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Knowledge sharing in virtual teams

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Title:
Knowledge sharing in virtual teams the impact on trust, collaboration, and team effectiveness
Creator:
Alsharo, Mohammad K. ( author )
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
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English
Physical Description:
1 online resource (132 pages) : ill. ;

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Subjects / Keywords:
Teams in the workplace ( lcsh )
Virtual work teams ( lcsh )
Teams in the workplace ( fast )
Virtual work teams ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Review:
Virtual teams are utilized by organizations to gather experts to collaborate online in order to accomplish organizational tasks. However, the characteristics of these teams create challenges to effective collaboration and effective team outcome. Collaboration is an essential component of teamwork, the notion of forming teams in organizations is the need for members with complementary skills and expertise to collaborate in order to achieve the goal for which the team is established. Literature on virtual teams has been growing for over a decade with researchers investigating different aspects of virtual work. Trust among virtual team members has been investigated by information systems researchers as a crucial challenge for virtual teams success. Knowledge sharing and management in virtual teams has been the focus of recent research studies as it represents a challenge in virtual work environments; specifically because the knowledge is scattered among geographically distributed team members with the absence of face to face interaction. This study extends the literature on virtual teams by investigating the relationship between knowledge sharing, trust, and collaboration among team members in virtual team settings; and examining how these constructs ultimately affect virtual team effectiveness. We argue that knowledge as a valuable asset of virtual team members' is a key factor influencing virtual team effectiveness. This research introduces a conceptual model which describes the hypothesized relationship between knowledge sharing, trust, collaboration, and team effectiveness in virtual team settings. The model is developed based on an intensive review of the literature on the constructs of interest in both traditional and virtual team settings. The theoretical foundation for the model is found in the Knowledge Based Theory of The Firm, Social Capital Theory, and the Social Exchange Theory. The study extends the Knowledge Based Theory of the Firm by improving our understanding of how knowledge sharing impacts trust, collaboration, and virtual team effectiveness
Thesis:
Thesis (Ph.D.)--University of Colorado Denver. Computer science and information systems
Bibliography:
Includes bibliographic references.
General Note:
Department of Computer Science and Engineering
Statement of Responsibility:
by Mohmmad K. Alsharo.

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|University of Colorado Denver
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|Auraria Library
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All applicable rights reserved by the source institution and holding location.
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868707622 ( OCLC )
ocn868707622

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Full Text
KNOWLEDGE SHARING IN VIRTUAL TEAMS: THE IMPACT ON TRUST,
COLLABORATION, AND TEAM EFFECTIVENESS
by
Mohammad K. Alsharo
B.S. A1 Albayt University, 2005
M.S. Jordan University of Science and Technology, 2008
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Computer Science and Information Systems
2013


This thesis for the Doctor of Philosophy degree by
Mohammad K. Alsharo
has been approved for the
Computer Science and Information Systems Program
by
Judy Scott, Chair
Dawn Gregg, Advisor
Ronald Ramirez
Ilkyeun Ra
Date: 4/17/2013


Alsharo, Mohammad K. (Ph.D., Computer Science and Information Systems)
Knowledge Sharing in Virtual Teams: The Impact on Trust, Collaboration, and Team
Effectiveness
Thesis directed by Associate Professor Dawn Gregg
ABSTRACT
The practice of virtual teams has provided organizations with a convenient
solution for gathering experts to collaborate online in order to accomplish organizational
tasks. However, the dynamics and characteristics of virtual teams create challenges to
effective collaboration. Collaboration is an important element in teamwork, team
members need to collaborate to achieve the goal for which the team is established.
Literature on virtual teams has been growing for over a decade with research investigated
different aspects of virtual work. Trust among virtual team members has been
investigated by information systems researchers as a crucial challenge for virtual teams.
Knowledge sharing and management in virtual teams have been the focus of many
research recently as they represent a challenge for virtual work environment; because the
knowledge is scattered among geographically distributed team members with limited face
to face interaction. Yet, trust and knowledge sharing are not the final outcome of
teamwork, there is a gap in the literature on how trust and knowledge sharing affect
collaboration and ultimately team effectiveness in virtual settings. This study extends the
literature on virtual teams through investigating the relationship between knowledge
sharing, trust, and collaboration among team members in virtual team settings; and how
these constructs ultimately affect virtual team effectiveness. We argue that the
characteristics and structure of virtual teams requires a distinctive understanding on how


to make their members collaborate in a comparable and effective way to collocated
teams. We argue that knowledge as a valuable asset and a personal advantage of a virtual
team member could be a key for successful virtual team collaboration.
This research introduces a conceptual model which describes the hypothesized
relationship between knowledge sharing, trust, collaboration, and team effectiveness in
virtual team settings. The model is developed based on an intensive review of the
literature on virtual teams, knowledge sharing and management, trust, collaboration, and
team effectiveness in traditional and virtual settings. The theoretical foundation for the
model is found in the Knowledge Based Theory of The Firm, Social Capital Theory, and
the Social Exchange Theory. The survey research method will be used to test the
proposed model.
The form and content of this abstract are approved. I recommend its publication.
Approved: Dawn Gregg
IV


DEDICATION
I dedicate this work to my parents, for all the sacrifices they made to raise me and
provide me with the means to be a successful person.
I also dedicate this work to the love of my life, my wife Enas for her love, support, and
patience throughout this journey.
Finally, I dedicate this work to my sons Eyad and Hazem. For all the time I could not
spend with you because I was working on this research.
v


ACKNOWLEDGMENTS
I would like to thank my advisor, Pro. Dawn Gregg for all the support and effort
she put into this research over the past few years. I also would like to thank my
committee members for their feedback and insights. Especially I would like to
acknowledge the feedback and insight of Professor Ronald Ramirez which had a major
impact on the quality of this research.
vi


Table of Contents
1. Introduction...................................................................1
1.2. Importance of Topic...........................................................1
1.3. Research Problem and Scope....................................................2
1.4. Research Questions............................................................3
1.5. Research Contribution.........................................................4
1.6. Outline of Dissertation.......................................................4
2. Intention to Collaborate: Investigating Online Collaboration in Virtual Teams.5
2.1. Abstract......................................................................5
2.2. Keywords......................................................................5
2.3. Introduction..................................................................6
2.4. Theoretical Foundation........................................................7
2.4.1. Virtual Teams...............................................................7
2.4.2. Online Collaboration........................................................9
2.5. Measurement Scale Development Process........................................11
2.5.1. Stage 1: Literature Investigation and Construct Identification.............11
2.5.2. Stage 2: Item Creation.....................................................19
2.5.3. Stage 3: Scale Development.................................................20
2.5.4. Stage 4: Instrument Testing Field Study..................................25
vii


2.6. Limitations and Future Work..................................................34
2.7. Implications to Research and Practice........................................35
2.8. Conclusion...................................................................36
3. Knowledge Sharing in Virtual Teams: The Impact on Trust, Collaboration, and Team
Effectiveness.....................................................................37
3.1. Abstract.....................................................................37
3.2. Keywords.....................................................................38
3.3. Introduction.................................................................38
3.4. Literature Review............................................................43
3.4.1. Virtual Teams..............................................................43
3.4.2. Knowledge Sharing and Management...........................................47
3.4.2.1. Organizational Knowledge Management Processes............................50
3.4.3. Trust......................................................................52
3.4.4. Collaboration..............................................................57
3.4.5. Team Effectiveness.........................................................60
3.5. Research Model and Hypotheses Development..................................61
3.6. Methodology..................................................................69
3.6.1. Sample...................................................................69
3.6.2. Measures...................................................................70
3.7. Data Analysis................................................................72
3.7.1. Demographics and Descriptive Statistics....................................72
3.7.2. Reliability................................................................72
3.7.3. Validity...................................................................73
3.7.4. PLS Analysis...............................................................75
3.7.5. Mediation Analysis.........................................................78
3.7.6. Control Variable Analysis..................................................79
viii


3.7.7. Multi-group analysis....................................................79
3.8. Discussion................................................................80
3.9. Limitations...............................................................84
3.10. Conclusion...............................................................85
4. Conclusion..................................................................86
References.....................................................................88
Appendix
1: Linkedin Groups............................................................106
2: Survey.....................................................................107
IX


LIST OF TABLES
Table
1: Online Collaboration Dimensions.................................................13
2: Mapping Online Collaboration Dimensions.........................................14
3: Intention to Collaborate Online Scale Items.....................................21
4: Measurement Items after Pilot Study.............................................24
5: Number of items after Pilot Study...............................................25
6: Reliability Statistics Perceived Incentives...................................26
7: Correlation Matrix Perceived Incentives.......................................26
8: Reliability Statistics Repeat Perceived Incentives............................26
9: Correlation Matrix Repeat Perceived Incentives................................27
10: Reliability Statistics Perceived Voluntariness...............................27
11: Correlation Matrix Perceived Voluntariness...................................27
12: Reliability Statistics Repeat- Perceived Voluntariness.........................27
13: Correlation Matrix Repeat Perceived Voluntariness............................28
14: Reliability Statistics- Perceived Common Ground................................28
15: Correlation Matrix Perceived Common Ground...................................28
16: Reliability Statistics Repeat Perceived Common Ground........................28
17: Correlation Matrix Repeat Perceived Common Ground............................29
18: Reliability Statistics-Perceived Members Expertise............................29
19 : Correlation Matrix- Perceived Members Expertise..............................29
20: Reliability Statistics-Perceived Members Expertise............................29
21: Correlation Matrix Repeat Perceived Members Expertise.......................30
22: Items Correlation..............................................................31
x


23: Rotated Component Matrix..................................................32
24: Rotated Component Matrix Repeat.........................................33
25: Total Variance Explained..................................................33
26: Virtual Team Research Categories..........................................46
27: Levels of Knowledge Management Systems....................................49
28: Types of Trust in Previous Research.......................................54
29: Definitions of the Study Constructs.......................................71
30: Cronbachs alpha..........................................................73
31: Demographics and Descriptive Statistics...................................74
32: Convergent And Discriminant Validities...................................75
33: Cross Loading.............................................................76
34 : Summary of Hypotheses Tests..............................................78
35: Sobel Test for the Significance of Mediation.............................78
36: Control Variable Effect...................................................79
37: Multi-group moderating effect (Trust).....................................81
38 : Multi-group moderating effect (Collaboration)............................82
xi


LIST OF FIGURES
Figure
1: Intention to Collaborate Online.............................................14
2: Spiral of knowledge Adapted from Nonaka and Takeuchi (1995)...............48
3: CSCW Matrix Adapted From Johansen (1988)................................. 59
4: Theoretical Research Model..................................................62
5: Measurement Model...........................................................71
6: PLS SEM Results.............................................................77
7: Control Variable Analysis...................................................79
xii


1. Introduction
The research presented in this dissertation addresses virtual team effectiveness.
This research sheds light on the importance of knowledge sharing in virtual team settings
and how the behavior of knowledge sharing has the potential to compensate for the
absence of observed physical behaviors. This study extends the previous literature on
virtual teams through investigating the role of knowledge sharing and trust in enabling
collaboration in virtual team settings; and eventually makes the team outcome more
effective. Individuals consider knowledge to be a personal advantage and sharing it leads
to loss of ownership of this knowledge and consequently loss of power and potential
replacement which makes them hoard the knowledge and be reluctant to share it
(Kankanhalli et. al. 2005). Nonetheless, sharing knowledge and exchanging ideas is
crucial for team collaboration. We argue that an efficient virtual team, is one which its
team members put the success of the team ahead of their personal tendency to hoard
knowledge for themselves.
1.2. Importance of Topic
As globalization and open markets continue to manifest, organizations are
realizing more the prominence of virtual teams as a convenient way to enable teamwork
in situations where people of expertise cannot be brought together into the same location.
Virtual teams have a set of characteristics and dynamics that are different from traditional
face -to- face teams since are composed of individuals of complex traits and diverse
backgrounds. Yet, these individuals are expected to be effective and collaborate to
achieve an organizational goal.


Because virtual teams normally do not have face-to-face interaction, their
effectiveness is more challenging compared to traditional teams. Although the literature
shows that virtual teams can reach a level of effectiveness which could be compared to
traditional teams effectiveness, this process in virtual environment takes more time and
effort.
Knowledge is a critical factor in improving team effectiveness and sharing
knowledge is essential for team members collaboration. In virtual teams, however,
knowledge is fragmented among already distributed individuals whose only mean of
interaction is mediated by technology. Therefore, sharing knowledge in virtual teams is
more critical and challenging than in traditional teams.
Trust is considered to be a crucial but challenging factor of successful teams. This
characteristic is difficult to establish and foster among team members especially when
those members are geographically distributed, have different perceptions of trust due to
their diverse backgrounds, and they are challenged with limited observed behaviors of
fellow team members.
In this research, we investigate how sharing knowledge influences trust and
collaboration among virtual team members. We also investigate the impact of these
relationships on virtual team effectiveness.
1.3. Research Problem and Scope
A main distinction we make in this research is between virtual teams and open
online communities of practice. A key characteristic of online communities of practice is
that their members are personally motivated to join these communities based on shared
2


interest. Virtual teams, however, members join them to collaborate on an organizational
task mainly because of the organization need for their expertise.
Online communities of practice are formed and managed beyond the
organizational boundaries and their members are not governed or concerned with
organizational structure or reward system. Virtual teams are established of members who
work in an organization or across an inter-organizational system. They are employees
who are expected to work together and collaborate to achieve an organizational goal
within specific time and limited resources.
Early virtual teams were described as temporary teams whose members are
brought together by technology to work on a complex problem. Although this type of
virtual teams still exists, nowadays virtual teams are more embedded into the
organization and their members are working on traditional everyday tasks for relatively
long periods of time.
Similar to traditional teams, virtual teams are expected to collaborate to
accomplish a common goal. However, the structure and characteristics of virtual teams
impose challenges on how effective these teams can be.
1.4. Research Questions
This research aims to answer the following research questions:
1. What factors influence a virtual team members intention to collaborate
online?
2. How do different factors combine to influence a virtual team members
intention to collaborate online?
3. How can we measure online collaboration in virtual team settings?
3


4. Does knowledge sharing significantly influence trust and collaboration in
virtual team setting?
1.5. Research Contribution
The research included in this dissertation has two main contributions. The first is
a literary contribution. From conducting an intensive literature review on virtual teams,
an observation was made. The literature investigates how different factors influence trust
and knowledge sharing in virtual teams. Yet, trust and knowledge sharing are not the
final outcomes of team work. As such, the final contribution of this dissertation is an
empirical one.
1.6. Outline of Dissertation
The focus of this dissertation virtual teams effectiveness, the role of knowledge
sharing, trust, and collaboration. It consists of four chapters: (1) Introduction, (2)
Intention to Collaborate: Investigating Online Collaboration in Virtual Teams. (3) Virtual
Teams Effectiveness: The Role Of Knowledge Sharing, Trust, And Collaboration. (4)
Comprehensive Conclusion.
4


2. Intention to Collaborate: Investigating Online Collaboration in Virtual Teams.
2.1. Abstract
The emergence of online tools supporting collaboration has allowed more people
to work together online, some in open online communities, others in professional groups
within organizations, and others in professional groups across inter-organizational
systems. The advancement of information and communication technology provided the
opportunity for organizations to establish virtual teams with needed expertise regardless
of geographical boundaries. Virtual team members are expected to collaborate in order to
solve predefined problems and organizational tasks. In this paper, we introduce a
conceptual model and a measurement scale which are derived from investigating the
literature on online collaboration and virtual teams. The proposed scale makes important
contributions to both research and practice. For research, it will provide a validated scale
to measure perceived intention to collaborate in virtual team settings, which will support
further research in this important field. For practice, it will help identify what contributes
to a virtual team member's intention to collaborate and can assist in the establishment of
virtual teams in organizations.
2.2. Keywords
Online Collaboration, Virtual Teams, Conceptual Model, Measurement Scale.
5


2.3.
Introduction
Globalization along with continuous improvement in information and
communication technologies has led more professionals to work together online. The web
is providing a platform for collaboration and it is continuously shifting towards a more
user centric experience (Lai and Turban, 2008). On an organizational level, information
technology offers organizations the opportunity to form partnerships, communicate with
each other, and coordinate activities. These inter-organizational systems are important in
a market where products and services require multi-organizational collaboration; which is
considered to be a shift from organizational competition to inter-organizational
collaboration (Kumar and Dissel, 1996; Zwass, 2003). This change in the organizational
structure and in the way organizations conduct their business, makes the practice of
virtual teams an organizational necessity. Web 2.0 and the variety of tools it provides
(e.g. blogs, wikis, and social bookmarking) have led to the emergent of new kinds of
services such as social networks, aggregation services, and cloud based office-style
software. These tools provide a means for organizations to establish teams based on
required expertise regardless of physical location. They allow professionals to establish
and join open online communities that relate to their field of expertise and to serve as a
virtual work place for knowledge exchange, collaboration, and problem solving.
Moreover, this technology advancement has resulted in a new innovative form of online
collaboration between producers and consumers; which is outsourcing product
development to digital consumer networks (Arakji and Lang, 2007).
This study investigates the factors which influence a virtual team members
intention to collaborate in organizational settings. Prior research on online collaboration
6


has focused on finding new constructs that contribute to online collaboration; whereas,
this study improves our understanding of how different factors combine to influence an
individuals intention to collaborate. The foundation of this approach is based in the
Socio-Technical Theory and the Theory of Reasoned Action.
This research proposes a conceptual model of virtual teams collaboration along
with a measurement scale derived from the literature. The research is designed to answer
the following research questions:
1. What factors influence a virtual team members intention to
collaborate online?
2. How do different factors combine to influence a virtual team
members intention to collaborate online?
3. How can we measure the intention to collaborate in virtual team
settings?
Answering these questions will allow researchers to better understand virtual
teams and the factors that contribute to collaboration among virtual team members
The next section outlines the theoretical foundation of this research. It is followed
by the development of a theoretical model for online collaboration and a measurement
scale to test the model. The scale is then refined through a pretest and tested in a field
study. The paper concludes with the implications for future research.
2.4. Theoretical Foundation
2.4.1. Virtual Teams
A virtual team is defined by Powel et al as a group of geographically,
organizationally and/or time dispersed workers brought together by information and
7


communication technologies to accomplish one or more organizational tasks (Powel et
al. 2004). Organizations incorporated virtual teams into their structure to address their
business needs, Townsend et al. (1998) attributes the need for virtual teams in the
organization to five factors. These factors are the change of organizational structure from
vertical to horizontal, the need for inter-organizational collaboration, employees
preference and expectations, Organizations are moving towards providing services rather
than manufacturing products, and due to globalization (Townsend et al. 1998).
Traditionally, virtual teams were established based on an organization need to
gather necessary expertise to solve complex problems, they used to be temporary teams,
and they suffered low commitment among team members. (Jarvenpaa et al. 1998; Squire
and Johnson, 2000; Kanawattanachai and Yoo, 2002). Recently, however, more
organizations are establishing virtual teams to work on everyday tasks; and many
organizations are allowing their employees to work remotely from places of their choice.
Griffith et al. (2003) describe this as the degree of virtualness, that is, some teams are
completely virtual, while other teams are a combination of co-located and distributed
members.
Moving to virtual teams has an impact on organizational structure. It is reported in
the literature that virtual teams create forms that are more reconfigurable, flexible, and
require mass collaboration (DeSanctis and Monge, 1999; Zammuto et al. 2007). The
challenges to virtual teams include time difficulties, feedback delays, misinterpretation,
cultural barriers, scheduling, and lack of communication and response (Fussell et al.
1998; Jarvenpaa and Leidner, 1998; Powell et al, 2004). These challenges if not
addressed and managed by the organization can threaten virtual teams success and
8


effectiveness (Piccoli et al. 2004). We argue that virtual teams can only thrive if
individual team members can overcome the challenges to virtual collaboration and
manage to work in a coordinated effort to solve problems together. This suggests the
need for an improved understanding of how to create virtual teams that work effectively.
2.4.2. Online Collaboration
Collaboration can be successfully accomplished in traditional face-to-face-teams.
However, the change from a physical to a virtual work space brings challenges to how
members collaborate within the same team, with solely relying on information
technology for communication and coordination. Information and communication
technologies have significantly improved over the last two decades, and virtual teams are
equipped with variety of tools and technologies to support their work. Therefore,
researchers have called for investigating the social factors of virtual teams which could
impact their effectiveness (Holton, 2001; Kirkman et al. 2004; Powell et al, 2004;
Henttonen and Blomqvist, 2005). Durate and Snyder (2006) discuss seven types of virtual
teams (i.e. networked teams, parallel teams, product development teams, production
teams, service teams, management teams, and action teams), they argue that all of these
teams have in common that team members must communicate and collaborate to achieve
effective outcomes. Evidence in the literature suggests that when given sufficient time
and managed properly, virtual teams could collaborate effectively compared to traditional
teams (Holton, 2001; Webster and Wong, 2008).
From an organizational perspective, Kumar and Dissel (1996) argue that three
arguments are needed to explain collaboration in an inter-organizational system,
economic, technical, and socio-political. The social system in workplace is a platform for
9


continuous interplay between the technical process and the social process to meet the
demands of an emerging and continuously changing work environment (Taylor, 1975).
The importance of both technology usage and social relationships to a virtual team
success is consistent with the Socio-Technical theory which states that organizations
consist of two interacting systems; the social and the technical (Bostrom and Heinen,
1977). While the technical system includes the process, task, and technology dimensions,
the social system explains how the relationships and interaction among individuals affect
the system outcome (Bostrom and Heinen, 1977). An organizational system design which
doesnt successfully integrate both the technical and social systems is unlikely to produce
effective outcome (Appelbaum, 1997).
Collaboration is different than coordination. While sometimes used
interchangeably, a main difference between the two is the degree to which the work is
coordinated among individuals. It is noted that collaboration requires higher level of
coordination among individuals than cooperation (Dillenbourg, 1999). Previous studies
have investigated why virtual team members share their knowledge especially outside the
organization boundaries (Bechky, 2003, Wasko and Faraj, 2005). However, collaboration
goes beyond sharing information or knowledge through a form of an information system,
its about working together in a coordinated effort through continuous discussion and
communication in order to jointly and collectively solve a problem. In face-to -face
setting, Hoegl and Gemuenden (2001) define a team as a social system of three or more
people, which is embedded in an organization, whose members perceive themselves as
such and are perceived as members by others, and whose members collaborate on a
10


common task. Kudaravalli and Faraj (2008) argue that online collaboration has received
limited attention in the literature.
2.5. Measurement Scale Development Process
The development of the measurement scale was carried out in four stages. The
first stage involved investigating relevant literature to find out the constructs which
influence a virtual team member intention to collaborate online. The second stage was
item creation stage; the purpose of this stage is to create a pool of items for the constructs
which were identified in the previous stage. The third stage was the scale development
stage which included a panel of judges who were asked to categorize and sort the items
created in the second stage. The final stage was the instrument testing stage which
included a pilot test with a small sample of respondents to get an indication of the scale
content validity and reliability. This stage also included a full scale study to test the final
instrument. The following sections will describe these stages in detail.
2.5.1. Stage 1: Literature Investigation and Construct Identification
The first step in developing a measurement model of online collaboration is a
thorough investigation of the literature on virtual teams, collaboration, and surrounding
research areas. While there is a considerable volume of research which investigates
online collaboration, much of it has focused on finding new constructs that contribute to
online collaboration as opposed to understanding how different factors combine to
influence an individuals intention to collaborate.
Investigation of the literature on virtual teams collaboration and surrounding
research areas revealed eleven dimensions. These dimensions, the studies in which they
11


were reported, and the context of each study are summarized in Table 1. It is noteworthy
to point out that the list of studies in Table 1 is not inclusive; however, we found them to
be most suitable to answer our research questions. Dimensions illustrated in Table 1 are
mapped into three categories based on the context of the original studies and the
theoretical support in the literature. These categories are intention to collaborate online
dimensions, online collaboration dimensions, and moderating dimensions. The mapping
of these factors is illustrated in Table 2.
Building on the preceding discussion, a research model depicting the constructs
and relationships examined in this study is depicted in Figurel. In the model, five
antecedent dimensions identified in prior literature influence a virtual team members
intention to collaborate online. Intention is relevant for this model because without intent,
actual collaboration will not occur. This is consistent with the theory of reasoned action
(TRA) which states that a persons action is a function of his intention (Fishbein and
Ajzens, 1975). As such "intention" is seen as the primary driver of actual collaboration
online. The relationship between intention to collaborate online and online collaboration
is moderated by the ability to meet in person and the availability of IT support. The
remainder of this paper will focus on further understanding the relationship between these
five dimensions and the intention to collaborate online. The study of actual online
collaboration is beyond the scope of this study.
2.5.1.l.Perceived Incentives
Organizations offer incentives to motivate individuals to contribute activity, and
the reward system is reported to affect organizational behavior (Clark and Wilson, 1961).
12


Table 1: Online Collaboration Dimensions
Study Context dimension(s)
Agarwal and Prasad (1997) Technology Acceptance Voluntariness
Bardram(1998) CSCW IT Support, communication, coordination.
Fussell et al. (1998) Virtual Teams Communication, Coordination
Jarvenpaa et al. (1998) Virtual Teams, Trust Voluntariness, Coordination
Cramton (2001) Virtual Teams, Knowledge sharing Communication
Holton(2001) Virtual Teams, Trust, and Collaboration Communication, Meet in person
Montoya-Weiss et al. (2001) Virtual Teams, Management Coordination
Desanctis et al.(2003) Online Communities Communication, IT Support, Openness
Hall and Graham (2004) Online Communities Incentives, Meet in person, Common Ground
Hertel, et al. (2004) Virtual Teams, Management Incentives
Leinonen et.al. (2005) Virtual Teams, Collaboration Communication, Coordination
Wasko and Faraj (2005) Online Communities, Collaboration Background similarities, level of expertise, Incentives
Durate and Snyder (2006) Virtual Teams, Management Voluntariness
Metiu (2006) Virtual Teams Background similarities, Tension
Kanawattanachai and Yoo (2007) Virtual Teams, Knowledge Coordination Communication
Kudaravalli and Faraj (2008) Online Communities, Collaboration Background similarities, Communication, Different level of expertise, Voluntariness
Bjorn and Ngwenyama (2009) Virtual Teams, Collaboration Communication, common ground, Coordination
Hemetsberger and Reinhardt (2009) Online Communities, Collaboration IT Support, Openness, Tension
13


Table 2. Mapping Online Collaboration Dimensions
Figure 1: Intention to Collaborate Online
Normally, there are incentives for a virtual team member to collaborate, some of
which could be tangible (e.g. reward, bonus, promotion ...etc.) while others could be
intangible (reputation, recognition, personal satisfaction ... etc.). Organizations
implement reward systems to induce employees to contribute and teams to succeed
(Hertel et al, 2004).
The literature on knowledge sharing reported that organizations had implemented
reward systems and offer variety of incentives to promote knowledge sharing (Bock et al.
14


2005). Hall and Graham (2004) reported that code breakers joined an online group to
share knowledge and collaborate to break a code in the hope of winning an award. Hall
and Graham (2004) also reported that some members were also interested achieving
personal satisfaction and enhanced reputation which is consistent with the findings of
Wasko and Faraj (2005) who argue that members contribute their knowledge when they
perceive that it enhances their professional reputations.
The Social Exchange Theory explains social interaction in terms of reward
expectations (Blau, 1964). Individuals have a tendency to maximize their rewards and
reduce their cost (Emerson, 1976). Therefore if a reward system is implemented, virtual
team members will be expected to work towards maximizing their benefits and will be
more motivated to collaborate online with each other.
2.5.I.2. Perceived Voluntariness
Professionals join online communities voluntarily based on shared interest
without any obligation to collaborate or any commitment to the community and its
objectives. However, when organizations form teams, team members are mandated -or at
least expected- to collaborate in order to solve problems and achieve a common goal.
Since virtual team members can cooperate but not collaborate, we could look at
collaboration as a form of voluntary cooperation in which team members not only work
on a task, but they communicate with each other and coordinate their activities
(Kudaravalli and Faraj, 2008). Thus, voluntariness could affect collaboration practices in
virtual teams, mainly because individuals are more effective when they are guided by
their own behavior and not forced to act in a certain way (Eisenberger and Cameron,
1996).
15


Virtual teams can be formed without a choice of their members, which could
influence members intention to collaborate online because voluntariness is considered to
be a form of social influence and voluntary membership is a principal element of
collaboration (Roberts and Bradley, 1991; Durate and Snyder, 2006; Karahanna et al.,
1999). Perceived voluntariness could have a bigger influence on behavior than actual
voluntariness (Moore and Benbasat, 1991) and it has the potential to significantly affect
individual behavior and intentions (Agarwal and Prasad, 1997).
The Unified Theory of Acceptance and Use of Technology (UTAUT) model -
which is an extended version of the Technology Acceptance Model (TAM) posits that
voluntariness has an effect on behavioral intention and use behavior (Venkatesh et al.,
2003). Hemetsberger and Reinhardt (2009) suggest that team leaders can at least
coordinate tasks while giving virtual team members the freedom to choose what task each
one of them would like to work on. Based on the preceding discussion, we posit that
perceived voluntariness of online collaboration contribute to a virtual team members
intention to collaborate online.
2.5.I.3. Perceived Common Ground
Crossing geographical boundaries affects the way in which virtual teams
communicate and collaborate which makes reaching a common ground crucial for
effective collaboration (Durate and Snyder, 2006; Alavi and Tiwana, 2002). The unique
characteristics of virtual teams create challenges to develop common ground among team
members (Alavi and Tiwana, 2002). Virtual team members are sometimes distributed
across vast geographical areas and live in different time zones which constrain their
ability to communicate and achieve a common ground.
16


In the absence of face-to-face interaction, developing common ground and shared
understanding in technology mediated communication can be challenging. Empirical
research has highlighted that virtual settings negatively affects the perceptions of their
members (Burke et al. 1999). Studies have also demonstrated negative associations
between the degree of virtualeness and communication which could affect virtual team
members ability to establish a common ground in a timely manner (Cohen and Gibson,
2003; Webster and Wong, 2008). In order to overcome the challenges of virtual
environments, virtual team members need to establish immediate, frequent, and effective
communication channels among each other to reach a common ground in a timely
manner (Kanawattanachai and Yoo, 2007).
Establishing common ground is a process of creating a shared meaning context
among team members (Bjorn and Ngwenyama, 2009). This shared meaning need to be
established in an early stage of virtual work or else the virtual team will be exposed to
challenges which could lead to failure in achieving effective collaboration (Cramton,
2001). The establishment of common ground and shared context in virtual teams is
complicated and challenging. Virtual team members -especially in global virtual team
context- have different backgrounds, different work experience, different expectations,
and sometimes language and cultural barriers. These challenges if not addressed properly
could lead to failure in communication and interpretation which ultimately leads to
failure in collaboration (Cramton, 2001). Based on the preceding discussion we argue that
the perception of establishing mutual understanding and common ground among virtual
team members impacts their intention to collaborate online because common ground
facilitates communication and collaboration.
17


