership from friend-
ship networks, cliques were identified via the information
exchange network in Phase III.
Given that collaborative learning and knowledge con-
struction take place through shared practice and social
interactions among multiple actors, the two variables
represent the extent to which an individual explored and
utilised new social resources as s/he participated in a new
learning or work environment, such as a DLC.
3.3 Analysis
To test the influence of pre-existing friendship networks on
the emergent collaborative networks (RQ2), the association
between the pre-existing and emergent networks was
measured. It was assumed that if the two social networks
at two different timeframes (for example, the pre-existing
network and the information exchange network in phase
III) were highly correlated to each other, the internal
characteristics of the first one significantly remained in the
following network, indicating that the former significantly
influenced the formation of the latter. The significance of
the association between two social networks was calculated
using the quadratic assignment procedure (QAP). QAP
calculates Pearson’s correlation coefficient as well as simple
matching coefficient between corresponding cells of the two
data matrices. By repeating such calculations thousands of
times using random permutations, QAP can test if the
observed association between the two networks is statisti-
cally significant (see Hubert and Schultz 1976, Krackhardt
1988 for reviews).
RQ3 and H1 were tested using multiple regression
analyses. Each dependent variable was regressed onto two
network variables to test whether initial network positions
significantly influenced the way individuals developed their
social/network capital.
4. Results
4.1 Descriptive results
At first, we examined how students from two distant
locations built collaborative work and learning networks
over time (RQ1). Figures 1 through 3 visually represent
how social networks evolved into a CSCSN as students
from two distant locations participated in a DLC. Note
that the social networks were measured at three different
periods. Figure 1 represents the social network of the class
at the beginning of the semester (Phase I: Pre-existing
Figure 1. Pre-existing social network.
440 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
friendship network). At this point students at different
locations did not have any interaction, clearly indicated in
the diagram. Of 31 students, five were identified as social
isolates.
Figure 2 shows students from both universities formed
large communication networks in Phase II. Note that this
study measured two different types of social networks—
social support and information exchange as described in
the network diagrams. With few exceptions, students
indicated that they exchanged social support and informa-
tion with the same partners. Finally, figure 3 shows the
network structures of Phase III. Students formed several
subgroups, mostly with co-located partners. It is interesting
to note that most students did not attempt to interact with
remote partners at this point. Inter-group communication
only occurred through liaisons connecting those subgroups.
Figure 2. CSCSN in Phase II.
Figure 3. CSCSN in Phase III.
Development of CSCSN 441
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
4.2 The effects of a pre-existing friendship network on the
formation of CSCSN
This study questioned to what extent a pre-existing
friendship network influences formation and change of
CSCSNs in a DLC (RQ3). Table 1 reports the results of
QAP correlation analyses testing the association between
two network matrices at different timeframes. Results show
that all five networks measured at different periods are
significantly correlated with each other. This is not
surprising given that the composition of network members
remained constant throughout the study period, except for
one participant who dropped the course after the first
semester (a2 in the figures). However, it is interesting to
note that the pre-existing friendship network significantly
influenced the emerging structures of collaborative learn-
ing networks (r=0.17, r=0.33, p5 0.01 for information
exchange networks in Phase II and III, and r=0.32,
r=0.42, p 5 0.01 for social support networks). The results
indicate that, despite abrupt changes in communication
structures (i.e. convergence of two distributed sub-networks
as the students participated in a collaborative learning
project) internal characteristics of the pre-existing friend-
ship network remained.
Another interesting finding in this analysis is that the
associations between Phase I and III networks (r=0.34,
0.42) are greater than those between Phases I and II
(r=0.17, 0.32) and Phases II and III (r=0.16, 0.37). This
indicates that the network structures became more similar
to the pre-existing friendship network in the final Phase III,
than in Phase II. In other words, networks regressed into
the initial pre-existing friendship network as time passed.
