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Virtual Teams: Effects of Technological Mediation on Team Performance

December 2003
Group Dynamics Theory Research and Practice 7:297-323

December 2003
7:297-323

DOI:10.1037/1089-2699.7.4.297

Authors:

Florida Maxima Corporation

Recent advances in networking environments and telecommunications have led to the proliferation of teams that do not work face-to-face but interact over a computer-mediated communications network. Although some have asserted that virtual teams transcend boundaries of time or distance, others have claimed that working remotely in a mediated team environment differs in significant ways from working face-to-face. In this article, the authors examine the effects of technological mediation on team processes such as cohesiveness, status and authority relations, counternormative behavior, and communication. They discuss conditions under which distance matters in virtual team interaction.

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Virtual Teams: Effects of Technological Mediation on

Team Performance

James E. Driskell

Florida Maxima Corporation Paul H. Radtke

Naval Air Warfare Center Training

Systems Division

Eduardo Salas

University of Central Florida

Recent advances in networking environments and telecommunications have led to the

proliferation of teams that do not work face-to-face but interact over a computer-

mediated communications network. Although some have asserted that virtual teams

transcend boundaries of time or distance, others have claimed that working remotely in

a mediated team environment differs in signiﬁcant ways from working face-to-face. In

this article, the authors examine the effects of technological mediation on team

processes such as cohesiveness, status and authority relations, counternormative be-

havior, and communication. They discuss conditions under which distance matters in

virtual team interaction.

Recent advances in networking environments

and telecommunications have led to the prolif-

eration of teams that do not work face-to-face

but interact over a computer-mediated commu-

nications network. We use the term virtual team

to refer to a team or group whose members are

mediated by time, distance, or technology.

Other closely related terms that have been used

to describe this type of environment include

computer-mediated communications and com-

puter-supported cooperative work. Although

there are differences in the type of technology

used and the types of communication enabled in

virtual team environments, for our purposes, the

core feature of a virtual team is that it is one in

which interdependent group members work to-

gether on a common task while they are spa-

tially separated.

Cairncross (1997) has coined the phrase “the

death of distance,” suggesting that distance may

no longer be a limiting factor in our ability to

communicate and is quickly becoming irrele-

vant to the way people interact. Proponents of

this view presume a future (or present) in which

all time and space restrictions have been re-

moved from the communication process and

where “face-to-face communication can be

done across oceans if video conferencing facil-

ities are available” (Burgelman, 2000, p. 3).

However, most researchers take issue with the

view that the technology that mediates human

interaction is seamless or transparent. For ex-

ample, Olson and Olson (2000) argued that

“distance matters” and that group members who

are remotely located or distributed from one

another are likely to face obstacles in coordi-

nating group efforts. The general consensus is

that the nature of interaction in virtual teams

may differ in a number of important ways from

“normal” face-to-face team interaction. How-

ever, if distance matters to how group members

interact, then how does it matter? How does this

phenomenon—that virtual team members must

work interdependently but remotely in a com-

James E. Driskell, Florida Maxima Corporation, Winter

Park, Florida; Paul H. Radtke, Naval Air Warfare Center

Training Systems Division, Orlando, Florida; Eduardo

Salas, Department of Psychology, University of Central

Florida.

The views expressed herein are those of the authors and

do not reﬂect the opinion, policy, or views of the Depart-

ment of Defense.

Correspondence concerning this article should be ad-

dressed to James E. Driskell, Florida Maxima Corporation,

507 North New York Avenue, R-1, Winter Park, Florida

32789. E-mail: james.driskell@rollins.edu

2003, Vol. 7, No. 4, 297–323 1089-2699/03/$12.00 DOI: 10.1037/1089-2699.7.4.297

297

puter-mediated environment—impact the na-

ture of team interaction and performance?

The purpose of this article is to examine the

effects of technological mediation on team pro-

cesses such as cohesiveness, status and author-

ity relations, counternormative behavior, and

communication. We ﬁrst present a model of the

effects of technological mediation on team per-

formance. We then examine how performing in

a virtual team environment may impact team

processes. Finally, we discuss factors, such as

the nature of the task, that may moderate the

effects of technological mediation on team

interaction.

Team Interaction in Computer-Mediated

Environments

In face-to-face interaction, group members

share the same physical location, can see and

hear one another, receive messages in “real

time”as they are produced, and send and re-

ceive information simultaneously and in se-

quence. During face-to-face interaction, group

members can see another’s nods and gestures;

they can observe eye contact, facial expressions,

and posture; they can hear the other’s tone of

speech and dialect; they are aware of the timing

of speech and who responds to whom; and they

experience the immediacy of interacting and

being involved with a physically present team

member. These types of contextual cues provide

important information about the individual with

whom one is interacting (i.e., Does the speaker

give evidence of being competent or experi-

enced? Is the speaker a high-status or low-status

team member?), how the message is being con-

veyed (i.e., Is the speaker angry, tense, or con-

ﬁdent?), and whether the message is being con-

veyed successfully (i.e., Are others paying at-

tention? Do they look puzzled?). Groups whose

members are distributed apart from one another

may lose some of these communicative

capabilities.

A substantial body of research on computer-

mediated communication has been conducted in

the past several decades. Some studies have

indicated that the distribution of team members

over remote networks tends to impair team in-

teraction in comparison with face-to-face inter-

action. McLeod (1992) found that computer-

mediated interaction led to an increase in the

time required to make a decision and a decrease

in team member satisfaction. Straus (1997) and

Warkentin, Sayeed, and Hightower (1997) re-

ported that virtual teams developed lower cohe-

siveness than face-to-face teams. However,

other studies have produced conﬂicting results

that are somewhat more difﬁcult to interpret.

For example, whereas Dubrovsky, Kiesler, and

Sethna (1991) found that status distinctions

among team members were reduced in comput-

er-mediated groups, Weisband, Schneider, and

Connolly (1995) found little evidence of status

equalization. Although some researchers have

reported a greater incidence of counternorma-

tive or uninhibited behavior in computer-medi-

ated groups (Siegel, Dubrovsky, Kiesler, &

McGuire, 1986), others have failed to ﬁnd these

differences (Walther & Burgoon, 1992). A rea-

sonable and cautious interpretation of the evi-

dence at this point is that, indeed, distance mat-

ters—that working remotely in a mediated team

environment is different from working face-

to-face—but that the manner in which media-

tion affects team interaction warrants closer

examination.

There are several overarching points that are

relevant to the following discussion. First, when

we use the term computer-mediated communi-

cations, we must remain cognizant of the fact

that there are a number of different types of

mediated environments. Clark and Brennan

(1991) described several characteristics of face-

to-face and distributed settings that determine

the nature of communication:

Copresence: Group members occupy the same physi-

cal location.

Visibility: Group members can see one another.

Audibility: Group members can hear one another.

Cotemporality: Communication is received at the ap-

proximate time it is sent.

Simultaneity: Group members can send and receive

messages simultaneously.

Sequentiality: Group members’speaking turns stay in

sequence.

As shown in Table 1, computer-mediated en-

vironments differ according to the communica-

tion capabilities that are enabled. In face-to-face

interaction, group members share the same

physical location, can see and hear one another,

receive messages in real-time as they are pro-

duced, and send and receive information simul-

taneously and in sequence. Teams that are dis-

tributed over various types of computer-medi-

298 DRISKELL, RADTKE, AND SALAS

ated environments lose certain capabilities. In a

videoconference setting, distributed groups may

interact over networked computer systems and

exchange live video as well as audio and text.

On the other hand, what we have termed com-

puter chat refers to the computer-mediated elec-

tronic dialogue between two or more group

members, in which the users exchange mes-

sages via text in real-time. Group communica-

tion over this type of distributed environment is

cotemporal, simultaneous, and sequential, but

group members lack the capability to see one

another and to hear the timing or intonation of

their speech.

The important point that this classiﬁcation

illustrates is that all of the settings in Table 1

(other than the face-to-face setting) may be de-

ﬁned as a computer-mediated environment.

They are all alike in that group members work

on a common task but do not share the same

spatial location. However, they differ quite con-

siderably regarding the communication capabil-

ities that are enabled. Therefore, when we dis-

cuss virtual teams, we must be aware that vir-

tual teams may operate in different types of

communication environments and that the type

of communication environment implemented

will have a signiﬁcant impact on team

interaction.

A second broad point of consideration is that

just as there are different types of technologi-

cally mediated environments, there are different

types of teams. Teams may be ad hoc (a tem-

porary team assembled solely for a speciﬁc

task) or intact (an existing team in which team

membership is stable). Teams may work to-

gether on a task over time, or interaction may be

short and time limited. Teams may meet ini-

tially prior to interaction, or team members may

be completely anonymous. Team members may

expect future interaction with one another after

an initial task is performed, or the task may be

a one-shot transaction. Teams can be composed

of many members or few. All of these factors

play a role in how teams develop and interact in

a mediated environment.

Finally, a third point is that the distribution of

team members over computer-mediated sys-

tems can disrupt team interaction under some

conditions and facilitate interaction under other

conditions. The focus of most research on com-

puter-mediated team performance has been on

the disadvantages incurred when team members

must perform apart from one another. Clearly,

coordination of team activities can become

more difﬁcult when team members are not in

the same physical location. However, team

membership can be a double-edged sword. For

example, research indicates that under certain

conditions, being in a team can lead to negative

consequences such as increased pressure for

conformity and impaired decision making

(Mullen, Anthony, Salas, & Driskell, 1994).

Thus, it follows that there are some circum-

stances in which separating team members apart

from one another may serve to overcome the

negative consequences of team membership.