2.5.1.4. Perceived Background Similarities
Transforming from traditional team work into virtual team work changes the way
work is carried out, which has an effect on the social aspects of team work. As members
from different backgrounds join a virtual team, online collaboration becomes more
difficult and can add problems and complications to virtual team work (Bechky 2003;
Durate and Snyder, 2006). The existence of different backgrounds among team members
could affect the structure and effectiveness of a virtual team, mainly because members of
different backgrounds tend to have different ways of working together and collaborate
with each other (Durate and Snyder, 2006).
The social categorization and the identity argument states that the more similar
the group members are to one another, the more they identify with the group which in
turn could influence members collaboration because individuals with similar
backgrounds tend to develop relationships and collaborate together faster (Abrams et al.
2005; Webster and Wong, 2008).
According to social identity theory, people seek out certainty by identifying
themselves within groups of members of similar attributes and characteristics (Ashforth
and Mael, 1989). Considering that the uncertainty is relatively high in virtual teams, it is
reasonable for team members to tend to identify themselves within groups of similar
backgrounds (Tajfel and Turner, 1986). Based on the preceding discussion, we argue that
background similarities among virtual team members impact their intention to collaborate
online.
18


2.5.1.5. Perceived Members Expertise
Organizations establish teams in order to gather members of different skills and
experience to collaborate on a common organizational task (Johnson, 2001). The
prevalence of organizations establishing virtual teams and accept their challenges and
overhead could be attributed to the need to recruit expert members to work across or
outside the physical boundaries of organizations. Virtual teams enable members to bring
different skills and expertise to help solve problems regardless of their physical location
(Johnson, 2001; Wasko and Faraj, 2005).
The integration of expert users into virtual teams increases opportunities by
enlarging the workforce and specialized knowledge within the team. Collaboration
depends on the extent to which team members are able to locate the necessary knowledge
within the team and retrieve it (Kanawattanachai and Yoo, 2007).
Diversity of expertise has a positive impact on online collaboration (Kudaravalli
and Faraj, 2008). Having expert members in a virtual team leads to members benefit from
each other, gain access to new information and ideas not available locally, and problem
solving process is improved (Wasko and Faraj, 2005). Based on the preceding discussion
we argue that having varying levels of expertise within a virtual team will have a positive
influence on a virtual team members intention to collaborate.
2.5.2. Stage 2: Item Creation
The methodology used to develop the scale in this research is based on procedures
described in Moore and Benbasat (1991) and in MacKenzie et al. (2011). The first stage
was to create a number of items for the scale, the initial scale items were adopted from
the literature on collaboration, virtual teams, online communities, computer supported
19


collaborative work, and knowledge sharing. Additional items were created through
interviews and group discussions. An initial set of 75 items were developed in this stage.
These initial items were reduced to 65 to eliminate narrow scope and ambiguous
questions; we also made sure that each construct has at least 10 questions. Items were
written as a statement to which the respondent can relate and indicate a degree of
agreement or disagreement with using a seven point Likert scale ranging from strongly
disagree to strongly agree.
2.5.3. Stage 3: Scale Development
The next stage was a pretest to refine the initial set of items in order to assess their
content validity. In this stage a panel of seven expert judges was asked to sort the items
into categories based on the similarities and differences among them. The judges were all
PhD students with four of them have experience in virtual team work. They were given
the set of items randomly distributed and the five constructs on which the items should be
categorized along with an additional category for ambiguous or unclear items. The judges
were also asked to rank order the items within each factor based on the closeness in
meaning with the factor itself.
The card sort analysis revealed a confusion caused by the items of Perceived
Members Expertise items and Perceived background similarities. All the judges
misplaced items from both of these construct, the judges reported that these two
constructs could easily be confused with one another. As this overlap between constructs
represents a threat to the internal validity of the model we had to make a choice of either
dropping one of the constructs, or dropping the items which threatened the internal
validity and modifying the rest. We chose the second option.
20


Table 3: Intention to Collaborate Online Scale Items
Factor Item Developed based on
Background Similarities The background of my online team members does not influence my intention to collaborate with them Im more likely to collaborate online with members with whom I share a similar culture Im more likely to collaborate online with individuals with whom I share similar background My intention to collaborate online is positively affected by the diversity of my team members background and expertise Having team members of different backgrounds makes me less likely to collaborate online I tend to collaborate online with team members that have different backgrounds than mine (Webster and Wong, 2008) (Jarvenpaa et al. 1998) (Webster and Wong, 2008) (Webster and Wong, 2008) (Webster and Wong, 2008) (Webster and Wong, 2008)
Common Ground Im more likely to collaborate online when the team reaches common ground from the beginning Reaching a common ground has nothing to do with my intention to collaborate online Collaborating online requires me to engage in continuous communication with other team members to reach common ground I collaborate online even when my team members and I do not fully share the same vision of the problem we are trying to solve. Mutual understanding is essential for me to collaborate online It is not essential to have a mutual understanding with other team members for me to collaborate online For me to collaborate online, the team should share a common understanding of problems to be addressed. (Kudaravalli and Faraj, 2008) (Kudaravalli and Faraj, 2008) (Leinonen et al. 2005), (Leinonen et al. 2005) (Leinonen et al. 2005), (Kudaravalli and Faraj, 2008) (Leinonen et al. 2005)
Members Expertise Having expert members on my team makes me more willing to collaborate online Diversity of members expertise in a virtual team encourages me to collaborate online I collaborate online with members who have expertise I can benefit from I collaborate online with team members who can benefit from my expertise Virtual team members' expertise does not influence my intention to collaborate with them online. (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005)
Incentives I collaborate with my online team members regardless of any incentives I expect to be rewarded by my organization or team supervisor when I collaborate online. I expect something in return when I collaborate with team members online I collaborate with others online to improve my image within the team (Kankanhalli et al. 2007) (Kankanhalli et al. 2007) (Kankanhalli et al. 2007) (Kankanhalli et al. 2007)
21


Table 3 (Cont.): Intention to Collaborate Online Scale Items
Incentives (Cont.) I only collaborate online when there are incentives for my collaboration Im less likely to collaborate online without getting something in return Collaborating online enhances my professional reputation (Kankanhalli et al. 2007) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997)
Voluntariness I only collaborate with online team members when Im asked to Im more likely to collaborate online when I voluntarily join the team I collaborate with other team members online even when not mandated by my organization Im less Likely to collaborate with other team members online when Im forced to do so Mandating online collaboration makes me less willing to collaborate with team members Mandating online collaboration makes me more willing to collaborate with team members Im more likely to collaborate online when I choose to join a team (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997)
Post card sorting interviews were conducted with the judges to further refine and
improve the items. These interviews resulted in rewording some items to improve their
clarity and dropping the items that the judges felt did not adequately represent the
underlying construct they were measuring. This process resulted in a refined scale
containing 33 items as listed in Table3.
2.5.3.I. Pilot Study
Before administering the scale on a large population, a pilot study was conducted
to insure that the scale under development used clear and appropriate language and had
no obvious errors (Johanson and Brooks, 2010). The objective of this pilot study was to
provide additional insights regarding the content validity, clarity, and appropriateness of
the survey questions and to further refine the survey items before using them in a larger
study. The respondents first were asked to complete the questionnaire, and then comment
22


on the questions length, wording, redundancy, and total number of questions. The scale
items were transformed into seven point Likert scale survey which was implemented on
the web. A filtering question related to virtual team experience was added to the survey to
insure that respondents have the background necessary to complete the survey.
The target population for the pilot study was MBA students at the University of
Colorado Denver Business School. MBA students were a good fit for the pilot study
because most of them are professionals working for companies in the Denver area.
Control questions were also added to control for years of experience, membership and
leadership, current virtual team membership, and global virtual team experience. A
convenient sample of 15 MBA students was invited to participate in the survey via an
invitation from the University of Colorado Denver MBA student club president. The pilot
test with the MBA students was used to identify any additional problem with the survey.
The respondents provided feedback regarding the background similarities
construct arguing that it was vague and difficult to answer its questions. The respondents
reported that the definition of the word background was unclear and that they could
interpret it in different contexts such as experience background, cultural background, and
educational background. Furthermore, the respondents reported that some questions were
vague and redundant. Eventually, based on the feedback of this pilot study the construct
background similarities and 5 items from the remaining constructs were dropped from
the study to improve the scale reliability and validity. The revised scale has 4 constructs
with 21 questions, table 4 shows the final list of questions used in the survey.
23


Table 4: Measurement Items after Pilot Study
Factor Item
Common Ground Im more likely to collaborate online when the team reaches common ground from the beginning Reaching a common ground has nothing to do with my intention to collaborate online Collaborating online requires me to engage in continuous communication with other team members to reach common ground I collaborate online even when my team members and I do not fully share the same vision of the problem we are trying to solve. For me to collaborate online, the team should share a common understanding of problems to be addressed.
Incentives I collaborate with my online team members regardless of any incentives I expect to be rewarded by my organization or team supervisor when I collaborate online. I expect something in return when I collaborate with team members online I collaborate with others online to improve my image within the team Im less likely to collaborate online without getting something in return Collaborating online enhances my professional reputation
Members Expertise Having expert members on my team makes me more willing to collaborate online Diversity of members expertise in a virtual team encourages me to collaborate online I collaborate online with members who have expertise I can benefit from I collaborate online with team members who can benefit from my expertise Virtual team members' expertise does not influence my intention to collaborate with them online.
Voluntariness I only collaborate with online team members when Im asked to Im more likely to collaborate online when I voluntarily join the team I collaborate with other team members online even when not mandated by my organization Im less Likely to collaborate with other team members online when Im forced to do so Mandating online collaboration makes me less willing to collaborate with team members
24


Table 5: Number of items after Pilot Study
Construct Number of deleted items Number of final items
Perceived Incentives 1 6
Perceived Common Ground 2 5
Perceived Member Expertise 0 5
Perceived Voluntariness 2 5
2.5.4. Stage 4: Instrument Testing Field Study
Once the measurement model has been formally specified, data need to be
obtained from a sample of respondents in order to examine the scale and to evaluate its
convergent, discriminant, and nomological validity (MacKenzie et al. 2011). The
measurement scale was implemented as an online survey and respondents were recruited
from professional groups in linkedin.com. The groups we used as a sample frame are
listed in Appendix I. The total number of members in these groups was 1372, 118
responses are obtained with a response rate of 8.6%. The number of usable responses
after eliminating incomplete surveys is 103.
The statistical analysis implemented in this study is adapted from Moore and
Benbasat (1991). The constructs are first tested for reliability and validity. Reliability is
tested using Cronbach's alpha with a value of at least 0.70 indicates adequate reliability.
In order to improve the reliabilities of the corresponding constructs, one or more
questions could be dropped from the study.
The scale is tested for validity using factor analysis with principal components
analysis. Convergent validity is assessed by checking loadings to see if items within the
same construct correlate highly amongst themselves. Furthermore, discriminant validity
is assessed by examining the factor loadings to see if questions loaded more highly on
25


their intended constructs than on other constructs (Kankanhalli et al. 2005). Loadings of
0.63 to 0.70 are considered very good, and above 0.71 are considered excellent (Hair et
al. 2009). Questions which could load onto other constructs are dropped.
2.5.4.I. Perceived Incentives
Reliability test for perceived incentives resulted in a very poor alpha value.
According to the correlation table (Table 6), two of the six perceived incentives items
have very poor correlation with rest of the items. So we decided to drop these items and
run the reliability test again without them. The reliability results for perceived incentives
after dropping items 4 and 6 are shown in table 8 which illustrates that alpha value now is
0.962, which indicates excellent reliability.
Table 6: Reliability Statistics Perceived Incentives
Cronbach's Alpha Cronbach's Alpha N of Items
.411 .757 6
Table 7: Correlation Matrix Perceived Incentives
INC1 INC2 INC3 INC4 INC5 INC6
INC1 1.000
INC2 .901 1.000
INC3 .814 .862 1.000
INC4 .045 .000 -.021 1.000
INC5 .809 .858 .948 -.018 1.000
INC6 .046 .093 .106 -.442 .121 1.000
Table 8: Reliability Statistics Repeat Perceived Incentives
Cronbach's Alpha Cronbach's Alpha N of Items
.962 .963 4
26


Table 9: Correlation Matrix Repeat Perceived Incentives
INC1 INC2 INC3 INC5
INC1 1.000
INC2 .901 1.000
INC3 .814 .862 1.000
INC5 .809 .858 .948 1.000
2.5.4.2.Perceived Voluntariness
Reliability test for perceived voluntariness resulted in an adequate alpha value of
(0.78). However, according to the correlation matrix (Table 11), item VOL5 has a very
poor correlation with most of the items. So we decided to drop this item in order to
improve the scales reliability. After removing VOL5 we ran the reliability test again.
The reliability results for perceived voluntariness is shown in table 12 which illustrates
that alpha value now is 0.959, which indicates excellent reliability.
Table 10: Reliability Statistics Perceived Voluntariness
Cronbach's Alpha Cronbach's Alpha Based N of Items
.780 .850 5
Table 11: Correlation Matrix Perceived Voluntariness
VOL IT VOL2 VOL3 VOL4 VOL5
VOL IT 1.000
VOL2 .958 1.000
VOL3 .836 .840 1.000
VOL4 .790 .793 .926 1.000
VOL5 .004 .041 .090 .040 1.000
Table 12: Reliability Statistics Repeat- Perceived Voluntariness
Cronbach's Alpha Cronbach's Alpha N of Items
.959 .960 4
27


Table 13: Correlation Matrix Repeat Perceived Voluntariness
VOL IT VOL2 VOL3 VOL4
VOL IT 1.000
VOL2 .958 1.000
VOL3 .836 .840 1.000
VOL4 .790 .793 .926 1.000
2.5.4.3.Perceived Common Ground
Reliability test for perceived common ground resulted in an adequate alpha value
of (0.762). However, according to the correlation matrix (Table 15), item CGD3 has a
very poor correlation with most of the items in the scale. So this item was dropped in
order to improve the scales reliability. After removing CGD3 we ran the reliability test
again. The reliability results for perceived voluntariness is shown in table 16 which
illustrates that alpha value now is 0.938, which indicates excellent reliability.
Table 14: Reliability Statistics- Perceived Common Ground
Cronbach's Alpha Cronbach's Alpha N of Items
.762 .827 5
Table 15: Correlation Matrix Perceived Common Ground
CGD1 CGD2 CGD3 CGD4 CGD5T
CGD1 1.000
CGD2 .851 1.000
CGD3 .030 .056 1.000
CGD4 .762 .803 -.011 1.000
CGD5T .762 .783 -.017 .863 1.000
Table 16: Reliability Statistics Repeat Perceived Common Ground
Cronbach's Alpha Cronbach's Alpha N of Items
.938 .943 4
28


Table 17: Correlation Matrix Repeat Perceived Common Ground
CGD1 CGD2 CGD4 CGD5T
CGD1 1.000
CGD2 .851 1.000
CGD4 .762 .803 1.000
CGD5T .762 .783 .863 1.000
2.5.4.4. Perceived Members Expertise
Reliability test for perceived members expertise resulted in an adequate alpha
value of (0.812). However, according to the correlation matrix (Table 19), item EXP5
has a very poor correlation with most of the items. So this item was dropped in order to
improve the scales reliability. After removing EXP5 we ran the reliability test again. The
reliability results for perceived voluntariness is shown in table 20 which illustrates that
alpha value now is 0.959, which indicates excellent reliability.
Table 18: Reliability Statistics- Perceived Members Expertise
Cronbach's Alpha Cronbach's Alpha N of Items
.812 .869 5
Table 19 : Correlation Matrix- Perceived Members Expertise
EXP1 EXP2 EXP3 EXP4 EXP5T
EXP1 1.000
EXP2 .916 1.000
EXP3 .842 .824 1.000
EXP4 .823 .809 .911 1.000
EXP5 .122 .148 .175 .132 1.000
Table 20: Reliability Statistics- Perceived Members Expertise
Cronbach's Alpha Cronbach's N of Items
.959 .959 4
29


Table 21: Correlation Matrix Repeat Perceived Members Expertise
EXP1 EXP2 EXP3 EXP4
EXP1 1.000
EXP2 .916 1.000
EXP3 .842 .824 1.000
EXP4 .823 .809 .911 1.000
Factor analysis was used as another assessment of construct validity. Principal
Components analysis was conducted with VARIMAX rotation. The initial analysis
included all the items including those which demonstrated poor correlation with items in
the same variable. The results in table 22 indicate that the items resulted in 8 factors,
while our proposed model includes only 4 factors.
The rotated factor matrix was examined for items which either did not load
strongly on any factor (<0.40), or were too complex -which loaded highly or relatively
equally on more than one factor. The rotated factor matrix in table 23 shows that the
items which affected the reliability analysis are the same items which either have poor
loading, load on a different construct than the one it should load onto, or load on a
separate construct. Thus, these items were dropped from the scale, and a second factor
analysis was conducted. This analysis again used Principal Components with VARIMAX
rotation. While the first analysis was exploratory, this analysis was conducted to confirm
that the items dropped in the previous validation steps resulted in factors that loaded onto
the correct construct (e.g. Moore and Benbasat, 1991). As demonstrated in the factor
matrix in table 22, a fairly simple factor structure emerged. No item loaded highly on
more than one factor. Furthermore, all items remaining in the various scales loaded
together on the target factor in the excellent range. These results indicate that the various
scales achieved a high degree of unidimensionality.
30


Table 22: Items Correlation
Correlation -Sig. (1- tailed] INC1 INC2 z o SON I INC4 INC6 VOL1T VOL2 VOL3 VOL4 VOL5 CGD1 CGD2 CGD3 CGD4 CGD5T EXP1 EX P2 EX P3 EX P4 EX P5T
INC1 1.00
INC2 .901 1.00
INC3 .814 .862 1.00
INC5 .809 .858 .948 1.00
INC4 .045 .000 ,021 ,018 1.00
INC6 .046 .093 .106 .121 ,442 1.00
V0L1T .086 .153 .094 .118 ,033 .053 1.00
V0L2 .099 .151 .092 .116 .010 .017 .958 1.00
V0L3 .130 .141 .143 .135 .036 .003 .836 .840 1.00
V0L4 .106 .117 .108 .113 .102 .038 .790 .793 .926 1.00
V0L5 ,048 ,096 ,058 ,096 .114 .244 .004 .041 .090 .040 1.00
CGD1 .139 .119 .121 .166 ,244 .251 .027 .015 .120 .127 .155 1.00
CGD2 .231 .217 .187 .228 ,213 .304 .035 .011 .159 .149 .150 .851 1.00
CGD3 .062 .075 .089 .129 ,182 .232 .074 .080 .104 .088 .130 .030 .056 1.00
CGD4 .121 .110 .120 .135 .182 .292 .091 .059 .203 .259 ,12 .762 .803 ,01 1.00
CGD5T .124 .160 .155 .185 ,190 .325 .087 .068 .187 .294 ,14 .762 .783 ,01 .863 1.00
EXP1 .176 .149 .109 .157 ,066 .102 .106 .122 .254 .271 ,07 .302 .326 .256 .201 .316 1.00
EXP2 .138 .129 .077 .139 ,059 .150 .168 .174 .266 .284 ,07 .265 .301 .255 .175 .292 .916 1.00
EXP3 .140 .118 .100 .172 ,041 .101 .083 .116 .195 .186 ,09 .288 .320 .139 .149 .239 .842 .824 1.00
EXP4 .109 .087 .071 .145 .009 .064 .130 .155 .231 .248 ,08 .259 .285 .148 .128 .247 .823 .809 .911 1.00
EXP5T ,099 ,060 ,142 ,145 .155 ,19 ,016 ,04 ,07 ,07 ,12 .000 .079 ,07 .083 .031 .122 .148 .175 .132 1.00
n


Table 23: Rotated Component Matrix
Raw Rescaled
Component Component
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
INC1 .15 .01 -.03 .028 -.01 .276 .254 .424 .251 .019 -.05 .044 -.02 .435 .401 .670
INC2 .17 .01 -.04 -.019 -.01 .242 .262 .450 .280 .001 -.07 -.03 -.01 .383 .415 .713
INC3 .15 .01 -.09 .015 -.01 .255 .260 .425 .246 -.01 -.14 .024 -.02 .412 .420 .687
INC5 .18 .02 -.07 .018 -.03 .261 .249 .399 .305 -.03 -.12 .030 -.05 .431 .411 .660
INC4 -.3 .73 .726 -.198 -.05 .799 .272 -.40 -.26 .492 .484 -.13 -.03 .533 .181 -.27
INC6 .51 .65 -.56 .017 .015 -.13 .058 -.15 .391 -.50 -.43 .013 .011 -.10 .044 -.11
VOL1 .59 .65 -.27 -.401 -.00 -.20 -.06 .085 .551 .611 -.26 -.37 -.00 -.19 -.05 .079
VOL2 .58 .71 -.26 -.363 -.03 -.17 -.06 .081 .537 .659 -.24 -.33 -.03 -.15 -.06 .074
VOL3 .61 .64 -.21 -.201 .039 -.05 -.01 -.02 .627 .650 -.21 -.20 .039 -.05 -.01 -.02
VOL4 .73 .70 -.21 -.261 .084 .02 -.07 -.12 .642 .618 -.18 -.22 .073 .021 -.01 -.12
VOL5 -.46 .94 -.20 1.17 .365 -.35 .239 .022 -.27 .559 -.12 .694 .215 -.21 .141 .013
CGD1 .41 .24 -.02 .110 .410 .143 .057 -.09 .537 -.31 -.03 .142 .528 .183 .074 -.12
CGD2 .46 .25 .014 .108 .433 .140 .136 -.05 .580 -.31 .018 .136 .545 .176 .171 -.07
CGD3 .41 .21 -.18 .064 -.71 -.28 .813 -.35 .317 -.16 -.14 .049 -.55 -.21 .627 -.27
CGD4 .47 .21 -.05 .025 .619 .128 .154 -.17 .523 -.23 -.05 .027 .683 .142 .170 -.18
CGD5 .60 .28 -.04 .094 .633 .209 .099 -.17 .595 -.25 -.04 .092 .620 .204 .097 -.17
EXP1 .69 .03 .375 .392 -.24 .051 -.10 .000 .716 -.04 .385 .402 -.24 .052 -.10 .000
EXP2 .70 .01 .368 .353 -.25 .003 -.10 -.01 .724 -.01 .378 .362 -.25 .003 -.11 -.01
EXP3 .71 .06 .490 .409 -.25 .060 -.23 .078 .670 -.06 .457 .382 -.23 .056 -.21 .073
EXP4 .66 .04 .436 .360 -.25 .091 -.23 .011 .669 .004 .437 .360 -.25 .091 -.23 .011
EXP5 .07 .05 1.24 -.420 .397 -.72 .292 .165 .048 -.03 .777 -.26 .249 -.45 .183 .103
32


Table 24: Rotated Component Matrix Repeat
Raw Rescaled
Component Component
1 2 3 4 1 2 3 4
INC1 .586 .049 .027 .045 .925 .078 .042 .072
INC2 .600 .031 .052 .044 .950 .049 .083 .070
INC3 .586 .012 .031 .047 .948 .020 .050 .076
INC5 .569 .052 .034 .060 .940 .086 .056 .099
VOL1 .071 .031 1.022 -.015 .066 .029 .952 -.014
VOL2 .075 .062 1.040 -.047 .069 .056 .953 -.043
VOL3 .068 .142 .917 .106 .069 .144 .931 .108
VOL4 .034 .171 1.042 .199 .030 .150 .910 .174
CGD1 .052 .135 -.001 .674 .067 .174 -.001 .867
CGD2 .121 .157 .006 .699 .153 .198 .007 .880
CGD4 .035 .021 .094 .852 .039 .023 .104 .941
CGD5 .070 .147 .102 .950 .068 .144 .099 .929
EXP1 .078 .898 .091 .171 .080 .921 .093 .175
EXP2 .050 .888 .133 .139 .051 .912 .137 .143
EXP3 .077 1.011 .049 .126 .072 .944 .045 .118
EXP4 .035 .930 .101 .103 .035 .931 .101 .103
Table 25: Total Variance Explained
Component Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total %of Variance Cumulative % Total %of Variance Cumulative %
Raw 1 5.155 38.715 38.715 1.425 10.706 10.706
2 3.242 24.348 63.063 3.608 27.099 37.805
3 2.160 16.226 79.289 4.114 30.896 68.701
4 1.300 9.765 89.055 2.710 20.354 89.055
Rescaled 1 5.173 32.329 32.329 3.605 22.528 22.528
2 3.154 19.715 52.043 3.592 22.450 44.978
3 2.606 16.285 68.328 3.583 22.397 67.375
4 3.269 20.431 88.759 3.422 21.385 88.759
33


2.6.
Limitations and Future Work
A limitation of this research is that it examines self-reported intention to
collaborate as opposed to actual collaboration. This study relies on the theoretical
foundation in the Theory of Reasoned Action (TRA) which argues that a persons action
is a function of his intention. However, intention to collaborate is only a proxy for the
actual outcome organizations wish to achieve, actual collaboration. One benefit of
developing this model and measurement scale for intention to collaborate online is that,
future studies can use this scale to address actual collaboration in a real world setting.
This will allow researchers to better understand whether factors influence intention to
collaborate have the same effect on actual observable collaboration.
A second limitation of this research is that it utilized subjects recruited from a
social network website instead of a traditional organizational setting. This environment is
appropriate to recruit members because a number of online groups do coordinate as
virtual teams (e.g. for group members working for the same organization). However,
recruiting in such an environment may have implications with respect to the types of
antecedents to virtual team participation that are present in the sample. Specifically, the
sample may include more members that are participating in virtual teams voluntarily than
would be seen in a more traditional organizational setting. Future research should
examine a sample from an organizational setting so the scale can be tested with a
population which may have exposure to a different set of antecedent factors influencing
virtual team participation and collaboration.
Lastly, because items could be added, dropped, or reworded in the scale test
process, future work could retest the measurement model using a new sample of data.
34


2.7. Implications to Research and Practice
This study provides important implications for both research and practice. The
study proposes a conceptual research model and a validated measurement scale for virtual
team members intention to collaborate. The theoretical background which supports the
study is found in the Socio-Technical Theory and the Theory of Reasoned Action.
For research, this study calls for a better understanding of the social aspects
surrounding virtual team members collaboration. Building upon the Socio-Technical
Theory this research suggests that technology advancement alone does not facilitate
online collaboration in virtual teams, social aspects of virtual teams plays an important
role in their collaboration as well. Prior research has reported several factors which
contribute to virtual team collaboration. This study, however, investigated how these
factors combine to influence a virtual team members intention to collaborate. The
contribution of this study will enable researchers to rely on a validated measurement
scale and conceptual model to further investigate online collaboration construct and its
interaction with other constructs in virtual team settings. Future work could retest the
model presented in this paper or use this model in a study where online collaboration is
one of the constructs.
For practice, this study provides noteworthy implications regarding the
importance of social characteristics and social relationships among virtual team members
in fostering an environment of collaboration within the team in the organization. Today
there is increased adoption of virtual teams in organizations and there is a need for
improved inter-organizational cooperation. Given that collaboration is essential for team
work, this study provides key implications to practice on how to establish and manage
35


virtual teams which collaborate effectively. It also provides insights into how to integrate
technology and social characteristics in a way that serves virtual team objectives.
2.8. Conclusion
The advancement in information and communication technologies along with
globalization and inter-organizational cooperation allowed organizations to establish
teams regardless of members physical locations. Virtual team members are expected to
collaborate online to solve problems. This study investigated the factors which influence
a virtual team members intention to collaborate online.
Prior research on virtual teams collaboration focused primarily on technical
factors with little attention to the social issues surrounding their work. Research which
investigated social aspects of virtual teams collaboration has focused on finding new
constructs that contribute to online collaboration without examining how different factors
combined influence a virtual team member intention to collaborate.
This study investigated the factors which influence online collaboration in virtual
team settings that were reported in the literature. The contributions of this study include a
conceptual model for online collaboration and a measurement scale to test this model.
The measurement scale was pretested and refined through a card sort exercise and
validated through a pilot and a field study. Additional research is necessary to further
validate the measurement scale and evaluate its generalizability across different virtual
team environments.
36