To examine whether the associations between Phases I and
III are statistically more significant than any other
combinations, Fisher’s z transformation method was used
to test the statistical significance of the difference between
two correlation values. Since the sampling distribution of
Pearson r is not normally distributed, r is converted to
Fisher’s z according to the r to z transformation formula
[z=0.5log[(1 + r)/(1 7 r)] for computing the confidence
intervals of the given correlation values. The values of
Fisher’s z in the confidence interval were then converted
back to Pearson’s r using the equation r=[(e2z71)/(e2z +
1)]. If the confidence intervals of the different correlation
values overlap each other, there is no significant difference
between them. The lower diagonal of Table 1 reports the
confidence intervals for each correlation coefficient.
As reported, for information exchange networks, the
association between Phases I and III is significantly
stronger than those between Phases II and III or I and II.
For social support networks, the difference between
correlation values was not significant. The results indicate
that for social support networks, internal characteristics of
the pre-existing friendship network remained essentially
constant, regardless of development phase of the DLC. On
the contrary, for information exchange networks, the
influence of the pre-existing friendship on the emergent
collaborative networks is less significant in Phase II, and
stronger in Phase III. This indicates that members in this
DLC, at first, explored and added new information
exchange partners in Phase II, but reverted back to their
old friends and social circles in the final phase. Note that all
correlations reported in Table 1 are significant at the 0.01
level. Therefore, the discussions here refer to relative
strengths of associations.
Since this result was unexpected, additional analyses
were conducted to confirm the finding. A variable measur-
ing the degree to which an individual changed the
composition of his/her ego network partners in phases II
and III was created. We counted the number of new
network partners a given individual added in his/her ego
network in Phases II and III compared to Phase I network
(the pre-existing friendship network). Similarly, we also
counted the number of friends (identified in Phase I) who
remained in an ego’s network in Phases II and III. Using
those numbers, we tested at what point people kept more
friends or added more new contacts in their social circles.
Figure 4 shows the results of these comparisons. On
average, students kept their old friends quite consistently
(M=2.72 (phase II), M=2.76 (phase III); t=0.157,
Table 1. Matrix correlation results using quadratic assignment procedure (QAP).
Phase IPhase II Phase III
Friendship Social Information Social Information
Friendship – 0.322** 0.171** 0.425** 0.332**
Social II 0.26 – 0.38 – 0.850** 0.366** 0.260**
Information II 0.11 – 0.23 0.83 – 0.87 – 0.214** 0.164**
Social III 0.37 – 0.48 0.31 – 0.42 0.15 – 0.27 – 0.861**
Information III 0.27 – 0.39 0.20 – 0.32 0.10 – 0.23 0.84 – 0.88 –
Upper diagonal: Pearson correlation coefficients based on QAP analysis.
Lower diagonal: Confidence interval (CI: 95%) using Fisher’s z-Transformation method.
** p40.01.
442 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
p4 0.05) throughout the study period. However, the way
they managed their new social capital differed significantly
across time. That is, students substantially reduced the
number of new contacts in later phases (M=3.80 (phase
II), M=2.08 (phase III), t=6.42, p5 0.01). These results
are consistent with the QAP analysis results. People
explored and added new social ties in the second phase,
but in the final phase they removed those new ties/links
from their ego networks. As a result, the final network
structure became similar to the initial pre-existing network.
In summary, the pre-existing friendship network sig-
nificantly influenced the emerging patterns of collaborative
learning and working networks during all time periods, but
the magnitude of influence varied over time (less significant
in Phase II and more significant in Phase III). This indicates
that the effects of pre-existing friendship networks are
significant, but also dependent on the developmental phase
of the DLC.
4.3 The effect of pre-existing network on individual social
capital
For RQ3 and H1, we investigated whether a pre-existing
network would facilitate or constrain an individual’s ability
to develop network capital as they became embedded in the
emergent CSCSN. Regression analyses were performed to
predict each of the dependent variables (change propensity
and clique membership) from two network centrality
variables. Note that this study centered the centrality
variables in order to correct for the multicollinearity
problem given that network variables tend to have high
intercorrelations. To check the severity of multicollinearity
among the independent variables, we examined the
conditioning index and variance proportions associated
with each independent variable (see Belsley et al. 1980, for a
discussion). According to Tabachnik and Fidell (1996, pp.