Therefore, the question is not simply “How is

performance impaired in virtual teams?”In-

stead, we expect that performing in a virtual

team environment will affect teams in ways that

may be advantageous as well as disadvanta-

Table 1

Characteristics of Face-to-Face and Mediated Environments

Type of environment

Media characteristics

Copresence Visibility Audibility Cotemporality Simultaneity Sequentiality

Face-to-face X X X X X X

Real-time audio/video

(videoconference) X X X X X

Audio-only (telephones,

conference calls) X X X X

Real-time electronic dialogue,

text-only (computer chat) X X X

E-mail

Note. From “Grounding in Communication,”by H. H. Clark & S. E. Brennan, in L. B. Resnick, J. M. Levine, & S. D.

Teasley (Eds.), Perspectives on Socially Shared Cognition (p. 142). Washington, DC: American Psychological Association.

299VIRTUAL TEAMS

geous. As a practical strategy, we may wish to

mitigate the costs of computer-mediated com-

munication in circumstances in which it inter-

feres with effective team functioning; at the

same time, we want to realize the beneﬁts in

circumstances in which computer-mediated

communications may facilitate team interaction.

A Research Model

Figure 1 presents a model that illustrates the

effects of computer-mediated communications

on team performance. This model adopts a gen-

eral input–process–output framework. The in-

put factor, shown in column 1, is the computer-

mediated environment. Note that, as we dis-

cussed above, the term computer-mediated

communications is a general term and that spe-

ciﬁc types of computer-mediated environments

offer different communicative capabilities. In

fact, the type of communications environment

over which team members interact is one of the

primary moderators that we intend to examine

in the following sections.

The second column in Figure 1 describes

several team processes of interest. Research has

examined the effect of computer-mediated en-

vironments on cohesiveness (Straus, 1997;

Figure 1. Input–process–output model of the effects of mediation on team interaction.

CMC ⫽computer-mediated communications.

300 DRISKELL, RADTKE, AND SALAS

Warkentin et al., 1997), status and authority

relations (Dubrovsky et al., 1991; Hiltz, Turoff,

& Johnson, 1989; Silver, Cohen, & Crutchﬁeld,

1994), counternormative behavior (Dubrovsky

et al., 1991; Kiesler, Zubrow, Moses, & Geller,

1985; Siegel et al., 1986, Sproull & Kiesler,

1986), and communication (Sellen, 1995;

Thompson & Coovert, 2003). Although this re-

view must by necessity be selective, we chose

these speciﬁc team processes to examine be-

cause they have been examined in prior reviews

of team performance in general and in reviews

of team performance in computer-mediated en-

vironments (cf. Hollingshead & McGrath,

1995; Straus, 1997) and because evidence sug-

gests these team processes are affected by tech-

nological mediation.

In Baron and Kenny’s (1986) terms, we view

process variables as mediators. That is, team

process variables are patterns of group interac-

tion through which input factors inﬂuence or

affect team outcomes (see Hackman & Morris,

1975). In reality, this input–process–output re-

lationship is often not as linear or static as this

model implies. If we consider team perfor-

mance over time, team outcomes such as the

performance of the group can in turn impact

team processes such as cohesiveness. More-

over, given the speciﬁc interests of the re-

searcher, variables such as cohesiveness can be

considered as input variables and outcome vari-

ables as well.

The third column describes performance out-

comes. Note that we restrict our focus to the

effects of technological mediation on perfor-

mance in task groups. Furthermore, although

performance can be deﬁned by more speciﬁc

outcome measures such as accuracy, speed, and

variability of performance, it is appropriate for

our purposes to simply address team perfor-

mance, broadly deﬁned.

Finally, moderators are factors that affect the

strength of the relation between two variables

(Baron & Kenny, 1986) or that can account for

observed variation in a relationship. Typically,

each speciﬁc research literature, such as the

research on group status or on communication,

has its own idiosyncratic cluster of moderators

that are dictated by research concerns and ﬁnd-

ings within that domain. However, there are

some moderators that emerge at least to some

extent across most domains. We examine three

such variables that seem most relevant to virtual

teams: (a) type of computer-mediated environ-

ment, (b) type of task, and (c) temporal context.

We have previously described differences in

types of computer-mediated environments (see

Table 1). Types of computer-mediated environ-

ments (e.g., audio–video vs. audio only vs. text

only) differ considerably in the richness of com-

munications afforded (Daft & Lengel, 1986).

Thus, we would expect the effect of technolog-

ical mediation on team processes to vary de-

pending on the type of computer-mediated com-

munications environment.

Perhaps no factor has a greater effect on team

interaction than the type of task that the team is

engaged in. There have been a number of at-

tempts to classify group tasks along a variety of

dimensions, such as the cognitive versus phys-

ical requirements of the task or the requirement

for cooperation or interdependence (cf.

McGrath, 1984; Shaw, 1973; Steiner, 1972).

One of the most recent and comprehensive clas-

siﬁcation systems relevant to teams has been

offered by Devine (2002), who identiﬁed 14

team types on the basis of the type of work that

they are engaged in, and further classiﬁed them

along seven contextual dimensions. Driskell,

Hogan, and Salas (1987) also classiﬁed group

tasks on the basis of the primary activities or

behaviors required of team members, resulting

in six task categories: (a) mechanical/technical

tasks, requiring the construction or operation of

things; (b) intellectual/analytic tasks, requiring

generation of ideas, reasoning, or problem solv-

ing; (c) imaginative/aesthetic tasks, requiring

creativity or artistic endeavor; (d) social tasks,

requiring training, supporting, or assisting oth-

ers; (e) manipulative/persuasive tasks, requiring

motivation or persuasion of others; and (f) log-

ical/precision tasks, requiring performance of

routine, detailed, or standardized tasks. We

adopt this typology, focusing on the behaviors

required for task completion, to examine the

effects of type of task. We would expect the

effect of technological mediation on team pro-

cesses to vary depending on the type of task.

A third factor that we would expect to have a

signiﬁcant effect on the relationship between

technological mediation and team interaction is

the temporal context of the team. McGrath

(1997) has noted that research is often con-

ducted with ad hoc laboratory groups with no

past or anticipated future, in contrast to dynamic

groups that are intact and perform over a longer

301VIRTUAL TEAMS

period of time (see also Hollingshead &

McGrath, 1995; McGrath, 1990). Teams that

perform over time gain experience with the task

and the communications technology, which

may reduce performance decrements (Hollings-

head, McGrath, & O’Conner, 1993). Although

there are a number of temporal distinctions that

can be drawn related to the synchronicity, pac-

ing, and sequencing of performance, we focus

on the simple distinction between ad hoc teams

that interact for a single session and dynamic

teams that interact over time. We would expect

the effect of technological mediation on team

processes to vary depending on the temporal

context of the team.

These moderators may have an impact on the

relationships depicted in Figure 1 at two sepa-

rate points. A moderator such as the type of task

may impact the extent to which a variable such

as cohesiveness affects team performance (re-

ﬂected in the rightmost arrow from the “Mod-

erator”box shown in Figure 1). However, the

relationship between cohesiveness and perfor-

mance is not unique to the topic of virtual

teams, and the extent to which this general

relationship is moderated by the type of task is

captured within that more general research lit-

erature. Our more speciﬁc interest is in exam-

ining factors that moderate the extent to which

the computer-mediated communications envi-

ronment affects team processes (the leftmost

arrow from the “Moderator”box in Figure 1).

Thus, our focus, for example, is on whether the

effects of technological mediation on cohesive-

ness may vary according to the type of task the

team is engaged in.

In summary, this model is intended to orga-

nize or structure examination of the effects of

technological mediation on team interaction.

We propose that technological mediation may

impact team performance because of changes in

cohesiveness, status, counternormative behav-

ior, and communication and that these changes

may be moderated by factors such as the type of

communications environment, the type of task,

and the temporal context of the team. In the

following sections, we use the framework of

this model to examine each of the team pro-

cesses identiﬁed. In each case, we brieﬂy de-

scribe the team process, discuss how that pro-

cess may be affected within a virtual team

environment, discuss possible performance ef-

fects, and examine potential moderators.

Cohesiveness

Cohesiveness is considered to be one of the

most fundamental aspects of groups. Golem-

biewski (1962) described it as “the essential

small group characteristic”(p. 149). Moreover,

research has shown that cohesive groups tend to

interact more (Back, 1951), agree more readily

(Lott & Lott, 1961), report greater satisfaction

with the group (Curtis & Miller, 1986), and at

least under some circumstances, outperform

less cohesive groups (Mullen & Copper, 1994).

However, there is some concern that virtual

teams may experience greater difﬁculty in de-

veloping strong relational bonds that underlie

group identity, cohesiveness, and trust (Jarven-

paa & Leidner, 1999; Rocco, 1998; Warkentin

et al., 1997).

Within the research literature, cohesiveness

has been viewed as group pride, loyalty, shared

understanding, bonding, interpersonal attrac-

tion, trust, task commitment, and mutual aid,

leading some researchers to question the clarity

and uniformity of existing conceptualizations

(Levine & Moreland, 1990). However, it is im-

portant to note that even the earliest conceptu-

alizations of cohesiveness treated it as a multi-

dimensional construct. Festinger (1950) pro-

vided the seminal deﬁnition of cohesiveness as

the forces that act on team members to remain

in the team, including interpersonal attraction to

other team members, group prestige, and attrac-

tion to the activities in which the group engages.

Most subsequent research on group cohesive-

ness has elaborated one or more of these three

major dimensions of cohesiveness.

Mullen and Copper (1994), in a meta-analy-

sis of the effects of group cohesiveness on per-

formance, examined separately the three com-

ponents of cohesiveness ﬁrst identiﬁed by Fest-

inger. Interpersonal attraction reﬂects affective

relations or attraction to other team members.