3. Knowledge Sharing in Virtual Teams: The Impact on Trust, Collaboration, and
Team Effectiveness.
3.1. Abstract
Virtual teams are utilized by organizations to gather experts to collaborate online
in order to accomplish organizational tasks. However, the characteristics of these teams
create challenges to effective collaboration and effective team outcome. Collaboration is
an essential component of teamwork, the notion of forming teams in organizations is the
need for members with complementary skills and expertise to collaborate in order to
achieve the goal for which the team is established. Literature on virtual teams has been
growing for over a decade with researchers investigating different aspects of virtual
work. Trust among virtual team members has been investigated by information systems
researchers as a crucial challenge for virtual teams success. Knowledge sharing and
management in virtual teams has been the focus of recent research studies as it represents
a challenge in virtual work environments; specifically because the knowledge is scattered
among geographically distributed team members with the absence of face to face
interaction. This study extends the literature on virtual teams by investigating the
relationship between knowledge sharing, trust, and collaboration among team members in
virtual team settings; and examining how these constructs ultimately affect virtual team
effectiveness. We argue that knowledge as a valuable asset of virtual team members is a
key factor influencing virtual team effectiveness.
This research introduces a conceptual model which describes the hypothesized
relationship between knowledge sharing, trust, collaboration, and team effectiveness in
virtual team settings. The model is developed based on an intensive review of the
37


literature on the constructs of interest in both traditional and virtual team settings. The
theoretical foundation for the model is found in the Knowledge Based Theory of The
Firm, Social Capital Theory, and the Social Exchange Theory. The study extends the
Knowledge Based Theory of the Firm by improving our understanding of how
knowledge sharing impacts trust, collaboration, and virtual team effectiveness.
3.2. Keywords: Virtual team, Knowledge sharing, Trust, Collaboration, Team
effectiveness.
3.3. Introduction
The web and surrounding technologies are continuously and rapidly improving.
The web is providing a platform for individuals to communicate and collaborate, and it is
continuously shifting towards a more user centric experience (Lai and Turban, 2008). On
an organizational level, the Web offers organizations a means to address their business
needs and collaborate with each other (Zwass, 2003). Web 2.0 and the variety of tools it
provides (e.g. blogs, wikis, and social bookmarking) have led to the emergence of new
kinds of services such as social networks, aggregation services, and cloud based software
applications. These tools provide a means for organizations to establish teams based on
required expertise regardless of the physical location of individual team members.
Literature on inter-organizational systems highlights the role of information
technology in enabling organizations to form alliances and collaborate with each other to
deliver a new product or service. Nowadays, inter-organizational collaboration became
crucial for a rapid response in a market where new products require numerous
organizations working together to produce (Kumar and Dissel, 1996; Zwass, 2003). The
38


significance of establishing virtual teams is that physical boundaries and barriers vanish,
which enables a more efficient inter-organizational collaboration.
In the knowledge-based view of the firm (Nonaka 1994), organizations treat
knowledge as an asset and a key factor for improving both the organization and its
individuals. Organizations nowadays are considered knowledge-focused systems as they
are continuously realizing the significance of knowledge as a valuable asset which has
the potential to prosper them in their markets, maximize their economic value, and
improve their effectiveness (Gold et. al. 2001; Alavi and Leidner, 2001). There exists a
considerable body of research in Information Systems literature which has investigated
knowledge management systems (KMS). The objective of deploying KMSs in
organizations is to support and improve the different components of knowledge
management process which are knowledge creation, storage, transfer, and application
within and among different entities of the organization (Alavi and Leidner, 2001).
Organizations form teams to work on sophisticated organizational tasks. The
advantage of team structure is that it integrates the knowledge that is distributed among
team members, which facilitate achieving more effective problem solving (Lam, 2000).
Teams are organizational instrument which often considered being the solution for large,
complex, and non-routine tasks which if managed properly can lead to an increase in
organizational value (Alavi and Tiwana, 2002). Virtual teams are groups of individuals
who are distributed across different physical locations and required to communicate and
collaborate using information technology (Jarvenpaa, 1998; Powel et al. 2004). Virtual
teams provide a convenient solution for integrating knowledge that is distributed across
the organization or across different organizations. The steady increase of organizational
39


reliance on the practice of virtual teams is attributed to five main reasons according to
Townsend et al. (1998) which are the modern structure of organizations which tends to be
horizontal rather than vertical, the need for inter-organizational collaboration to produce
quality products and services, continuous globalization were organizations are spanning
across vast geographical locations, the interest of organizations in providing services not
manufacture products, and to employee preferences in which organizations address their
need for team members who hold specialized knowledge regardless of their physical
location, while employees can belong to an organization without the need to move to its
physical location. Nevertheless, virtual teams also offer unique challenges as they
encompass members with complex traits and characteristics including absence of prior
shared work history, different cultures and backgrounds, and the chance of working with
members outside the organization boundaries. These challenges, if not addressed and
managed properly by the organization, could jeopardize virtual teams effectiveness and
success.
The theme for establishing teams in organizations is to bring together members
with the necessary expertise and skills to collaborate on an organizational task (Hoegl
and Gemuenden, 2001). When the team is a virtual one, collaboration becomes more
complex since team members are separated through time and/or space which indicates
that online collaboration among virtual team members requires more effort and different
means for communication and coordination to be effective (Riegelsberger et. al. 2003;
Piccoli et al. 2004).
This study extends the literature on virtual teams through investigating the role of
knowledge sharing and trust in enabling collaboration in virtual team settings; and how
40


this will ultimately affect the team outcome by measuring its effectiveness. Individuals
consider knowledge to be a personal advantage and sharing it leads to loss of ownership
of this knowledge and consequently loss of power and potential replacement, which
makes them hoard their knowledge for themselves and be reluctant to share it
(Kankanhalli et. al. 2005). Nonetheless, sharing knowledge and exchanging ideas is
crucial for team collaboration. An effective virtual team, is one which its team members
put the success of the team ahead of their personal tendency to hoard knowledge for
themselves.
Several studies have reported the importance of trust as a factor to virtual teams
success and effectiveness (Jarvenpaa and Leidner, 1998; Zolin et al. 2004; Glen, 2002;
Steinfield, 2002; Henttonen and Blomqvist. 2005, Ulriksson and Ayani 2005). However,
in virtual team settings, building trust among team members is a complex task mainly
because of the absence of observed behaviors which members of traditional face-to-face
teams rely upon to establish and maintain trust. Therefore, virtual team members need to
rely on different behaviors to assess trustworthiness among each other in order for them
to compensate for the lack of physically observed behaviors. As a personal advantage and
a valuable personal asset, we argue that knowledge sharing could be considered a
significant behavior which virtual team members can observe and use to build trust.
The practice of virtual teams has provides organizations the ability to work across
physical and geographical boundaries. Yet, this structure brings with it some unique
challenges (Boudreau et. al. 1998). Knowledge exists in the individual and in the group
(Nonaka 1994); individuals create knowledge in the first place, organizations and teams
do not create knowledge by themselves. The purpose of a team is to create a social
41


knowledge through the interaction and collaboration of team members (Alavi and
Leidner 2001). Nevertheless, transforming individual knowledge into a social knowledge
is not an easy task. Even if the proper technology is in place, individuals tend to hoard
knowledge for different reasons; primarily they hoard their knowledge and selectively
release part of it in order to appear valuable to their organization (Gilmour, 2003; Bock
et.al. 2005). Therefore, knowledge sharing across the organization depends on
employees' willingness to share and contribute their knowledge through a form of a
knowledge management system (Bock et al. 2005). The reluctance of employees to share
knowledge has serious consequences which have the potential to hinder team
collaboration and could lead to a team that is unsuccessful in achieving its goals (Van den
Bosch etal. 1999).
The purpose of this research is to investigate the hypothesized relationship
between knowledge sharing, trust, collaboration, and team effectiveness among virtual
team members. We argue that knowledge as a valuable asset and higher trust among
virtual team members, will lead to better collaboration and a more effective virtual team.
This research is designed to answer the following research questions:
1. Does sharing knowledge influence trust in virtual teams?
2. Does sharing knowledge influence collaboration in virtual teams?
3. Does collaboration influence virtual team effectiveness?
4. Does trust influence the relationship between collaboration and team effectiveness
in virtual teams?
Answering these questions will allow researchers to better understand virtual
teams and the factors which contribute to their effectiveness. This study will also provide
42


guidance to organizations about how to design and mange virtual teams in a way which
encourages collaboration and enhances team effectiveness.
The reminder of this paper is structured as follows: Section 2 presents a literature
review which focuses on virtual teams, trust, knowledge sharing and management,
collaboration, and team effectiveness. Section 3 presents the theoretical model of the
research and hypotheses development. Section 4 presents the methodology we used to
conduct this research. Section 5 presents a discussion of the results. Section 6 discusses
the research limitation. Finally, the paper concludes with a summary and implications for
future research.
3.4. Literature Review
Virtual teams have been widely investigated by researchers over the past two
decades with trust and collaboration being identified as crucial factors for virtual team
success. The literature on knowledge sharing and management in the organization has
been growing tremendously in the past few years. Organizations are increasingly
realizing the importance of knowledge as an asset for competitive and sustainable
advantage. In this section, we present streams of research in the literature which
investigated knowledge sharing, trust, and collaboration in virtual team settings.
3.4.1. Virtual Teams
Following Hoegl and Gemuenden we define a team as a social system of three or
more people, which is embedded in an organization, whose members perceive themselves
as such and are perceived as members by others, and whose members collaborate on a
common task(Hoegl and Gemuenden, 2001).
43


Teams are one form of a social system that is embedded within an organization.
As a social system, there is a set of human relationships in which the act of one individual
affects all other individuals in the same social system. Organizations establish teams to
work collaboratively on a common task, in their definition of a team Katzenbach and
Smith (1993) argue that the characteristics of team members should include
complementary skills, they must be committed to achieve a common goal that is set by
the organization, and they need to hold themselves mutually accountable for the success
or failure of the team mission.
Organizations rely on teams to work on complex and non-routine tasks, and to
achieve an effective outcome, the underlying assumption is that team members are
expected to collaborate to achieve quality collective performance which exceeds an
individual team members performance (Gardner, 2012; Griffith et al. 2003). The
structure of teams has changed dramatically over the past two decades. Teams are
increasingly becoming virtual, in that they are often geographically dispersed and mainly
rely on using information technology to communicate and collaborate (Jarvenpaa and
Leidner 1999). Different terms and synonyms are cited in the literature to describe virtual
teams such as distributed teams (Gorton and Motwani, 1996; Mortensen and Hinds, 2001;
Hinds and Bailey, 2003) and Technology Mediated Teams (Henttonen and Blomqvist,
2005; Fuller et.al, 2006) with the majority using the term Virtual Teams. Following
Powel et al. we define virtual teams as A group of geographically, organizationally
and/or time dispersed workers brought together by information and communication
technologies to accomplish one or more organizational tasks Powel et al. (2004).
44


Traditionally, virtual teams used to be established to work on temporary projects
with a short life cycle; these teams were mainly established based on a need to quickly
gather necessary expertise to solve complex or non-routine problems. Squire and Johnson
(2000) argued that virtual teams are formed as need for them arises in the organization,
which means that as soon as virtual team members finish the required work task the team
is disassembled. These characteristics of early virtual teams made them more of task
oriented with limited opportunities to form social relationships (Jarvenpaa et al. 1998).
Recently, however, organizations are increasingly establishing virtual teams to work on
everyday routine and non-routine tasks. Furthermore, several organizations are allowing
their employees to work virtually from the physical location of their preference. Griffith
et al. (2003) describe this as the degree of team virtualness (i.e. some teams are
completely distributed while others are a combination of co-located and distributed
members).
Powel et al. (2004) identifies four major categories of virtual team research. They
include inputs (i.e. design, culture, technical, and training), socio-emotional process (i.e.
relationship building, cohesion, and trust), task process (i.e. communication,
coordination, and task-technology-structure fit), and outputs (i.e. performance and
satisfaction). Table 26 illustrates research in the literature which investigated one or more
of those research categories.
Moving from traditional to virtual teams practice has an impact on organizational
structure. While virtual teams offer unique benefits for organizations, they are not
without their challenges. Virtual team challenges mainly stem from the fact that members
are distributed among different physical locations; and their communication and
45


collaboration is mediated by technology. Reported challenges include time difficulties,
feedback delays, misinterpretation, cultural barriers, scheduling conflicts, lack of
communication, and delayed responses (Fussell et al. 1998; Jarvenpaa and Leidner, 1998;
Alavi and Tiwana 2002; Powell et al, 2004). These challenges, if not addressed properly
and managed, can threaten the success and effectiveness of virtual teams (Piccoli et al.
2004).
Table 26: Virtual Team Research Categories
Category Research Research Topic
Input Kristof et. al. 1995 Design
Snow et. al. 1996 Design
Gorton and Motwani, 1996 Design
Townsend et. al. 1998 Design, culture
Boudreau et. al. 1998 Design, technical
Squire and Johnson, 2000 Technical
Socio-Emotional Processes Handy, 1995 Trust
Meyersonet. al. 1996 Trust
Jarvenpaa et. al. 1998 Trust
Mortensen and Hinds, 2001 Cohesion
Bhattacheijee, 2002. Trust
Hinds and Bailey, 2003 Relationship building, Cohesion
Henttonen and Blomqvist, 2005 Trust
Chandra et. al. 2011 Trust
Koehne et. al. 2012 Relationship building, Cohesion
Task Processes DeSanctis, 1999 Communication processes
Alavi and Tiwana, 2002 Knowledge sharing
Steinfield et. al. 2002 Communication
Griffith et. al. 2003 Task-Technology- Structure fit
Chiu et. al. 2006 knowledge sharing
Kanawattanachai and Yoo, 2007 Coordination
Outputs Piccoli et. al. 2004 Effectiveness
Fuller, 2006 Efficacy
Kanawattanachai and Yoo, 2007 Performance
Heath et. al. 2011 Performance
46


Team effectiveness has to do with group-produced outputs and the reward system
the organization implements for team members (Piccoli et al. 2004). An effective virtual
team needs to achieve its objectives, perform as a cohesive group in which everyone is
accountable for the outcome, produce high quality output, and team members need to
have a sufficient level of satisfaction with the work outcome and with one another.
Accordingly, team effectiveness in virtual settings has two dimensions, performance and
satisfaction (Lurey and Raisinghani, 2001; Piccoli et al. 2004).
The principle for investigating virtual teams in Information Systems is to expand
our understanding of how technology mediated interaction affect virtual team
performance and effectiveness especially when compared to traditional face-to-face team
settings (Potter and Balthazard, 2002). Nonetheless, virtual team performance should not
be attributed solely to the technology which team members use for communication and
collaboration; a major influence on virtual team performance should be attributed to the
social interaction and social capital among team members (Potter and Balthazard, 2002).
According to social presence theory, communication is effective if the communication
medium has the appropriate social presence required for the level of interpersonal
involvement required for a task (Salinas et al. 2000). In this research, we extend the
literature by investigating how the factors of knowledge sharing and trust affect team
effectiveness through enabling collaboration among virtual team members.
3.4.2. Knowledge Sharing and Management
Knowledge is an organizational asset and a significant organizational resource
which has the potential to improve an organization competitive advantage (Nonaka,
1994; Alavi and Leidner, 2001). Nonaka and Takeuchi (1995) define knowledge as a
47


dynamic process ofjustifying personal belief towards the truth and Liebeskind (1996)
defines it as information whose validity has been established through tests of proof'.
Nonaka (1994) conceptualized two dimensions of knowledge in organizations: tacit and
explicit. The tacit dimension of knowledge is considered to be personal, complex,
difficult to explain or share, and comprised of both cognitive and technical elements
(Nonaka 1994). The cognitive element is considered to be an individual's mental models
which were developed through experience, test, and proof in the mind of an individual.
The technical element refers to knowledge which can be codified, shared, and
communicated from one individual to another (Nonaka, 1994; Alavi and Leidner, 2001).
The interaction between these two knowledge dimensions results in four modes of
knowledge conversion according to Nonaka and Takeuchi (1995) who introduced the
spiral of knowledge (Figure 2) to describe these dimensions. These modes are
socialization, extemalization, combination, and internalization. These four modes are
considered essential for knowledge creation and sharing in the organization.
To Tacit Knowledge To Explicit Knowledge
Figure 2: Spiral of knowledge Adapted from Nonaka and Takeuchi (1995)
48


Since knowledge is considered a valuable organizational asset, managing it,
enforcing adequate policies, and implementing Knowledge Management Systems (KMS)
become an organizational necessity. The process of knowledge management in the
organization aims to identifying knowledge throughout the organization, make it
available and accessible, and use it to improve the organization competitive advantage
(Davenport and Prusak, 1998; von Krogh, 1998; Alavi and Tiwana, 2002).
KMS is a category of information systems which organizations implement to
support the processes of knowledge creation, storage, transfer, and application throughout
the organization (Alavi and Leidner, 2001; Gallupe, 2001) describes three levels of
knowledge management technologies illustrated in Table 27 which are: KMS tools, KMS
generators, and Specific KMS. There exist a rich literature on user technology acceptance
(e.g. Davis, 1985) when it comes to using technology, and in the case of knowledge
sharing and management an important issue arises which is users willingness to share
their knowledge and seek others knowledge using a KMS (Bock et.al. 2005; Kankanhalli
et. al. 2005). While technological capabilities are important, implementing a KMS does
not guarantee a successful knowledge management process. For a KMS to be used and
utilized effectively, the organization needs to address both the social and technical
dimensions of KMSs usage (Kankanhalli et. al. 2005).
Table 27: Levels of Knowledge Management Systems.
Level Description Examples
Level 1 KMS Tools Programming languages, Database Management Systems
Level 2 KMS Generators Lotus Notes, emails
Level 3 Specific KMS OracleKM, SalesForce
49


3.4.2.I. Organizational Knowledge Management Processes
Following Alavi and Leidner (2001), an organizational knowledge management
process includes a set of four processes: knowledge creation, knowledge storage/
retrieval, knowledge transfer, and knowledge application. Organizational knowledge
creation includes developing new knowledge or replacing an existing one (Pentland
1995). Knowledge is normally created by an individual within the organization, but the
interchange and the spiral of knowledge as described by Nonaka (1994) (See Figure 2
section 3.4.2) integrates and transforms the knowledge to make it available throughout
the organization (Nonaka 1994; Alavi and Leidner, 2001).
Organizational memory refers to the information and coded knowledge which are
stored in a form of repository within the organization and accessible by its individual
(Walsh and Ungson, 1991). Knowledge could be stored in various forms within the
organization including written documentation, digital repositories, and as tacit knowledge
in the mind of the individuals (Alavi and Leidner 2001; Tan and Hung, 2006). However,
the main challenge in the organizational knowledge management process is making the
stored knowledge accessible by individuals throughout the organization. From a technical
perspective, KMSs are implemented to support this process. Nevertheless, technology
support does not guarantee a successful integration and transfer of knowledge within the
organization; unless the organization implement a comprehensive knowledge
management strategy which takes into consideration both the technical and the social
dimensions of knowledge, the effectiveness of the KMS will be difficult to estimate
(Alavi and Leidner, 1999; Kankanhalli et. al. 2005).
50


Transactive memory refers to the process of sharing and exchanging knowledge
within a group. In traditional team settings, transactive memory is developed through
interaction among team members and is considered to have an influence on team
performance and effectiveness (Alavi and Tiwana, 2002; Choi et al. 2010). However, in
virtual team setting transactive memory is more complex and takes longer time to
develop, mainly because team members are geographically distributed and interact
through a communication medium which has the potential to negatively impact team
effectiveness. In traditional team settings, research has found that knowledge sharing is
critical for team effectiveness (Powell et al. 2004). Considering the distributed nature of
organizational cognition, an important process of knowledge management in
organizational settings is the transfer of knowledge to locations where it is needed and
can be used. The distributed and fragmented nature of virtual teams leads to the
fragmentation of knowledge among different team members who reside in different
locations which make knowledge sharing and integration difficult to be accomplished
when compared to traditional team settings.
In summary, knowledge is a personal advantage for individuals and a valuable
asset for teams and organizations. Organizations have employed various techniques to
integrate and transfer knowledge; and a new model of knowledge management is
emerging in the organization which aims to motivate individuals to share their knowledge
(Al-Alawi et al. 2007). Handy (1995) describes virtual teams as a concept without a place
and a new organizational view to their employees to be human assets and not human
costs. The asset which Handy (1995) refers to here is the knowledge which virtual team
members possess and bring to the team. In this study, we argue that sharing knowledge in
51


virtual team setting has an effect on trust among team members as it compensate for
some of the observed behaviors which are missing in the virtual environment. Also, we
argue that sharing knowledge affects collaboration among virtual team members which
ultimately influences the team effectiveness.
3.4.3. Trust
Trust is a key factor in forming and maintaining social relationships and it is a key
for cooperative relationships and effective teamwork (Jarvenpaa et. al 1998; Jarvenpaa et
al. 2004; Zaheer et.al, 1998; Powell et al. 2004). In co-located organizational settings,
research has reported that trust has many benefits such as better productivity, facilitates
resolution of conflicts disagreements, and improves effectiveness (Earley, 1986; Hagen
and Choe, 1998; Zaheer et.al, 1998). High-trust teams tend to exchange ideas more
openly, have clearer goals, more motivated and satisfied, and less willing to leave the
team (Zand, 1972; Jarvenpaa et. al 1998).
There is no one agreed upon a definition of trust, but generally, trust includes
elements of risk, vulnerability, and uncertainty. Mayer et.al, (1995) defines trust as the
willingness of a party to be vulnerable to the actions of another party based on the
expectation that the other will perform a particular action important to the trustor ,
Baba (1999) defines trust to be the subjective expression of one actors expectations
regarding the behavior of another actor in a way that is safe and secure, and
McAllister (1995) defines trust among team members to be the extent to which a person
is confident in, and willing to act on the basis of the words, actions, and decisions of
another.
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Mayer et al. (1995) argue that the attributes of trust are ability (i.e. skills of trustee
which makes him capable of performing his task), benevolence (i.e. willingness to do
good), and integrity (i.e. dependability and reliablity). Numerous researchers have built
on Mayers (1995) conceptualization in investigating trust in virtual teams (e.g.
Jarvenpaa et al. 1998; Jarvenpaa and Leidner, 1999; Dirks and Ferrin 2001; McKnight et.
al. 2002; Gefen et. al. 2003). In this study, we also adopt the trust construct as
conceptualized in Mayer et al. (1995).
The literature on trust reported different types of trust in different contexts. Lewis
and Wiegert (1985) distinguished between cognitive and affective based trust, Lewicki
and Bunker (1995) argue that trust can be calculus based, knowledge based, or
identification based, and Meyerson et al. (1996) brought the concept of swift trust in
temporary teams. These types of trust are illustrated in Table 28.
Lewis and Wiegert (1985) argue two distinctive types of trust: the cognitive and
the affective. Individuals build cognitive trust based on evidence of observed behavior
rather than emotion and genuine caring which are considered to be the foundations for
affective based trust. In organizational settings, McAllister (1995) argues that cognitive
trust is built among individuals based on their performance, cultural or ethnical
similarities, and higher professional credentials, while affective trust is built based on
citizenship and interaction frequency. Lewicki and Bunker (1995) argue that trust is
dynamic and changing phenomenon which takes different shapes in different stages of a
relationship. Therefore, as individuals interact and observe each others behaviors
overtime, trust will become more evident (Panteli and Sockalingam, 2005).
53


Lewicki and Bunker (1995) suggest three types of trust, each corresponding to a
different stage of the relationship: Calculus-Based Trust (CBT), Knowledge-Based Trust
(KBT) and Identification- Based Trust (IBT). CBT is based on the concepts of reward
and punishment for maintaining or violating trust. KBT is similar to cognitive based trust
in which individuals build a trust decision based on observing behaviors throughout
interactions over time. IBT is considered a higher level of trust which takes longer time to
be developed. IBT requires a high degree of mutual understanding among individuals and
a more established trust to the point which one can act on behalf of the other (Rousseau,
1998; Lewicki and Wiethoff, 2000).
Table 28: Types of Trust in Previous Research
Research Type of Trust Definition
Lewis and Wiegert 1985 Cognition-based trust Individuals consciously choose those in whom they trust based on perceptions of evidence for their trustworthiness.
Affect-based trust Individuals demonstrate their genuine caring and concern for one another over time
Lewicki and Bunker (1995) Calculus-Based Trust Assessments of costs and rewards for violating or sustaining trust.
Knowledge-Based Trust Individuals have enough information and understanding about each other to predict behavior.
Identification- Based Trust Parties take time to develop their common interests, values, perceptions, motives and goals
Meyerson et. al. (1996) Conventional Trust Traditional trust which results from observing individuals behavior.
Swift Trust Trust developed in temporary short lived groups which presume clear roles and responsibilities.
54


The literature on virtual teams recognizes trust as a critical requirement for team
success and effectiveness (Jarvenpaa et. al 1998; Sarker et al. 2001; Kirkman et al. 2002;
Powell et al. 2004). Having trust in an organization and among members of a virtual team
is considered to be a key element of success and a necessity to overcome the obstacles
which team members face due to the absence of face-to-face interaction (Kristof et.al.
1995; Furst et al. 1999; Kirkman et al. 2002;).
As organizations are becoming more distributed, concerns about how to build
trust among team members are increasing. Kirkman et al. (2002) argue that building trust
is the biggest challenge for virtual team success. For instance, as organizations are
becoming more distributed, more team members find themselves to be working with
others whom they have never met and whose cultures and societies they know little
about, but with whom they must collaborate through technology to achieve a predefined
organizational goal (Jarvenpaa et al. 1998; Kirkman et al. 2002). This creates a
significant challenge for organizations on how to establish trust and collaborative
relationship among members of virtual teams.
Considering the characteristics of virtual teams, developing trust is not an easy
task or even similar to the development of trust in traditional teams. In practice, most
people judge an individual trustworthiness in terms of a persons observed behaviors. But
observed behaviors in virtual teams are different from those of traditional teams. Within
the traditional organizational literature, an important factor of trust is the degree of
familiarity with other people (i.e. the more we get to know others, the more likely it is
that we trust them) (Lewicki and Bunker, 1996). However, virtual team members do not
possess this characteristic, they are separated in space and/or time, and their interaction is
55


mediated by technology. Therefore, the observed behaviors and the signals which exist in
traditional team setting do not apply to virtual team settings, which means that virtual
team members lose key information which help build a trust decision. This loss of
information normally leads to increase uncertainty, and lower trust among virtual team
members (Riegelsberger et. al. 2003). Furthermore, it is reported in the literature that
information technology increases the potential for faulty first impressions (Ferreira et al.
2012). Therefore, we need to consider other factors, behaviors, and standards especially
designed for virtual teams in order for members to trust each other.
Meyerson et al. (1996) present the concept of swift trust in which they argue that
trust can be developed in temporary team settings in spite of the short life cycle and the
absence of trust determining behaviors found in regular teams. Instead, the swift trust
argument is built on establishing clear roles and responsibilities and assuming that a
sufficient level of trust already exists among team members (Jarvenpaa et al. 1998;
Panteli and Sockalingam, 2005). Nonetheless, swift trust was originally introduced to
describe trust in temporary traditional co-located teams. The early literature on virtual
teams reported that virtual team members exhibit trust characteristics which are most
similar to swift trust because virtual teams were mainly temporary teams as well
(Jarvenpaa et al. 1998). Therefore, swift trust applies to temporary virtual teams with a
short life cycle which makes it difficult to establish strong trust among members.
Nowadays, however, virtual teams are embedded into the organization structure and they
are not necessarily created work on temporary tasks but rather to work on everyday tasks.
This form of virtual teams in todays organizations should have the time, technology, and
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means to build a stronger trust beyond swift trust in order to sustain a long relationship
and produce effective outcome.
Kanawattanachai and Yoo, (2002) found that virtual team members rely more on
cognition-based trust than on affect-based trust in which they base their trust decision on
perceptions of evidence for their trustworthiness not on genuine caring and emotion. In
this research we investigate the relationship between trust and knowledge sharing and its
effect on collaboration and team effectiveness. We argue that sharing knowledge in
virtual team setting will influence trust mainly because virtual team members who share
and contribute their knowledge provide evidence for their trustworthiness which would
compensate for the lack of trust signals exist in traditional team settings.
3.4.4. Collaboration
An organization is a social system of individuals who are required to work
collectively and collaboratively on accomplishing a common goal (Alavi and Tiwana,
2002). Collaboration in work settings is defined by Aram and Morgan as the presence of
mutual influence between persons, open and direct communication and conflict
resolution, and support for innovation and experimentation (Aram and Morgan, 1976).
Collaboration is an essential part of team work and an effective collaboration
leads to an effective team outcome (Aram and Morgan, 1976). To collaborate effectively,
the knowledge that is distributed among team members must be properly and adequately
integrated (Gray, 2000). In virtual team setting, integrating knowledge to achieve an
effective collaboration is challenging as knowledge is distributed among physically
separated team members.
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Virtual team members are chosen and assigned based on the need for their unique
knowledge and expertise. However, recruiting expert members does not guarantee
effectiveness collaboration (Kudaravalli and Faraj, 2008). In order for virtual teams to
collaborate effectively, they need establish an open communication and efficiently
coordinate activities among each other (Hemetsberger and Reinhardt, 2009).
Coordinating activities in group work is already difficult for co-located teams and is even
more challenging for virtual teams (Fussell et al. 1998). When collaborating in traditional
face-to-face settings, conversations which take place in the shared physical space
facilitate coordinating activities (Kudaravalli and Faraj, 2008). However, in virtual team
settings where members are geographically distributed and communication is mediated
by technology, coordination becomes more complex. If virtual team members are not
properly coordinating their activities and are unaware of the work progress across the
team, the team is likely to face serious obstacles which impact collaboration.
Durate and Snyder (2006) discuss seven types of virtual teams, they argue that all
of them have in common that team members must collaborate to accomplish their work.
Virtual team members need to overcome the challenges they face by keeping open
communication channels, coordinate activities, and collaborate effectively. Otherwise,
they will not achieve effective outcomes. We argue that virtual teams can only thrive if
individual team members can overcome the challenges to virtual collaboration and
manage to work in a coordinated effort to solve problems together. This suggests the
need for an improved understanding of how to create virtual teams that work, share
knowledge, and collaborate effectively.
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The literature on Computer Supported Collaborative Work (CSCW) addresses
how collaborative activities can be supported by means of computer information systems
(Schmidt, 2011). CSCW investigates collaborative work in groups in both face-to-face
and virtual team settings and aims to provide a better understanding of the technology
and the social aspects group work. Johansen (1988) introduced the CSCW Matrix which
conceptualizes CSCW systems in terms of the context of system use. The matrix
considers work contexts along two dimensions, time and space. The matrix distinguishes
between the needs of different work groups whether they are co-located or geographically
distributed, and whether they collaborate synchronously or asynchronously. The CSCW
matrix is illustrated in Figure 3.
According to the CSCW Matrix, when team members are collaborating from
different distributed places and in different times they need CSCW systems that facilitate
communication and coordination activities. In traditional team settings, communication
and coordination are also reported as indicators for team collaboration (Aram and
Morgan, 1976; Rousseau et al. 2006).
Same Place
Co-located
Different Place -
Distributed
Same Time Synchronous Different Time-Asynchronous
Face-to-face Interactions Continuous Tasks
Remote Interactions Communication + Coordination
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Figure 3: CSCW Matrix Adapted From Johansen (1988)