86 – 87), a conditioning index greater than 30 and at least
two variance proportions greater than 0.50 indicates
serious multicollinearity. The multicollinearity diagnostics
showed that none of regression analyses had serious
multicollinearity problems after the measures were cen-
tered.
Table 2 reports the results of the regression analyses used
to predict three dependent variables (change propensity in
Phases I and II, and clique membership). Note that the
analyses excluded six people who were identified as social
isolates or close to an isolate (those who had only one
connection) in the pre-existing network, because computing
the change propensity (i.e. adding new members in their
ego networks) for these people would be less meaningful
and would bias the results of the analyses.2 As shown in the
table, degree centrality displayed significant associations
with the dependent variable, change propensity at two
timeframes. Note that the direction of the associations is
negative (b=7 0.569, p5 0.01 at T1 and b=7 0.410,
p=0.052 at T2). That is, those who had many relational
partners in the pre-existing friendship network were less
likely to form new ties and links in later periods. Although
these centrality variables did not display any significant
relationships with the other dependent variable, clique
membership, the directions were the same, indicating that
central actors in a pre-existing network did not move into
different social circles in the emerging social structures.
Thus, H1 was partially supported.
5. Discussion
This study first examined how social networks emerged
over time in a distributed learning environment. Special
attention was placed on how pre-existing friendship
Table 2. Regression analyses predicting change propensity andclique membership.
B p R-Square
Dependent variable: change
propensity (T1)
Degree centrality 70.410 0.052
Betweeness 70.135 N.S. 0.224
Dependent variable: change
propensity (T2)
Degree centrality 70.569 0.008
Betweeness 70.034 N.S. 0.340
Dependent variable:
clique membership
Degree centrality 70.059 N.S.
Betweeness 70.006 N.S. 0.004
Figure 4. Comparison of change propensity between Phases
II and III.
2To test whether the deletion of these people made any significant changes
in the results, additional analyses were conducted including those people in
the model. The results were almost identical except for small changes in
coefficient values. Overall, the model fits got lowered and degree centrality
became a less significant predictor (but still significant at 0.01 level).
Development of CSCSN 443
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
networks influenced the formation of emergent collabora-
tive learning and work relationships in a DLC. We
discovered that pre-existing friendship networks signifi-
cantly influenced the formation of community level
CSCSN, and individual level social capital in an emerging
DLC. Results demonstrate that network structures were
somewhat fixed, from the start, even though the distributed
learning environment strongly encouraged students to form
new relational ties using collaborative learning projects and
technologies.
The patterns of associations between networks at
different timeframes are noteworthy. Network structures
became more similar to the pre-existing friendship network
in Phase III, in comparison to Phase II. In other words,
networks regressed into the initial pre-existing friendship
network over time. Intuitively, it was predicted that the
effects of pre-existing networks on the formation of
emerging collaborative networks would be greater in the
initial phase and then would diminish later as people
incrementally develop new ties and links over time.
Developing social or trustful information exchange links
is time-consuming, and may be especially true in a DLC
where members are physically located at different places
and faced with high levels of uncertainty. As Uncertainty
Reduction Theory (URT) suggests (Berger and Calabrese
1975, Berger and Bradac 1982), people develop interper-
sonal relationships after they reduce uncertainties about
each other via ongoing interactions and observations.
Members of this DLC were expected to keep their old
social circles (friends) for a while, and then expand their
networks as they reduced uncertainty. Hence, it was
expected that the internal characteristics of pre-existing
networks would be less likely to remain in later phases of
DLC development.
However, we found that the direction of change was
opposite. That is, structural characteristics of the emergent
social networks became more similar to (in other words,
reverted back to) pre-existing networks as time passed.
Although this finding was unexpected, URT assumes that
people interact more frequently when they have higher
uncertainties about each other and when they expect that
they will develop future relationships. In this distributed
learning community, for instance, students joined an
electronic community with remote participants. Because
they had higher uncertainties with the remote participants
and they expected future relationships with these people
(for example, collaborating on design projects), they might
have been quickly involved in frequent interactions to
reduce uncertainty about each other. In this case, high-
uncertainty may have pushed people to explore new social
ties in the early phase of the project. But as uncertainty
levels declined via interaction, so did information seeking
behaviours. People may have become less motivated to
explore or maintain new social and information links. In
some cases, they may have either gone back to their old
friends if the interactions with new partners had not been
satisfactory, or tried to keep a smaller number of network
partners for future interactions (combining old and new
partners).