Group pride reﬂects group prestige, loyalty, and

normative bonds. Task commitment reﬂects

commitment to the task or goals of the team.

Mullen and Copper found that group cohesive-

ness had a positive effect on performance and

further found that this relationship was primar-

ily a function of the task commitment compo-

nent of cohesiveness.

Furthermore, some research has shown that

the components of cohesiveness may have dif-

ferential effects depending on the type of task.

302 DRISKELL, RADTKE, AND SALAS

For example, on an additive task, group mem-

bers pool individual products to produce a team

outcome but are not required to coordinate ac-

tions among themselves. Zaccaro and Lowe

(1986) found that higher task-based cohesive-

ness led to greater performance on an additive

task (folding paper tents), whereas interpersonal

cohesiveness had no effect. However, on a “sur-

vival exercise”disjunctive task that required

team members to communicate and coordinate

individual efforts, Zaccaro and McCoy (1988)

found that team performance was affected by

both task cohesiveness and interpersonal cohe-

siveness. Craig and Kelly (1999) also found that

both task cohesiveness and interpersonal cohe-

siveness enhanced performance on an interac-

tive group creativity task.

Cohesiveness in Virtual Teams

Generally speaking, researchers have sug-

gested that the process of team development

may be more complex in a virtual environment.

In contrast to face-to-face interaction in which

individuating information on team members is

abundant, some have argued that members of

virtual teams are more anonymous and deindi-

viduated. Thus, interaction that is mediated by

technology may lead to less intimacy and difﬁ-

culty in establishing relationships among team

members. Furthermore, some research has

shown that weaker relational ties in virtual

teams can lead to lower cohesion (Straus, 1997;

Warkentin et al., 1997).

However, we have noted that cohesiveness is

composed of three primary components—inter-

personal attraction, group pride, and task com-

mitment—and we propose that technological

mediation may have a differential effect on

these three components of cohesiveness. First, it

is likely that a weakening of social cues or a

reduction in personalizing information among

virtual team members may lead to weaker af-

fective bonds and a decrease in intimacy

(Straus, 1997; Weisband & Atwater, 1999).

Thus, we would expect technological mediation

to have a negative impact on cohesiveness,

when deﬁned as interpersonal attraction.

Second, technological mediation may affect

the group pride component of cohesiveness. To

the extent that normative bonds may be weak-

ened in computer-mediated teams, the distribu-

tion of team members over a computer-medi-

ated network may lead to lower commitment to

the team. However, the impact of mediation on

group pride may be minimized if group mem-

bers possess a strong preexisting sense of pride

and loyalty to the group or organization to

which they belong. In other words, ad hoc teams

that have never met prior to the virtual team

interaction may have difﬁculty in developing a

strong team commitment, whereas intact teams

may have developed strong normative bonds

prior to interaction in a virtual team setting.

Thus, we would expect technological mediation

to have a negative impact on cohesiveness,

when deﬁned as group pride, in some situations

but not in others.

Third, technological mediation may also im-

pact the task commitment component of cohe-

sion. Task commitment is an instrumental bond,

rather than an interpersonal or normative bond,

reﬂecting attractiveness of and satisfaction with

the task or the group’s activities (Mullen &

Copper, 1994). Some studies suggest team

members mediated by technology experience

less satisfaction with the task, although this may

be a function of the requirements of the task or

the newness of the task experience (Straus,

1996; Straus & McGrath, 1994). In general, we

would expect technological mediation to have a

negative impact on cohesiveness, when deﬁned

as task commitment.

Performance Effects

The empirical evidence for the effect of co-

hesiveness on performance is mixed. Studies

report that cohesiveness may have a positive

effect on performance (Tziner & Vardi, 1982),

no effect on performance (Terborg, Castore, &

DeNinno, 1976), or either positive or negative

effects depending on the performance standards

of the group (Schachter, Ellertson, McBride, &

Gregory, 1951). However, meta-analyses of the

cohesiveness–performance literature report

overall positive mean effects of cohesiveness on

performance (Evans & Dion, 1991; Mullen &

Copper, 1994; Oliver, Harman, Hoover, Hayes,

& Pandhi, 1999). Furthermore, the Mullen and

Copper (1994) meta-analysis reported that the

strongest effect of cohesiveness on performance

occurred when cohesiveness was operational-

ized as task commitment and that the interper-

sonal attraction and group pride components of

cohesiveness had weaker effects on perfor-

303VIRTUAL TEAMS

mance. This evidence suggests that cohesive-

ness enhances performance and that it does so

primarily because cohesive team members are

more committed to successful task perfor-

mance. This suggests that team task perfor-

mance would be most degraded through the

negative effect of technological mediation on

task commitment and that there is less of a

potential effect on performance of reduced so-

cioemotional bonds or normative bonds. In

other words, technological mediation is more

likely to impact team performance through its

effect on task commitment rather than through

its effect on socioemotional bonds or normative

bonds.

In brief, we would expect technological me-

diation to affect the different components of

cohesiveness, with the potential to reduce inter-

personal attraction, group pride, and task com-

mitment. In the following, we consider factors

that may moderate the effect of technological

mediation on cohesiveness.

Moderators

Type of task. In keeping with our focus on

the three primary components of cohesiveness,

we propose that the effect of technological me-

diation on cohesiveness will differ according to

the type of task that the team is performing and

the components of cohesiveness that are most

relevant to that task. To the extent that techno-

logical mediation impairs interpersonal attrac-

tion, this should have a greater impact on cohe-

siveness for tasks in which interpersonal rela-

tions are most salient. Thus, we would expect

the impact of technological mediation on the

interpersonal attraction component of cohesive-

ness to be greater for social tasks or manipula-

tive/persuasion tasks that emphasize affective

relations. To the extent that technological me-

diation impairs group pride, this would have a

greater impact on tasks that require strong

shared beliefs and normative consensus. We

would expect the impact of technological me-

diation on the group pride component of cohe-

siveness to be greater for teams organized for

ideological or special-interest purposes. Finally,

to the extent that technological mediation im-

pairs task commitment, this would have a

greater impact on tasks that are primarily per-

formance or productivity oriented and that em-

phasize instrumental relations. Thus, we would

expect the impact of technological mediation on

the task commitment component of cohesive-

ness to be greater for mechanical/technical or

intellectual/analytic tasks. Unfortunately, there

is little empirical evidence to support these

predictions.

Type of computer-mediated environment.

Two major approaches to understanding the ef-

fects of different types of communications me-

dia, theories of social presence (Short, Wil-

liams, & Christie, 1976) and media richness

(Daft & Lengel, 1984), hold that the capacity to

transmit communicative information (visual,

verbal, and contextual cues) is progressively

restricted as one moves from face-to-face to

audio–video to audio-only to textual modes of

communication. The type of information that is

restricted includes verbal and nonverbal expres-

sive cues, body language, gaze, gesture, appear-

ance, and voice tone. This loss of contextual

information across various communication

modes may differentially impact the three pri-

mary components of cohesiveness.

It is likely that the loss of expressive contex-

tual information, especially in audio-only and

text communication modes, will lead to weaker

interpersonal bonds. Straus (1997) found that

computer-mediated teams (engaged in com-

puter conferencing via text messaging) reported

less cohesiveness, measured as interpersonal at-

traction, than did face-to-face teams. Weisband

and Atwater (1999) reported that teams com-

municating via a text-messaging network liked

other team members less than did face-to-face

teams. Examining trust across different types of

media, Bos, Olson, Gergle, Olson, and Wright

(2002) found that teams using text had the

greatest difﬁculty establishing trust, and that

teams using video conferencing and audio con-

ferencing took longer to establish trust than

face-to-face teams. Thus, although it is reason-

able to expect that strong interpersonal bonds

can develop in less rich modes of communica-

tion such as chat or e-mail, it is likely that these

bonds will develop more slowly and with

greater difﬁculty.

The richness of the mode of communica-

tion may also moderate the effect of techno-

logical mediation on task commitment.

Doherty-Sneddon et al. (1997) found that task

efﬁciency and understanding were lessened in

videoconferencing and audio-only communi-

cations versus face-to-face communications.

304 DRISKELL, RADTKE, AND SALAS

Straus and McGrath (1994) similarly found

that productivity, or the time it took to com-

plete a given task, was less for computer-

mediated communications (computer confer-

encing via text messaging) than with face-to-

face communications. Straus and McGrath

also reported that computer-mediated team

members had a harder time understanding one

another than did face-to-face teams, and Straus

(1996) found that computer-mediated team

members were less satisﬁed with the task pro-

cess than face-to-face members. Thus, we

would expect that richer modes of communica-

tion may allow more satisfactory task participa-

tion and accomplishment (especially for more

demanding tasks), which would be reﬂected in

greater task commitment.

Finally, the richness of the mode of commu-

nication may moderate the effect of technolog-

ical mediation on group pride. No studies that

we are aware of have tested this relationship

directly. However, group pride is related to nor-

mative consensus, shared beliefs, and satisfac-

tion with the team, and some research has

shown that less rich modes of communication

can result in longer time to reach consensus and

agreement (Connolly, Jessup, & Valacich,

1990; Hiltz, Johnson, & Turoff, 1986) and

lower identiﬁcation with the group (Cappel &

Windsor, 2000). Sellen (1995) examined face-

to-face teams, teams who interacted via audio–

video communications, and teams who inter-

acted via audio-only communications. She

found that compared with face-to-face groups,

technology-mediated teams (with or without

video) exhibited clear symptoms of depersonal-

ization and psychological distance. Moreover,

she found that overall, the use of audio versus

video mattered less than whether group mem-

bers were mediated or interacted face-to-face.