Collaboration depends upon trust to enable channels of open communication
(Scott, 2000). Trust is reported to reduce task uncertainty, improves task coordination
process among team members, and lead to an effective collaboration (Kollock, 1994;
Holton, 2001). Weick and Roberts (1993) argue that to coordinate knowledge among
team members, they need to trust each others capabilities. In this research, we
investigate the how knowledge sharing and trust and influence effective collaboration in
virtual teams setting.
3.4.5. Team Effectiveness
Teams are fundamental component of the organizational structure, they enable
organizations reach better solutions and more effective outcomes (Gardner et al. 2012).
Organizations increasingly rely on virtual teams to meet the demands of a changing
marketplace (Luery and Raisinghani, 2001). Because of their unique characteristics,
virtual teams can be difficult to manage and could take longer time to reach an effective
outcome. In traditional team settings, Cohen and Bailey (1997) categorize team
effectiveness into three major dimensions: performance, member attitudes, and
behavioral outcomes. In virtual team settings, however, Lurey and Raisinghani (2001)
argue that the dimensions of virtual team effectiveness are team performance and team
members satisfaction which is consistent with the work of Mathieu et al (2008). Team
performance is measured by evaluating the team outcome and comparing it to the
requirements of the assigned task, while satisfaction represents team members approval
of work process, commitment to the team objectives, and chances of personal growth
(Lurey and Raisinghani, 2001). Virtual teams will not be effective if the team members
themselves are not satisfied with the way the team functions. Team members need to
60


have a sense of belonging to the team; this can only exist if they are satisfied with the
work experience.
In this study, we investigate how knowledge sharing, trust, and collaboration
influence virtual teams outcome by measuring perceived virtual team effectiveness. We
argue that a virtual team where knowledge is freely shared and trust is well established
will be a more effective team. We argue that knowledge sharing affect team effectiveness
through improving team collaboration while trust moderates the relationship between
collaboration and team effectiveness.
3.5. Research Model and Hypotheses Development
Even though information and communication technologies impact knowledge
sharing, trust, and collaboration, social factors also have the potential improve or
jeopardize virtual team work (Zakaria et al. 2004). Organizations are distributed
knowledge systems and the ability of the organization to identify knowledge resources,
leverage them, and make them available for its employees can lead to a distinctive
competitive advantage (Tsoukas, 1996; Davenport and Prusak, 1998; Alavi and Tiwana,
2002). In this section we present the research model, along with the research hypotheses,
which explain the relationships between knowledge sharing, trust, collaboration, and their
impact on virtual team effectiveness.
The theoretical research model is demonstrated in Figure 4. The model represents
a correlational research in which knowledge sharing is proposed to positively influence
trust, collaboration and team effectiveness. Furthermore, we argue that trust moderates
the relationship between collaboration and team effectiveness in which the higher levels
of trust; the higher is collaboration influence on team effectiveness.
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Figure 4: Theoretical Research Model
The Knowledge Based Theory of The Firm states that a firm is a knowledge
creating entity (Nonaka 1994; Nonaka et. al. 2000). Nonaka and Konno introduced the
concept of her which they define as a shared space which serves as a foundation for
hwwledge creation (Nonaka and Konno, 1998). An organization is considered to be a
ba according to Nonaka and Konno (1998), which means that an organization is a
shared space for individuals to create knowledge and improve together. Grant (1996)
emphasizes the role of the individual within the organization in creating knowledge and
argues that the role of the organization is to integrate, store, and apply the knowledge
created by its individuals. If effectively utilized and integrated, created knowledge could
be transformed into an organizational asset which has the potential to improve the
organization competitive advantage (Nonaka and Konno 1998; Nonaka et. al. 2000).
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Virtual teams are assembled of knowledgeable and skilled individuals who are
expected to perform an organizational task to the best of their abilities. Ability is one of
Mayers (1995) three attributes of trust (i.e. Ability, Benevolence, and Integrity); virtual
team members are then left with other two attributes they need to establish among each
other which are benevolence and integrity. The characteristics of virtual teams, especially
the technology mediated communication and lack of face-to-face interaction, cause
virtual team members to lose important observed behavior they need to evaluate each
others trust (Kanawattanachai and Yoo, 2002). With the lack of physical interaction
which takes place in collocated team settings, virtual team members need to demonstrate
different and unique behaviors to their team mates in order to prove their benevolence
and integrity. By sharing the knowledge they possess, we argue that virtual team
members demonstrate their willingness to do well and that they are dependable and
reliable.
Sharing knowledge in virtual team settings is indeed a controversial and a
complex issue. On one hand, knowledge is viewed by virtual team members as a valuable
personal asset and sharing it leads to the loss of their unique relative advantage to the
organization while it enables others to free-ride on their effort (Wasko and Faraj, 2005).
On the other hand, organizations continue to form and rely on virtual teams and team
members seem to share knowledge for variety of reasons, it is also reported that virtual
team members share their knowledge to appear valuable to their organization (Gilmour,
2003).
Trust among virtual team members is expected to affect team performance and
effectiveness as it enables an environment of open information exchange and assist team
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members overcome the physical barrier (Scott, 2000; Kanawattanachai and Yoo, 2002).
Since trust is a dynamic phenomenon which changes throughout time, and since virtual
teams are becoming part of the organizational structure and not necessarily temporary
anymore (Lewicki and Bunker, 1995; Kanawattanachai and Yoo, 2002; Griffith et al.
2003; Panteli and Sockalingam, 2005), virtual team members nowadays have sufficient
time to build social capital and make a sound trust decision. The challenge in virtual
environments is identifying unique and distinctive behaviors to assist team members in
making the trust decision.
The type of trust which develops in virtual setting is reported to be a cognitive
based trust (Kanawattanachai and Yoo, 2002), this is primarily for two reasons: 1)
information technologies are not successful in transferring feelings and emotions which
affect based trust depends upon; 2) trust decisions are often built based on team members
ability, integrity, and benevolence which in the absence of face-to-face interaction virtual
team members need to provide evidence for (Sproull and Kiesler 1986; Kanawattanachai
and Yoo, 2002; Mayer et al. 1995). Overall, virtual team members need to provide solid
evidence of their trustworthiness for other team members to trust them.
In the early phase of virtual team work, team members could quickly develop
affect based trust by assuming the good in each other. This is consistent with Mayerson et
al. concept of swift trust which Jarvenpaa and Leidner (1998) describe as fragile and
temporal. However, since trust is cognitively assessed in virtual teams and since trust
takes different shapes along time, we argue that in virtual team settings, the behavior of
sharing knowledge provides evidence of virtual team members trustworthiness which
leads to higher levels of trust among the team members.
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HI: In virtual team settings, knowledse sharins has a positive influence on
trust amons team members.
Teams perform better when they comprise members with the expertise relevant to
the task they are supposed to accomplish (Gardner, 2012). When virtual teams are able to
locate and access organizational knowledge, they perform better and they produce a more
effective outcome (Civi, 2000; Gardner et al. 2012).
Social Exchange Theory explains human behavior in social exchange (Blau
1964). The basic principle behind The Social Exchange Theory is that individuals within
a social system exchange favors with a general expectation of some future but unclear
return. Therefore, Social Exchange Theory assumes a long-term relationship where
individuals have enough time to exchange favors (Blau 1964; Molm et al. 2000). Fulk et
al. argues that knowledge sharing can be seen as a form of generalized social exchange,
where individuals share their knowledge without a clear expectation what the return
would be but on a promise of a long mutual relationship.
Resources (tangible and intangible) are considered to be the currency of social
exchange. Social Exchange Theory posits that people behave in ways that maximize their
benefits and minimize their costs (Molm et. al. 2000). The main cost of sharing
knowledge, especially in virtual tern setting, is the loss of a personal relative advantage
while the main benefit is effective collaboration and integration of diverse resources to
reach new insights. Furthermore, organizations are continuously implementing reward
systems to encourage team members to contribute and share knowledge and punish them
if they refuse to share the knowledge they possess (Bartol and Srivastava, 2002).
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To collaborate effectively, virtual teams require the knowledge that is distributed
among their team members to be adequately located and integrated. Otherwise, virtual
teams will suffer high costs associated with searching for the necessary knowledge to
perform their job (Gray, 2001). Based on the preceding discussion, we argue that sharing
knowledge in virtual team settings has a positive effect on collaboration among team
members.
H2: In virtual team settings, knowledge sharing has a positive influence on
collaboration.
Virtual teams characteristics influence the way in which team members work
together and have the potential to hinder team success and effectiveness. The ability of
virtual teams to achieve effective outcomes without face-to-face interaction is a
controversial matter in the literature since virtual teams tend to take longer time to reach
common ground and collaborate effectively (Holton, 2001; Potter and Balthazard, 2002;
Kirkman et al. 2004). On the other hand, it is reported that virtual team members tend to
express their opinions more freely and openly regardless of any social or managerial
constrains. Consequently, virtual team members are able to assess each other more
accurately based on performance and contribution; they also show less bias compared to
traditional teams when evaluating each others performance and contribution (Weisband
and Atwater, 1999).
Virtual team effectiveness has two dimensions: team performance and individual
satisfaction (Lurey and Raisinghani, 2001; Piccoli et al. 2004). An effective virtual team
is the one which delivers high task performance and sufficient members satisfaction in
terms of work experience, task load, and working with one another (Peters and Manz,
66


2007). To achieve these two effectiveness dimensions, with the absence of face-to-face
interaction, and solely relying on information technology medium for communication and
collaboration, virtual team members need to contribute more effort into collaboration by
exchanging more ideas, share more knowledge, and sufficiently coordinate tasks among
each other. Based on the proceeding discussion, we argue that an effective and successful
collaboration will ultimately influence the output of the virtual team in terms of team
effectiveness.
H3: In virtual teams, collaboration among team members has a positive
influence on team effectiveness.
A virtual team is a social system of individuals who are expected to collaborate on
a common organizational task; and the act of one team member affects all other team
members (Hoegl and Gemuenden, 2001). Social Capital Theory explains how changes in
relations among individuals in a social system facilitate action, coordination,
collaboration, and resource exchange (Colman, 1988, Adler and Kwon, 2002; Chiu et al,
2006).
Colman describes the social capital process in this paragraph If A does something
for B and trusts B to reciprocate in the future, this establishes an expectation in A and
obligation on the part of B. This obligation can be conceived as a credit slip held by A for
performance by B... Unless the placement of trust has been unwise and these are bad
debts that will not be repaid (Colman, 1988).
Based on Colman (1988) argument, we conclude that in order to build a social
capital in social systems, two components are necessary: action and trust among the
individuals in the system. For the action to take place a trust decision must be made; and
67


based on the act of the other parties trust is either confirmed or turn to be misplaced.
Trust is reported in the literature to be the foundation for effective collaboration (Mayer
et al. 1995; Rousseau et al. 1998; Paul and McDaniel 2004). In social systems such as
teams, trust is considered a key factor in reducing complexity and uncertainty and
enabling a positive atmosphere of collaboration among individuals within the system
(Kollock, 1994; Paul and McDaniel, 2004)
Theoretically, when a virtual team is newly established trust levels among virtual
team members is expected to be significantly low (Jarvenpaa et al. 1998). The reason is
that virtual team members normally lack a past history working together, communicate
via technology with little to no chance to meet in person, and unable to observe each
others behaviors therefore they would be unable to make a trust decision (Robert et. al.
2009). Consistent with Colmans (1988) description, we argue that when team members
trust each other they have expectation of certain behaviors and certain performance based
on this trust. Trust facilitates transactions by reducing the uncertainty and risk of
collaboration and team members with higher trust are more likely to work together
cooperatively (Baba, 1999; Jarvenpaa et al. 1998).
Team collaboration is the backbone that supports and drives team success and
effectiveness. In spite of the availability of advanced information and telecommunication
technologies, trust continues to influence collaboration among virtual team members
since the ability to collaborate depends heavily upon trust to facilitate sharing of
information and knowledge across the team (Koehne et al. 2012; Scott, 2000). Based on
the preceding argument, we argue that trust moderates the relationship between
collaboration and team effectiveness in virtual team setting.
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H4: (a) In high trust context, there will be a positive association between
collaboration and team effectiveness, (b) In low trust contexts, this association between
collaboration and team effectiveness will be significantly less strong.
3.6. Methodology
The research presented in this project can be described as quantitative, positivist
research. The survey method for data collection is used to test the proposed research
model. The unit of analysis is at the individual level and behavior level as virtual team
members perceptions of trust, collaboration, and team effectiveness in an open
knowledge sharing environment.
3.6.1. Sample
The theoretical population comprises of any and all virtual team members who
work in an organizational setting. In order to avoid sampling bias, we chose to focus on a
specific industry. Therefore, the study population includes virtual team members who
work in the information technology industry (e.g. software engineers, and developers).
To acquire a representative sample, the sample frame in this study was mainly acquired
from social media websites (e.g. Linkedin and Facebook) where we were able to identify
members who work in virtual teams in the IT industry. Also, a sample frame was
developed of individual known to be virtual team members. This sample frame was
developed by an effort of the investigator and the list included members who fit the
description of the study population.
Purposive sampling was used to target individuals who work in virtual settings.
The data was collected in two phases. In the first phase, we identified individuals, pages,
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and groups which include members who best represent the population. In the second
phase, emails were sent to group admins, directly to group members, and directly to
virtual team members in the sample frame developed by the investigator seeking their
response to the survey. In the second phase, a brief description of the research was given
along with a link to access the survey online. A total of 193 subjects were recruited for
participation in this study. Given the method the sample was gathered, response rate
could not be estimated.
3.6.2. Measures
The survey measures are derived from previously published studies in the
literature. The variables of interest are knowledge sharing, trust, collaboration, and team
effectiveness. The measure for knowledge sharing is adopted from the work by Phang et
al. (2009). Additional survey items are adopted from a survey used in practice by MITRE
Corporation (www.mitre.org) to obtain a baseline of knowledge sharing behaviors and
enablers. These items are evaluated for content validity by the researchers advisor,
expert judges, professional virtual team members, and researchers in the field.
The measure for trust is derived from Mayer et al. (1995) which is considered the
most widely cited researches on trust in the organization with over 7000 citations, and the
measure they developed is widely used and accepted in the literature (e.g. Jarvenpaa and
Leidner 1998, Jarvenpaa et al. 1999, Dirks and Ferrin 2001, and Bhattacherjee 2002).
The measure for collaboration is derived from Aram and Morgan (1976).
Although this measure was developed to measure collaboration in traditional team
settings, it has been adopted in studies which investigate online collaboration (e.g. Zhu et
al. 2010; Chandra et al. 2011; and Heath et al. 2011).
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The measure for team effectiveness is derived from Lurey and Raisinghani (2001)
which measures virtual team effectiveness in terms of team performance and team
members satisfaction. The formal construct definitions and sources are given in Table 29
below and the actual items used in the survey are given in APPENDIX II.
Figure 5: Measurement Model
Table 29: Definitions of the Study Constructs
Construct Definition
Knowledge Sharing The degree to which knowledge is shared (contribute/seek) among virtual team members
Trust The belief in the good intent and the ability of other virtual team members.
Collaboration The degree to which team members work together to accomplish an organizational task
Team Effectiveness Group-produced outputs and the consequences a group has for its members
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3.7. Data Analysis
Data analysis includes demographics and descriptive analysis. The model is then
tested for reliability using Cronbachs alpha. The validity of the model is assessed by
evaluating content validity, convergent validity, and discriminant validity. The partial
least squares (PLS) method is used to examine the hypotheses, as it is recommended for
complex models focusing on prediction, and allows for minimal demands on
measurement scales, sample size, and residual distribution (Chin et al., 2003). Finally the
Sobel test of mediation is used along with control variable analysis and multi-group
analysis.
3.7.1. Demographics and Descriptive Statistics
Respondents were asked to indicate their gender and age. Respondents were also
asked to indicate how long they worked in virtual teams, how long they have been
members of the same team, if they participated in pure virtual environment or in both
virtual and face-to-face environments, if they have been members of global virtual teams,
if they ever been virtual team leader, and if they work for the same organization (Table
31).
3.7.2. Reliability
Reliability of the measurement model is assessed by examining internal
consistency and indicator reliability. Internal consistency measures the reliability of a set
of indicators, represented by Cronbachs alpha. Indicator reliability is defined as the
proportion of the indicator variance explained by the corresponding latent variable, and is
represented by indicator loading, described as follows: fair (.45 .54), good (.55 .62),
72


very good (.63 .70), and excellent (.71 and higher) (Comrey, 1973). Cronbachs alpha
values for the constructs in the model are illustrated in Table 30 and all constructs show
high and adequate alpha values.
3.7.3. Validity
Validity of the measurement model is assessed by examining content validity,
internal consistency and discriminant validity. Content validity is an assessment of the
degree of correspondence between the items selected to constitute a summated scale and
its conceptual definition (Hair et. al. 2005). Content validity is ensured by utilizing
measurement items validated in existing research (Section 4.2).
The psychometric properties of the research model were evaluated by examining
item loadings, internal consistency, and discriminant validity. Researchers suggest that
item loadings and internal consistencies greater than .70 are considered acceptable (Hair
et. al. 2005). As can be seen by the shaded cells in Table 33, all item loadings surpass
this threshold. Internal consistency is evaluated by a constructs composite reliability
score. The composite reliability scores are located in the leftmost column of Table 32
which shows adequate reliability scores for all constructs.
Table 30: Cronbachs alpha
Construct Cronbachs alpha
Knowledge Sharing 0.93
Trust 0.83
Collaboration 0.91
Team Effectiveness 0.92
Communication 0.90
Coordination 0.91
Performance 0.93
Satisfaction 0.92
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Table 31: Demographics and Descriptive Statistics
Category Participants Mean Standard Deviation Sample Variance
Gender Male 77% 1.23 0.42 0.18
Female 23%
Age 1. 18-20 0 2.81 0.88 0.88
2. 21 -29 43%
3. 30-39 40%
4. 41 -50 12%
5. 51 -60 5%
Experience 1. less than a year 12% 2.27 0.74 0.77
2. 1 5 54%
3. 6 -10 27%
4. 10 -15 6%
Member of Current Team 1. less than a year 28% 2.27 0.74 0.55
2. 1 5 54%
3. 6 -10 27%
4. 10 -15 6%
5. more than 15 0%
Virtual Team Type 1. Online only 6% 1.85 0.65 0.42
2. Combined online and face-to-face members 34%
3. Both Types 59%
4. Face-to-face only 0%
Leader VS. Member 1. Leader 42% 0.42 0.49 0.24
2. Member 58%
Global Virtual Team Member 1. Yes 60% 0.42 0.49 0.24
2. No 40%
Members work for the same organization 1. Yes 82% 0.60 0.49 0.24
2. No 18%
Mandatory Vs. Voluntarily 1. Yes 76% 0.82 0.39 0.15
2. No 24%
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Evaluating discriminant validity has two parts; firstly, each item should load
higher on its respective construct than on the other constructs in the model, and secondly,
the Average Variance Extracted (AVE) for each construct should be higher than the inter-
construct correlations (Agarwal and Karahanna, 2000). In Table 33, we can see that all
items load higher on their respective construct than the other constructs in the research
model. Likewise, in Table 32, we can see that the square root of the AVE in the diagonal
for each construct is higher than the inter-construct correlations on the same row and the
same column. These two comparisons suggest that the model has good discriminant
validity.
3.7.4. PLS Analysis
Partial Least Squares (PLS) method is used to examine the hypotheses; a two-
stage analysis has been performed using confirmatory factor analysis to assess the
measurement model followed by examination of the structural relationships. PLS is an
extension of the multiple linear regression model; it is a linear model specifies the
relationship between a dependent variable (Y) and a set of predictor variables (X's).
Table 32: Convergent And Discriminant Validities
CR AVE Knowledge Sharing
Knowledge Sharing 0.95 0.84 0.92
Trust 0.85 0.88 0.65
Collaboration 0.95 0.71 0.70
Effectiveness 0.96 0.75 0.64
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Table 33: Cross Loading
Collaboration Effectiveness Knowledge Sharing Trust
COM1 0.91 0.56 0.61 0.55
COM2 0.80 0.49 0.59 0.52
COM3 0.79 0.57 0.63 0.48
COM4 0.78 0.54 0.67 0.48
COM5 0.73 0.46 0.57 0.48
COR1 0.88 0.68 0.57 0.42
COR2 0.85 0.64 0.57 0.35
COR3 0.86 0.63 0.56 0.35
COR4 0.72 0.55 0.49 0.43
COR5 0.70 0.56 0.45 0.43
PERI 0.64 0.90 0.59 0.29
PER2 0.62 0.87 0.59 0.27
PER3 0.61 0.88 0.53 0.30
PER4 0.60 0.82 0.50 0.29
SAT1 0.65 0.88 0.65 0.36
SAT2 0.57 0.70 0.49 0.36
SAT3 0.67 0.83 0.55 0.28
SAT4 0.64 0.84 0.58 0.28
SAT5 0.65 0.86 0.59 0.34
KN1 0.60 0.48 0.86 0.49
KN2 0.61 0.50 0.86 0.54
KN3 0.65 0.64 0.90 0.52
KN4 0.64 0.64 0.88 0.48
KN5 0.67 0.64 0.87 0.43
KN6 0.62 0.60 0.88 0.41
TR1 0.46 0.29 0.42 0.80
TR2 0.43 0.26 0.47 0.82
TR3 0.59 0.49 0.48 0.83
TR4 0.32 0.12 0.42 0.82
The PLS method allows for simultaneous analysis of the measurement and
structural models, and allows each indicator to vary in how much it contributes to the
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composite score of the latent variable (Chin et al., 2003). PLS also allows for latent
variable modeling of interaction effects, necessary for the proposed model as it includes a
moderating variable.
The results of the PLS SEM analysis are presented in Figure 6. Knowledge
Sharing has an R-Squared value of 0 because it is not being predicted by any other
construct. Trust has an R-Squared value of 0.31, collaboration has an R-Squared value of
0.489, and team effectiveness has an R-Squared value of 0.50. This means that 31% of
the variance in Trust and 49% of the variance in Collaboration is explained by
Knowledge Sharing, and 50% of the variance in Team Effectiveness is explained by
Collaboration (Agarwal and Karahanna, 2000). The path coefficients between
knowledge sharing, trust, collaboration, and team effectiveness were significant at .001.
However, the path coefficient for trust moderating the relationship between collaboration
and team effectiveness is insignificant. In summary, three out of the four hypotheses were
supported as illustrated in table 34.
Figure 6: PLS SEM Results
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Table 34 : Summary of Hypotheses Tests
Hypothesis Supported
HI: Knowledge Sharing Trust Yes
H2: Knowledge Sharing -> Collaboration Yes
H3: Collaboration -> Team Effectiveness Yes
H4: Trust x Collaboration -> Team Effectiveness No
3.7.5. Mediation Analysis
Sobel Test for the Significance of Mediation is used to test for the significance of
collaboration mediating the relationship between knowledge sharing and team
effectiveness. The Sobel test is a specialized t test that provides a method to determine
whether the reduction in the effect of the independent variable, after including the
mediator in the model, is a significant reduction and therefore whether the mediation
effect is statistically significant. Table 35 represents the results of the Sobel test. As
shown in the table the Sobel test for mediation is significant at the 0.01 level, which
indicates that collaboration mediates the relationship between knowledge sharing and
team effectiveness.
Table 35: Sobel Test for the Significance of Mediation
Path Coefficient Standard Error
Knowledge Sharing Collaboration 0.722 0.0536
Collaboration -> Team Effectiveness 0.685 0.2231
Sobel Test Statistics 2.99358902
One-tailed probability 0.00137859
Two-tailed probability 0.00275717
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3.7.6. Control Variable Analysis
In this study we controlled for the effect of the design process on the dependent
variables (i.e. trust and team effectiveness). Conducting PLS analysis on the model after
adding the control variable resulted in minor changes in the R-Squared values for trust
and team effectiveness as shown in table 36. Also as shown in figure 7, the path
coefficient between the control variable and both trust and team effectiveness are not
strong.
000 0.489 0.593
Figure 8: Control Variable Analysis
Table 36: Control Variable Effect
Construct R-Squared: No Control R-Squared: With Control Change Sig.
Trust 0.31 0.314 <0.1 -
Team Effectiveness 0.50 0.593 <0.1 **
3.7.7. Multi-group analysis
Multigroup analysis is conducted to determine if there is any significant effect for
specific individual team characteristics on the knowledge sharing behavior. These
characteristics are how long the respondents has been working as a virtual team member,
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how long the respondents has been member of his current team, if the respondent has
been a virtual team leader, if the respondent ever participated in a global virtual team, if
team members met in person, and if sharing knowledge is voluntarily or mandatory.
For each variable, the data set was separated based on the different values of the
variable. PLS analysis was calculated for both sets, and the results were tested for
significance for both trust and collaboration. The trust results are summarized in Table 37
and the collaboration results are summarized in Table 38. The only variable which shows
a significant difference between the two groups is being a member of a global virtual
team which has a 0.05 significant level for trust. This result provides an indication that
virtual team members cultural and background diversity does affect the level of
knowledge shared between team members and the levels of trust among them.
3.8.Discussion
The objective of this study is to investigate the relationship between knowledge
sharing, trust, and collaboration and how this relationship ultimately affects the
effectiveness of virtual teams. Our final results provide support for the theoretical model
and qualified support for most of our hypothesized relationships. The results show that
knowledge sharing has a significant influence on both trust and collaboration in virtual
team settings. This provides support for our hypotheses HI and H2. The results indicate
that knowledge sharing in virtual settings could be a crucial factor in establishing a social
capital among virtual team members. Consistent with the Knowledge-Based Theory of
the Firm and Nonakas concept of ba (1995), a virtual team could be considered a place
where members share their knowledge and transform their environment to reach new
insights. Furthermore, in this study we extend the literature on virtual teams to claim that
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sharing knowledge is crucial for virtual team members to collaborate, trust each other,
and be effective.
Table 37: Multi-group moderating effect (Trust)
Variable Group 1 Group 2 Significance
<= 5 Years > 5 Years
Experience Sample Size 124 69 Not Significant
Regression Weight 0.69 0.554
Standard Error (S.E.) 0.0595 0.1054
t-statistic 1.220
p-value (2-tailed) 0.224
Member of the current virtual team <= 5 Years > 5 Years Not Significant
Sample Size 159 34
Regression Weight 0.634 0.726
Standard Error (S.E.) 0.0617 0.0534
t-statistic 0.594
p-value (2-tailed) 0.554
Leader Vs. Member Yes NO Not Significant
Sample Size 82 111
Regression Weight 0.655 0.647
Standard Error (S.E.) 0.084 0.0626
t-statistic 0.078
p-value (2-tailed) 0.938
Global virtual team Yes No Significant at 0.05
Sample Size 113 80
Regression Weight 0.753 0.546
Standard Error (S.E.) 0.0654 0.0588
t-statistic 2.238
p-value (2-tailed) 0.027
Mandatory or voluntarily Mandatory Voluntarily Not Significant
Sample Size 52 141
Regression Weight 0.747 0.591
Standard Error (S.E.) 0.0472 0.0747
t-statistic 1.162
p-value (2-tailed) 0.247
Meet in Person Yes No Not Significant
Sample Size 45 148
Regression Weight 0.724 0.627
Standard Error (S.E.) 0.0621 0.0556
t-statistic 0.848
p-value (2-tailed) 0.398
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Table 38 : Multi-group moderating effect (Collaboration)
Variable Group 1 Group 2 Significance
<= 5 Years > 5 Years
Experience Sample Size 124 69 Not Significant
Regression Weight 0.698 0.74
Standard Error (S.E.) 0.0556 0.825
t-statistic 0.071
p-value (2-tailed) 0.944
Member of the same virtual team <= 5 Years > 5 Years Not Significant
Sample Size 159 34
Regression Weight 0.69 0.792
Standard Error (S.E.) 0.0649 0.0437
t-statistic 0.628
p-value (2-tailed) 0.531
Leader Vs. Member Yes NO Not Significant
Sample Size 82 111
Regression Weight 0.719 0.703
Standard Error (S.E.) 0.0695 0.0662
t-statistic 0.164
p-value (2-tailed) 0.870
Global virtual team Yes No Not Significant
Sample Size 113 80
Regression Weight 0.655 0.776
Standard Error (S.E.) 0.0902 0.0383
t-statistic 2.238
p-value (2-tailed) 0.027
Mandatory or voluntarily Mandatory Voluntarily Not Significant
Sample Size 52 141
Regression Weight 0.747 0.591
Standard Error (S.E.) 0.0472 0.0747
t-statistic 1.162
p-value (2-tailed) 0.247
Meet in Person Yes No Not Significant
Sample Size 45 148
Regression Weight 0.848 0.67
Standard Error (S.E.) 0.0308 0.075
t-statistic 1.191
p-value (2-tailed) 0.235
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The results show that the relationship between collaboration and team
effectiveness is significant which confirms hypothesis H3. The Sobel test of mediation
also indicates that collaboration mediates the relationship between knowledge sharing
and team effectiveness. This provides additional support for the importance of knowledge
sharing in virtual team settings.
Hypothesis 4 was not supported; in this hypothesis we argue a moderating effect
for trust on the relationship between collaboration and team effectiveness. However,
there was no significant support that such an impact exists. Aubert and Kelsey (2003)
investigated the influence of trust on virtual team performance and found that the level of
trust among virtual team members does not have a significant impact on team
performance. This is consistent with the results of our study and suggests that the impact
of trust may have a limited influence on virtual team effectiveness. Jarvenpaa et al.
(2004) argue that trust effects are sensitive to the context of the virtual team, which might
suggest that our results could be limited to the population of the study. Nevertheless,
further research is needed to further investigate the impact of trust on virtual teams
collaboration and effectiveness.
The multi-group moderation effect results were negative for all items except for
global virtual team members perception of trust. This result may indicate that trust is
higher among virtual team members who have similar characteristics (i.e. culture,
language).
The results of this research have interesting implications for both research and
practice. For research, this research provides implications for the importance of
knowledge sharing in virtual team setting. Furthermore, this research suggests that more
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research is necessary to better understand the influence of trust on virtual team outcome.
For practice, this research highlights the role which knowledge sharing plays in virtual
teams. This suggests that organizations should support their virtual team members
knowledge sharing by providing them with the tools to do so.
3.9. Limitations
A limitation of this study is that the research investigates knowledge sharing
independent from the technology. KMSs vary from a simple blog or discussion board to a
more sophisticated software application especially designed for organization knowledge
needs. It is reported in the literature that technology could influence the quality and
quantity of knowledge shared. This research is concerned with the social aspect of
sharing knowledge in virtual teams independent from the knowledge management
system (KMS) technology. Therefore, the model should be applied with care to contexts
which use different KMS technology than the one in this study sample context.
A second limitation is that we relied on purposive sampling. The study targeted a
specific sample of virtual team members in a specific industry. Future studies need to
examine other dimensions of the theoretical population of the study.
A third limitation is that we investigate team members in organizational settings.
There exist a literature that investigates knowledge sharing, trust, and collaboration in
online communities of practice outside the organization. Future studies could test the
proposed model in online communities of practice environment beyond the
organizational setting.
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3.10. Conclusion
This research proposes a conceptual model which represents the hypothesized
relationship between knowledge sharing, trust, collaboration, and team effectiveness in
virtual team settings. The model is developed based on an intensive review of the
literature on knowledge management and sharing, virtual teams, trust, and collaboration.
The theoretical foundation for the model is found in the Knowledge Based Theory of The
Firm, Social Capital Theory, and the Social Exchange Theory.
The model is tested using a survey research design developed based on measures
from previous research. The results of this research support three hypotheses which
explain the relationship between knowledge sharing, trust, and collaboration. For
research, the results of our research imply the need of further research to investigate how
different factors affect virtual teams effectiveness. For practice, the results of this
research calls for better understanding for the role of knowledge sharing in virtual teams.
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4. Conclusion
The work presented in this dissertation consists of two parts. The first investigates
online collaboration in virtual teams and the second investigates the impact of knowledge
sharing on trust, collaboration, and team effectiveness in virtual team settings. The
theoretical foundation which supports the first study is found in the Socio-Technical
Theory and the Theory of Reasoned Action. The theoretical foundation which supports
the second study is found in the Social Capital Theory, Social Exchange Theory, and The
Knowledge Based Theory of The Firm.
The result of the first part is a theoretical model and a measurement scale for
intention to collaborate online. The model and measurement scale were tested and
validated through a pilot study and in a field study. However, additional research is
necessary to further validate the measurement scale and evaluate its generalizability
across different virtual team environments.
The first study provides important implications for both research and practice. For
research, this study calls for a better understanding of the social aspects surrounding
virtual team members collaboration. For practice, this study provides noteworthy
implications regarding the importance of social characteristics and social relationships
among virtual team members in fostering an environment of collaboration within the
team in the organization.
The results of the second part is a conceptual model which describes the
hypothesized relationship between knowledge sharing, trust, collaboration, and team
effectiveness in virtual team setting. The model is tested using a survey research design
developed based on measures from previous research.
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The results of this research support three hypotheses which explain the
relationship between knowledge sharing, trust, and collaboration. For research, the results
of this research imply the need of further research to investigate how different factors
affect virtual teams effectiveness especially trust among virtual team members. For
practice, the results of this research calls for better understanding for the role of
knowledge sharing in virtual team effectiveness.
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Full Text

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KNOWLEDGE SH ARING IN VIRTUAL TEAMS : THE IMPACT ON TRUST, COLLABORATION AND TEAM EFFECTIVENESS by Mohammad K. Alsharo B.S. Al Albayt University, 2005 M.S. Jordan University of Science and Technol o gy, 2008 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Computer Science and Information Systems 2013

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ii This thesis for the Doctor of Philosophy degree by Mohammad K. Alsharo has been approved for the Computer Science and Information Systems Program by Jud y Scott Chair Dawn Gregg Advisor Ronald Ramirez Ilkyeun Ra Date: 4/17/2013

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iii Alsharo, Mohammad K. (Ph.D., Computer Science and Information Systems) Knowledge Sharing in Virt ual Teams: The Impact on Trust, Collaboration and Team Effectiveness Thesis directed by Associate Professor Dawn Gregg ABSTRACT The practice of virtual teams has provided organizations with a convenient solution for gathering experts to collaborate onlin e in order to accomplish organizational tasks. However, the dynamics and characteristics of virtual teams create challenges to effective collaboration. Collaboration is an important element in teamwork, team members need to collaborate to achieve the goal for which the team is established. Literature on virtual teams has been growing for over a decade with research investigated different aspects of virtual work. Trust among virtual team members has been investigated by information systems researchers as a c rucial challenge for virtual teams. Knowledge sharing and management in virtual teams have been the focus of many research recently as they represent a challenge for virtual work environment; because the knowledge is scattered among geographically distribu ted team members with limited face to face interaction. Yet, trust and knowledge sharing are not the final outcome of teamwork, there is a gap in the literature on how trust and knowledge sharing affect collaboration and ultimately team effectiveness in vi rtual settings. This study extends the literature on virtual teams through investigating the relationship between knowledge sharing, trust, and collaboration among team members in virtual team settings; and how these constructs ultimately affect virtual te am effectiveness. We argue that the characteristics and structure of virtual teams requires a distinctive understanding on how

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iv to make their members collaborate in a comparable and effective way to collocated teams. We argue that knowledge as a valuable as set and a personal advantage of a virtual team member could be a key for successful virtual team collaboration. This research introduces a conceptual model which describes the hypothesized relationship between knowledge sharing, trust, collaboration, and team effectiveness in virtual team settings. The model is developed based on an intensive review of the literature on virtual teams, knowledge sharing and management, trust, collaboration, and team effectiveness in traditional and virtual settings. The the oretical foundation for the model is found in the Knowledge Based Theory of The Firm, Social Capital Theory, and the Social Exchange Theory. The survey research method will be used to test the proposed model. The form and content of this abstract are app roved. I recommend its publication. Approved: Dawn Gregg

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v DEDICATION I dedicate this work to my parents for all the sacrifices they made to raise me and provide me with the means to be a successful person. I also dedicate this work to the love of m y life, my wife Enas for her love, support, and patience throughout this journey. Finally, I dedicate this work to my sons Eyad and Hazem. For all the time I could not spend with you because I was working on this research.

