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Transcript
Revisiting Communication and Trust in
Globally Distributed Teams: A Social
Network Perspective
Manju Ahuja
Kelley School of Business
Coathors:
Saonee Sarker, Suprateek Sarker (Washington State University)
Sarah Almbjerg (Copenhagen Business School)
Agenda






State of knowledge on globally distributed
teams
The theorized relationships among
communication, trust, and performance
Communication and Trust from a Social
Network Perspective
Research Methodology
Findings
Discussion
Research on Trust in VTs

Key areas of research in globally distributed teams


Trust (e.g., Jarvenpaa, Shaw, and Staples 2004; Piccoli
and Ives, 2003; Sarker, Valacich, and Sarker 2003)
Communication (e.g., Piccoli, Powell, and Ives 2004;
Galvin and Ahuja 2001; Jarvenpaa and Leidner 1998)

“The most widely researched of the issues surrounding
virtual teams” (Powell et al. 2004, p. 17)
Trust is Generally a dependent variable
Research in virtual teams

Focus on group performance



Need to investigate individual performance (Mehra et
al. 2001)
Need to identify the high performing team members
(e.g., Powell et al. 2004)
Reliance primarily on individual trait-based or
sometimes behavior-based explanations


Need structural/relational approach (Tichy 1981)
Research on the structural position of individuals can
answer “why are some people better performers than
others” (Mehra et al. 2001)
RESEARCH QUESTION
What is the role of communication
and trust centrality in determining an
individual’s performance within a
globally distributed team?
The approach - “networked individualism.”
Networked Individualism


Noted researchers have observed that ICTmediated groups are moving towards
“networked individualism” (Wellman et al.
2003)
“By bringing to bear measures and
constructs of social structure, we can begin
to how simple notions of .. autonomous
individuals are incomplete” (Rice 1994,
p. 181)
“Networked Individualism” (contd.)

“If you took away my computer, my
colleagues, my office, my books, my desk,
my telephone I wouldn’t be a sociologist
writing papers, delivering lectures, and
producing knowledge. I’d be something
quite other – and the same is true for all of
us.” (Law 1992)
Virtual Teams as a Social Network

We conceptualize a distributed team as
a social network, and each individual
having a structural position within
that network.
Communication& Trust-based
Stru. Position
Performance
Three Models
 We explore three perspectives regarding the
nature of influence of trust and
communication on individual performance in
globally distributed teams
 They represent three Strands of Theorizing
about the role of Communication and Trust
 an additive model
 an interaction model, and
 A mediation model.
The Additive Model

Twin predictions concerning performance

One preditcs a strong linkage between trust and performance
(Hossain and Wigand 2004; Coppola, Hiltz, and Rotter 2004)


“Prevailing view of trust in the IS literature contends that trust
has direct positive effects on .. performance” (e.g., Iacono and
Weisband 1997; Jarvenpaa and Leidner 1999)”
The other predicts that “Ineffective communications,” may
“hinder” performance (Scarnati 2001)
Trust
Individual
Performance
Communication
The Interaction Model



Model suggests that both trust and communication are
necessary for higher individual performance
That is, trust and communication interact to affect outcome
(Jarvenpaa et al. 2004)
E.g., team member may be perceived as a low performer by
peers if he/she exhibits low communication and does not
enjoy the trust of other members (Jarvenpaa et al. 2004)
Trust
Communication
Individual Performance
The Mediation Model


Any effect of communication on performance is due to trust
 Communication leads to trust, and trust leads to performance
(Coppola, Hiltz, and Rotter 2004).
 “Trust is developed through communication” (Handfield 1994)
 “High levels of trust will cause the trustor .. to perceive good
performance” (Jarvenpaa et al. 2004)
“Several empirical studies on the trust development process
suggest that video and audio.. are nearly as good as face-to-face
contacts provided that participants engage in various gettingacquainted activities..” (Hossain and Wigand 2004)
Communication
Trust
Individual
Performance
Ego-centric Network View

Communication Centrality


Trust Centrality


The extent to which a member is communicatively
connected with each of the other members within
a team
The extent to which a member enjoys the trust of
each of the other members within a team
(trustworthiness)
Degree-based
Communication Centrality
Legend:
Blue nodes: Location A team members
Red nodes: Location B team members
Size of nodes: Communication centrality
Trust Centrality
Legend:
Blue nodes: Location A team members
Red nodes: Location B team members
Size of nodes: Trust centrality
Research Methodology


A field study of hybrid virtual teams
Sample

US-Norway student teams engaged in systems
development


US-Denmark student teams engaged in systems
analysis


Duration: 1 semester
Duration: 6 weeks
N=111
Measures


In-degree centrality
 In-degree centrality is relatively stable even at a
low sampling level (Valente and Davis 1999)
 Freeman’s (1979) measure of relative in-degree
centrality (i.e., the actual number of lines relative
to the total number that it could sustain) was used
Performance
 “.. the effects of networks on performance..
measured by supervisor ratings, may contain
political aspects” (Brass 2003)
 Consistent with the above comment, each team
member was asked to rate the performance of
every other team member
Analysis Technique

Additive Model


Interaction Model


Linear Regression
Hierarchical Regression (Mehra et al. 2001)
Mediation Model

Linear Regression following the guidelines
of Baron and Kenney (1986)
Results - Additive Model

Model Summary




Effect of communication (b = .001, p> .10)
Effect of Trust (b = .519, p < .05)
R-square = .646
Results fail to support the Additive
Model
Interaction Model

Model Summary

1st block with communication centrality and trust centrality as
predictors (R-square = .646, 2nd model R-square = .781)


2nd block included the above predictors and an additional interaction
term



R-square change is .134 (F-change is significant)
The ANOVA model (1st Model (F= 98.736, p < .01), 2nd Model (F= 126.85, p<
.01, Role of communication (b= -.064, p> .10), role of trust (b= .562, p< .01),
role of interaction (b= -.444, p< .01)
2nd Model has better fit.
However, direction of the interaction is anomalous
Mediation Model



Model Summary (Baron and Kenney, 1986)
 Commun. centrality affects trust centr. (b= .832, p<.01)
 Commun. centrality affects performance (b= .432, p< .01)
 Trust centrality affects performance (b= .519, p< .01) and
effect of commun. centrality disappears (b= .001, p> .10)
Thus, full mediation exists (Baron and Kenney
1986)
Results support the mediation model
Discussion

Complete mediation of trust on the
relationship between communication
and performance


That is, high levels of communication
cannot lead to high performance until
he/she is trusted by the other team
members
‘More [communication] is not always
better” (Krackhardt and Hansen 2003)
What about the anomaous
Moderation Model?

To understand anomalous moderating
model, we split the sample into




High trust centrality
Low trust centrality
In hi-trust group, the interaction effect is
positive; negative in the low-trust group
Less trustworthy members are harmed by
more communication
Possible effect of task? No!

We split the sample into:
 those involved in systems analysis tasks
(US-Denmark), &
 those involved in systems development
tasks (US-Norway)
 Results are consistent, showing robustness
Continuing Research


Continuing to qualitatively explore the
three models
Initial exploration supports regression
results
Questions?