Sellen concluded that some forms of interaction

may be fundamentally altered when team mem-

bers do not share the same space. Thus, for the

development and maintenance of strong norma-

tive bonds, the important distinction may be

between mediated and face-to-face teams, with

the type of mediated communication environ-

ment less relevant.

Temporal context. Some research has sug-

gested that team processes develop more slowly

in virtual teams and that the weaker relational

ties observed in virtual teams may be the simple

result of these teams needing longer to develop

(Grifﬁth & Neale, 1999). Walther and Burgoon

(1992) found that members of computer-medi-

ated groups initially rated each other unfavor-

ably. Yet over the course of several weeks,

group members’ratings of composure/relax-

ation, informality, receptivity, trust, and social

orientation became more positive. Thus, deﬁcits

in the development of team processes in virtual

teams may be temporary, and some research

suggests that virtual teams can function as ef-

fectively as face-to-face teams provided they

have sufﬁcient time to develop.

Furthermore, most of the research that has

suggested a weakening of cohesiveness in vir-

tual teams examined teams that had never met,

had no history, and were not likely to meet

again after the interaction ceased. Research sug-

gests that these ad hoc teams may experience

difﬁculty in development of team processes

such as trust (Jarvenpaa & Leidner, 1999).

However, some research has suggested that

trust can develop in virtual teams but that it

develops in a different manner from the gradual

development of trust in face-to-face groups.

Meyerson, Weick, and Kramer (1996) argued

that such groups may develop “swift trust,”

based on existing bonds of organizational mem-

bership rather than on interpersonal feedback

from the task.

Status and Authority Relations

Several decades of research conducted with

mock juries, military teams, and problem-solv-

ing groups have revealed how status differences

among group members structure the nature of

group interaction. In a typical work group, those

with higher status assume a leadership position

and command more of the group’s resources—

they talk more, they direct group activities, and

they are more likely to exert their opinion dur-

ing decision making. In brief, individuals who

are perceived as having high status within the

group command more of the group’s resourc-

es—they dominate conversation, their ideas are

accepted more often, and they are seen as more

competent and leaderlike (see Driskell &

Mullen, 1990; Driskell, Olmstead, & Salas,

1993).

The theory of status characteristics and ex-

pectation states provides one of the most com-

prehensive and well documented explanations

of how status differentials emerge and are main-

305VIRTUAL TEAMS

tained in small groups (see Berger, 1992; Wag-

ner & Berger, 1993; Webster & Driskell, 1983).

According to this theory, status and inﬂuence in

task groups are determined by the performance

expectations formed for group members relative

to one another. The higher the performance

expectations formed for a group member are,

the more likely that person is to be given op-

portunities to perform in the group, initiate in-

teraction, receive positive evaluations and

agreement from others, and exert inﬂuence.

To illustrate this process, consider a decision-

making group that has initially gathered to per-

form a task. The group members are task ori-

ented, they want to achieve a successful out-

come of the task, and they search for

information regarding each others’capabilities

to help them achieve this goal. It is in each

individual’s self-interest, in order to accomplish

the task, to defer to others on the basis of the

others’expected task contributions. In other

words, it is in Person A’s best interest to defer

to Person B (i.e., to allow B to command more

time speaking, to accept B’sinﬂuence attempts,

etc.) if Person B seems to possess superior task

capability. Therefore, deference is exchanged

by some members of the group for the perceived

superior task contributions of others.

There are several types of characteristics of

individuals that are typically salient in face-to-

face groups and that provide a basis for the

formation of performance expectations. We will

discuss two types, status characteristics and sta-

tus cues. Status characteristics are external

characteristics that differentiate individuals,

such as race, gender, occupation, and even

physical attractiveness (Webster & Driskell,

1983). In most cases, the status hierarchy that

emerges within the small group reﬂects these

cultural stereotypes: Males, Whites, and those

of higher occupational status enact a more pro-

active role and command more of the group’s

resources, whereas females, ethnic minorities,

and those of lower occupational status are less

active and generally more compliant during

group deliberations. There are two types of sta-

tus characteristics: (a) diffuse status character-

istics such as race, gender, and age, which

evoke broad, general expectations for perfor-

mance, and (b) speciﬁc status characteristics,

which include speciﬁc skills or abilities and

have more speciﬁc, delimited expectations for

performance. In brief, status characteristics are

visible characteristics of individuals that struc-

ture such group behaviors as who speaks to

whom, who speaks more often, and whose ideas

are more likely to be solicited and accepted.

Status cues are expressive behaviors that peo-

ple exhibit, such as body position, intonation,

gaze, gestures, and other expressive cues. In

general, individuals who speak rapidly, with

few verbal disﬂuencies or hesitations, who se-

lect the head of the table, who speak more often,

and who maintain eye contact, especially while

speaking, are perceived as more competent and

occupy a higher position in the group status

hierarchy (see Berger, Webster, Ridgeway, &

Rosenholtz, 1986; Erickson, Lind, Johnson, &

O’Barr, 1978; Mullen, Salas, & Driskell, 1989;

Ridgeway, 1987). These types of expressive

status cues may have as strong an informational

impact as one’s“formal”status. For example,

when expressive cues and formal status provide

contradictory information (e.g., when someone

is a task group leader by virtue of his or her

organizational position but sounds hesitant and

looks confused), we attend to the information

provided by both types of characteristics. In this

example, the group leader may be afforded less

inﬂuence in the group than when his or her

expressive behavior suggests competence.

Status in Virtual Teams

A number of studies have examined the ef-

fects of computer-mediated environments on

status and authority relations in teams.

Dubrovsky et al. (1991) examined the effects of

status on groups who communicated either

face-to-face or through e-mail. Their results in-

dicated that in groups communicating via e-

mail, the high-status team member’s relative

dominance in participation over the low-status

team member was reduced compared with face-

to-face groups, resulting in greater equality of

interaction. Note that status distinctions were

diminished but not eliminated; high-status team

members participated more than low-status

team members in both the face-to-face and e-

mail groups. Sproull and Kiesler (1986) re-

ported that the use of e-mail breaks down status

barriers, resulting in a less hierarchical pattern

of communication that is less likely to reﬂect

differences in rank or status among group mem-

bers. Sproull and Kiesler (1991) concluded,

“The high status group member participated

306 DRISKELL, RADTKE, AND SALAS

less when the group communicated electroni-

cally . . . the low status members spoke more”

(p. 62). However, other studies have shown no

effect of technological mediation on reducing

status differentials. For example, Linville, Lieb-

haber, Obermayer, and Fallesen (1991) exam-

ined the effect of computer-mediated interac-

tion on a simulated military task and found little

evidence that status or leadership was impaired.

Weisband et al. (1995) and Silver et al. (1994)

found status differences to persist in both face-

to-face and computer-mediated groups. Saun-

ders, Robey, and Vaverek (1994) reported that

status differentials were maintained among hos-

pital personnel (physicians and nurses) when

communicating via computer conferencing.

Although the available research suggests that

technological mediation may affect the status

structure of the group, there is a good bit that is

not known at this point. For example, the status

characteristics theory describes a process

whereby certain evaluated characteristics that

differentiate group members lead to the forma-

tion of performance expectations, which then

determine behavioral inequalities in the group,

such as the rate of speaking. Figure 2 illustrates

this status 3expectations 3behavior process.

One question that is unclear from the existing

literature is, at what point in this process does

technological mediation affect status? We pro-

pose that there are three primary mechanisms

through which mediation may impact status

processes, differing in the stage at which this

impact occurs. First, it is clear that some com-

puter-mediated communications systems may

block the transmission of status information

(status characteristics and status cues) that dif-

ferentiates group members. Thus, in some

cases, because information that differentiates

other group members is not available or acces-

sible, initial status differentials are not estab-

lished as they would be in normal face-to-face

interaction. Thus, computer-mediated systems

may block the transmission of differentiating

information on group members that initiates the

operation of status processes.

A second possibility is that the effects of

status cues are dampened in computer-mediated

environments. Status cues such as strong eye

contact while speaking, ﬂuid gestures, and a

well-moderated voice tone have been shown to

structure interaction in face-to-face settings

(Driskell et al., 1993). However, these expres-

sive behaviors may not have the same impact

when expressed by a remote group member

over a computer-mediated network. Indeed,

some have argued that gestures and expressions

may lose their interactional signiﬁcance when

abstracted from the environment in which they

are produced and mediated via a video image

(Doherty-Sneddon et al., 1997; Heath & Luff,

1992). Thus, it is possible that status cues may

be dampened in a computer-mediated environ-

ment and performance expectations that are

formed on the basis of these weakened cues

may be attenuated.

A third possibility is that the manner in which

expectations are translated into behavior may

differ in a computer-mediated environment. The

status structure in a group is a normatively

Figure 2. Stages at which technological mediation may impact status processes.

307VIRTUAL TEAMS

supported process (Ridgeway & Diekema,

1989). As we noted earlier, group members may

defer to Member A because Member A shows

competence or other evidence of helping the

group reach its cooperative goal. Behaviors in-

consistent with this status structure, as when

Member B repeatedly overrules the higher sta-

tus Member A, are deemed to be a violation of

group norms and typically result in negative

reactions toward the norm violator. In a normal

group setting, negative reactions to a norm vi-

olation may range from a disapproving glance

to a face-to-face confrontation. Given that vir-

tual team members are more remote and less

immediately accessible than members of face-

to-face groups, it is possible that the pressure to

conform to these norms of status allocation may

be weaker. In this case, even if status differen-

tials are salient and performance expectations

are formed on this basis, there may be less

pressure to behave in a manner consistent with

these expectations. It is likely that previous re-

sults showing a status equalization effect in

computer-mediated groups may be a combina-

tion of (a) blocking of status information, (b)

dampening of status cues, and (c) weakening of

the normatively supported process by which

expectations are translated into behavior.