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vi ACKNOWLEDGMENTS I would like to thank my advisor, Pro. Dawn Gregg for all the support and effort she put into this research over the past few years. I also would like to thank my committee members for their feedback and insights. Especially I would like to acknowledge the feedback and insight of Professor Ronald Ramirez which had a major impact on the quality of this research.

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vii Table of Contents 1. Introduction ................................ ................................ ................................ ..................... 1 1.2. Importance of Topic ................................ ................................ ................................ ..... 1 1.3. Research Problem and Scope ................................ ................................ ....................... 2 1.4. Research Questions ................................ ................................ ................................ ...... 3 1.5. Research Contribution ................................ ................................ ................................ 4 1.6. Outline of Dissertation ................................ ................................ ................................ 4 2. Intention to Collaborate: Inve stigating Online Collaboration in Virtual Teams. ............ 5 2.1. Abstract ................................ ................................ ................................ ........................ 5 2.2. Keywords ................................ ................................ ................................ ..................... 5 2.3. Introduction ................................ ................................ ................................ .................. 6 2.4. Theoretical Foundation ................................ ................................ ................................ 7 2.4.1. Virtual Teams ................................ ................................ ................................ ............ 7 2.4.2. Online Collaboration ................................ ................................ ................................ 9 2.5. Measurement Scale Development Process ................................ ................................ 11 2.5.1. Stage 1: Literatu re Investigation and Construct Identification ............................... 11 2.5.2. Stage 2: Item Creation ................................ ................................ ............................ 19 2.5.3. Stage 3: Scale Development ................................ ................................ ................... 20 2.5.4. Stage 4: Instrument Testing Field Study ................................ .............................. 25

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viii 2.6. Limitations and Future Work ................................ ................................ ..................... 34 2.7. Implications to Research and Practice ................................ ................................ ....... 35 2.8. Conclusion ................................ ................................ ................................ ................. 36 3. Knowledge Sharing in Virtual Teams: The Impac t on Trust, Collaboration, and Team Effectiveness. ................................ ................................ ................................ .................... 37 3.1. Abstract ................................ ................................ ................................ ...................... 37 3.2. Keywords ................................ ................................ ................................ ................... 38 3.3. Introduction ................................ ................................ ................................ ................ 38 3.4. Literature Review ................................ ................................ ................................ ...... 43 3.4.1. Virtual Teams ................................ ................................ ................................ .......... 43 3.4.2. Knowledge Sharing and Management ................................ ................................ .... 47 3.4.2.1. Organizational Knowledge Management Processes ................................ ............ 50 3.4.3. Trust ................................ ................................ ................................ ........................ 52 3.4.4. Collaboration ................................ ................................ ................................ ........... 57 3.4.5. Team Effectiveness ................................ ................................ ................................ 60 3.5. Research Model and Hypotheses Development ................................ ........................ 61 3.6. Methodology ................................ ................................ ................................ .............. 69 3.6.1. Sample ................................ ................................ ................................ ..................... 69 3.6.2. Measures ................................ ................................ ................................ ................. 70 3.7. Data Analysis ................................ ................................ ................................ ............. 72 3.7.1. Demographics and Descriptive Statistics ................................ ................................ 72 3.7.2. Reliability ................................ ................................ ................................ ................ 72 3.7.3. Validity ................................ ................................ ................................ ................... 7 3 3.7.4. PLS Analysis ................................ ................................ ................................ ........... 75 3.7.5. Mediation Analysis ................................ ................................ ................................ 78 3.7.6. Control Variable Analysis ................................ ................................ ....................... 79

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ix 3.7. 7. Multi group analysis ................................ ................................ ............................... 79 3.8. Discussion ................................ ................................ ................................ .................. 80 3.9. Limitations ................................ ................................ ................................ ................. 84 3.10. Conclusion ................................ ................................ ................................ ............... 85 4. Conclusion ................................ ................................ ................................ .................... 86 References ................................ ................................ ................................ ......................... 88 Appendix 1 : Linkedin Groups ................................ ................................ ................................ ......... 106 2: Survey ................................ ................................ ................................ ......................... 107

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x LIST OF TABLES Table 1: Online Collaboration Dimensions ................................ ................................ ................ 13 2: Mapping Online Collaboration Dimensions ................................ ................................ 14 3: Intention to Collaborate Online Scale Items ................................ ................................ 21 4: Measurement Items after Pilot Study ................................ ................................ ............ 24 5: Number of items after Pilot Study ................................ ................................ ................ 25 6: Reliability Sta tistics Perceived Incentives ................................ ................................ 26 7: Correlation Matrix Perceived Incentives ................................ ................................ .... 26 8: Reliability Statistics Repeat Perceived I ncentives ................................ ..................... 26 9: Correlation Matrix Repeat Perceived Incentives ................................ ........................ 27 10: Reliability Statistics Perceived Voluntariness ................................ ......................... 27 11: Correlation Matrix Perceived Voluntariness ................................ ............................ 27 12: Reliability Statistics Repeat Perceived Voluntariness ................................ .............. 27 13: Correlation Matrix Repeat Perceived Voluntariness ................................ ................ 28 14: Reliability Statistics Perceived Common Ground ................................ .................... 28 15: Correlation Matrix Perceived Common Ground ................................ ...................... 28 16: Reliability Statistics Repeat Perceived Common Ground ................................ ....... 28 17: Correlation Matrix Repeat Perceived Common Ground ................................ .......... 29 18: Reliability Statistics ................................ ................ 29 19 : Correlation Matrix ................................ ................. 29 20: Reliability Statistics ................................ ................ 29 2 1: Correlation Matrix Repeat ................................ ..... 30 22: Items Correlation ................................ ................................ ................................ ........ 31

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xi 23: Rotated Component Matrix ................................ ................................ ........................ 32 24: Rotated Component Matrix Repeat ................................ ................................ .......... 33 25: Total Variance Explained ................................ ................................ ........................... 33 26: Virtual Team Research Categories ................................ ................................ ............. 46 27: Levels of Knowledge Management Systems. ................................ ............................. 49 28: Types of Trust in Previous Research ................................ ................................ .......... 54 29: Definitions of the Study Constructs ................................ ................................ ............ 71 alpha ................................ ................................ ................................ ........ 73 31: Demographics and Descriptive Statistics ................................ ................................ ... 74 32: Convergent And Discriminant Validities ................................ ................................ .. 75 33: Cross Loading ................................ ................................ ................................ ............. 76 34 : Summary of Hypotheses Tests ................................ ................................ .................. 78 35: Sobel Test for the Significa nce of Mediation ................................ ............................. 78 36: Control Variable Effect ................................ ................................ ............................... 79 37: Multi group moderating effect (Trust) ................................ ................................ ....... 81 38 : Multi group moderating effect (Collaboration) ................................ ......................... 82

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xii LIST OF FIGURES Figure 1: Intention to Collaborate Online ................................ ................................ .................... 14 2: Spiral of knowledge Adapted from Nonaka and Takeuchi (1995) ............................ 48 3: CSCW Matrix Adapted From Johansen (1988) ................................ ......................... 59 4: Theoretical Research Model ................................ ................................ ......................... 62 5: Measurement Model ................................ ................................ ................................ .... 71 6: PLS SEM Results ................................ ................................ ................................ .......... 77 7: Control Variable Analysis ................................ ................................ ............................ 79

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1 Introduction The research presented in this dissertation addresses virtual team effectiveness. Th is research sheds light on the importance of knowledge sharing in virtual team settings and how the behavior of knowledge sharing has the potential to compensate for the absence of observed physical behaviors. This study extends the previous literature on virtual teams through investigating the role of knowledge sharing and trust in enabling collaboration in virtual team settings; and eventually makes the team outcome more effective. Individuals consider knowledge to be a personal advantage and sharing it l eads to loss of ownership of this knowledge and consequently loss of power and potential replacement which makes them hoard the knowledge and be reluctant to share it (Kankanhalli et. al. 2005). Nonetheless, sharing knowledge and exchanging ideas is crucia l for team collaboration. We argue that a n efficient virtual team, is one which its team members put the success of the team ahead of their personal tendency to hoard knowledge for themselves. 1.2. Importance of Topic As globalization and open markets continue to manifest, organizations are realizing more the prominence of virtual teams as a convenient way to enable teamwork in situations where people of expertise cannot be brought together into the same location. Virtual teams have a set of characteristics and dynamics that are different from traditional face to face teams since are composed of individuals of complex traits and diverse backgrounds. Yet, these individuals are expected to be effective and collaborate to achieve an organizational goal.

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2 Because vi rtual teams normally do not have face to face interaction, their effectiveness is more challenging compared to traditional teams. Although the literature shows that virtual teams can reach a level of effectiveness which could be compared to traditional tea ms effectiveness, this process in virtual environment takes more time and effort. Knowledge is a critical factor in improving team effectiveness and sharing knowledge is fra gmented among already distributed individuals whose only mean of interaction is mediated by technology. Therefore, sharing knowledge in virtual teams is more critical and challenging than in traditional teams. Trust is considered to be a crucial but chall enging factor of successful teams. This characteristic is difficult to establish and foster among team members especially when those members are geographically distributed, have different perceptions of trust due to their diverse backgrounds, and they are challenged with limited observed behaviors of fellow team members. In this research, we investigate how sharing knowledge influences trust and collaboration among virtual team members. We also investigate the impact of these relationships on virtual team e ffectiveness. 1.3. Research Problem and Scope A main distinction we make in this research is between virtual teams and open online communities of practice. A key characteristic of online communities of practice is that their members are personally motivated to join these communities based on shared

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3 interest. Virtual teams, however, members join them to collaborate on an organizational task mainly because of the organization need for their expertise. Online communities of practice are formed and managed beyond t he organizational boundaries and their members are not governed or concerned with organizational structure or reward system. Virtual teams are established of members who work in an organization or across an inter organizational system. They are employees w ho are expected to work together and collaborate to achieve an organizational goal within specific time and limited resources. Early virtual teams were described as temporary teams whose members are brought together by technology to work on a complex probl em. Although this type of virtual teams still exists, nowadays virtual teams are more embedded into the organization and their members are working on traditional everyday tasks for relatively long periods of time. Similar to traditional teams, virtual team s are expected to collaborate to accomplish a common goal. However, the structure and characteristics of virtual teams impose challenges on how effective these teams can be. 1.4. Research Questions This research aims to answer the following research questions: 1. What factors influence a virtual team member intention to collaborate online? 2. How do different factors combine to influence a virtual team member intention to collaborate online? 3. How can we measure online collaboration in virtual team settings?

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4 4. Does kn owledge sharing significantly influence trust and collaboration in virtual team setting? 1.5. Research Contribution The research included in this dissertation has two main contributions. The first is a literary contribution From conducting an intensive litera ture review on virtual teams, an observation was made. The literature investigates how different factors influence trust and knowledge sharing in virtual teams. Yet, trust and knowledge sharing are not the final outcomes of team work. As such, the final co ntribution of this dissertation is an empirical one. 1.6. Outline of Dissertation The focus of this dissertation virtual teams effectiveness, the role of knowledge sharing, trust, and collaboration It consists of four chapters: (1) Introduction, (2) Intentio n to Collaborate: Investigating Online Collaboration in Virtual Teams. (3) Virtual Teams Effectiveness: The Role Of Knowledge Sharing, Trust, And Collaboration. (4) Comprehensive Conclusion.

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5 2. Intention to Collaborate: Investigating Online Collab oration in Virtual Teams. 2.1. Abstract The emergence of online tools supporting collaboration has allowed more people to work together online, some in open online communities, others in professional groups within organization s and others in professional grou ps across inter organizational system s. The advancement of information and communication technology provided the opportunity for organizations to establish virtual teams with needed expertise regardless of geographical boundaries. Virtual team members are expected to collaborate in order to solve predefined problems and organizational tasks In this paper, we introduce a conceptual model and a measurement scale which are derived from investigating the literature on online collaboration and virtual teams. Th e proposed scale makes important contributions to both research and practice. For research, it will provide a validated scale to measure perceived intention to collaborate in virtual team settings which will support further research in this important fiel d. For practice, it will help identify what contributes to a virtual team member's intention to collaborate and can assist in the establish ment of virtual teams i n organizations. 2.2. Keywords Online Collaboration, Virtual Teams, Conceptual Model, Measurement Scale.

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6 2.3. Introduction Globalization along with continuous improvement in information and communication technologies has led more professionals to work together online. The web is providing a platform for collaboration and it is continuously shifting towa rds a more user centric experience (Lai and Turban, 2008) On an organizational level, information technology offers organizations the opportunity to form partnerships communicate with each other, and coordinate activities These inter organizational syst ems are important in a market where pr oducts and services require multi organizational collaboration ; which is considered to be a shift from organizational competition to inter organizational collaboration ( Kumar and Dissel, 1996 ; Zwass, 2003) This change in the organization al structure and in the way organizations conduct their business make s the practice of virtual teams an organizational necessity Web 2.0 and the variety of tools it provides (e.g. blogs, wikis, and social bookmarking) have led to the emergent of new kinds of services such as social networks, aggregation services, and cloud based office style software. These tools provide a means for organizations to establish teams based on required expertise regardless of physical location. T hey allow professionals to establish and join open online communities that relate to their field of expertise and to serve as a virtual work place for knowledge exchang e collaboration, and problem solving. Moreover, this technology advancement has resulted in a ne w innovative form of online collaboration between producers and consumers ; which is outsourcing product development to digital consumer networks (Arakji and Lang, 2007) intention to collaborate in organizational setting s P rior research on online collaboration

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7 ha s focused on finding new constructs that contribute to online collaboration ; whereas, this study improves our understanding of how different factors combine to influence an Socio T echnical T heory and the Theory of Reasoned Action. This research proposes a conceptual model of virtual teams collaboration along with a measurement scale deri ved from the literature. The research is designed to answer the following research questions: 1. collaborate online? 2. How do different factors combine to influence a virtual team o collaborate online? 3. How can we measure the intention to collaborate in virtual team settings? Answering these questions will allow researchers to better understand virtual teams and the factors that contribute to collaboration among virtual team members The next section outlines the theoretical foundation of this research. It is followed by the development of a theoretical model for online collaboration and a measurement scale to test th e model. The scale is then refined through a pretest and tested in a field study The paper concludes with the implications for future research. 2.4. Theoretical Foundation 2.4.1. Virtual Teams A virtual team is defined by Powel et al a group of geographically, organizationally and/or time dispersed workers brought together by inf ormation and

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8 communication technologies to accomplish one or more organizational tasks (Powel et al. 2004). O rganization s incorporated virtual teams into their structure to address their business needs, Townsend et al. ( 1998) attribute s the need for virtu al teams in the organization to five factors. T hese factors are the change of organizational structure from vertical to horizontal, the need for inter organizational collaboration preference and expectations, O rganizations are moving towards pr oviding services rather than manufacturing products, and due to globalization (Townsend et al. 1998) Traditionally, virtual teams were established based on a n organization need to gather necessary expertise to solve complex problems they used to be temp orary teams, and they suffered low commitment among team members. (Jarvenpaa et al. 1998; Squire and Johnson, 2000 ; Kanawattanachai and Yoo 2002 ) R ecently h owever, more organizations are establishing virtual teams to work on everyday tasks ; and many org anizations are allowing their employees to work remotely from places of their choice that is, some teams are completely virtual, while other teams are a combination of co located and dis tributed members Moving to virtual teams has an impact on organizational structure. It is reported in the literature that virtual teams create forms that are more reconfigurable, flexible and require mass collaboration ( DeSanctis and Monge, 1999 ; Zammut o et al. 2007). The challenges to virtual teams include time difficulties, feedback delays, misinterpretation, cultural barriers, sch eduling, and lack of communication and respons e (Fussell et al. 1998 ; Jarvenpaa and Leidner, 1998; Powell et al, 2004) The se challenges if not addressed and managed by the organization can threaten virtual team s success and

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9 effectiveness ( Piccoli et al. 2004 ). We argue that virtual teams can only thrive if individual team members can overcome the challenges to virtual collab oration and manage to work in a coordinated effort to solve problems together. This suggests the need for an improved understanding of how to create virtual teams that work effectively. 2.4.2. Online Collaboration Collaboration can be successfully accomplished in traditional face to face teams. However, the change from a physical to a virtual work space brings challenges to how member s collaborate within the same team with solely relying on information technology for communication and coordination Information an d communication technologies have significantly improved over the last two decades, and virtual teams are equipped with variety of tools and technologies to support their work. Therefore, research ers have called for investigating the social factors of virt ual teams which could impact their effectiveness ( Holton, 2001; Kirkman et al. 2004; Powell et al, 2004 ; Henttonen and Blomqvist, 2005 ). Durate and Snyder (2006) discuss seven types of virtual teams (i.e. networked teams, parallel teams, product developmen t teams, production teams, service teams, management teams, and action teams ), they argue that all of these teams have in common that team members must communicate and collaborate to achieve effective outcomes Evidence in the literature suggests that when given sufficient time and managed properly, virtual teams could collaborate effectively compared to traditional teams (Holton, 2001 ; Webster and Wong 2008 ) From an organizational perspective, Kumar and Dissel (1996) argue that three arguments are needed to explain collaboration in an inter organizational system, economic, technical, and socio political. T he social system in workplace is a platform for

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10 continuous inte rplay between the technical process and the social process to meet t he demands of an emer ging and continuously changing work environment (Taylor, 1975) The importance of both technology usage and social relationships to a virtual team success is consistent with the Socio Techni cal theory which states that organization s consist of two interact ing systems; the social and the technical (Bostrom and Heinen, 1977) While t he technical system includes the process, task, and technology dimensions, the social system explains how the relationships and interaction among individuals affect the system out come (Bostrom and Heinen, 1977). An organizational system design which effective outcome ( Appelbaum 1997). Collaboration is different than coordination. While some times used interchangeably, a main difference between the two is the degree to which the work is coordinated among individuals It is noted that collaboration requires higher level of coordination among individuals than cooperation (Dillenbourg, 1999). Pre vious studies have investigated why virtual team members share their knowledge especially outside the organization boundaries (Bechky, 2003, Wasko and Faraj, 2005). However, collaboration goes beyond sharing information or knowledge through a form of an in formation system communication in order to jointly and collectively solve a problem. In face to face a social syst em of three or more people, which is embedded in an organization, whose members perceive themselves as such and are perceived as members by others, and whose members collaborate on a

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11 common task. online c ollaboratio n has received limited attention in the literature 2.5. Measurement Scale Development Process The development of the measurement scale was carried out in four stages. The first stage involved investigating relevant literature to find out the constructs which influence a virtual team member intention to collaborate online. The second stage was item creation stage; t he purpose of this stage is to create a pool of items for the constructs which were identified in the previous stage. The third stage was the scale development stage which included a panel of judges who were asked to categorize and sort the items created in the second stage. The final stage was the instrument testing stage which included a pilot test with a small sample of respondents to get an indica tion of the scale content validity and reliability. This stage also included a full scale study to test the final instrument. The following sections will describe these stages in detail. 2.5.1. Stage 1: Literature Investigation and Construct Identification The fi rst step in developing a measurement model of online collaboration is a thorough investigation of the literature on virtual teams, collaboration, and surrounding research areas. While there is a considerable volume of research which investigates online col laboration, much of it has focused on finding new constructs that contribute to online collaboration as opposed to understanding how different factors combine to Investigation of the literature on virtua research areas revealed eleven dimensions. These dimensions, the studies in which they

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12 were reported, and the context of each study are summarized in Table 1. It is noteworthy to point out that the list of studies in Table 1 is not inclusive; however, we found them to be most suitable to answer our research questions. Dimensions illustrated in Table 1 are mapped into three categories based on the context of the original studies and the theoretical support in the litera ture. These categories are intention to collaborate online dimensions, online collaboration dimensions, and moderating dimensions. The mapping of these factors is illustrated in Table 2. Building on the preceding discussion, a research model depicting th e constructs and relationships examined in this study is depicted in Figure1. In the model, five antecedent dimensions intention to collaborate online. Intention is relevant for this model b ecause without intent, actual collaboration will not occur. This is consistent with the theory of reasoned action ary driver of actual collaboration online. The relationship between intention to collaborate online and online collaboration is moderated by the ability to meet in person and the availability of IT support. The remainder of this paper will focus on furthe r understanding the relationship between these five dimensions and the intention to collaborate online The study of actual online collaboration is beyond the scope of this study. 2.5.1.1. Perceived Incentives Organizations offer incentives to motivate individuals to contribute activity, and the reward system is reported to affect organizational behavior (Clark and Wilson, 1961).

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13 Table 1 : Online Collaboration Dimensions Study Context dimension (s) Agarwal and Prasad (1997) Technology Accept ance Voluntariness Bardram(1998) CSCW IT Support, communication, coordination. Fussell et al. (1998) Virtual Teams Communication, Coordination Jarvenpaa et al. (1998) Virtual Teams, Trust Voluntariness, Coordination Cramton (2001) Virtual Teams, Knowle dge sharing Communication Holton(2001) Virtual Teams, Trust, and Collaboration Communication, Meet in person Montoya Weiss et al. (2001) Virtual Teams, Management Coordination Desanctis et al.(2003) Online Communities Communication, IT Support, Openness Hall and Graham (2004) Online Communities Incentives, Meet in person, Common Ground Hertel, et al. (2004) Virtual Teams, Management Incentives Leinonen et.al. (2005) Virtual Teams, Collaboration Communication, Coordination Wasko and Faraj (2005) Onlin e Communities, Collaboration Background similarities, level of expertise, Incentives Durate and Snyder (2006) Virtual Teams, Management Voluntariness Metiu (2006) Virtual Teams Background similarities, Tension Kanawattanachai and Yoo (2007) Virtual Team s Knowledge Coordination Communication Kudaravalli and Faraj (2008) Online Communities, Collaboration Background similarities, Communication, Different level of expertise, Voluntariness Bjrn and Ngwenyama (2009) Virtual Teams, Collaboration Communicat ion, common ground, Coordination Hemetsberger and Reinhardt (2009) Online Communities, Collaboration IT Support, Openness, Tension

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14 Table 2 Mapping Online Collaboration Dimensions Intention to Collaborate Online Dimensions Modera tors Online Collaboration Dimensions Perceived Background similarities IT Support Communication Perceived Common Ground Meet in person Coordination Perceived Different level of expertise Perceived Incentives Perceived Voluntariness Normall y, there are incentives for a virtual team member to collaborate, some of intangible (reputation, recognition, person O rganization s implement reward systems to induce employees to contribut e and teams to succeed (Hertel et al, 2004) The literature on knowledge sharing reported that organizations had implemented reward systems and offer variety of incentives to promote knowledge sharing (Bock et al. Figure 1 : Intention to Collaborate Online

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15 2 005) Hall and Graham (2004) reported that code breakers joined an online group to share knowledge and collaborate to break a code in the hope of winning an award. Hall and Graham (2004) also reported that some members were also interested achieving person al satisfaction and enhanced reputation which is consistent with the findings of Wasko and Faraj (2005) who argue that members contribute their knowledge when they perceive that it enhances their professional reputations. The Social Exchange T heory explai ns social interaction in terms of reward expectations (Blau, 1964 ) I ndividuals have a tendency to maximiz e their rewards and reduce their cost (Emerson, 1976) Therefore if a reward system is implemented, virtual team members will be expected to work towa rds maximizing their benefits and will be more motivated to collaborate online with each other 2.5.1.2. Perceived Voluntariness Professionals join online communities voluntarily based on shared interest without any obligation to collaborate or any commitment to t he community and its objectives However, when o rganizations form teams, team members are mandate d or at least expected to collaborat e in order to solve problems and achieve a common goal Since virtual team members can cooperate but not collaborate, we could look at c ollaboration as a form of voluntary cooperation in which team members not only work on a task, but they communicate with each other and coordinate their activities (Kudaravalli and Faraj, 2008) Thus, voluntariness could affect collaboration practices in virtual teams, mainly because individuals are more effective when they are guid ed by their own behavior and not forced to act in a certain way (Eisenberger and Cameron, 1996).

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16 V irtual teams can be formed without a choice of their members whi ch could influence members intention to collaborate online because voluntariness is considered to be a form of social influence and voluntary membership is a principal element of collaboration ( Roberts and Bradley, 1991 ; Durate and Sny der, 2006 ; Karahanna et al., 1999). P erceived voluntariness could have a bigger influence on behavior than actual voluntariness ( Moore and Benbasat 1991) and it has the potential to significant ly affect individual behavior and intentions (Agarwal and Prasad, 1997) The Unif ied Theory of Acceptance and Use of Technology (UTAUT) model which is an extended version of the Technology Acceptance Model (TAM) posits that voluntariness has a n effect on behavioral intention and use behavior (Venkatesh et al., 2003). Hemetsberger an d Reinhardt (2009) suggest that team leaders can at least coordinate tasks while giving virtual team members the freedom to choose what task each one of them would like to work on. Based on the preceding discussion, we posit that perceived voluntariness of online collaboration contribute to intention to collaborate online. 2.5.1.3. Perceived Common Ground Crossing geographical boundaries affects the way in which virtual teams communicate and collaborate which makes reaching a common ground c rucial for effective collaboration (Durate and Snyder, 2006 ; Alavi and Tiwana 2002) The unique characteristics of virtual teams create challenges to develop common ground among team members (Alavi and Tiwana 2002) Virtual team members are sometimes dis tributed across vast geographical areas and live in different time zones which constrain their ability to communicate and achieve a common ground.

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17 In the absence of face to face interaction, developing common ground and shared understanding in technology m ediated communication c an be challenging Empirical research has highlighted that virtual settings negatively affects the perceptions of their members (Burke et al. 1999) Studies have also demonstrate d negative associations between the degree of virtualen ess and communication which could affect virtual team ( Cohen and Gibson, 2003 ; Webster and Wong 2008 ). In order to overcome the challenges of virtual environments, virtual team members need to establish immediate, frequent and effective communication channels among each other to reach a common ground in a timely manner (Kanawattanachai and Yoo 2007) Establishing common ground is a process of cr eating a shared meaning context among team me mbers ( Bjrn and Ngwenyama 2009 ) This shared meaning need to be established in an early stage of virtual work or else the virtual team will be exposed to challenges which could lead to failure in achieving effective collaboration (Cramton, 2001). The es tablishment of common ground and shared context in virtual teams is complicated and challenging. Virtual team members especially in global virtual team context have different backgrounds, different work experience, different expectations, and sometimes l anguage and cultural barriers. These challenges if not addressed properly could lead to failure in communication and interpretation which ultimately leads to failure in collaboration (Cramton, 2001). Based on the preceding discussion we argue that the perc eption of establishing mutual understanding and common ground among virtual team members impacts the ir intention to collaborate online because common ground facilitates communication and collaboration

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18 2.5.1.4. Perceived Background Similarities Transfor ming from t raditional team work into virtual team work changes the way work is carried out which has an effect on the social aspects of team work As mem bers from different backgrounds join a virtual team, online collaboration becomes more difficult and can add pro blems and complications to virtual team work (Bechky 2003 ; Durate and Snyder, 2006 ). The existence of different backgrounds among team members could affect the structure and effectiveness of a virtual team mainly because members of different backgrounds t end to have different ways of working together and collaborate with each other ( Durate and Snyder, 2006). The social categorization and the identity argument states that the more similar the group members are to one another, the more they identify with the group which in turn could influence members collaboration because individuals with similar backgrounds tend to develop relationships and collaborate together faster (Abrams et al. 2005 ; Webster and Wong, 2008 ). According to social identity theory, p eople seek out certainty by identifying themselves with in group s of members of similar attributes and characteristics ( Ashforth and Mael, 1989 ) Considering that the uncertainty is relatively high in virtual teams, it is reasonable for team members to tend to i dentify themselves within groups of similar backgrounds (Tajfel and Turner, 198 6) Based on t h e preceding discussion, we argue that background similarities among virtual team members impact their intention to collaborate online.