Previous research typically tested whether or not people
could develop strong interpersonal relationships using
CMC channels (Walther 1992, Walther et al. 2001).
However, they rarely investigated how people maintain
CMC relationships for an extended period of time.
Findings in this study show that CMC relationships are
fragile and task-oriented, rather than resilient. Since the
finding is descriptive, future studies would benefit from
analysing this research topic with more rigorous research
design and measurement.
RQ3 and H1 investigated the effects of initial structural
position on development of social capital in emerging
networks. Central actors in the pre-existing network were
less likely to form new relationships and explore new social
worlds. On the other hand, those who were least connected
with other members early on quickly formed new links with
other members. Central actors in a pre-existing friendship
network, by contrast, maintained their old social circles
throughout the study period. This indicates that, contrary
to our intuition, an actors’ centrality in pre-existing
networks acted as a social liability that significantly
constrained an actor’s ability to explore new social contacts
and resources. Strong personal relationships had negative
impacts on network capital development by ‘locking’
individuals in pre-existing social relationships.
Many researchers and practitioners highlight the im-
portance of strong interpersonal relationships among
community members, since they are a building block of a
community (Preece 2001). However, our finding suggests
that the qualities that make a community an ideal structure
for learning and work—shared perspective, trust, commu-
nal identity based on long-standing, strong personal
relationships—are the same qualities that can suppress its
potential for success. That is, the community can become
an ‘ideal structure for avoiding learning’ (Wenger et al.
2002, p. 254). As Wenger et al. (2002) note, too much
intimacy can create a barrier to newcomers, a blinder to
new ideas, or a reluctance to critique each other.
Granovetter (1973) also argues that tight bonds tend to
create closed social circles and these can become exclusive
and present an insurmountable barrier to new social ties.
6. Conclusion and directions for future studies
It is often said that traditional approaches to collaborative
learning or cooperative work suffer from too narrow
theoretical and methodological orientations which, in turn,
have led researchers to examine individuals or task groups,
rather than considering the larger social structures and how
444 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
they constrain or enable individual as well as collective
behaviour (Brown and Duguid 1991, Nardi et al. 2002,
Woodruff 2002). By adopting a relatively new analytical
tool in this field—social network analysis—we examined
the building processes of emergent social infrastructures in
a distributed learning community. Also, the study tested
whether pre-existing social networks had functional or
dysfunctional effects for individual learners in terms of
developing social/network capital. Although the above
findings are only a first step, they do provide a model for
understanding the processes of emergent collaborative
social networks in relatively new social systems like DLCs.
As social networks and social capital come to be more
important in learning and work places, social network
analysis has been increasingly utilised by researchers to
uncover the social dynamics of CSCW (Fisher and
Dourish 2003, McDonald 2003), CSCL (Haythorthwaite
1998, Cho et al. 2002), knowledge management (Cohen
and Prusak 2001, Seufert et al. 2003), information
visualisation (Terveen et al. 1999, Gloor et al. 2003) and
so forth. We believe that this study extended the
theoretical and empirical basis of social network analysis
in several ways. First, this study explored the building
processes of a DLC in a longitudinal context. Social
network analysis is fundamentally process-oriented and
emphasises the emergent nature of social systems. How-
ever, few previous social network studies adopted a
longitudinal approach to examine how networks emerge
over time, and what influences such processes of change.
As a result, social network studies have been criticised for
not having a clear explanation about the ‘origins of the
networks’ (Brass 1984, Emirbayer and Goodwin 1994).
The incorporation of a time dimension in this study
enabled the researcher to examine the way two discon-
nected pre-existing friendship networks evolved into new
collaborative learning and working networks, as students
from two universities joined design project groups using
online collaboration tools.