There may be some aspects of computer-

mediated communications that bolster or

strengthen status effects. For example, in nor-

mal group settings, one’s position in the group

status structure is established on the basis of

multiple indicators of status: A person may have

the relative disadvantage of being a lower level

employee in a group of managers but may also

provide evidence of specialized skills and train-

ing, professional demeanor, and competency.

Research indicates that people combine multi-

ple items of differentiating information to form

composite performance expectations for others

(Webster & Driskell, 1983). To the extent that

computer-mediated environments restrict the

transmission of differentiating information (es-

pecially expressive and other contextual cues),

interaction may be more likely to be patterned

simply on a single salient distinction, such as

the primary employee–manager distinction in

this example. In fact, the social identity model

of deindividuation effects suggests that when

individuating information is scarce, relevant

group membership cues may become more in-

ﬂuential (Postmes, Spears, & Lea, 2002). Thus,

it is possible that major status characteristics

that are salient, such as occupational position,

may have a relatively greater impact in comput-

er-mediated environments in which other differ-

entiating information is suppressed.

Performance Effects

It is important to note that status processes in

groups may have desirable and undesirable con-

sequences. Furthermore, any potential attenua-

tion of status differences in computer-mediated

groups can likewise have desirable as well as

undesirable effects. Ideally, status processes

provide structure to the team so that it can use

its resources more effectively to accomplish a

task. Status processes operate in groups to pro-

duce a hierarchical patterning of interaction,

such that those with higher status (i.e., those

who are perceived to be more competent) tend

to dominate group discussion. To the extent that

status differentials within the group are based

on cultural stereotypes (such as race or gender)

this may result in loss of resources to the group

and undesirable barriers to equal participation

for females and ethnic minorities. In this case, a

ﬂattening of the status hierarchy may lead to

more open and equal participation and greater

resources for the group to draw upon. On the

other hand, status generalization may have pos-

itive effects when it operates in general accor-

dance with the distribution of ability in the

group. To the extent that status differences re-

ﬂect actual differences in ability, expertise, and

competence, it is desirable for higher status

group members to be more active and inﬂuen-

tial. In this case, the dampening of status effects

can lead to lower participation by those with

greater competence.

There are ways in which status can affect

performance other than by determining the ex-

tent that team members participate or contribute

to the task. Under conditions in which status

distinctions are blurred, when there is uncer-

tainty about status, or when team members hold

conﬂicting expectations, status incongruence

may arise. One type of status incongruence may

occur when Team Member A possesses high

status (perhaps having more experience or hav-

ing speciﬁc content knowledge) and is ignored

or afforded fewer opportunities to contribute to

the task. Status incongruence can lead to dissat-

isfaction and lower productivity (Nixon, 1979).

308 DRISKELL, RADTKE, AND SALAS

Furthermore, for technology-mediated teams, it

may be more difﬁcult to correct normative vio-

lations of the status structure. In normal inter-

action, when there is a discrepancy between a

team member’s status, behavior, or rewards

(e.g., if the low-ability team member dominates

conversation), team members tend to act to cor-

rect these discrepancies. In situations in which

status distinctions are unclear, these normative

violations are less likely to be perceived or

addressed.

Moderators

Type of task. In general, a hierarchical sta-

tus structure is potentially more advantageous,

and more likely to be established, for tasks that

are ambiguous, uncertain, and complex versus

tasks that are highly structured and routine

(Nixon, 1979). Thus, in general, technological

mediation should impact status processes less

for more routine logical/precision tasks than for

intellectual/analytic tasks.

Examining the moderating effects of type of

task more precisely requires that we also con-

sider the temporal context of the team and the

consequences of changes in the status structure.

One point that we have touched on previously is

that status processes in teams are not passive but

are normatively supported by team members,

both high status and low status. Given that the

status structure is a means to organize interac-

tion within the team to achieve effective task

performance, changes to this structure are likely

to be resisted, especially if the status structure

supports the effective accomplishment of the

task. In other words, real groups that have a

history of interaction are likely to resist changes

to an established status structure.

Straus and McGrath (1994) noted that idea

generation tasks require very little coordination

or consensus to accomplish. And, in fact, status-

differentiated groups tend to do more poorly on

idea generation tasks than status-undifferenti-

ated groups (Silver et al., 1994). Thus, if tech-

nological mediation suppresses status effects on

idea generation tasks, there will probably be

little effort by team members to restore a struc-

ture that does not support successful task per-

formance in the ﬁrst place. However, judgment

tasks are tasks for which team members must

seek consensus on a preferred alternative. Struc-

turing interaction so that those who have better

ideas contribute more to team deliberations

should result in better performance. Technolog-

ical mediation may suppress status effects on

judgment tasks, but it may be temporary, as

team members seek to restore an effective status

structure. Status differentiation may be some-

what less beneﬁcial for intellective tasks that

have a correct answer, given that as long as one

person has the answer, consensus is not re-

quired. However, to the extent that status pro-

cesses operate to place those who are more

likely to have a correct answer in an advantaged

position in the group, an appropriate status

structure should support effective performance.

Again, for this type of task, technological me-

diation may initially suppress status effects, but

this effect may be temporary, as team members

attempt to restore an effective status structure.

Type of computer-mediated environment.

Some computer-mediated communications sys-

tems restrict the transmittal of visible as well as

expressive indicators of group members’status.

For example, group members who are separated

over a computer network with no visual capa-

bilities lose access to visual cues of others’

status that are present in face-to-face interac-

tion, such as eye contact, style of dress, select-

ing the head of the table, or perhaps even the

race, age, and gender of other group members.

The relative anonymity of other group members

(compared with face-to-face groups) may result

in less hierarchically structured and more equal

participation across group members.

Computer-mediated environments in which

group members communicate primarily via text,

with no visual or verbal communication, may

also limit the transmittal of expressive status

cues such as rate of speech, verbal ﬂuency, and

loudness and tone of speech. Furthermore,

many computer-mediated systems impose stan-

dardized forms of communication that further

limit the expression of status. In fact, more

formalized means of communication may allow

team members to speak uninterrupted, which

may in itself lead to changes in patterns of team

member interaction. By masking cues to status

or by imposing more standardized forms of

communications, we would expect interaction

in these types of mediated environments to be

less hierarchical and more equal.

Temporal context. Teams may be com-

posed of members who are initially differenti-

ated by status distinctions (i.e., teams of mixed

309VIRTUAL TEAMS

organizational status, gender, or ability) or they

may be composed of status equals, undifferen-

tiated with regard to observable status differ-

ences. In status-unequal teams, a status structure

develops very quickly at the onset of interac-

tion, on the basis of the observable status char-

acteristics that differentiate team members. In

status-equal teams, this structure develops more

slowly on the basis of the contributions that

team members make during team interaction.

There are several implications of this distinc-

tion. First, in teams that interact over a period of

time, the strong initial impact of status charac-

teristics declines as interaction becomes pat-

terned more on the quality of the contributions

that team members make. Thus, the effect of

technological mediation on blocking observable

indicators of status may become less relevant to

team interaction over time. Second, the impact

of status characteristics in structuring interac-

tion is relatively more salient when the task

situation is ambiguous and team members do

not have much information regarding the per-

formance abilities of each other. This is more

likely to be the case for ad hoc, newly formed

teams than for teams that are experienced and

have interacted over time.

Counternormative Behavior

Reviews of computer-mediated interaction

invariably address differences in counternorma-

tive or uninhibited behavior in face-to-face and

computer-mediated groups. In fact, this is per-

haps one of the most widely cited yet unimpres-

sively supported phenomena in this literature.

This ambiguity leads to conﬂicting conclusions

drawn by reviewers that uninhibited communi-

cation is either “a common, if not universal,

feature of computer-based conferences”(Selfe

& Meyer, 1991, p. 170) or “a comparatively

rare occurrence in [computer-mediated commu-

nications]”(Lea, O’Shea, Fung, & Spears,

1992, p. 108).

It is important to note that the term counter-

normative refers to behavior that deviates from

the norm, and it can refer to behavior that is

more positive than normal or behavior that is

more negative than normal (Blanton & Christie,

2003). Within the computer-mediated commu-

nications literature, counternormative or unin-

hibited behavior is often discussed in terms of

hostile or negative communication (i.e., ﬂam-

ing), but this association may not be consistent

and can be misleading. In fact, authors of one

study that is often cited as providing supporting

evidence for negative communications in com-

puter-mediated communications (Kiesler et al.,

1985) included in their measure of uninhibited

behavior both impolite statements and swearing

as well as exclamations (e.g., “Hooray!”) and

expressions of high positive regard. Thus, cau-

tion is advised in that some may treat counter-

normative or uninhibited behavior as both de-

sirable and undesirable behaviors that deviate

from the norm, whereas others may discuss

counternormative or uninhibited behaviors as

referring speciﬁcally to negative behavior.

Counternormative Behavior in Virtual

Teams

Certainly the primary concern prevalent in

the literature is with counternormative behavior

as negative behavior, such as verbal aggression,

expressions of hostility, or negative socioemo-

tional content. Dubrovsky et al. (1991) argued

that the lack of contextual means of communi-

cating in computer-mediated environments

forces interactants to be more forceful and less

inhibited. Further, Kiesler et al. (1985) noted

that computer-mediated systems transmit social

information poorly and that computer-mediated

interaction is likely to be characterized by less

attention to others, less social feedback, and

depersonalized communication. Sproull and

Kiesler (1991) concluded, “People interacting

on a computer are isolated from social cues. . . .