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19 2.5.1.5. Perceived Members Expert ise Organizations establish teams in order to gather members of different skills and experience to collaborate on a common organizational task (Johnson, 2001). The prevalence of organizations establishing v irtual teams and accept their challenges and overh ead could be attributed to the need to recruit expert members to work across or outside the physical boundaries of organizations Virtual teams enable members to bring different skills and expertise to help solve problems regardless of their physical locat ion (Johnson, 2001; Wasko and Faraj, 2005). The integration of expert users into virtual teams increases opportunities by enlarging the workforce and specialized knowledge with in the team C ollaboration depends on the extent to which team members are able to locate the necessary knowledge within the team and retrieve it (Kanawattanachai and Yoo 2007). D iversity of expertise has a positive impact on online collaboration ( Kudaravalli and Faraj 2008) Having expert members in a virtual team leads to m ember s benefit from each other, gain acces s to new information and ideas not available locally, and problem solving process is improved (Wasko and Faraj, 2005). Based on the preceding discussion we argue that having varying levels of expertise within a virtual team will have a positive 2.5.2. Stage 2: Item Creation The methodology used to develop the scale in this research is based on procedures described in Moore and Benbasat (1991) and in MacKenzie et al. (2011). The first stage was to create a number of items for the scale, the initial scale items were adopted from the literature on collaboration, virtual teams, online communities, computer supported

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20 collaborative work, and knowledge sharing. Additional i tems were created through interviews and group discussions. An initial set of 75 items were developed in this stage. These initial items were reduced to 65 to eliminate narrow scope and ambiguous questions; we also made sure that each construct has at leas t 10 questions. Items were written as a statement to which the respondent can relate and indicate a degree of agreement or disagreement with using a seven point Likert scale ranging from strongly disagree to strongly agree. 2.5.3. Stage 3: Scale Development The next stage was a pretest to refine the initial set of items in order to assess their content validity. In this stage a panel of seven expert judges was asked to sort the items into categories based on the similarities and differences among them. The judges were all PhD students with four of them have experience in virtual team work. They were given the set of items randomly distributed and the five constructs on which the items should be categorized along with an additional category for ambiguous or unclear items. The judges were also asked to rank order the items within each factor based on the closeness in meaning with the factor itself. The card sort analysis revealed a confusion caused by the items of Perceived Members Expertise items and Perceived backg round similarities. All the judges misplaced items from both of these construct, the judges reported that these two constructs could easily be confused with one another. As this overlap between constructs represents a threat to the internal validity of the model we had to make a choice of either dropping one of the constructs, or dropping the items which threat ened the internal validity and modifying the rest. We chose the second option.

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21 Table 3 : Intention t o Collaborate Online Scale Items Factor Item Developed based on Background Similarities The background of my online team members does not influence my intention to collaborate with them with whom I share a similar culture re likely to collaborate online with individuals with whom I share similar background My intention to collaborate online is positively affected expertise Having team members of different backgrounds makes me less likely to collaborate online I tend to collaborate online with team members that have different backgrounds than mine (Webster and Wong, 2008) (Jarvenpaa et al. 1998) (Webster and Wong, 2008) (Webster and Wong, 2008) (Webster and Wong, 2008) (Web ster and Wong, 2008) Common Ground reaches common ground from the beginning Reaching a common ground has nothing to do with my intention to collaborate online Collaborating online requires me to engage in continuous communication with other team members to reach common ground I collaborate online even when my team members and I do not fully share the same vision of the problem we are trying to solve. Mutual understanding is essential for me to collaborat e online It is not essential to have a mutual understanding with other team members for me to collaborate online For me to collaborate online, the team should share a common understanding of problems to be addressed. (Kudaravalli and Faraj, 2008) (Kudarava lli and Faraj, 2008) (Leinonen et al. 2005), (Leinonen et al. 2005) (Leinonen et al. 2005), (Kudaravalli and Faraj, 2008) (Leinonen et al. 2005) Members Expertise Having expert members on my team makes me more willing to collaborate online Diversity of encourages me to collaborate online I collaborate online with members who have expertise I can benefit from I collaborate online with team members who can benefit from my expertise Virtual team members' expertise does not influence my intention to collaborate with them online. (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) (Wasko and Faraj, 2005) Incentives I collaborate with my online team members regardless of any in centives I expect to be rewarded by my organization or team supervisor when I collaborate online. I expect something in return when I collaborate with team members online I collaborate with others online to improve my image within the team (Kankanhalli et al. 2007) (Kankanhalli et al. 2007) (Kankanhalli et al. 2007) (Kankanhalli et al. 2007)

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22 Table 3 (Cont.) : Intention t o Collaborate Online Scale Items Incentives (Cont.) I only collaborate online when there are incentives for my collaboration kely to collaborate online without getting something in return Collaborating online enhances my professional reputation (Kankanhalli et al. 2007) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) Voluntariness I only collaborate with online team mem bers when voluntarily join the team I collaborate with other team members online even when not mandated by my organization ed to do so Mandating online collaboration makes me less willing to collaborate with team members Mandating online collaboration makes me more willing to collaborate with team members join a team (Agar wal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) (Agarwal and Prasad, 1997) Post card sorting interviews were conducted with the judges to further refine and improve the items. These interviews resulted in rewording some items to improve their clarity and dropping the items that the judges felt did not adequately represent the underlying construct they were measuring. This process resulted in a refi ned scale containing 33 items as listed in Table3. 2.5.3.1. Pilot Study Before administering the scale on a large population, a pilot study was conducted to insure that the scale under development use d clear and appropriate language and ha d no obvious errors (Joha nson and Brooks, 2010). The objective of this pilot study was to provide additional insights regarding the content validity, clarity and appropriateness of the survey questions and to further refine the survey items before using them in a larger study. Th e respondents first were asked to complete the questionnaire, and then comment

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23 on the questions length, wording, redundancy, and total number of questions. T he scale items were transformed into seven point Likert scale survey which was implemented on the w eb. A filtering question related to virtual team experience was added to the survey to insure that respondents have the background necessary to complete the survey. The target population for the pilot study was MBA students at the University of Colorado D enver Business School. MBA students were a good fit for the pilot study because most of them are professionals working for companies in the Denver area. Control questions were also added to control for years of experience, members hip and leaders hip curren t virtual team member ship and global virtual team experience A convenient sample of 15 MBA students was invited to participate in the survey via an invitation from the University of Colorado Denver MBA student club president The pilot test with the MBA students was used to identify any additional problem with the survey. The respondents provided feedback regarding th e background similarities construct arguing that it was vague and difficult to answer its questions. The respondents reported that the defi nition of the word background was unclear and that they c ould interpret it in different contexts such as experience background, cultural background, and educational background. Furthermore, the respondents reported that some questions were vague and redund ant. Eventually, based on the feedback of this pilot study the construct the study to improve the scale reliability and validity. The revised scale h as 4 constructs with 21 q uestions, table 4 shows the final list of questions used in the survey

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24 Table 4 : Measurement Items after Pilot Study Factor Item Common Ground ground from the beginning Reaching a common ground has nothing to do with my intention to collaborate online Collaborating online requires me to engage in continuous communication with other team members to reach common ground I collaborate online even when my team m embers and I do not fully share the same vision of the problem we are trying to solve. For me to collaborate online, the team should share a common understanding of problems to be addressed. Incentives I collaborate with my online team members regardless of any incentives I expect to be rewarded by my organization or team supervisor when I collaborate online. I expect something in return when I collaborate with team members online I collaborate with others online to improve my image within the team ss likely to collaborate online without getting something in return Collaborating online enhances my professional reputation Members Expertise Having expert members on my team makes me more willing to collaborate online a virtual team encourages me to collaborate online I collaborate online with members who have expertise I can benefit from I collaborate online with team members who can benefit from my expertise Virtual team members' expertise does not influence my inten tion to collaborate with them online. Voluntariness I collaborate with other team members online even when not mandated b y my organization Mandating online collaboration makes me less willing to collaborate with team members

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25 Table 5 : Number of i tems after Pilot St udy Construct Number of deleted items Number of final items Perceived Incentives 1 6 Perceived Common Ground 2 5 Perceived Member Expertise 0 5 Perceived Voluntariness 2 5 2.5.4. Stage 4: Instrument Testing Field Study Once the measurement model has been f ormally specified, data need to be obtained from a sample of respondents in order to examine the scale and to evaluate its convergent, discriminant, and nomological validity ( MacKenzie et al. 2011) The measurement scale was implemented as an online survey and respondents were recruited from professional groups in linkedin.com. The groups we used as a sample frame are listed in Appendix I The total number of members in these groups was 1372, 118 responses are obtained with a response rate of 8.6%. The numb er of usable responses after eliminating incomplete surveys is 103. The statistical analysis implement ed in this study is adapted from Moore and Benbasat (1991). The constructs are first tested for reliability and validity. Reliability is tested using Cron bach's alpha with a value of at least 0.70 indicates adequate reliability. In order to improve the reliabilities of the corresponding constructs, one or more questions could be dropped from the study. The scale is tested for validity using factor analysis with principal components analysis. Convergent validity is assessed by checking loadings to see if items within the same construct correlate highly amongst themselves. Furthermore, discriminant validity is assessed by examining the factor loadings to see i f questions loaded more highly on

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26 their intended constructs than on other constructs (Kankanhalli et al. 2005). Loadings of 0.63 to 0.70 are considered very good, and above 0.71 are considered excellent (Hair et al. 2009). Questions which could load onto o ther constructs are dropped. 2.5.4.1. Perceived Incentives Reliability test for perceived incentives resulted in a very poor alpha value. According to the correlation table (Table 6 ), two of the six perceived incentives items have very poor correlation with rest o f the items. So we decided to drop these items and run the reliability test again without them. The reliability results for perceived incentives after dropping items 4 and 6 are shown in table 8 which illustrates that alpha value now is 0.962, which indica tes excellent reliability. Table 6 : Reliability Statistics Perceived Incentives Cronbach's Alpha Cronbach's Alpha N of Items .411 .757 6 Table 7 : Correlation Matrix Perceived Incentives INC1 INC2 INC 3 INC4 INC5 INC6 INC1 1.000 INC2 .901 1.000 INC3 .814 .862 1.000 INC4 .045 .000 .021 1.000 INC5 .809 .858 .948 .018 1.000 INC6 .046 .093 .106 .442 .121 1.000 Table 8 : Reliability Statistics Repeat Perceiv ed Incentives Cronbach's Alpha Cronbach's Alpha N of Items .962 .963 4

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27 Table 9 : Correlation Matrix Repeat Perceived Incentives INC1 INC2 INC3 INC5 INC1 1.000 INC2 .901 1.000 INC3 .814 .862 1.000 INC5 .809 .858 .948 1.000 2.5.4.2. Perceived Voluntariness Reliability test for perceived v oluntariness resulted in an adequate alpha value of (0.78) However, a ccording to the correlation matrix (Table 1 1 ), very poor correlation with most of the items. So we deci ded to drop th is item in order to After removing VOL5 we ran the reliability test again The reliability results for perceived v oluntariness is shown in table 1 2 which illustrates that alpha value now is 0.9 59 which indica tes excellent reliability. Table 10 : Reliability Statistics Perceived Voluntariness Cronbach's Alpha Cronbach's Alpha Based N of Items .780 .850 5 Table 11 : Correlation Matrix Perceived Voluntarines s VOL1T VOL2 VOL3 VOL4 VOL5 VOL1T 1.000 VOL2 .958 1.000 VOL3 .836 .840 1.000 VOL4 .790 .793 .926 1.000 VOL5 .004 .041 .090 .040 1.000 Table 12 : Reliability Statistics Repeat Perceived Voluntariness Cronbach's Alph a Cronbach's Alpha N of Items .959 .960 4

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28 Table 13 : Correlation Matrix Repeat Perceived Voluntariness VOL1T VOL2 VOL3 VOL4 VOL1T 1.000 VOL2 .958 1.000 VOL3 .836 .840 1.000 VOL4 .790 .793 .926 1.000 2.5.4.3. Perceived Common Ground Reliability test for perceived common ground resulted in an adequate alpha value of (0.762) However, a ccording to the correlation matrix (Table 1 5 ), CGD3 very poor correlation with most of the items in the scale So this item was drop ped in After removing CGD3 we ran the reliability test again The reliability results for perceived v oluntariness is shown in table 1 6 which illustrates that alpha value now is 0.9 38 which indicates excellent reli ability. Table 14 : Reliability Statistics Perceived Common Ground Cronbach's Alpha Cronbach's Alpha N of Items .762 .827 5 Table 15 : Correlation Matrix Perceived Common Ground CGD1 CGD2 CGD3 CGD4 CGD5 T CGD1 1.000 CGD2 .851 1.000 CGD3 .030 .056 1.000 CGD4 .762 .803 .011 1.000 CGD5T .762 .783 .017 .863 1.000 Table 16 : Reliability Statistics Repeat Perceived Common Ground Cronbach's Alpha Cronbach's Alpha N of Items .938 .943 4

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29 Table 17 : Correlation Matrix Repeat Perceived Common Ground CGD1 CGD2 CGD4 CGD5T CGD1 1.000 CGD2 .851 1.000 CGD4 .762 .803 1.000 CGD5T .762 .783 .863 1.000 2.5.4.4. Reliabilit y test for perceived resulted in an adequate alpha value of (0. 81 2). However, according to the correlation matrix (Table 19 EXP5 has a very poor correlation with most of the items. So this item was dropped in order to improve th EXP5 we ran the reliability test again. The reliability results for perceived voluntariness is shown in table 2 0 which illustrates that alpha value now is 0.9 59 which indicates excellent reliability. Table 18 : Reliability Statistics Cronbach's Alpha Cronbach's Alpha N of Items .812 .869 5 Table 19 : Correlation Matrix EXP1 EXP2 EXP3 EXP4 EXP5T EXP1 1.000 EXP2 .916 1.000 EXP3 .842 .824 1.000 EXP4 .823 .809 .911 1.000 EXP5 .122 .148 .175 .132 1.000 Table 20 : Reliability Statistics Cronbach's Alpha Cronbach's N of Items .959 .959 4

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30 Table 21 : Correlation Matrix Repeat EXP1 EXP2 EXP3 EXP4 EXP1 1.000 EXP2 .916 1.000 EXP3 .842 .824 1.000 EXP4 .823 .809 .911 1.000 Factor analysis was used as another assessment of construct valid ity. Principal Components analysis was conducted with VARIMAX rotation. The initial analysis included all the items including those which demonstrated poor correlation with items in the same variable. The results in table 22 indicate that the items resulte d in 8 factors, while our proposed model includes only 4 factors. The rotated factor matrix was examined for items which either did not load strongly on any factor (<0.40), or were too complex which loaded highly or relatively equally on more than one fa ctor. The rotated factor matrix in table 2 3 shows that the items which affected the reliability analysis are the same items which either have poor loading, load on a different construct than the one it should load onto, or load on a separate construct. Th us, these items were dropped from the scale, and a second factor analysis was conducted. This analysis again used Principal Components with VARIMAX rotation. While the first analysis was exploratory this analysis was conducted to confirm that the items dr opped in the previous validation steps resulted in factors that loaded onto the correct construct ( e.g. Moore and Benbasat, 1991) As demonstrated in the factor matrix in table 2 2 a fairly simple factor structure emerged. No item load ed highly on more tha n one factor. Furthermore, all items remaining in the various scales loaded together on the target factor in the excellent range. These results indicate that the various scales achieved a high degree of unidimensionality.

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31 Table 2 2 : Items Correlation

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32 Table 23 : Rotated Component Matrix Raw Rescaled Component Component 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 INC1 .15 .01 .03 .028 .01 .276 .254 .424 .251 .019 .05 .044 .02 .435 .401 .670 INC2 .17 .01 .04 .019 .01 .242 .262 .450 .280 .001 .07 .03 .01 .383 .415 .713 INC3 .15 .01 .09 .015 .01 .255 .260 .425 .246 .01 .14 .024 .02 .412 .420 .687 INC5 .18 .02 .07 .018 .03 .261 .249 .399 .305 .03 .12 .030 .05 .431 .411 .660 INC4 .3 .73 .726 .198 .05 .799 .272 .40 .26 .492 .484 .13 .03 .533 .181 .27 INC6 .51 .65 .56 .017 .015 .13 .058 .15 .391 .50 .43 .013 .011 .10 .044 .11 VOL1 .59 .65 .27 .401 .00 .20 .06 .085 .551 .611 .26 .37 .00 .19 .05 .079 VOL2 .58 .71 .26 .363 .03 .17 .06 .081 .537 .659 .24 .33 .03 .15 .06 .074 VOL3 .61 .64 .21 .201 .039 .05 .01 .02 .627 .650 .21 .20 .039 .05 .01 .02 VOL4 .73 .70 .21 .261 .084 .02 .07 .12 .642 .618 .18 .22 .073 .021 .01 .12 VOL5 .46 .94 .20 1.17 .365 .35 .239 .022 .27 .559 .12 .694 .215 .21 .14 1 .013 CGD1 .41 .24 .02 .110 .410 .143 .057 .09 .537 .31 .03 .142 .528 .183 .074 .12 CGD2 .46 .25 .014 .108 .433 .140 .136 .05 .580 .31 .018 .136 .545 .176 .171 .07 CGD3 .41 .21 .18 .064 .71 .28 .813 .35 .317 .16 .14 .049 .55 .21 .627 .27 CGD4 .47 .21 .05 .025 .619 .128 .154 .17 .523 .23 .05 .027 .683 .142 .170 .18 CGD5 .60 .28 .04 .094 .633 .209 .099 .17 .595 .25 .04 .092 .620 .204 .097 .17 EXP1 .69 .03 .375 .392 .24 .051 .10 .000 .716 .04 .385 .402 .24 .052 .10 .000 EXP2 .70 .01 .368 .353 .25 .003 .10 .01 .724 .01 .378 .362 .25 .003 .11 .01 EXP3 .71 .06 .490 .409 .25 .060 .23 .078 .670 .06 .457 .382 .23 .056 .21 .073 EXP4 .66 .0 4 .436 .360 .25 .091 .23 .011 .669 .004 .437 .360 .25 .091 .23 .0 11 EXP5 .07 .05 1.24 .420 .397 .72 .292 .165 .048 .03 .777 .26 .249 .45 .183 .103

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33 Table 24 : Rotated Component Matrix Repeat Raw Rescaled Component Component 1 2 3 4 1 2 3 4 INC1 .586 .049 .027 .045 .925 .078 .042 .0 72 INC2 .600 .031 .052 .044 .950 .049 .083 .070 INC3 .586 .012 .031 .047 .948 .020 .050 .076 INC5 .569 .052 .034 .060 .940 .086 .056 .099 VOL1 .071 .031 1.022 .015 .066 .029 .952 .014 VOL2 .075 .062 1.040 .047 .069 .056 .953 .043 VOL3 .068 .142 917 .106 .069 .144 .931 .108 VOL4 .034 .171 1.042 .199 .030 .150 .910 .174 CGD1 .052 .135 .001 .674 .067 .174 .001 .867 CGD2 .121 .157 .006 .699 .153 .198 .007 .880 CGD4 .035 .021 .094 .852 .039 .023 .104 .941 CGD5 .070 .147 .102 .950 .068 .144 .099 .929 EXP1 .078 .898 .091 .171 .080 .921 .093 .175 EXP2 .050 .888 .133 .139 .051 .912 .137 .143 EXP3 .077 1.011 .049 .126 .072 .944 .045 .118 EXP4 .035 .930 .101 .103 .035 .931 .101 .103 Table 25 : Total Variance Explained Com ponent Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Raw 1 5.155 38.715 38.715 1.425 10.706 10.706 2 3.242 24.348 63.063 3.608 27.099 37.805 3 2.160 16.226 79 .289 4.114 30.896 68.701 4 1.300 9.765 89.055 2.710 20.354 89.055 Rescaled 1 5.173 32.329 32.329 3.605 22.528 22.528 2 3.154 19.715 52.043 3.592 22.450 44.978 3 2.606 16.285 68.328 3.583 22.397 67.375 4 3.269 20.431 88.759 3.422 21.385 88.759

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34 2.6. L imitations and Future Work A limitation of this research is that it examines self reported intention to collaborate as opposed to actual collaboration. This study relies on the theoretical foundation in the Theory of Reasoned Action (TRA) which argues that is a function of his intention. However, intention to collaborate is only a proxy for the actual outcome organizations wish to achieve, actual collaboration. One benefit of developing this model and measurement scale for intention to col laborate online is that, future studies can use this scale to address actual collaboration in a real world setting. This will allow researchers to better understand whether factors influence intention to collaborate have the same effect on actual observabl e collaboration. A second limitation of this research is that it utilized subjects recruited from a social network website instead of a traditional organizational setting. This environment is appropriate to recruit members because a number of online groups do coordinate as virtual teams (e.g. for group members working for the same organization ). However, recruiting in such an environment may have implications with respect to the types of antecedents to virtual team participation that are present in the samp le. Specifically, the sample may include more members that are participating in virtual teams voluntarily than would be seen in a more traditional organizational setting. Future research should examine a sample from an organizational setting so the scale can be tested with a population which may have exposure to a different set of antecedent factors influencing virtual team participation and collaboration. Lastly, because items could be added, dropped, or reworded in the scale test process, future work cou ld retest the measurement model using a new sample of data.

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35 2.7. Implications to Research and Practice This study provides important implications for both research and practice. The study proposes a conceptual research model and a validated measurement scale for virtual study is found in the Socio Technical Theory and the Theory of Reasoned Action. For research, this study calls for a better understanding of the social aspect s Technical Theory this research suggests that technology advancement alone does not facilitate online collaboration in virtual teams, social aspects of virtual teams plays an importa nt role in their collaboration as well. Prior research has reported several factors which contribute to virtual team collaboration. This study, however, investigated how these he contribution of this study will enable researchers to rely on a validated measurement scale and conceptual model to further investigate online collaboration construct and its interaction with other constructs in virtual team settings. Future work could retest the model presented in this paper or use this model in a study where online collaboration is one of the constructs. For practice, this study provides noteworthy implications regarding the importance of social characteristics and social relationships among virtual team members in fostering an environment of collaboration within the team in the organization. Today there is increased adoption of virtual teams in organizations and there is a need for improved inter organizational cooperation. Given that collaboration is essential for team work, this study provides key implications to practice on how to establish and manage

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36 virtual teams which collaborate effectively. It also provides insights into how to integrate technology and social characteristics in a way that serves virtual team objectives. 2.8. Conclusion The advancement in information and communication technologies along with globalization and inter organizational cooperation allowed organizations to establish tions. Virtual team members are expected to collaborate online to solve problems. This study investigate d the factors which influence a virtual team member intention to collaborate online. primarily on technical factors with little attention to the social issues surrounding their work. Research which constructs that contribute to online collaboration without examin ing how different factors combined influence a virtual team member intention to collaborate. This study investigate d the factors which influence online collaboration in virtual team settings that were reported in the literature. The contributions of this s tudy include a conceptual model for online collaboration and a measu rement scale to test this model. T he measurement scale was pretested and refined through a card sort exercise and validated through a pilot and a field study. Additional research is necess ary to further validat e the measurement scale and evaluate its generalizability across different virtual team environments

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37 3 Knowledge Sharing in Virtual Teams: The Impact on Trust, Collaboration, and Team Effectiveness 3.1. Abstract V irtual teams are utilized by organizations to gather experts to collaborate online in order to accomplish organizational tasks However, the characteristics of these teams create challenges to effective collaboration and effective team outcome Collaboration is an essentia l component of teamwork, the notion of forming teams in organizations is the need for members with complementary skills and expertise to collaborate in order to achieve the goal for which the team is established. Literature on virtual teams has been growin g for over a decade with research ers investigat ing different aspects of virtual work. Trust among virtual team members has been investigated by information systems researchers as a crucial challenge for virtual teams success Knowledge sharing and manageme nt in virtual teams has been the focus of recent research studies as it represent s a challenge in virtual work environment s ; specifically because the knowledge is scattered among geographically distributed team members with the absence of face to face inte raction. This study extends the literature on virtual teams by investigating the relationship between knowledge sharing, trust, and collaboration among team members in virtual team settings; and examining how these constructs ultimately affect virtual team effectiveness. We argue that knowledge as a valuable asset of virtual team member is a key factor influencing virtual team effectiveness This research introduces a conceptual model which describes the hypothesized relationship between knowledge sharin g, trust, collaboration, and team effectiveness in virtual team settings. The model is developed based on an intensive review of the

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38 literature on the constructs of interest in both traditional and virtual team settings. The theoretical foundation for the model is found in the Knowledge Based Theory of The Firm, Social Capital Theory, and the Social Exchange Theory. The study extends the Knowledge Based Theory of the Firm by improving our understanding of how knowledge sharing impacts trust, collaboration and virtual team effectiveness. 3.2. Keywords : Virtual team, Knowledge sharing, Trust, Collaboration, Team effectiveness. 3.3. Introduction The web and surrounding technologies are continuously and rapidly improving. T he web is providing a platform for individual s to communicate and collaborate, and it is continuously shifting towards a more user centric experience (Lai and Turban, 2008). On an organizational level, t he Web offers organizations a means to address their business needs and collaborate with each othe r (Zwass, 2003). Web 2.0 and the variety of tools it provides (e.g. blogs, wikis, and social bookmarking) have led to the emergence of new kinds of services such as social networks, aggregation services, and cloud based software applications. These tools p rovide a means for organizations to establish teams based on required expertise regardless of the physical location of individual team members Literature on inter organizational systems highlights the role of information technology in enabling organizati ons to form alliances and collaborate with each other to deliver a new product or service Nowadays, i nter organizational collaboration became crucial for a rapid response in a market where new products require numerous organization s working together to pr oduce ( Kumar and Dissel, 1996; Zwass, 2003). The

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39 significance of establishing virtual teams is that physical boundaries and barriers vanish, which enables a more efficient inter organizational collaboration. In the knowledge based view of the firm ( Nonaka 1994 ) organizations treat knowledge as an asset and a key factor for improving both the organization and its individuals. Organizations nowadays are considered knowledge focused systems as they are continuously realizing the significance of knowledge as a valuable asset which has the potential to prosper them in their markets maximize their economic value and improve their effectiveness (Gold et. al. 2001 ; Alavi and Leidner, 2001 ). There exists a considerable body of research in Information Systems liter ature which has investigated knowledge management systems (KMS). The objective of deploying KMS s in organizations is to support and improve the different components of knowledge management process which are knowledge creation, storage, transfer, and applic ation with in and among different entities of the organization ( Alavi and Leidner, 2001) Organizations form teams to work on sophisticated organizational tasks T he advantage of team structure is that it integrate s the knowledge that is distributed among t eam members which facilitate achieving more effective problem solving (Lam, 2000) Teams are organizational instrument which often considered being the solution for large, complex, and non routine tasks which if managed properly can lead to a n increase in organizational value (Alavi and Tiwana, 2002). V irtual team s are groups of individuals who are distributed across different physical locations and required to communicate and collaborate using information technology (Jarvenpaa, 1998; Powel et al. 2004). V irtual teams provide a convenient solution for integrating knowledge that is distributed across the organization or across different organizations The steady increase of organizational

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40 reliance on the practice of virtual team s is attributed to five main r easons according to Townsend et al. ( 1998) which are the modern structure of organizations which tends to be horizontal rather than vertical, the need for inter organizational collaboration to produce quality products and services, continuous globalization were organizations are spanning across vast geographical locations the interest of organizations in providing services not manufacture products, and to employee preferences in which organizations address their need for team members who hold specialized k nowledge regardless of their physical location, while employees can belong to an organization without the need to move to its physical location Nevertheless virtual teams also offer unique challenges as they encompass members with complex traits and char acteristics including absence of prior shared work history, different cultures and backgrounds, and the chance of work ing with members outside the organization boundaries These challenges if not addressed and managed properly by the organization, could j eopardize virtual effectiveness and success. The theme for establishing team s in organization s is to bring together members with the necessary expertise and skills to collaborate on a n organizational task (Hoegl and Gemuenden, 2001). When the team is a virtual one, collaboration becomes more complex since team members are separated through time and/or space which indicates that online collaboration among virtual team members requires more effort and different means for communication and coordination to be effective (Riegelsberger et. al. 2003 ; Piccoli et al. 2004 ) This study extends the literature on virtual teams through investigating the role of knowledge sharing and trust in enabling collaboration in virtual team settings; and how

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41 this will ulti mately affect the team outcome by measuring its effective ness Individuals consider knowledge to be a personal advantage and sharing it leads to loss of ownership of this knowledge and consequently loss of power and potential replacement which makes them hoard the ir knowledge for themselves and be reluctant to share it (Kankanhalli et. al. 2005). Nonetheless, sharing knowledge and exchanging ideas is crucial for team collaboration. An effective virtual team, is one which its team members put the success of the team ahead of their personal tendency to hoard knowledge for themselves. Several suc cess and effectiveness (Jarvenpaa and Leidner 1998; Zolin et al 2004 ; Glen, 2002; Steinfi eld, 2002; Henttonen and Blomqvist 2005, Ulriksson and Ayani 2005). However, i n virtual team settings, building trust among team members is a complex task mainly because of the absence of observed behaviors which members of traditional face to face teams rely upon to establish and maintain trust Therefore, virtual team members need to rely on different behaviors to assess trustworthiness among each other in order for them to compensate for the lack of physically observed behaviors. As a personal advantage and a valuable personal asset, we argue that knowledge sharing could be considered a significant behavior which virtual team members can observe and use to build trust. T he practice of virtual teams has provid es organizations the ability to work across p hysical and geographical boundaries Yet, this structure brings with it some unique challenges (Boudreau et. al. 1998) Knowledge exists in the individual and in the group (Nonaka 1994); individual s create knowledge in the first place, organizations and te ams do not create knowledge by themselves. The purpose of a team is to create a social

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42 knowledge through the interaction and collaboration of team members (Alavi and Leidner 2001). Nevertheless, transforming individual knowledge into a social knowledge is not an easy task. Even if the proper technology is in place, individuals tend to hoard knowledge for different reasons ; primarily they hoard their knowledge and selectively release part of it in order to appear valuable to the ir organization (Gilmour, 2003 ; Bock et.al. 2005 ). Therefore, knowledge sharing across the organization depends on employees' willingness to share and contribute their knowledge through a form of a knowledge management system (Bock et al. 2005) The reluctance of employees to share kno wledge has serious consequences which have the potential to hinder team collaboration and could lead to a team that is unsuccessful in achieving its goals (Van den Bosch et al. 1999) The purpose of this research is to investigate the hypothesized relatio nship between knowledge sharing, trust, collaboration, and team effectiveness among virtual team members. We argue that knowledge as a valuable asset and higher trust among virtual team members will lead to better collaboration and a more effective virtua l team. This research is designed to answer the following research questions: 1. Does sharing knowledge influence trust in virtual teams? 2. Does sharing knowledge influence collaboration in virtual teams? 3. Does collaboration influence virtual team effectiveness ? 4. Does trust influence the relationship between collaboration and team effectiveness in virtual teams? Answering these questions will allow researchers to better understand virtual teams and the factors which contribute to their effective ness T his study w ill also provide

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43 guidance to organizations about how to design and mange virtual teams in a way which encourages collaborat ion and enhances team effectiveness. The reminder of this paper is structured as follows: S ection 2 presents a literature review whic h focuses on virtual teams, trust, knowledge sharing and management, collaboration, and team effectiveness Section 3 presents the theoretical model of the research and hypotheses development. Section 4 presents the methodology we use d to conduct this rese arch. Section 5 presents a discussion of the results Section 6 discusses the research limitation. Finally, t he paper concludes with a summary and implications for future research. 3.4. Literature Review Virtual teams have been widely investigated by researche rs over the past two decades with trust and collaboration being identified as crucial factors for virtual team success. The literature on knowledge sharing and management in the organization has been growing tremendously in the past few years O rganization s are increasingly realizing the importance of knowledge as an asset for competitive and sustainable advantage. In this section, we present streams of research in the literature which investigated knowledge sharing, trust, and collabor ation in virtual team settings. 3.4.1. Virtual Teams a social system of three or more people, which is embedded in an organization, whose members perceive themselves as such and are perceived as members by others, and whose members c ollaborate on a common task.