Further, the study examined whether pre-existing social
ties exerted functional or dysfunction effects on the
emerging pattern of individual level social capital. While
a large body of social network research focuses on the
benefits of social capital, the literature on its risks, or
negative effects, is much sparser (Adler and Kwon 2002). In
general, many researchers posit that social networks can be
an important resource providing structural advantages to
individuals, such as power and influence in decision-making
(Brass 1984), and better performance (Baldwin et al. 1997,
Sparrowe et al. 2001). However, others increasingly see this
position as one-sided (Leenders and Gabbay 1999). Social
structures change over time and so do their effects on
individuals. Hence, relationships beneficial to goal attain-
ment in the past may thwart goal attainment in the future.
By using a longitudinal approach, we demonstrated how
the social network that once conferred social capital and
relational assets could become a constraint, or social
liability, restricting one’s ability to adapt to changing
conditions.
Overall, the findings presented herein provide practical
implications for managers and designers of collaborative
learning and work environments. As discussed above,
members in this distributed community tended to treat
interactions as simple transactions. When topics changed or
the task was completed, distant relationships tended to
quickly disappear. While the real power of ‘intensional
social networks’ is their ability to quickly form and
disintegrate (Nardi et al. 2002), too much refreshment is
also problematic in terms of building and sustaining
cohesive communities. That is, community members need
a meaningful ‘sense of shared identity’, one that binds
members beyond specific exchanges. To overcome the
limitation of task-oriented interactions, community mem-
bers or sponsors may employ team building exercises and
socialisation activities. Assuming that members in new
DLCs tend to feel disconnected and experience high
uncertainty, studies often suggest providing community-
building exercises in the beginning phases of community
development (Preece 2001). However, the findings of this
study suggests that the relationships in DLCs tend to be
more ephemeral in the later phase, suggesting that at least
an equal amount of attention should be directed toward the
later period.
Finally, this study contains several methodological and
theoretical limitations, which warrant attention in future
studies. This research has positioned itself at a considerable
distance from cognitive or motivational research. While the
importance of building social infrastructures is clearly
identified in this study, these merely create opportunity for
collaboration; building a DLC requires not only establish-
ing social ties but also nurturing motivation and providing
resources (Haythornwaite 1998). Future research would
benefit from a more interdisciplinary approach that links
structural and psychological factors, and tests how these
combine to affect various processes and outcomes in a
DLC. For instance, future studies may test how social
embeddedness affects a sense of community membership,
and how this cognitive element, in turn, leads to actors’
voluntary participation in, and contribution to, the
development of collective knowledge.
This study also revealed that social networks could have
both functional and dysfunctional effects on individual
learners, and the learning community as a whole. While we
understand much about market failures and technology
failures, we still lack knowledge about how and when a
social network or social capital fails (Leenders and Gabbay
1999). An important task for future research is to identify
when and how social networks serve different functions
using increasingly rigorous research plans. Similarly, this
Development of CSCSN 445
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
study did not test whether the constraint effect of pre-
existing social ties ultimately led to poor outcomes such as
poor learning performance. Future research would benefit
from directly testing the relationships between network
constraints and task coordination failures or performance
outcomes.
Finally, the particular sites that this study examined
consisted of relatively small, homogeneous learning
groups. Having a small sample size is not unusual in
social network or longitudinal studies, due to the extreme
difficulties gathering rich and complex information. Yet,
the small sample size restricts researchers from general-
ising the findings to broader social settings. Similarly, the
two research sites were situated in an educational
context; participants were all college students. Although
this subject group accounts for a significant portion of
learning and knowledge communities, care should be
taken when generalising these findings to other settings
where social dynamics/structures might be significantly
different. For instance, knowledge sharing and collabora-
tion in business firms would be very different from those
in an educational setting because of high competition,
hierarchical structures, and rules commonly found in
these environments. The findings in the current study
might be further validated or modified when researchers
test the structural model of this study in different
contexts.
Acknowledgement
The authors gratefully acknowledge the support of NASA
Langley Research Center, through Cooperative Agreement
No. NCC-1-01004. Additional support was provided by the
State of New York and the AT&T Foundation.