This feeling of privacy makes them feel less

inhibited with others”(p. 48). However, the

existing research on the effects of computer-

mediated communications on counternormative

behavior is much more equivocal. Although

early research studies revealed more negative

socioemotional content in computer-mediated

groups than in face-to-face groups, more recent

reports have called this relationship into

question.

Siegel et al. (1986) argued that the deindi-

viduation stemming from the relative absence

of social cues and social feedback in computer-

mediated groups would lead to greater uninhib-

ited behavior. They compared three-person

groups who either met face-to-face or were

physically separated and communicated via a

computer-mediated text messaging program. In

310 DRISKELL, RADTKE, AND SALAS

the computer-mediated groups, they observed

signiﬁcantly greater instances of uninhibited be-

havior (swearing, insults, and name calling)

than in the face-to-face groups. Dubrovsky et al.

(1991) found greater swearing, name calling,

and threats in groups communicating via e-mail

than in face-to-face groups. Sproull and Kiesler

(1986) reported more ﬂaming in groups com-

municating via e-mail, on the basis of research

subjects’self-reports of how much ﬂaming they

observed in e-mail messages versus how much

they observed in everyday conversations.

However, other studies have found that there

is little difference in uninhibited negative be-

havior between computer-mediated and face-to-

face groups. Hiltz, Johnson, and Turoff (1986)

compared communication in computer-medi-

ated and face-to-face groups using the Bales

Interaction Process Analysis categories. Al-

though they found evidence of fewer socioemo-

tional statements in computer-mediated groups,

they also found other results that were more

equivocal. Computer-mediated groups made

more statements relating to showing tension

than face-to-face groups on one experimental

task; however, for the other task, this difference

was reversed. There was no difference in state-

ments showing antagonism between the two

groups. Hiltz, Turoff, and Johnson (1989) found

little evidence of uninhibited behavior (e.g., in-

sults, profanity) in either computer-mediated or

face-to-face groups composed of corporate

managers. Walther and Burgoon (1992) re-

ported no difference in communications show-

ing tension or composure between face-to-face

and computer-mediated groups. Walther, An-

derson, and Park (1994), in a meta-analysis of

negative communication in computer-mediated

interaction, concluded that the overall propor-

tion of communications considered negative

was quite small and that the difference between

negative communication in computer-mediated

and face-to-face groups was minute (d⫽.017).

The most reasonable conclusion that can be

drawn from this research is that negative coun-

ternormative behavior is not inevitable in com-

puter-mediated groups but may occur under

some situations. There are several explanations

that can account for the potential for greater

counternormative behavior in computer-medi-

ated groups, including the effect of the impov-

erished computer-mediated communications

environment, anonymity and deindividuation,

lack of accountability, frustration, and confu-

sion. We consider each of these potential expla-

nations in the following.

One explanation for uninhibited behavior in

computer-mediated communication is directly

related to the impoverishment of the medium

itself. Normal communication involves both

verbal and nonverbal channels, and researchers

have drawn a distinction between verbal (con-

tent) and nonverbal (expressive) communica-

tion (Ambady & Rosenthal, 1992; DePaulo,

Rosenthal, Eisenstat, Rogers, & Finkelstein,

1976). Moreover, when the content of a mes-

sage and the expressive behavior accompanying

the message are discrepant—for instance, when

a person states “That’s horrible!”but with a

warm voice tone, a smile, and open, relaxed

gestures—we are likely to interpret the message

as a friendly jibe rather than an insult or a threat.

Computer-mediated systems that do not convey

expressive behavior may result in more misin-

terpretations and mistakes in interpreting mes-

sage intent. Computer-mediated systems that do

not provide an audio channel block paralinguis-

tic information, those that do not provide a

video channel block the transmission of visual

expressive behavior, and even those that pro-

vide “head and shoulders”video may distort

expressive behavior when it is abstracted from

the context in which it is produced and pre-

sented via a restricted video image (Heath &

Luff, 1992). Furthermore, we may ﬁnd greater

evidence of overt disagreements or more stri-

dent statements in computer-mediated groups

simply because more subtle forms of disagree-

ment (such as shaking one’s head) are rendered

less useful by the communications medium. In

this case, one form of disagreement may serve

as a substitute in computer-mediated communi-

cations for another form of disagreement that is

suppressed.

A number of researchers have argued that the

relative anonymity and loss of social cues in

computer-mediated interaction lead to greater

depersonalization or deindividuation. The tradi-

tional perspective holds that anonymity (such as

induced by wearing a mask or being in a large

crowd) leads to a loss of self-awareness, and in

this state of lowered self-attentiveness, people

fail to regulate their behavior with the prevail-

ing standards of performance (Carver &

Scheier, 1981; Mullen & Baumeister, 1987).

Thus, those “lost in a crowd”are more likely to

311VIRTUAL TEAMS

become deindividuated and commit atrocities

(Mullen, 1987), and those depersonalized in a

virtual team environment are less likely to be

concerned with prevailing team standards and

more likely to exhibit counternormative behav-

ior. The social identity model of deindividua-

tion (SIDE) provides an alternative and compet-

ing explanation (Postmes & Spears, 1998;

Spears & Lea, 1992). According to the SIDE

perspective, deindividuation leads to greater,

not less, conformity under certain conditions.

More speciﬁcally, if group standards are salient

(i.e., group membership is emphasized), then

deindividuation will lead to greater adherence

to group norms. Further, if the reference group

supports the expression of uninhibited behavior

(as SIDE proponents imply that many online

groups do), then this increased responsivity to

group norms will lead to greater uninhibited

behavior.

A related argument is that deindividuated vir-

tual team members, isolated from the physical

presence of other team members, may be less

accountable for their actions. Normative bonds

are consensual—we adhere to norms because it

is the right thing to do. But we also conform to

norms because of the coercive power of others.

For example, I may choose not to insult some-

one sitting across the table from me because he

or she may take umbrage and respond. Virtual

team members are under less coercive control,

stemming from the lack of immediate presence

of other team members, and are less account-

able for their actions. Furthermore, in ad hoc

one-shot or single-meeting virtual teams, not

only do we not have someone who can ﬁgura-

tively reach over and thump us on the ears, but

there is no potential for future interaction in

which transgressions can be righted. Thus, the

loss of the coercive power of other physically

present team members may lead to greater un-

inhibited behavior in virtual teams.

It is reasonable to expect that there is greater

“mechanical friction”(i.e., frustration with new

or awkward procedures and systems) involved

in using computer-mediated systems, which

may lead to greater annoyance and emotionality

(Poole, Holmes, Watson, & DeSanctis, 1993). It

is further likely that these procedural difﬁculties

and resulting frustrations would subside over

time. However, because of the short-term, tem-

porary nature of many research studies, there is

little evidence available regarding whether or

how this type of adaptation occurs.

Finally, there may be greater counternorma-

tive behavior in computer-mediated teams be-

cause interaction is more confusing than in face-

to-face communication (Thompson & Coovert,

2003). Thompson and Coovert claim that virtual

teams have greater difﬁculty establishing mu-

tual or shared knowledge because they lack the

direct knowledge gained from ﬁrsthand, imme-

diate experience with other team members.

They found that in comparison with teams that

worked face-to-face, teams that interacted via

text-based computer conferencing reported

greater confusion and less understanding of

team discussions. These results suggest that

technological mediation may lead to greater

counternormative behavior, especially uninten-

tional norm violations, because virtual team

members may ﬁnd it more difﬁcult to under-

stand just what the rules are.

Performance Effects

There is little direct research on the effects of

counternormative behavior on performance in

computer-mediated groups. However, assuming

that team norms support performance, adher-

ence to team norms should generally result in

greater productivity (Postmes, Spears, & Cihan-

gir, 2001). Moreover, negative or hostile com-

munications may lead to greater task-irrelevant

communications and may disrupt team interac-

tion and interpersonal relations among team

members.

Moderators

Type of task. Many of the studies that have

examined counternormative behavior in com-

puter-mediated groups have used judgment or

choice dilemma tasks, including studies that

have reported greater counternormative behav-

ior in computer-mediated groups versus face-to-

face groups (Dubrovsky et al., 1991; Siegel et

al., 1986) as well as those reporting no differ-

ence (Hiltz et al., 1989). It is likely that the

relative anonymity, lack of accountability, frus-

tration, and confusion that may stem from in-

teraction in computer-mediated environments

would have similar effects on a broad range of

tasks. However, the tendency for these condi-

tions to result in negative socioemotional out-

312 DRISKELL, RADTKE, AND SALAS

bursts may be greater for tasks in which socio-

emotional relations are more salient, such as

social or persuasive tasks, than in more routin-

ized mechanical/technical or logical/precision

tasks.

One further characteristic of the task is rele-

vant to the effect of deindividuation on coun-

ternormative behavior. The SIDE model argues

that deindividuation leads to greater identiﬁca-

tion with the group and stronger normative

commitment when social identity is salient

(Postmes, Spears, & Lea, 1998). That is, for

tasks in which group members perceive strong

membership to the group, deindividuation may

reinforce conformity to group norms (i.e., team

members are less self-aware and more group-

aware). When group members do not identify

strongly with the group, deindividuation may

weaken social inﬂuence and increase counter-

normative behaviors.

Type of computer-mediated environment.

Research on counternormative behavior has al-

most exclusively compared face-to-face groups

with groups communicating via e-mail or text-

based computer conferencing systems (cf.

Dubrovsky et al., 1991; Hiltz et al., 1989;

Kiesler et al., 1985; Siegel et al., 1986; Sproull

& Kiesler, 1986; Walther & Burgoon, 1992).