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44 Teams are one form of a social system that is embedded within an organization. As a social system, there is a set of human relationships in which the act of one individual affects all other individ uals in the same social system. Organizations establish teams to work c ollaboratively on a common task, i n their definition of a team Katzenbach and Smith (1993) argue that the characteristics of team members should include complementary skills, they must be committed to achieve a common goal that is set by the organization and they need to hold themselves mutually accountable for the success or failure of the team mission Organizations rely on teams to work on complex and non routine tasks, and to achie ve an effective outcome the underlying assumption is that team members are expected to collaborate to achieve quality collective performance which exceeds an ( Gardner, 2012; Griffith et al. 2003). The structure of team s has changed dramatically over the past two decades. Teams are increasingly becoming virtual, in that they are often geographically dispersed and mainly rely on using information technology to communicate and collaborate (Jarvenpaa and Leidner 1999). Diff erent terms and synonyms are cited in the literature to describe virtual teams such as distributed teams (Gorton and Motwani, 1996; Mortensen and Hinds, 2001; Hinds and Bailey, 2003) and Technology Mediated Teams (Henttonen and Blomqvist, 2005; Fuller et.a l, 2006) with the majority using the term Virtual Teams. Following Powel et al. we define virtual teams A group of geographically, organizationally and/or time dispersed workers brought together by information and communication technologies to accompli sh one or more organizational tasks Powel et al. (2004)

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45 Traditionally, virtual teams used to be established to work on temporary projects with a short life cycle; these teams were mainly established based on a need to quickly gather necessary expertise t o solve complex or non routine problems. Squire and Johnson ( 2000) argue d that v irtual teams are formed as need for them arises in the organization, which means that as soon as virtual team members finish the required work task the team is disassembled Th ese characteristics of earl y virtual teams made them more of task oriented with limited opportunities to form social relationships (Jarvenpaa et al. 1998) R ecently however, organizations are increasingly establishing virtual teams to work on everyday rou tine and non routine tasks Furthermore, several organizations are allowing their employees to work virtually from the physical location of their preference Griffith et al. (2003) d escribe this as the degree of team v irtualness (i.e. some teams are comple tely distributed while others are a combination of co located and distributed members). Powel et al. (2004) identifies four major categories of virtual team research They include inputs (i.e. design, culture, technical, and training), socio emotional pro cess (i.e. relationship building, cohesion, and trust), task process (i.e. communication, coordination, and task technology structure fit), and outputs (i.e. performance and satisfaction). Table 2 6 illustrates research in the literature which investigated one or more of those research categories. Moving from traditional to virtual team s practice has an impact on organizational structure. W hile virtual teams offer unique benefits for organizations they are not without their challenges Virtual team challen ges mainly stem from the fact that members are distributed among different physical locations ; and their communication and

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46 collaboration is mediated by technology Reported challenges include time difficulties feedback delays, misinterpretation, cultural barriers, scheduling conflicts lack of communication, and delayed responses (Fussell et al. 199 8 ; Jarvenpaa and Leidner, 1998; Alavi and Tiwana 2002 ; Powell et al, 2004 ) These challenges if not addressed properly and managed, can threaten the success an d effectiveness of virtual team s ( Piccoli et al. 2004 ) Table 26 : Virtual Team Research Categories Category Research Research Topic Input Kristof et. al. 1995 Design Snow et. al. 1996 Design Gorton and Motwani 1996 Design T ownsend et. al. 1998 Design, culture Boudreau et. al. 1998 Design, technical Squ ire and Johnson 2000 Technical Socio Emotional Processes Handy 1995 Trust Meyerson et. al. 1996 Trust Jarvenpaa et. al. 1998 Trust Mortensen and Hinds 2001 Cohe sion Bhattacherjee, 2002. Trust Hinds and Bailey 2003 Relationship building, Cohesion Henttonen a nd Blomqvist, 2005 Trust Chandra et. al. 2011 Trust Koehne et. al. 2012 Relationship building, Cohesion Task Processes DeSanctis 1999 Communic ation processes Alavi and Tiwana, 2002 Knowledge sharing Steinfield et. al. 2002 Communication Griffith et. al. 2003 Task Technology Structure fit Chiu et. al. 2006 knowledge sharing Kanawattanachai and Yoo 2007 Coordination Outputs Piccoli et. al. 2004 Effectiveness Fuller 2006 Efficacy Kanawattanachai and Yoo 2007 Performance Heath et. al. 2011 Performance

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47 T eam effectiveness has to do with group produced outputs and the reward system the organization implements for team members ( Piccoli et al. 2004 ). An e ffective virtual team need s to achieve its objectives perform as a cohesive group in which everyone is accountable for the outcome, produce high quality output and team members need to have a sufficient level of satisfaction wit h the work outcome and with one another Accordingly, t eam effectiveness in virtual settings has two dimensions, performance and satisfaction ( Lurey and Raisinghani 2001 ; Piccoli et al. 2004 ). The principle for investigating virtual teams in Information S ystems is to expand our understand ing of how technology mediated interaction affect virtual team performance and effectiveness especially when compared to traditional face to face team settings (Potter and Balthazard, 2002). Nonetheless, virtual team perfo rmance should not be attributed solely to the technology which team members use for communication and collaboration ; a major influence on virtual team performance should be attributed to the social interaction and social capital among team members (Potter and Balthazard, 2002). According to social presence theory, communication is effective if the communication medium has the appropriate social presence required for the level of interpersonal involvement required for a task (Sallnas et al. 2000). In this research, we extend the literature by investigating how the factors of knowledge sharing and trust affect team effectiveness through enabling collaboration among virtual team members. 3.4.2. Knowledge Sharing and Management Knowledge is an organizational asset a nd a signi ficant organizational resource which has the potential to improve an organization competitive advantage ( Nonaka, 1994; Alavi and Leidner, 2001) a

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48 dynamic process of justifying personal belief towar and Liebeskind (1996) defines it as Nonaka (1994) conceptualized two dimensions of knowledge in organizations: tacit and explicit. T he tacit dimension of knowledge is c ons idered to be personal, complex, difficult to explain or share, and comprised of both cognitive and technical elements (Nonaka 1994). The cognitive element is considered to be an individual's mental models which were developed through experience, test, a nd proof in the mind of an individual. The technical element refers to knowledge which can be codified, shared, and communicated from one individual to another (Nonaka, 1994; Alavi and Leidner, 2001) The interaction between these two knowledge dimensions results in four modes of knowledge conversion according to Nonaka and Takeuchi (1995) who introduced the spiral of knowledge (Figure 2) to describe these d imensions. These modes are socialization, externalization, combination, and internalization These fo ur modes are considered essential for knowledge creation and sharing in the organization Figure 2 : Spiral of knowledge Adapted from Nonaka and Takeuchi (1995)

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49 Since knowledge is considered a valuable organizational asset, man aging it, enforcing adequate policies, and implementing Knowledge Management Systems (KMS) become an organizational necessity. The process of knowledge management in the organization aims to identifying knowledge throughout the organization, make it availa ble and accessible, and use it to improve the organization competitive advantage (Davenport and Prusak, 1998; von Krogh 1998; Alavi and Tiwana, 2002) KMS is a category of information systems which organizations implement to support the processes of knowl edge creation, storage, transfer, and application throughout the organization (Alavi and Leidner, 2001 ; Gallupe 2001) describes three levels of knowledge manageme nt technologies illustrated in T able 2 7 which are: KMS tools, KMS generators, and Specific KM S. There exist a rich literature on user technology acceptance (e.g. Davis, 1985) when it comes to using technology, and in the case of knowledge sharing and management an important issue arises which is users willingness to share their knowledge and seek Kankanhalli et. al. 2005). While techno l ogical capabilities are important, implementing a KMS does not guara ntee a successful knowledge management process For a KMS to be used and utilized effectively, the organization needs to address both the social and technical dimensions of KM Ss usage ( Kankanhalli et. al. 2005 ). Table 27 : Levels of Knowledge Management Systems. Level Description Examples Level 1 KMS Tools Programming languages, Database Management Systems Level 2 KMS Generators Lotus Notes, emails Level 3 Specific KMS OracleKM, SalesForce

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50 3.4.2.1. Organizational Knowledge Management Processes Following Alavi and Leidner ( 2001) an organizational knowledge management process include s a set of four processes: knowledge creation, knowledge storage/ retrieval, knowledge transfer, and knowledge application. Organizational knowledge creation includes developing new knowledge or replacing an existing one (Pentland 1995). Knowledge is norma lly created by an individual within the organization, but the interchange and the spiral of knowledge as described by Nonaka (1994) (See Figure 2 section 3.4.2) integrates and transforms the knowledge to make it available throughout the organization (Nonak a 1994; Alavi and Leidner, 2001) Organizational memory refers to the information and coded knowledge which are stored in a form of repository within the organization and accessible by its individual (Walsh and Ungson, 1991). K nowledge could be stored in various forms within the organization including written documentation digital repositories, and as tacit knowledge in the mind of the individuals ( Alavi and Leidner 2001; Tan and Hung, 2006). However, the main challenge in the organizational knowledge man agement process is making the stored knowledge accessible by individuals throughout the organization. From a technical perspective, KMSs are implemented to support this process Nevertheless, technology support does not guarantee a successful integration a nd transfer of knowledge within the organization; unless the organization implement a comprehensive knowledge management strategy which takes into consideration both the technical and the social dimensions of knowledge, the effectiveness of the KMS will be difficult to estimate (Alavi and Leidner, 1999 ; Kankanhalli et. al. 2005 ).

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51 Transactive memory refers to the process of sharing and exchanging knowledge within a group. In traditional team settings, transactive memory is developed through interaction amon g team members and is considered to have an influence on team performance and effectiveness (Alavi and Tiwana, 2002; Choi et al. 2010 ). However, in virtual team setting transactive memory is more complex and takes longer time to develop mainly because tea m members are geographically distributed and interact through a communication medium which has the potential to negatively impact team effectiveness. In traditional team settings, research has found that knowledge sharing is critical for team effectiveness (Powell et al. 2004). Considering the distributed nature of organizational cognition, an important process of knowledge management in organizational settings is the transfer of knowledge to locations where it is needed and can be used. The distributed and fragmented nature of virtual teams leads to the fragmentation of knowledge among different team members who reside in different locations which make knowledge sharing and integration difficult to be accomplished when compared to traditional team settings In summary, knowledge is a personal advantage for individuals and a valuable asset for teams and organizations. Organizations have employed various techniques to integrate and transfer knowledge ; and a new model of knowledge management is emerging in the organization which aims to motivate individuals to share their knowledge (Al Alawi et al. 2007) Handy (1995) describes virtual teams as a concept without a place and a new organization al view to their employees to be human assets and not human costs. The asset which Handy (1995) refers to here is the knowledge which virtual team members possess and bring to the team. In this study, we argue that sharing knowledge in

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52 virtual team setting has an effect on trust among team members as it compensate for some o f the observed behaviors which are missing in the virtual environment. Also, we argue that sharing knowledge affects collaboration among virtual team members which ultimately influences the team effectiveness 3.4.3. Trust Trust is a key factor in forming and mai ntaining social relationships and it is a key for cooperative relationships and effective teamwork ( Jarvenp a a et. al 1998 ; Jarvenpaa et al. 2004; Zaheer et.al, 1998 ; Powell et al. 2004 ). In co located organizational settings, research has reported that tru st has many benefits such as better productivity facilitates resolution of conflicts disagreements and improves effectiveness (Earley, 1986 ; Hagen and Choe, 1998; Zaheer et.al, 1998). High trust teams tend to exchange ideas more openly, have clearer goal s, more motivated and satisfied, and less willing to leave the team (Zand, 1972 ; Jarvenp a a et. al 1998 ). There is no one agreed upon a definition of trust, but generally, trust includes elements of risk, vulnerability, and uncertainty Mayer et.al, (1995) the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor the subjective expression o regarding the behavior of another actor in a way that is safe and secure the extent to which a person is confident in, and willing to act on the basis of, the words actions, and decisions of another

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53 Mayer et al. (1995) argue that the attributes of trust are ability (i.e. skills of trustee which makes him capable of performing his task), benevolence (i.e. willingness to do good), and integrity (i.e. dependability a nd reliabl ity ). Numerous researchers have built conceptualization in investigating trust in virtual teams ( e.g. Jarvenpaa et al. 1998; Jarvenpaa and Leidner, 1999; Dirks and Ferrin 2001; McKnight et. al. 2002; Gefen et. al. 2003). In this study, we also adopt the trust construct as conceptualized in Mayer et al. (1995). The literature on trust reported different types of trust in different contexts. Lewis and Wiegert (1985) distinguished between cognitive and affective based trust, Lewick i and Bunker (1995) argue that trust can be calculus based, knowledge based, or identi fication based, and Meyerson et al. (1996) brought the concept of swift trust in temporary teams. These typ es of trust are illustrated in T able 2 8 Lewis and Wiegert (19 85) argue two distinctive types of trust: the cognitive and the affective. Individuals build cognitive trust based on evidence of observed behavior rather than emotion and genuine caring which are considered to be the foundations for affective based trust. In organizational settings, McAllister (1995) argue s that cognitive trust is built among individuals based on their performance, cultural or ethnical similarities, and h igher professional credentials, w hile affective trust is built based on citizenship an d interaction frequency. Lewicki and Bunker (1995) argue that trust is dyn amic and changing phenomenon which takes different shapes in different stages of a relationship. Therefore, a overtime, trus t will become more evident ( Panteli and Sockalingam, 2005).

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54 Lewicki and Bunker (1995) suggest three types of trust, each corresponding to a different stage of the relations hip: Calculus Based Trust (CBT), Knowledge Based Trust (KBT) and Identification Based Trust (IBT). CBT is based on the concepts of reward and punishment for maintaining or violatin g trust. KBT is similar to cognitive based trust in which individuals build a trust decision based on observing behaviors throughout interactions over time. IBT is considered a higher level of trust which takes longer time to be developed. IBT requires a high degree of mutual understanding among individuals and a more established trust to the point which one can act on behalf of the other ( Rousseau, 1998; Lewicki and Wiethoff 2000) Table 28 : Types of Trust in Previous Research Research Type of Trust Definition Lewis and Wiegert 1985 Cognition based trust Individuals consciously choose those in whom they trust based on perceptions of evidence for their trustworthiness. Affect based trust Individuals demonstrate their genuine caring and concern for one another over time Lewicki and Bunker (1995) Calculus Based Trust Assessments of costs and rewards for violating or sustaining trust. Knowledge Based Trust Individuals have enough information and understanding about each other to predict behavior. Identification Based Trust Parties take time to develop their common interests, values, perceptions, motives and goals Meyerson et. al. (1996) Conventional Trust Traditional trust which results from observing individuals behavior. Swift Trust Trust developed in temporary short lived groups which presume cl ear roles and responsibilities.

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55 The literature on virtual teams recognizes trust as a critical requirement for team success and effectiveness (Jarvenp a a et. al 1998; Sarker et al. 2001; Kirkman et al. 2002 ; Powell et al. 2004). Having trust in an organiz ation and among members of a virtual team is considered to be a key element of success and a necessity to overcome the obstacles which team members face due to the absence of face to face interaction (Kristof et.al. 19 95 ; F urst et al. 1999 ; Kirkman et al. 2002 ; ). As organizations are becoming more distributed, concerns about how to build trust among team mem bers are increasing. Kirkman et al. (2002) argue that building trust is the biggest challenge for virtual team success. For instance, as organizations are becoming more distributed, more team members find themselves to be working with others whom they have never met and whose cultures and societies they know little about, but with whom they must collaborate through technology to achieve a predefined orga nizational goal (Jarvenpaa et al. 1998; Kirkman et al. 2002) This creates a significant challenge for organizations on how to establish trust and collaborative relationship among members of virtual teams. Considering the characteristics of virtual teams, developing trust is not an easy task or even similar to the development of trust in traditional teams. In practice, most observed behaviors in virtual teams are diffe rent from those of traditional teams. Within the traditional organizational literature, an important factor of trust is the degree of familiarity with other people (i.e. the more we get to know others, the more likely it is that we trust them) (Lewicki and Bunker, 1996). However, virtual team members do not possess this characteristic, they are separated in space and/or time, and their interaction is

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56 mediated by technology Therefore, the observed behaviors and the signals which exist in traditional team se tting do not apply to virtual team setting s, which means that virtual team members lose key information which help build a trust decision This loss of information normally leads to increase uncertainty, and lower trust among virtual team members (Riegelsb erger et. al. 2003). Furthermore, it is reported in the literature that information technology increases the potential for faulty first impressions (Ferreira et al. 2012). Therefore, we need to consider other factors behaviors, and standards especially de signed for virtual teams in order for members to trust each other. Meyerson et al. (1996) present the concept of swift trust in which they argue that trust can be developed in temporary team settings in spite of the short life cycle and the absence of tru st determining behaviors found in regular teams Instead, the swift trust argument is built on establishing clear roles and responsibilities and assuming that a sufficient level of trust already exists among team members ( Jarvenpaa et al. 1998; Panteli and Sockalingam, 2005). Nonetheless, swift trust was originally introduced to describe trust in temporary traditional co located teams. The early literature on virtual teams re ported that virtual team members exhibit trust characteristics which are most similar to swift trust because virtual teams were mainly temporary teams as well (Jarvenpaa et al. 1998). Therefore, s wift trust applies to temporary virtual teams with a short l ife cycle which makes it difficult to establish strong trust among members. Nowadays, however, virtual teams are embedded into the organization structure and they are no t necessarily created work on temporary tasks but rather to work on everyday tasks Thi should have the time, technology, and

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57 means to build a stronger trust beyond swift trust in order to sustain a long relationship and produce effective outcome. Kanawattanachai and Yoo, (2002) found that virt ual team members rely more on cognition based trust than on affect based trust in which they base their trust decision on perceptions of evidence for their trustworthiness not on genuine caring and emotion. In this research we investigate the relationship between trust and knowledge sharing and its effect on collaboration and team effectiveness. We argue that sharing knowledge in virtual team setting will influence trust mainly because virtual team members who share and contribute their knowledge provide ev idence for their trustworthiness which would compensate for the lack of trust signals exist in traditional team settings. 3.4.4. Collaboration An organization is a social system of individuals who are required to work collectively and collaboratively on accompli shing a common goal (Alavi and Tiwana, 2002) Collaboration in work settings is defined by Aram and Morgan as the presence of mutual influence between persons, open and direct communication and conflict resolution, and support for innovation and experimen tation M organ, 1976) Collaboration is an essential part of team work and an e ffective collaboration leads to an effective team outcome (Aram and M organ, 1976) To collaborate effectively, the knowledge that is distributed among team members mus t be properly and adequately integrated (Gray, 2000) In virtual team setting, integrating knowledge to achieve an effective collaboration is challenging as knowledge is distributed among physically separated team members

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58 Virtual team members are chosen and assigned based on the need for their unique knowledge and expertise. However, recruiting expert members does not guarantee effectiveness collaboration (Kudaravalli and Faraj, 2008) In order for virtual teams to collaborate effectively, they need estab lish an open communication and efficiently coordinate activities among each other (Hemetsberger and Reinhardt, 2009). Coordinating activities in group work is already difficult for co located teams and is even more challenging for virtual teams (Fussell et al. 1998). When collaborating in traditional face to face settings, conversations which take place in the shared physical space facilitate coordinating activities (Kudaravalli and Faraj, 2008). However, in virtual team settings where members are geographi cally distributed and communication is mediated by technology, coordination becomes more complex. If virtual team members are not properly coordinating their activities and are unaware of the work progress across the team, the team is likely to face seriou s obstacles which impact collaboration. Durate and Snyder (2006) discuss seven types of virtual teams, they argue that all of them have in common that team members must collaborate to accomplish their work. Virtual team members need to overcome the challe nges they face by keeping open communication channels, coordinate activities, and collaborate effectively. Otherwise, they will not achieve effective outcomes We argue that v irtual teams can only thrive if individual team members can overcome the challeng es to virtual collaboration and manage to work in a coordinated effort to solve problems together. This suggests the need for an improved understanding of how to create virtual teams that work, share knowled ge, and collaborate effectively.

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59 The literature on Computer Supported Collaborative Work (CSCW) addresses how collaborative activities can be supported by means of computer information systems (Schmidt, 2011). CSCW investigates collaborative work in groups in both face to face and virtual team settings and aims to provide a better understanding of the technology and the social aspects group work Johansen (1988) introduced the CSCW Matrix which conceptualizes CSCW systems in terms of the context of system use. The matrix considers work contexts along two dimensions time and space. The matrix distinguishes between the needs of different work groups whether they are co located or geographically distributed, and whether they collaborate synchronously or asynchronously. The CSCW matrix is illustrated in Figu re 3. According to the CSCW Matrix, when team members are collaborating from different distributed places and in different times they need CSCW systems that facilitate communication and coordination activities. In traditional team settings, communication a nd coordination are also reported as indicators for team collaboration (Aram and Morgan, 1976; Rousseau et al. 2006) Figure 3 : CSCW Matrix Adapted From Johansen (1988)

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60 Collaboration depends upon trust to enable channels of open communication (Scott, 2000). Trust is reported to reduce task uncertai nty improves task coordination process among team members and lead to an effective collaboration ( Kollock 1994 ; Holton, 2001 ) Weick and Roberts (1993) argue that to coordinate knowledge among In th is research we investigate the how knowledge sharing and trust and influence effective collaboration in virtual teams setting. 3.4.5. Team Effectiveness Teams are fundamental component of the organizational structure, they enable organizations reach better solu tion s and more effective outcomes (Gardner et al. 2012) Organizations increasingly rely on virtual teams to meet the demands of a changing marketplace (Luery and Raisinghani, 2001) Because of their unique characteristics virtual teams can be difficult t o manage and could take longer time to reach an effective outcome In tradition a l team settings, Cohen and Bailey ( 1997 ) c ategorize team effectiveness into three major dimensions : performance, member attitudes, and behavioral outcomes. In virtual team sett ings, however, Lurey and Raisinghani ( 2001) argue that the dimensions of virtual team effectiveness are team performance and team which is consistent with the work of Mathieu et al (2008) Team performance is measured by evaluating th e team outcome and comparing it to the requirements of the assigned task, while satisfaction represents of work process, commitment to the team objectives, and chances of personal growth (Lurey and Raisinghani 2001) V irtual team s w ill not be effective if the team members themselves are not satisfied with the way the team functions. Team members need to

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61 have a sense of belonging to the team; this can only exist if they are satisfied with the work experience. In t his study, we invest igate how knowledge sharing, trust, and collaboration influence virtual team effectiveness We argue that a virtual team where knowledge is freely shared and trust is well established will be a more effective t eam. We argue that knowledge sharing affect team effectiveness through improving team collaboration while trust moderates the relationship between collaboration and team effectiveness. 3.5. Research Model and Hypotheses Development Even though information and communication technologies impact knowledge sharing, trust, and collaboration, social factors also have the potential improve or jeopardize virtual team work (Zakaria et al. 2004) O rganizations are distributed knowledge systems and the ability of the orga nization to identify knowledge resources, leverage them, and make them available for its employees can lead to a distinctive competitive advantage (Tsoukas, 1996; Davenport and Prusak, 1998; Alavi and Tiwana, 2002). In this section we present the research model, along with the research hypotheses, which explain the relationships between knowledge sharing, trust, collaboration, and their impact on virtual team effectiveness. The theoretical res earch model is demonstrated in F igure 4 The model represents a correlational research in which knowledge sharing is proposed to positively influence trust, collaboration and team effectiveness. Furthermore, we argue that trust moderates the relationship between collaboration and team effectiveness in which the higher levels of trust; the higher is collaboration influence on team effectiveness.

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62 Figure 4 : Theoretical Research Model The Knowledge Based Theory of The Firm states that a firm is a knowledge creating entity (Nonaka 1994; Nonaka et. al. 2000 ). Nonaka and Konno introduced the concept of ba a shared space which serves as a foundation for knowledge creation (Nonaka and Konno, 1998) An organization is considered to be a 8), which means that an organization is a shared space for individuals to create knowledge and improve together Grant (1996) emphasizes the role of the individual within the organization in creating knowledge and argues that the role of the organization i s to integrate, store, and apply the knowledge created by its individuals. If effectively utilized and integrated, created knowledge could be transformed into an organizational asset which has the potential to improve the organization competitive advantage ( Nonaka and Konno 1998; Nonaka et. al. 2000) Knowledge Sharing Collaboration Trust Virtual Team Effectiveness H2 H3 H4 Control Design Process Virtual Team Setting

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63 Virtual teams are assembled of knowledgeable and skilled individuals who are expected to perform an organizational task to the best of their abilities. Ability is one of t (i.e. Ability, Benevolence, and Integrity); virtual team members are then left with other two attributes they need to establish among each other which are benevolence and integrity. The characteristics of virtual teams, especially the technology mediated communication and lack of face to face interaction cause virtual team members to lose important observed behavior they need to evaluate each trust (Kanawattanachai and Yoo, 2002). With the lack of physical interaction which takes place in colloca ted team settings, virtual team members need to demonstrate different and unique behaviors to their team mates in order to prove their benevolence and integrity. By sharing the knowledge they possess, we argue that virtual team members demonstrate their wi llingness to do well and that they are dependable and reliable. S haring knowledge in virtual team settings is indeed a controversial and a complex issue On one hand, knowledge is viewed by virtual team members as a valuable personal asset and sharing it leads to the loss of their unique relative advantage to the organization while it enables others to free ride on their effort (Wasko and Faraj, 2005) On the other hand organizations continue to form and rely on virtual teams and team members seem to shar e knowledge for variety of reasons it is also reported that virtual team members share their knowledge to appear valuable to the ir organization ( Gilmour, 2003 ). Trust among virtual team members is expected to affect team performance and effectiveness as it enables an environment of open information exchange and assist team

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64 members overcome the physical barrier ( Scott, 2000 ; Kanawattanachai and Yoo, 2002) Since tr ust is a dynamic phenomenon which changes throughout time and since virtual teams are becomi ng part of the organizational structure and not necessarily temporary anymore (Lewicki and Bunker, 1995 ; Kanawattanachai and Yoo, 2002 ; Griffith et al. 2003 ; Panteli and Soc kalingam, 2005) virtual team members nowadays have sufficient time to build social capital and make a sound trust decision The challenge in virtual environments is identifying unique and distinctive behaviors to assist team members in making the trust de cision. The type of trust which develops in virtual setting is reported to be a cognitive based trust ( Kanawattanachai and Yoo, 2002 ) this is primarily for two reasons: 1) information technologies are not successful in transferring feelings and emotions w hich affect based trust depends upon; 2) trust decisions are often built based on team members ability, integrity, and benevolence which in the absence of face to face interaction virtual team members need to provide evidence for ( Sproull and Kiesler 1986 ; Kanawattanachai and Y oo, 2002; Mayer et al. 1995 ). Overall, virtual team members need to provide solid evidence of their trustworthiness for other team members to trust them. In the early phase of virtual team work, team members could quickly develop affe ct based trust by assuming the good in each other. This is consistent with Mayerson et al. concept of swift trust which Jarvenpaa and Leidner (1998) describe as fragile and temporal However, since trust is cognitively assessed in virtual teams and since trust takes different shapes along time, w e argue that in virtual team settings, the behavior of leads to higher levels of trust among the team members.