References
ADLER, P.S. and KWON, S.W., 2002, Social capital: Prospect for a new
concept. Academy of Management Review, 27, pp. 17 – 40.
ALAVI, M., 1994, Computer-mediated collaborative learning: An empirical
evaluation. MIS Quarterly, pp. 159 – 174.
BALDWIN, T.T., BEDELL, M.D. and JOHNSON, J.L., 1997, The social fabric
of a team-based M.B.A. program: Network effects on student satisfac-
tion and performance. Academy of Management Journal,, 40, pp. 1369 –
1397.
BELSEY, D.A., KUH, E. and WELSCH, R.E.C., 1980, Regression diagnostic:
Identifying Influential Data amd Sources of Collinearity, pp. 1 – 292
(New York: Wiley).
BERGER, C.R. and BRADAC, J.J., 1982, Language and Social Knowledge:
Uncertainty in Interpersonal Relations, (London: Arnold).
BERGER, C.R. and CALABRESE, R.J., 1975, Some explorations in initial
interaction and beyond: Toward a developmental theory of interpersonal
communication. Human Communication Theory, 1, pp. 99 – 112.
BIKSON, T., EVELAND, J.D. and GUTEK, B.A., 1989, Flexible interactive
technologies for multi-person tasks: Current problems and future
prospects. In Technological Support for Work Group Collaboration,
M.H. Olson (Ed.) (Hillsdale, NJ: Erlbaum).
BOLAND, R.J. and TENKASI, R.V., 1995, Perspective making and
perspective taking in communities of knowing. Organization Science, 6,
pp. 350 – 372.
BORGATTI, S.P., EVERETT, M.G. and FREEMAN, L.C., 1999, UCINET 5 for
Windows, pp. 1-49 (Harvard, MA: Analytic Technologies).
BRASS, D.J., 1984, Being in the right place: A structural analysis of
individual influence in an organization. Administrative Science Quarterly,
29, pp. 518 – 539.
BROWN, J.S. and DUGUID, P., 1991, Organizational learning and commu-
nities-of-practice: toward a unified view of working, learning, and
innovation. Organization Science, 2, pp. 40 – 57.
CHILD, J. AND LOVERIDGE, R., 1990, Information Technology In European
Services—Towards A Microelectronic Future (Oxford: Blackwell).
CHO, H., STEFANONE, M. and GAY, G., 2002, Social information sharing in
a CSCL community. In Proceedings of 2002 ACM CSCL conference, S.
Gery (Ed.) (Boulder, CO., USA: Lawrence Elbaum Associates).
COHEN, D. and PRUSAK, L., 2001, In Good Company: How Social Capital
Makes Organizations Work (Boston, MA: Harvard Business Press).
COLEMAN, J.S., 1990, Foundations of Social Theory (Cambridge: The
Belknap Press of Harvard University Press).
CONSTANT, D., SPROULL, L. and KIESLER, S., 1996, The kindness of
strangers: on the usefulness of electronic weak ties for technical advice.
Organization Science, 7, pp. 119 – 135.
DIRKS, K.T., SHAH, P. and CHERVANY, N., 2001, The impact of friendship
networks on team performance: A story of equifinality. Academy of
Management Proceedings 2001, OB: E1-E5.
EMIRBAYER, M. and GOODWIN J., 1994, Network analysis, culture, and
the problem of agency. American Journal of Sociology, 99, pp. 1411 –
1454.
ERICKSON, T. and KELLOGG, W.A., 2001, Knowledge communities: Online
environmentsforsupportingknowledgemanagementanditssocialcontext.
In Beyond Knowledge Management: Sharing Expertise, M. Ackerman, V.
Pipek and V.Wulf (Eds) (Cambridge, MA:MIT Press), pp. 299 – 326.
FISHER, D. and DOURISH, P., 2003, Social and temporal structures in
everyday collaboration. Proceedings of the Conference on Human Factors
in Computing Systems (CHI 2003), Ft Lauderdale, Florida, USA (New
York: ACM Press).
FREEMAN, L.C., 1979, Centrality in social networks, Conceptual clarifica-
tion. Social Networks, 1, pp. 215 – 239.