We would expect deindividuation or loss of

self-awareness to be more prevalent the more

that the communications medium restricts the

transmission of individual cues. Thus, we

would expect deindividuation to be greatest for

text-based communications systems, relatively

less likely to occur for audio-based systems, and

even less likely to occur for audio–video com-

munications. Frustration with the communica-

tions medium and confusion with team deliber-

ations are also likely to be greater for more

restrictive communications environments. On

the other hand, the loss of accountability stems

primarily from the physical separation of team

members, and this is likely to be salient in any

communications environment in which team

members are separated from one another.

Temporal context. Sproull and Kiesler

(1986) and Hiltz et al. (1989) examined coun-

ternormative behavior in intact work teams.

Sproull and Kiesler reported greater counternor-

mative behavior in e-mail communications, and

Hiltz et al. reported no differences between

teams communicating via text conferencing and

those interacting face-to-face. The vast majority

of other studies of counternormative behavior

examined newly formed groups whose time to-

gether lasted approximately 90 min or less.

Walther (1997) has argued that short-term com-

puter-mediated groups are more task oriented,

impersonal, and hostile than long-term groups.

He states that groups that anticipate future in-

teraction increase social information seeking,

show more positive affect, and enact more re-

lationally positive communication than ad hoc

one-shot groups. Indeed, Walther et al. (1994)

reported that differences in the extent of nega-

tive communications between face-to-face and

computer-mediated groups were reduced when

interaction was longer term versus time

restricted.

Communication

Communication is a multimodal process that

includes both verbal and nonverbal compo-

nents. As people speak, they also gesture to

elaborate speech, they change body position and

posture, and they vary facial expression and

gaze. In fact, one primary characteristic of com-

munication is that the literal meaning of speech

underspeciﬁes the speaker’s intended meaning.

To infer the speaker’s intended meaning re-

quires that the listener supplement speech con-

tent with contextual information that is not rep-

resented in speech. This contextual information

may include a speaker’s gaze or eye contact,

facial expressions, gestures, posture and body

movements, as well as paralinguistic cues such

as intonation and loudness of speech. For ex-

ample, expressive behaviors such as gestures

can serve multiple functions: they may supple-

ment and elaborate speech content, accent or

punctuate speech, substitute for speech, clarify

ambiguity, regulate the timing and sequence of

communication, and convey emotion (Driskell

& Radtke, in press). All of these functions can

serve to more fully convey information to the

listener. The promise of computer-mediated

communications systems is to produce an envi-

ronment that captures the richness of face-to-

face communication.

Communication in Virtual Teams

Communications systems that do not support

the transmission of contextual information are

viewed as impoverished and less informative

313VIRTUAL TEAMS

(Fussell & Benimoff, 1995). One perspective on

communication in computer-mediated groups

contends that transfer of information is re-

stricted in a computer-mediated environment

owing to a reduction in the amount and type of

cues available to interactants. More speciﬁcally,

the types of information reduced in a computer-

mediated environment are largely cues of con-

text—verbal and nonverbal behaviors that play

a large part in deﬁning the message that is

communicated. In brief, the available body of

research suggests that (a) communication in-

volves the transfer of information through mul-

tiple channels, (b) computer-mediated interac-

tion may ﬁlter out certain communicative cues

(primarily visual and contextual) found in face-

to-face interaction, and (c) this restriction in

contextual cues may lead to signiﬁcant changes

in group communication (see Culnan &

Markus, 1987).

One fundamental problem present in comput-

er-mediated communications is the failure to

establish and maintain mutual knowledge

(Cramton, 2001; Thompson & Coovert, 2003)

or common ground (Clark & Brennan, 1991).

Mutual knowledge refers to knowledge that

team members share and know they share

(Krauss & Fussell, 1990). Accordingly, a lack

of mutual knowledge would be represented by

team members who hold differing information

and do not realize they do so. Whittaker and

O’Conaill (1997) noted that group members

must establish common ground in regard to the

content of communication (mutual knowledge

regarding the content of conversation) and also

the process of communication (mutual knowl-

edge regarding who will speak, who will listen,

and how transitions are made).

Mutual knowledge is established through

several mechanisms, including direct knowl-

edge gained from shared experiences and ﬁrst-

hand observations of other team members and

through the dynamics of the interaction process

itself (Krauss & Fussell, 1990). Virtual team

members are less likely to gain direct knowl-

edge of other team members when they are

located remotely from one another. Further-

more, establishing mutual knowledge through

interaction is made more difﬁcult in a computer-

mediated environment because information ex-

change is less complete, is slower, and requires

more effort. Cramton (2001) has noted that one

reason communication is more difﬁcult in com-

puter-mediated environments is the greater ef-

fort required to convey nuances of speech with-

out the use of expressive and paralinguistic

cues.

For example, one of the most common ways

of establishing whether a statement is under-

stood is through acknowledgment. Acknowl-

edgments are attempts to provide positive evi-

dence of understanding. Acknowledgments are

often verbal but in many cases constitute what

are termed back-channel responses, including

signals or gestures such as head nods, shrugs, or

smiles. Another basic form of acknowledgment

is sustained attention. In normal communica-

tion, individuals typically monitor moment-to-

moment what other interactants are doing. We

detect cues of sustained attention from others,

often through eye gaze, that indicate their un-

derstanding. On the other hand, when someone

looks puzzled, breaks eye contact, or raises a

brow, this provides critical information that un-

derstanding has not been established. In a com-

puter-mediated environment, because this type

of contextual communication is often lacking, it

may be more difﬁcult to ground communication

and establish that a statement is understood;

additionally, the costs of grounding communi-

cation will be greater, as team members must

expend more effort to ensure that information is

understood by others.

A related problem in attempting to achieve

mutual knowledge in virtual teams is that re-

stricted feedback may make it more difﬁcult to

transfer information and to identify and correct

errors in the transfer of information as they

occur. Cooperative behavior in a team setting is

an iterative cycle of individual performance,

feedback from others, adjustment, and action.

Ashford and Tsui (1991) note that in many

real-world task settings, formal feedback mech-

anisms regulate behavior only loosely. That is,

the feedback that is received directly from ex-

ternal sources may in some cases be of less

relevance than that which is abstracted actively

from the environment by the individual. Draw-

ing feedback from other team members requires

an active process of seeking and evaluating

feedback, monitoring performance, and initiat-

ing and revising corrective actions.

Models of self-regulation emphasize the im-

portance of feedback that individuals generate

for themselves, rather than information that is

passively provided by others (Butler & Winne,

314 DRISKELL, RADTKE, AND SALAS

1995). Self-regulation refers to the active pro-

cess whereby individuals set performance stan-

dards, seek and evaluate feedback, detect dis-

crepancies in their performance, and take

actions to reduce those discrepancies. Self-

regulation is, in part, a social process. As team

members monitor their ongoing task perfor-

mance, they seek feedback from sources such as

other team members. This feedback allows the

learner to establish the validity of his or her own

perceptions regarding current task performance,

verify performance standards, and gather infor-

mation regarding potential strategies to correct

performance.

To the extent that team members are less

immediate and accessible, accessing peer feed-

back is more difﬁcult. Often, individuals may

seek feedback from a team member’s glance,

nod, or frown—nuanced behaviors that are not

easily transmitted in computer-mediated envi-

ronments. Not only is this type of feedback

more difﬁcult to perceive in a computer-medi-

ated environment, it may be less likely to be

provided by a team member who is sitting re-

motely at a computer screen rather than in the

direct presence of others. A second concern is

that the nature or valence of feedback provided

by members of virtual teams may differ from

that of face-to-face groups. Ashford and Tsui

(1991) have noted that there is a general ten-

dency for people to give each other positive

feedback spontaneously, while withholding

negative feedback. However, research suggests

that under certain circumstances, members of

computer-mediated groups may offer more neg-

ative or extreme evaluations of others than

members of face-to-face groups (Walther,

1997). Thus, there may be a tendency in com-

puter-mediated environments for team members

to be exposed to more negative feedback, dis-

approval, or censure. Finally, virtual team envi-

ronments may affect how feedback is evaluated.

Individuals do not simply absorb feedback but

actively evaluate the extent to which feedback is

an accurate reﬂection of their performance.

Feedback that is seen as inaccurate is discount-

ed; feedback that is seen as accurate is more

likely to be accepted. Some research suggests

that feedback is more likely to be accepted from

sources that are psychologically closer to the

source than from those more distant (Ilgen,

Fisher, & Taylor, 1979). Thus, we may accept

feedback less readily from others in a virtual

team environment who are seen as more remote

or removed from the immediate social setting.

Performance Effects

Cramton (2001) found several consequences

of the failure to establish mutual knowledge in

computer-mediated teams. Team members had

difﬁculty in communicating and obtaining con-

textual information regarding the speciﬁc con-

ditions and constraints under which remote

teammates worked. Information was distributed

unevenly among team members, and widely

varying impressions were formed regarding the

task. Team members had difﬁculty in commu-

nicating and in understanding what was most

important in the information transmitted. Fi-

nally, team members reported problems in re-

ceiving timely feedback from other team mem-

bers and in interpreting the meaning of other

teammates’silence or lack of response. Inter-

estingly, some team members interpreted oth-

ers’failure to communicate as an unwillingness

to work, reﬂecting the tendency to make nega-

tive personal attributions when situational infor-

mation is absent. Thompson and Coovert (2003)

found that a lack of mutual knowledge in com-

puter-mediated teams led to greater confusion

and inaccuracies in recording team decisions.

Finally, some research has shown that the sim-

ilarity of shared mental models among team

members predicts the quality of team processes

and performance (Mathieu, Heffner, Goodwin,

Salas, & Cannon-Bowers, 2000).