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65 H1: In virtual team settings, knowledge sharing has a positive influence on trust among team members. Teams perform better when they comprise members with the expertise relevant to the task they are supposed to accomplish (Gardner, 2012). When virtual team s are able to locat e and access organizational knowledge, they perform better and they produce a more effective outcome ( Civi, 2000 ; Gardner et al. 2012) Social E xchange T heory explains human behavior in social exchange (Blau 1964). The basic principle behind The Social Exc hange Theory is that individuals within a social system exchange favor s with a general expectation of some future but unclear return Therefore, S ocial E xchange Theory assumes a long term relationship where individuals have enough time to exchange favors ( Blau 1964 ; Molm et al. 2000 ). Fulk et al. argues that k nowledge sharing can be seen as a form of generalized social exchange where individuals share their knowledge without a clear expectation what the return would be but on a promise of a long mutual rel ationship Resources (tangible and intangible) are considered to be the currency of social exchange. Social Exchange T heory posits that people behave in ways that maximize their benefits and minimize their costs ( Molm et. al. 2000 ). The main cost of shari ng knowledge es pecially in virtual tem setting, is the loss of a personal relative advantage while the main benefit is effective collaboration and integration of diverse resources to reach new insights Furthermore, organizations are continuously implemen ting reward systems to encourage team members to contribute and share knowledge and punish them if they refuse to share the knowledge they possess ( Bartol and Srivastava 2002)

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66 To collaborate effectively, virtual teams require the knowledge that is distri buted among their team members to be adequately located and integrated Otherwise, virtual teams will suffer high costs associated with searching for the necessary knowledge to perform their job (Gray, 2001 ). Based on the preceding discussion, we argue tha t sharing knowledge in virtual team settings has a positive effect on collaboration among team members. H2: In virtual team settings, knowledge sharing has a positive influence on collaboration. Virtual team s characteristics influence the way in which team members work together and have the potential to hinder team success and effectiveness. The ability of virtual teams to achieve effective outcomes without face to face interaction is a controversial matter in the literature since virtual teams tend to take longer time to reach common ground and collaborate effectively (Holton, 2001 ; Potter and Balthazard, 2002 ; Kirkman et al. 2004 ). On the other hand, it is reported that virtual team members tend to express their opinions more free ly and openly regardless o f any social or managerial constrains Consequently, virtual team members are able to assess each other more accurately based on performance and contribution ; they also show less bias compa red to traditional teams a nd contribution (Weisband and Atwater, 1999 ) Virtual t eam effectiveness has two dimensions: team performance and individual satisfaction (Lurey and Raisinghani 2001 ; Piccoli et al. 2004) An effective virtual team is the one which delivers high task per in terms of work experience task load and working with one another (Peters and Manz,

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67 2007) To achieve these two effectiveness dimensions with the absence of face to face interaction, and solely relying on i nformation technology medium for communication and collaboration virtual team members need to contribute more effort into collaboration by exchanging more ideas, share more knowledge, and sufficiently coordinate tasks among each other. Based on the procee ding discussion, we argue that an effective and successful collaboration will ultimately influence the output of the virtual team in terms of team effectiveness. H3: In virtual teams, collaboration among team members has a positive influence on team effect iveness. A virtual team is a social system of individuals who are expected to collaborate on a common organizational task; and the act of one team member affects all other team members (Hoegl and Gemuenden, 2001) Social Capital Theory explains how changes in relations among individuals in a social system facilitate action coordination collaboration, and resource exchange (Colman, 1988, Adler and Kwon 2002; Chiu et al, 2006). Colman describes the social capital process in this paragraph If A does someth ing for B and trusts B to reciprocate in the future, this establishes an expectation in A and obligation on the part of B. This obligation can be conceived as a credit slip held by A for ese are bad debts that will not be repaid (Colman, 1988). Based on Colman (1988) argument, we conclude that in order to build a social capital in social systems, two components are necessary: action and trust among the individuals in the system. For the a ction to take place a trust decision must be made; and

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68 based on the act of the other parties trust is either confirmed or turn to be misplaced. Trust is reported in the literature to be the foundation for effective collaboration (Mayer et al. 1995; Roussea u et al. 1998 ; Paul and McDaniel 2004). In social systems such as teams, t rust is considered a key factor in reduc ing complexity and uncertainty and enabling a positive atmosphere of collaborat ion among individuals within the system ( Kollock 1994; Paul an d McDaniel 2004) Theoretically, when a virtual team is newly established trust levels among virtual team members is expected to be significantly low (Jarvenpaa et al. 1998) The reason is that virtual team members normally lack a past history working toge ther communicate via technology with little to no chance to meet in person and unable to observe each (Robert et. al. 2009). hat when team members trust each other they have expectation of certain behaviors and certain performance based on this trust. T rust facilitates transactions by reducing the uncertainty and ris k of collaboration and t eam members with higher trust are more likely to work together cooperatively (Baba, 1999 ; Jarvenpaa et al. 1998 ). Team collaboration is the backbone that supports and drives team success and effectiveness. In spite of the availability of advanced information and telecommunication technologies, trust continues to influence collaboration among virtual team members since the ability to collaborate depends heavily upon trust to facilitate sharing of information and knowledge across the team (Koehne et al. 2012 ; Scott, 2000) Based on the preceding argument, w e argue that trust moderate s the relationship between collaboration and team effectiveness in virtual team setting

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69 H 4 : (a) In high trust context, there will be a positive association between collaboration and team effectiveness. (b) In low tru st contexts, this association between collaboration and team effectiveness will be significantly less strong. 3.6. Methodology The research presented in this project can be described as quantitative, positivist research. The survey method for data collection is used to test the proposed research model. The unit of analysis is at the individual level and behavior level as virtual team perceptions of trust, collaboration, and team effectiveness in an open knowledge sharing environment 3.6.1. Sample The theor etical population comprises of any and all virtual team members who work in an organizational setting. In order to avoid sampling bias, we chose to focus on a specific industry. Therefore, the study population includes virtual team members who work in the information technology industry (e.g. software engineers, and developers). To acquire a representative sample, the sample frame in this study was mainly acquired from social media websites (e.g. Linkedin and Facebook) where we were able to identify member s who work in virtual teams in the IT industry. Also, a sample frame was developed of individual known to be virtual team members. This sample frame was developed by an effort of the investigator and the list included members who fit the description of the study population. Purposive sampling was used to target individuals who work in virtual settings. The data was collected in two phases. In the first phase, we identified individuals, pages,

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70 and groups which include members who best represent the populati on. In the second phase, emails were sent to group admins, directly to group members, and directly to virtual team members in the sample frame developed by the investigator seeking their response to the survey. In the second phase, a brief description of t he research was given along with a link to access the survey online. A total of 1 9 3 subjects were recruited for participation in this study. Given the method the sample was gathered, response rate could not be estimated. 3.6.2. Measures The survey measures are derived from previously published studies in the literature. The variables of interest are knowledge sharing, trust, collaboration, and team effectiveness. The measure for knowledge sharing is adopted from the work by Phang et al. (2009). Additional surve y items are adopted from a survey used in practice by MITRE Corporation ( www.mitre.org ) to obtain a baseline of knowledge sharing behaviors and s advisor, expert judges, professional virtual team members, and researchers in the field. The measure for trust is derived from Mayer et al. (1995 ) which is considered the most widely cited researches on trust in the organization with over 7000 citations, and the measure they developed is widely used and accepted in the lit erature (e.g. Jarvenpaa and L e i dner 1998, Jarvenpaa et al. 1999, Dirks and Ferrin 2001, and Bhattacherjee 2002). The measure for collaboration is derived from Aram and Morgan (1976). Al though this measure was developed to measure collaboration in traditional team settings, it has been adopted in studies which investigate online collaboration (e.g. Zhu et al. 2010; Chandra et al. 2011; and Heath et al. 2011).

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71 The measure for team effect iveness is derived from Lurey and Raisinghani (2001) which measures virtual team effectiveness in terms of team performance and team The formal construct definitions and sources are given in Table 29 below and t he actual items used i n the survey are given in APPENDIX II. Table 29 : Definitions of the Study Constructs Construct Definition Knowledge Sharing The degree to which knowledge is shared (contribute/seek) among virtual team members Trust The belief in the good intent and the ability of other virtual team members. Collaboration The degree to which team members work together to accomplish an organizational task Team Effectiveness Group produced outputs and the consequences a group has for its members Communication Coordination Performance Satisfaction Knowledge Sharing Collaboration Trust Virtual Team Effectiveness H2 H3 H4 Control Design Process Virtual Teams Figur e 5 : Measurement Model

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72 3.7. Data Analysis Data analysis includes demographics and descriptive analysis. The model is then tested for reliability using The validity of the model is assessed by evaluating content validity, convergent validity, and discriminant valid ity. The partial least squares (PLS) method is used to examine the hypotheses, as it is recommended for complex models focusing on prediction, and allows for minimal demands on measurement scales, sample size, and residual distribution (Chin et al., 2003) Finally t he Sobel test of mediation is used along with control variable analysis and multi group analysis. 3.7.1. Demographics and D escriptive Statistics Respondents were asked to indicate their gender and age. Respondents were also asked to indicate how long they worked in virtual teams, how long they have been members of the same team, if they participated in pure virtual environment or in both virtual and face to face environments, if they have been members of global virtual teams, if they ever been virtual team leader, and if they work for the same organization (T able 3 1 ). 3.7.2. Reliability Reliability of the measurement model is assessed by examining internal consistency and indicator reliability. Internal consistency measures the reliability of a set proportion of the indicator variance explained by the corresponding latent variable, and is represented by indicator loading, described as follows: fair (.45 .54), good (.55 .62),

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73 very good (.63 .70), and excellent (.71 and higher) (Comrey, 1973) s alpha values for the constructs in the model are illustrated in Table 3 0 and all constructs show high and adequate alpha values. 3.7.3. Validity Validity of the measurement model is assessed by examining content validity, internal consistency and discriminant v alidity. Content validity is an assessment of the degree of correspondence between the items selected to constitute a summated scale and its conceptual definition (Hair et. al. 2005). Content validity is ensured by utilizing measurement items validated in existing research (Section 4 .2). The psychometric properties of the research model were evaluated by examining item loadings, internal consistency, and discriminant validity. Researchers suggest that item loadings and internal consistencies greater than 70 are considered acceptable (Hair et. al. 2005). As can be seen by the shaded cells in Table 33 all item loadings surpass score. The composite reliability scores are located in the leftmost column of Table 32 which shows adequate reliability scores for all constructs. Table 30 : Construct Knowledge Sharing 0.93 Trust 0.83 Collaboration 0.9 1 Team Effective ness 0.92 Communication 0.90 Coordination 0.91 Performance 0.93 Satisfaction 0.92

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74 Table 31 : Demographics and Descriptive Statistics Category Participants Mean Standard Deviation Sample Variance Gender Male 77% 1.23 0.42 0.18 Female 23% Age 1. 18 20 0 2.81 0.88 0.88 2. 21 29 43% 3. 30 39 40% 4. 41 50 12% 5. 51 60 5% Experience 1. less than a year 12% 2.27 0.74 0.77 2. 1 5 54% 3. 6 10 27% 4. 10 15 6% Member of Current Te am 1. less than a year 28% 2.27 0.74 0.55 2. 1 5 54% 3. 6 10 27% 4. 10 15 6% 5. more than 15 0 % Virtual Team Type 1. Online only 6% 1.85 0.65 0.42 2. Combined online and face to face members 34% 3. Both Types 59% 4. F ace to face only 0 % Leader VS. Member 1. Leader 42% 0.42 0.49 0.24 2. Member 58% Global Virtual Team Member 1. Yes 60% 0.42 0.49 0.24 2. No 40% Members work for the same organization 1. Yes 82% 0.60 0.49 0.24 2. No 18% Mandatory Vs. V oluntarily 1. Yes 76% 0.82 0.39 0.15 2. No 24%

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75 Evaluating discriminant validity has two parts; firstly, each item should load higher on its respective construct than on the other constructs in the model, and secondly, the Average Variance Extracted (AVE) for each construct should be higher than the inter construct correlations (Agarwal and Karahanna, 2000). In Table 3 3 we can see that all items load higher on their respective construct than the other constructs in the research model. Likewise, in Table 3 2 we can see that the square root of the AVE in the diagonal for each construct is higher than the inter construct correlations on the same row and the same column. These two comparisons suggest that the model has good discriminant validity. 3.7.4. PLS Analysis Partial Least Squares ( PLS ) method is used to examine the hypotheses ; a two stage analysis has been performed using confirmatory factor analysis to assess the measurement model followed by examination of the structural relationships. PLS is an ex tension of the multiple linear regression model ; it is a linear model specifies the relationship between a dependent variable ( Y ) and a set of predictor variables ( X's ). Table 32 : Convergent And Discriminant Validities CR AVE Knowledge Sharing Trust Collaboration Effectiveness Knowledge Sharing 0.9 5 0.84 0.92 Trust 0.85 0.88 0.65 0.94 Collabor ation 0.95 0.71 0.70 0.61 0.84 Effectiveness 0.96 0.75 0.64 0.43 0.71 0.87

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76 Table 33 : Cross Loading Collaboration Effectiveness Knowledge Sharing Trust COM1 0.91 0.56 0.61 0.55 COM2 0.80 0.49 0.59 0.52 COM3 0.79 0.57 0.63 0.48 COM4 0.78 0.54 0.67 0.48 COM5 0.73 0.46 0.57 0.48 COR1 0.88 0.68 0.57 0.42 COR2 0.85 0.64 0.57 0.35 COR3 0.86 0.63 0.56 0.35 COR4 0.72 0.55 0.49 0.43 COR5 0.70 0.56 0.45 0.43 PER1 0.64 0.90 0.59 0.29 PER2 0.62 0.87 0.59 0.27 PER 3 0.61 0.88 0.53 0.30 PER4 0.60 0.82 0.50 0.29 SAT1 0.65 0.88 0.65 0.36 SAT2 0.57 0.70 0.49 0.36 SAT3 0.67 0.83 0.55 0.28 SAT4 0.64 0.84 0.58 0.28 SAT5 0.65 0.86 0.59 0.34 KN1 0.60 0.48 0.86 0.49 KN2 0.61 0.50 0.86 0.54 KN3 0.65 0.64 0 .90 0.52 KN4 0.64 0.64 0.88 0.48 KN5 0.67 0.64 0.87 0.43 KN6 0.62 0.60 0.88 0.41 TR1 0.46 0.29 0.42 0.80 TR2 0.43 0.26 0.47 0.82 TR3 0.59 0.49 0.48 0.83 TR4 0.32 0.12 0.42 0.82 The PLS method allows for simultaneous analysis of the mea surement and structural models, and allows each indicator to vary in how much it contributes to the

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77 composite score of the latent variable (Chin et al., 2003) PLS also allows for latent variable modeling of interaction effects, necessary for the proposed model as it includes a moderating variable. The results of the PLS SEM analysis are presented in Figure 6 Knowledge Shar ing has an R Squared value of 0 because it is not being predicted by any other construct. Trust has an R Squared value of 0. 31 collaboration has an R Squared value of 0.4 8 9, and team effectiveness has an R Squared value of 0.50. This means that 31 % of th e variance in Trust and 49 % of the variance in Collaboration is explained by Knowledge Sharing and 50 % of the variance in Team Effectiveness is explained by Collaboration (Agarwal and Karahanna, 2000). The path coefficients between knowledge sharing tru st collaboration, and team effectiveness were significant at .001 However, the path coefficient for trust moderating the relationship between collaboration and team effectiveness is insignificant In summary three out of the four hypotheses were support ed as illustrated in table 3 4 Figure 7 : PLS SEM Results Figure 6 : PLS SEM Results

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78 Table 34 : Summary of Hypotheses Tests Hypothesis Supported H1: Knowledge Sharing Trust Yes H2: Knowledge Sharing Collaboration Yes H3: Collaboration Team Effectiveness Yes H4: Trust x Collaboration Team Effectiveness No 3.7.5. Mediation Analysis Sobel Test for the Significance of Mediation is used to test for the significance of collaboration mediating the relationship between knowledge sharing and team effectiveness. The Sobel test is a specialized t test that provides a method to determine whether the reduction in the effect of the independent variable, after including the mediator in the model, is a significant reduction and therefore whether the mediation effect is statistically signi ficant. Table 3 5 represents the results of the Sobel t est. As shown in the table the S obel test for mediation i s significant at the 0.01 level, which indicates that collaboration mediates the relationship between knowledge sharing and team effectiveness. Table 35 : Sobel Test for the Significance of Mediation Path Coefficient Standard Error Knowledge Sharing Collaboration 0.7 22 0.0 536 Collaboration Team Effectiveness 0.6 85 0. 2231 Sobel Test Statistics 2.99358902 One tailed p robability 0.00137859 Two tailed probability 0.00275717

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79 3.7.6. Control Variable Analysis In this study we controlled for the effect of the design process on the dependent variables (i.e. trust and team effectiveness). Conducting PLS analysis on the model after adding the control variable resulted in minor changes in the R Squared values for trust and team effectiveness as shown in table 3 6 Also as shown in figure 7, the path coefficient between the control variable and both trust and team effectiveness are not strong. Figure 8 : Control Variable Analysis Table 36 : Control Variable Effect Construct R Squared: No Control R Squared: With Control Change Sig. Trust 0. 31 0. 314 < 0.1 Team Effectiveness 0.50 0.5 93 < 0.1 ** 3.7.7. Multi group analysis Multigroup analysis is conducted to determine if there is any significant effect for specific individual team characteristics on the knowledge sharing behavior These characteristics are how long the respondents has been work ing as a virtual team member,

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80 how long the respondents has been member of his current team, if the respondent has been a virtual team leader, if the respondent ever participated in a global virtual team, if team members met in person, and if sharing knowle dge is voluntarily or mandatory. For each variable, the data set was separated based on the different values of the variable PLS analysis was calculated for both sets, and the results were tested for significance for both trust and collaboration The trus t results are summarized in T able 3 7 and the collaboration results are summarized in T able 3 8 T he only variable which shows a significant difference between the two groups is being a member of a global virtual team which has a 0.05 significant level for t rust This result provide s an indication that does affect the level of knowledge shared between team members and the levels of trust among them. 3.8. Discussion The objective of thi s study is to investigate the relationship between knowledge sharing, trust, and collaboration and how this relationship ultimately affects t he effectiveness of virtual teams. Our final results provide support for the theoretical model and qualified suppor t for most of our hypothesized relationships. The results show that knowledge sharing has a significant influence on both trust and collaboration in virtual team settings. This provides support for our hypotheses H1 and H2. The results indicate that knowl edge sharing in virtual settings could be a crucial factor in establishing a social capital among virtual team members. Consistent with the Knowledge Based Theory of the Firm and (1995) a virtual team could be considered a place whe re members share their knowledge and transform their environment to reach new insights Furthermore, in this study we extend the literature on virtual teams to claim that

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81 sharing knowledge is crucial for virtual team members to collaborate, trust each othe r, and be effective. Table 37 : Multi group modera ting effect (Trust) Variable Group 1 Group 2 Significance <= 5 Years > 5 Years Experience Sample Size 12 4 6 9 Not Significant Regression Weight 0.69 0.554 Standard Error (S.E.) 0.0595 0.1054 t statistic 1.220 p value (2 tailed) 0.224 Memb er of the current virtual team <= 5 Years > 5 Years Not Significant Sample Size 1 5 9 3 4 Regression Weight 0.634 0.726 Standard Error (S.E.) 0.0617 0.0534 t statistic 0.594 p value (2 tailed) 0.554 Leader Vs. Member Yes NO Not Significant Sample Size 8 2 1 1 1 Regression Weight 0.655 0.647 Standard Error (S.E.) 0.084 0.0626 t statistic 0.078 p value (2 tailed) 0.938 Global virtual team Yes No Significant at 0.05 Sample Size 1 1 3 8 0 Regression Weight 0.753 0.546 Standard Error (S.E.) 0.0654 0.0588 t statistic 2.238 p value (2 tailed) 0.027 Mandatory or voluntarily Mandatory Voluntarily Not Significant Sample Size 5 2 1 4 1 Regression Weight 0.747 0.591 Standard Error (S.E.) 0.0472 0.0747 t statistic 1.162 p value (2 tailed) 0.247 Meet in Person Yes No Not Significant Sample Size 4 5 1 4 8 Regression Weight 0.724 0.627 Standard Error (S.E.) 0.0621 0.0556 t statistic 0.848 p value (2 tailed) 0.398

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82 Table 38 : Multi g roup moderating effect (Collaboration) Variable Group 1 Group 2 Significance <= 5 Years > 5 Years Experience Sample Size 1 2 4 6 9 Not Significant Regression Weight 0.698 0.74 Standard Error (S.E.) 0.0556 0.825 t statistic 0.071 p value (2 ta iled) 0.944 Member of the same virtual team <= 5 Years > 5 Years Not Significant Sample Size 1 5 9 3 4 Regression Weight 0.69 0.792 Standard Error (S.E.) 0.0649 0.0437 t statistic 0.628 p value (2 tailed) 0.531 Leader Vs. Member Yes NO Not Significant Sample Size 8 2 1 1 1 Regression Weight 0.719 0.703 Standard Error (S.E.) 0.0695 0.0662 t statistic 0.164 p value (2 tailed) 0.870 Global virtual team Yes No Not Significant Sample Size 1 1 3 8 0 Regression Weight 0.655 0.776 Standard Error (S.E.) 0.0902 0.0383 t statistic 2.238 p value (2 tailed) 0.027 Mandatory or voluntarily Mandatory Voluntarily Not Significant Sample Size 5 2 1 4 1 Regression Weight 0.747 0.591 Standard Error (S.E.) 0.0472 0.0747 t stati stic 1.162 p value (2 tailed) 0.247 Meet in Person Yes No Not Significant Sample Size 4 5 1 4 8 Regression Weight 0.848 0.67 Standard Error (S.E.) 0.0308 0.075 t statistic 1.191 p value (2 tailed) 0.235

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83 The results show that the relationship between collaboration and team effectiveness is significant whi ch confirms hypothesis H3. The S obel test of mediation also indicates that collaboration mediates the relationship between knowledge sharing and team effectiveness This provides additional support for the importance of knowledge sharing in virtual team settings. Hypothesis 4 was not supported; in this hypothesis we argue a moderating effect for trust on the relationship between collabo ration and team effectiveness. However, there was no significant support that such an impact exists Aubert and Kelsey (2003) investigated the influence of trust on virtual team performance and found that the level of trust among virtual team members does not have a significant impact on team performance. This is consistent with the results of our study and suggests that the impact of trust may have a limited influence on virtual team effectiveness. Jarvenpaa et al. (2004) argue that trust effects are sensi tive to the context of the virtual team which might suggest that our results could be limited to the population of the study Nevertheless, f urther research is needed to further investigate the impact of trust on virtual teams collaboration and effectiven ess The multi group moderation effect results were negative for all items except for This result may indicate that trust is higher among virtual team members who have similar characteristics (i.e. culture, language). The results of this research have interesting implications for both research and practice. For research, this research provides implications for the importance of knowledge sharing in virtual team setting. Furthermore, this research suggests t hat more

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84 research is necessary to better understand the influence of trust on virtual team outcome. For practice, this research highlights the role which knowledge sharing play s in virtual teams. This suggests that o rganizations should support their virtua l team members knowledge shar ing by providing them with the tools to do so. 3.9. Limitations A limitation of this study is that the research investigates knowledge sharing independent from the technology KMSs vary from a simple blog or discussion board to a m ore sophisticated software application especially designed for organization knowledge needs. It is reported in the literature that technology could influence the quality and quantity of knowledge shared. This research is concerned with the social aspect of system (KMS) technology. Therefore, the model should be applied with care to contexts which use different KMS technology than the one in this study sample context A second limi tation is that we relied on purposive sampling. The study target ed a specific sample of virtual team members in a specific industry Future studies need to examine other dimensions of the theoretical population of the study. A third limitation is that we i nvestigate team members in organizational settings. There exist a literature that investigates knowledge sharing, trust, and collaboration in online communities of practice outside the organization. Future studies could test the proposed model in online co mmunities of practice environment beyond the organizational setting.

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85 3.10. Conclusion This research proposes a conceptual model which represents the hypothesized relationship between knowledge sharing, trust, collaboration, and team effectiveness in virtual team settings. The model is developed based on an intensive review of the literature on knowledge management and sharing, virtual teams, trust, and collaboration. The theoretical foundation for the model is found in the Knowledge Based Theory of The Firm, Soci al Capital Theory, and the Social Exchange Theo ry. The model is tested using a survey research design developed based on measures from previous research The results of this research support three hypotheses which explain the relationship between knowled ge sharing, trust, and collaboration. For research, the results of our research imply the need of further research to investigate how different factors affect virtual teams effectiveness. For practice, the results of this research calls for better underst anding for the role of knowledge sharing in virtual teams.

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86 4. Conclusion The work presented in this dissertation consists of two parts. The first investigates online collaboration in virtual teams and the second investigates the impact of knowl edge sharing on trust, collaboration, and team effectiveness in virtual team settings. The theoretical foundation which supports the first study is found in the Socio Technical Theory and the Theory of Reasoned Action The theoretical foundation which supp orts the second study is found in the Social Capital Theory Social Exchange Theory, and The Knowledge Based Theory of The Firm. The result of the first part is a theoretical model and a measurement scale for intention to collaborate online. The model and measurement scale were tested and validated through a pilot study and in a field study. However, additional research is necessary to further validat e the measurement scale and evaluate its generalizability across different virtual team environments The fi rst study provides important implications for both research and practice. For research, this study calls for a better understanding of the social aspects surrounding For practice, this study provides noteworthy implicat ions regarding the importance of social characteristics and social relationships among virtual team members in fostering an environment of collaboration within the team in the organization. The results of the second part is a conceptual model which describ es the hypothesized relationship between knowledge sharing, trust, collaboration, and team effectiveness in virtual team setting. The model is tested using a survey research design developed based on measures from previous research

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87 The results of this re search support three hypotheses which explain the relationship between knowledge sharing, trust, and collaboration. For research, the results of this research imply the need of further research to investigate how different factors ectiveness especially trust among virtual team members For practice, the results of this research calls for better understanding for the role of knowledge sharing in virtual team effectiveness

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106 APPENDIX 1 : Linkedin Groups 1. Online Collaboration Group 2. Online collaboration software user group 3. Oracle ERP User Network 4. Ppt Collaboration: People, Process & Technology 5. The Project Manager Network #1 Group for Project Managers 6. Best Pr actice Transfer

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107 APPENDIX 2 : Survey Study 1 Survey Section 1: Demographics Please provide the following demographic information for analysis purposes. -No personal information will be collected 1. Please indicate whether you are Male Female 2. What is your age group? 18 24 25 29 30 39 40 49 50 59 60 or above 3. How long have you worked as a virtual team member? less than 1 year 2 5 years 6 10 years 11 15 years More than 15 years 4. Have you participated in a purely online virtual te am or in a team that combined online and face to face work? Participated in online team(s) only Participated in combined online and face to face teams Participated in both types of virtual teams I have not participated in a virtual team 5. Are you current ly a virtual team member? Yes No 6. Have you ever been a virtual team leader? Yes No 7. Have you ever been part of a global virtual team (members from different countries and/or cultures)? Yes No

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108 Section 2: Incentives The following section will be liker t scale questions, these questions will measure the extent of your agreement to a statement. 8. I expect to be rewarded by my organization or team supervisor when I collaborate online. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 9. I expect something in return when I collaborate with team members online. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 10. I collaborate with others online to improve my image within the team. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 12. Collaborating online enhances my professional reputation Strongly Disagree Disagree Somewhat Disagree Undecided

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109 Somewhat Agree Agree Strongly Agree 13. I collaborate with my online team members regardless of any incentives. Strongly Di sagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Section 3: Voluntariness Does making collaboration with virtual team members voluntary influence your intention to collaborate online? 14. I only collaborate with online t Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 15. I am more likely to collaborate online when I voluntarily join the team Strongly Disagree Disagree Somewhat Disagree Undecided Som ewhat Agree Agree Strongly Agree 16. I voluntarily collaborate with other team members online even when not mandated by my organization Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree

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110 17. Mandating online collabo ration makes me less willing to collaborate with my team members Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree so Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Background Similarities Do your virtual team members background (culture, education, nationality, ethnicity) influence your intention to collaborate with them online? 19 I am more likely to collaborate online with individuals with whom I share similar background Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 20. I am more likely to collaborate online with members with whom I s hare a similar culture Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree

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111 21. Having team members of different backgrounds makes me less likely to collaborate online Strongly Disagree Disagree Somewhat Disagree Unde cided Somewhat Agree Agree Strongly Agree 22. I tend to collaborate online with team members who have a different background than mine Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 23. The background of my onl ine team members does not influence my intention to collaborate with them Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Common Ground Does reaching a common ground with virtual team members' influence your inte ntion to collaborate with them online? 24. Collaborating online requires me to communicate with other team members in order to reach a common ground Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 25. I am more l ikely to collaborate online when the team reaches common ground from the beginning Strongly Disagree

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112 Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 26. I collaborate online even when my team members and I do not fully share the s ame vision of the problem we are trying to solve Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 27. For me to collaborate online, the team should share a common understanding of problems to be addressed Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 28. Reaching a common ground has nothing to do with my intention to collaborate online Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Members Expertise Does having virtual team members' with different levels of expertise influence your intention to collaborate online? 29. Having expert members on my team makes me more willing to collaborate online Strongly Disagree Disagree Some what Disagree Undecided Somewhat Agree Agree

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113 Strongly Agree Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 31. I collab orate online with members who have expertise I can benefit from Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 32. I collaborate online with team members who can benefit from my expertise Strongly Disagree Disag ree Somewhat Disagree Undecided Somewhat agree Agree Strongly Agree 33. Virtual team members' expertise does not influence my intention to collaborate with them online. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat agree Agree Strongly A gree 34. Please feel free to leave any comments or feedback you see appropriate Please feel free to leave any comments or feedback you see appropriate You Have Completed the Survey, Thank You For Participating, Best Regards Mohammad Alsharo CU Denver Bu siness School

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114 Study 2 Survey Section 1 out of 6 Please provide the following demographic information for analysis purposes. -No personal information will be collected -1. Please indicate whether you are Male Female 2. What is your age group? 18 20 2 1 29 30 39 40 49 50 59 60 or above 3. How long have you worked as a virtual team member? less than 1 year 1 5 years 6 10 years Over 10 years 4. How long have you been a member of your current virtual team? less than one year 1 5 years 6 10 years over 10 years 5. Have you participated in a purely online virtual team or in a team that combined online and face to face work? Participated in online team(s) only Participated in combined online and face to face teams Participated in both types of virtua l teams I have not participated in a virtual team 6. Are you currently a virtual team member? Yes No 7. Have you ever been a virtual team leader? Yes No 8. Have you ever been part of a global virtual team (members from different countries and/or culture s)?

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115 Yes No 9. Do all members of your team work for the same organization (company)? Yes No 10. In my organization, sharing knowledge is mandatory. Yes No Section 2 out of 6 Please answer the following questions. For the following questions, please ind icate to what extent you agree or disagree with each statement. 11. Team members were asked for their suggestions when the team was originally formed Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 12. Careful team. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree n otified that I will be part of this team. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree

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116 14. My role in the team was clearly explained to me. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 15. I have received training focused on becoming more effective in the virtual team setting. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 16. My virtual team is equipped with adequate tools and technologies to perform our tasks. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 17. The electronic methods we use to communicate with one another are effective. Strongly Disagree Disagree Somewhat Di sagree Undecided Somewhat Agree Agree Strongly Agree 18. My team members were given the opportunity to meet each other in person. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree

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117 Agree Strongly Agree my work effort Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Section 3 out of 6 Please answer the following questions about behaviors and practices in your virtual team. 20. I routinely share my knowledge wi th my team members Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 21. I routinely seek out knowledge from other team members Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly A gree 22. If I had my way, I wouldn't let the other team members have any influence over issues that are important to the project. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 23. I really wish I had a good wa y to oversee the work of the other team members on the project. Strongly Disagree

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118 Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 24. My virtual team members communicate in a positive manner to one another. Strongly Disagree Disag ree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 25. If a team member makes a mistake, others generally point out his error and discuss it with him Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agr ee 26. My team has been effective in reaching its goals Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 27. My team is meeting its business objectives. Strongly Disagree Disagree Somewhat Disagree Undecided Some what Agree Agree Strongly Agree Section 4 out of 6 Please answer the following

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119 28. Overall, I find the information I need to do my job. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 29. I would be comfortabl e giving the other team members complete responsibility for the completion of this project. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 30. I can turn to my team mates for help when needed Strongly Disagree D isagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 31. I would be comfortable giving the other team members a task or problem which was critical to the project, even if I could not monitor them. Strongly Disagree Disagree Somewhat Dis agree Undecided Somewhat Agree Agree Strongly Agree 32. I try to be honest about what I think and feel when working with my team members. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree

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120 Strongly Agree 33. When several team mem bers are discussing an issue, I can ask questions about anything I do not understand. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 34. My input is valued by my team members. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 35. My team members and I respect each other. Strongly Disagree Disagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree Strongly Disagree Di sagree Somewhat Disagree Undecided Somewhat Agree Agree Strongly Agree 49. Please feel free to leave any comments or feedback you see appropriate Please feel free to leave any comments or feedback you see appropriate You Have Completed the Survey, Thank Y ou For Participating, Best Regards Mohammad Alsharo CU Denver Business School