GARGIULO, M. and BENASSI, M., 1999, The dark side of social capital. In
Corporate Social Capital and Liability, R.A.J. Leenders and S.M.
Gabbay (Eds) (Boston: Kluwer Academic Publishers), pp. 298 – 322.
GLOOR, P.A., LAUBACHER, R. and DYNES, S.B.C., 2003, Visualization of
communication patterns in collaborative innovation networks—Analysis
of some W3C working groups. Proceedings of the 12th International
Conference on Information and Knowledge Management, New Orleans,
LA (New York: ACM Press).
GRANOVETTER, M.S., 1973, The strength of weak ties. American Journal of
Sociology, 78, pp. 1360 – 1380.
GRANOVETTER, M., 1985, Economic action and social structure: The
problem of embeddedness, American Journal of Sociology, 91, pp. 481 –
510.
GULATI, R., 1995, Does familiarity breed trust? The implications of
repeated ties for contractual choices in alliances. Academy of Manage-
ment Journal, 38, pp. 85 – 112.
HAYTHORNWAITE, C., 1998, A social network study of the growth of
community among distance learners. Information Research, 4, http://
informationr.net/ir/4-1/paper49.html
HAYTHORNWAITE, C. and WELLMAN, B., 1998, Work, friendship and media
use for information exchange in a networked organization. Journal of the
American Society for Information SCIENCE, 46, pp. 1101 – 1114.
HUBERT, L. AND SCHULTZ, J., 1976, Quadratic Assignment as a general data
analysis strategy. British Journal of Mathematical Statistical Psychology,
29, pp. 190 – 241.
446 H. Cho et al.
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
IBARRA, H. and STEVEN, A., 1993, Power, influence, and sense making:
Effects of network centrality and proximity on employee perceptions.
Administrative Science Quarterly, 38, pp. 277 – 304.
JEHN, K. and SHAH, P., 1997, Interpersonal relationships and task
performance: An examination of mediating processes in friendship and
acquaintance groups. Journal of Personality and Social Psychology, 72,
pp. 775 – 790.
JONES, S.G., 1994, Understanding community in the information age. In
CyberSociety: Computer-Mediated Communication and Community, S.G.
Jones (Ed.) (Thousand Oaks, CA: Sage), pp. 10 – 35.
KRACKHARDT, D., 1988, Predicting with networks: Nonparametric multiple
regression analysis of dyadic data. Social Networks, 10, pp. 359 – 381.
KRACKHARDT, D., 1992, The strength of strong ties. In Networks and
Organization, N. Noria and R.G. Eccles (Eds) (Cambridge, MA: HBS
Press), pp. 216 – 238.
LAVE, J. and WENGER, E., 1991, Situated Learning: Legitimate Peripheral
Participation (Cambridge, UK: Cambridge University Press).
LEENDERS, R.A.J. and GABBAY, S.M., 1999, Corporate Social Capital and
Liability (Boston: Kluwer Academic Publishers).
LIEVROUW, L.A., ROGERS, E.M., LOWE, C. and NADEL, E., 1987,
Triangulation as a research strategy for identifying invisible colleges
among biomedical scientists. Social Networks, 9, pp. 217 – 248.
MCDONALD, D., 2003, Recommending collaboration with social Networks:
A comparative evaluation. In Proceedings of the Conference on Human
Factors in Computing Systems (CHI 2003), Ft. Lauderdale, Florida
(New York: ACM Press).
NAHAPIET, J. and GHOSHAL, S., 1998, Social capital, intellectual capital,
and the organizational advantage. Academy of Management Review, 23,
pp. 242 – 266.
NARDI, B., WHITTAKER, S. and SCHWARZ, H., 2002, NetWorks and their
activity in intensional networks. Computer Supported Cooperative Work,
11, pp. 205 – 242.
O’REILLY, C., 1982, Variations in decision maker’s use of information
sources: the impact of quality and accessibility of information. Academy
of Management Journal,, 25, pp. 756 – 771.
PREECE, J., 2000, Online Communities: Designing Usability, Supporting
Sociability (Chichester, UK: Wiley).
