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Revisiting Communication and Trust in Globally Distributed Teams: A Social Network Perspective
Coathors: Saonee Sarker, Suprateek Sarker (Washington State
University) Sarah Almbjerg (Copenhagen Business School)
Manju AhujaKelley School of Business
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 ICT-mediated 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
Communication
Individual Performance
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)
Communication
Trust
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 getting-acquainted activities..” (Hossain and Wigand 2004) TrustCommunication Individual
Performance
Ego-centric Network View
Communication Centrality The extent to which a member is
communicatively connected with each of the other members within a team
Trust Centrality 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 membersRed nodes: Location B team membersSize of nodes: Communication centrality
Trust Centrality
Legend:Blue nodes: Location A team membersRed nodes: Location B team membersSize of nodes: Trust centrality
Research Methodology
A field study of hybrid virtual teams Sample
US-Norway student teams engaged in systems development
Duration: 1 semester US-Denmark student teams engaged in
systems analysis 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 Linear Regression
Interaction Model 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)
R-square change is .134 (F-change is significant) 2nd block included the above predictors and an additional
interaction term 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?