PREECE, J., 2001, Sociability and usability in online communities:
Determining and measuring success. Behaviour and Information Technol-
ogy, 20, pp. 347 – 356.
QUINN, C., MEHAN, H., LEVIN, J. and BLACK, S., 1983, Real education in
non-real time: The use of electronic message systems for instruction.
Instructional Science, 11, pp. 313 – 327.
RICE, R., 1994, Network analysis and computer-mediated communication
systems. In Advances in Social Network Analysis, S. Wasserman and J.
Galaskiewicz (Eds) (Thousand Oaks, CA: Sage), pp. 167 – 203.
ROBERTSON, M., Swan, J. and NEWELL, S., 1996, The role of networks in
the diffusion of technological innovations. Journal of Management
Studies, 33, pp. 333 – 359.
SCARDAMALIA, M. and BEREITER, C., 1994, Computer support for
knowledge-building Communities. Journal of the Learning Sciences, 3,
pp. 265 – 283.
SEUFERT, A., BACK, A. and KROGH, G., 2003, Unleashing the Power of
Networks environments: leveraging intellectual capital in virtual business
communities. In Knowledge Management and Networked Environment, J.
Beerili, S. Falk and D. Diemers (Eds) pp. 99 – 136 (New York:
AMACOM).
SPARROWE, R.T., LIDEN, R.C. and KRAIMER, M.L., 2001. Social networks
and the performance of individuals and groups. Academy of Management
Journal, 44, pp. 316 – 425.
SPROULL, L. and KIESLER, S., 1986, Reducing social context cues:
Electronic mail in organizational communication. Management Science,
32, pp. 1492 – 1512.
SPROULL, L. and KIESLER, S., 1991, Connections: New Ways of Working in
the Networked Organization (Cambridge, MA: MIT Press).
TABACHNIK, B.G. and FIDELL, L.S., 1996, Using Multivariate Statistics
(3rd Edn) (New York: HarperCollins College Publishers).
TERVEEN, L., HILL, W. and Amento, B., 1999, Constructing, organizing,
and visualizing collections of topically related Web resources. ACM
Transactions on Computer-Human Interaction, 6, pp. 67 – 94.
WALTHER, J.B., 1992, Interpersonal effects in computer-mediated interac-
tion: A relational perspective. Communication Research, 19, pp. 52 – 90.
WALTHER, J.B., SLOVACEK, C.L. and TIDWELL, L.C., 2001, Is a picture
worth a thousand words? Photographic images in long-term and short-
term computer-mediated communication. Communication Research, 28,
pp. 105 – 134.
WASSERMAN, S. and FAUST, K., 1994, Social Network Analysis (Cambridge,
UK: Cambridge University Press).
WELLMAN, B., HASS, A.Q., WITTE, J. and HAMPTON, K., 2001, Does the
Internet increase, decrease, or supplement social capital? Social net-
works, participation, and community commitment. The American
Behavioral Scientist, 45, pp. 436 – 455.
WELLMAN, B., SALAFF, J., DIMITROVA, D., GARTON, L., GULIA, M. and
HAYTHORNTHWAITE, C., 1996, Computer networks as social networks:
Virtual community, Computer Supported Cooperative Work and
telework. Annual Review of Sociology, 22, pp. 213 – 38.
WENGER, E., 1998, Communities of Practice, Learning, Meaning and Identity
(Cambridge, UK.: Cambridge University Press).
WENGER, E. C., MCDERMOTT, R. and SNYDER, W.C., 2002, Cultivating
Communities of Practice: A Guide to Managing Knowledge (Cambridge,
USA: Harvard Business School Press).
WOODRUFF, E., 2002, CSCL communities in post-secondary education and
cross-cultural settings, In CSCL 2: Carrying Forward the Conversation,
T. Kochmann, R. Hall and N. Miyake (Eds) (Mahwah, NJ: Lawrence
Erlbaum Associates), pp. 157 – 168.
Development of CSCSN 447
Dow
nloa
ded
by [
Uni
vers
idad
Aut
onom
a de
Bar
celo
na]
at 0
6:56
15
Oct
ober
201
4