Moderators

Type of task. Cramton (2001) noted that the

impact of computer-mediated communications

on mutual knowledge is likely to be greater for

tasks in which individual team members hold a

great amount of unique information and in

which contextual information between remote

sites differs. Furthermore, this problem is exac-

erbated by high requirements for complexity,

workload, and interdependence. Others have ar-

gued that technological mediation, especially

the loss of visual cues, may have a greater

impact on communication effectiveness for

tasks that have a greater interpersonal require-

ment, such as social, manipulative/persuasive,

or negotiation tasks (Sellen, 1995; Short, 1974;

Williams, 1977).

315VIRTUAL TEAMS

Type of computer-mediated environment.

The advantages of audio over text-based com-

munications are substantial, leading Whittaker

and O’Conaill (1997) to conclude that speech is

the critical medium for interpersonal communi-

cations. Thus, the negative impact of technolog-

ical mediation on communication should be re-

duced when audio is available. However, the

value of adding video to audio communications

is less apparent. Rudman, Hertz, Marshall, and

Dykstra-Erickson (1997) examined the role of

the video channel in supporting computer-me-

diated communication. They proposed that one

beneﬁt of adding video to audio and data chan-

nels in a computer-mediated environment is that

it allows teams to more effectively regulate the

ﬂow of interaction and to communicate emo-

tions and attitudes. Rudman et al. found that

team members using audio-only communica-

tions had difﬁculty knowing when others were

paying attention and could not tell when they

were understood because they could not see

others’nonverbal reactions. Without visual

feedback, team members delayed asking others

for information out of fear of interrupting their

work; further, they had problems knowing when

others needed help, because they could not see

others’level of frustration or confusion. In gen-

eral, Rudman et al. observed that without visual

input, team members had problems evaluating

others’level of attention and concentration, de-

termining how positive or negative others were

feeling, determining whether others needed

help, and knowing when to interrupt.

Nevertheless, the bulk of evidence on the

value of adding video capabilities to computer-

mediated systems is not supportive. In one of

the earliest such studies, Chapanis, Ochsman,

Parrish, and Weeks (1972) found that adding

video to group communications did not enhance

the efﬁciency of group problem solving or result

in higher quality outcomes. Other studies have

similarly shown little advantage of video over

audio-only communication in enhancing team

performance (Anderson et al., 1997; Boyle,

Anderson, & Newlands, 1994; Doherty-Sned-

don et al., 1997; Sellen, 1995; Williams, 1977).

In a recent review of this literature, Whittaker

and O’Conaill (1997) concluded, “Laboratory

studies to demonstrate the beneﬁts of adding a

visual communication modality to voice have in

general shown few objective improvements”

(p. 24).

Why does video apparently do so little to

enhance communication in computer-mediated

systems? First, Doherty-Sneddon et al. (1997)

note that a primary focus on task outcome may

be misleading and that a more useful evaluation

of communication requires consideration of

both outcome and communicative process. By

looking at outcome alone, their results showed

little difference between face-to-face, audio,

and audio–video communications. However,

they also found that the structure of communi-

cation varied across these conditions and that

the visual channel was useful to speakers, for

example, in facilitating conversational ﬂow.

However, one further factor underlies the ap-

parent ineffectiveness of video in computer-

mediated communications. Research suggests

that people are able to make fairly accurate

judgments of others’personality on the basis of

minimal interactions (Ambady, Hallahan, &

Rosenthal, 1995) and that people are able to

recognize emotions as well as deception from

demeanor (Frank & Ekman, 1997). However,

studies also show that the assessment of other

states, moods, or motivations from nonverbal

behavior can be more difﬁcult. For example,

Jecker, Maccoby, Breitrose, and Rose (1964)

found that assessing comprehension from non-

verbal behavior was difﬁcult and that teachers

were able to accurately assess student compre-

hension about 30% of the time from video re-

cordings. Furthermore, research that has at-

tempted to improve the ability to decode non-

verbal behavior suggests that training may

increase decoding accuracy (Costanzo, 1992).

However, most studies that contrast audio-only

with audio–video communications simply make

available a video channel (typically a facial

image on a computer screen window) for team

members to use. One reason that providing a

video channel to computer-mediated systems

has shown few beneﬁts may be that individuals

make poor use of the social cues that are avail-

able without specialized training to ensure ac-

curacy in decoding nonverbal behavior.

Temporal context. It is likely that commu-

nication difﬁculties, and especially problems in

establishing mutual knowledge, will be less for

preexisting teams that have a history of interac-

tion, team members who are more experienced

using the communications media, and more ma-

ture teams that can function with less rich in-

formation exchanges (McGrath & Hollings-

316 DRISKELL, RADTKE, AND SALAS

head, 1994). Moreover, the value of video to

enhancing communication in virtual teams may

be greater as team members gain familiarity

with one another. In ad hoc or newly formed

groups, expressions and gestures are idiosyn-

cratic. In other words, when we have not

worked with someone before, that person’s ges-

tures, posture, and expressions may hold little

meaning for us. It is only over time that these

idiosyncratic gestures gain meaning (e.g., we

learn that when Alice looks away, she is think-

ing, but when Bob looks away, he is bored).

Therefore, the use of video in computer-medi-

ated systems may be of less value for newly

formed teams, because the social information

provided may hold less meaning.

Discussion

Some see a future in which virtual team

members interact seamlessly over advanced

communications systems. Others are more cau-

tious and argue that virtual teams differ in sig-

niﬁcant ways from teams that work face-to-

face. For each of the topics we have discussed,

we have examined research that has compared

face-to-face teams and technology-mediated or

virtual teams. The existing evidence leads us to

conclude that overall, distance seems to mat-

ter—that being mediated by technology can

have a signiﬁcant impact on how teams per-

form. These performance effects may stem from

changes in cohesiveness, status structure, coun-

ternormative behavior, and communication.

Technological mediation can have a negative

effect on cohesiveness, affecting interpersonal

bonds (interpersonal attraction), normative

bonds (group pride), and instrumental bonds

(task commitment) among team members.

Team performance is likely to be affected espe-

cially to the extent that task commitment is

lowered.

Technological mediation may affect the sta-

tus structure of the team by blocking the trans-

mission of status information, dampening the

effects of status cues, or weakening the norma-

tive process that supports status-based behavior.

To the extent that more competent persons oc-

cupy higher positions in the status hierarchy of

the team, performance can be degraded if status

differentials are reduced.

Although counternormative behavior is not

necessarily a feature of computer-mediated

teams, technological mediation can lead to a

greater prevalence of behaviors, both desirable

and undesirable, that deviate from the norm.

Negative counternormative behavior may stem

from a number of causes, including deindividu-

ation, frustration, lack of accountability, and

confusion, and can disrupt the smooth interper-

sonal relations required for effective team

performance.

Technological mediation can make it more

difﬁcult to communicate information to others

and to interpret the communications of others.

The relative loss of contextual information in

computer-mediated communications can result

in greater difﬁculty in establishing mutual

knowledge and can lead to greater confusion

among team members and inaccuracies in

performance.

However, the effects of technological medi-

ation on these team processes are moderated by

the type of computer-mediated environment, the

type of task, and the temporal context of the

team. We described the effects of these three

moderators as overarching, and the evidence

reviewed suggests they are indeed important.

For example, there are few generalizations that

can be made about virtual teams without con-

sidering the nature of the communications en-

vironment. Overall, less rich communications

media serve to exacerbate the effects of techno-

logical mediation. Thus, to the extent that tech-

nological mediation impairs interaction, it im-

pairs interaction more so for text-only commu-

nications than for text–audio and for audio–

video communications. However, there also

seems to be something about the physical pres-

ence or immediacy of another that differentiates

face-to-face interaction from even high-quality

audio–video. Sellen (1995) concluded,

There appears to be something critically different

about sharing the same physical space that needs to be

examined more carefully. What aspects of interaction

are fundamentally altered when people no longer share

the same physical space? Why does the presence of a

video channel fail to compensate? (p. 440)

At present, there is no satisfactory explication

of this issue.

Similarly, few ﬁrm statements can be made

regarding team interaction in virtual environ-

ments without consideration of the type of task

that the team is performing. This variable is

somewhat more difﬁcult to examine, as there

are any number of dimensions on which tasks

317VIRTUAL TEAMS

may be classiﬁed. We have attempted to focus

on the behavioral requirements of the task; the

distinction that seems to be most useful is that

between tasks that have high interpersonal/so-

cial requirements (i.e., social and persuasive

tasks) and tasks that have greater instrumental

requirements (i.e., technical or logical tasks).

The comparison is not a simple one, but tech-

nological mediation may be more disruptive for

tasks that have high demands for either social or

instrumental interdependence than for tasks that

can be accomplished with less social or instru-

mental interaction.

Finally, McGrath (1990) has noted, “Groups

develop and exist in a temporal context”(p. 23),

an admonition that is all but ignored in most

research on virtual teams. Overall, time and

experience tend to brace teams against the del-

eterious effects of technological mediation.

Thus, some of the negative effects of techno-

logical mediation observed in ad hoc, short-

term teams may not be evident in intact or

experienced teams. Further, some effects of

technological mediation that are observed in the

short term may be temporary and subside over

time.

To draw ﬁrm conclusions from a model of

team performance such as that presented in Fig-

ure 1, a research literature is needed that clearly

and systematically varies important team and

task characteristics. However, as Hollingshead

and McGrath (1995) noted, this may be the

ideal, but the reality is that the body of literature

on computer-mediated teams virtually ignores

the operation of key team and task variables.

Accordingly, in many cases, our analyses are

speculative rather than conclusive, and more

research is needed to further elucidate the spe-

ciﬁc conditions under which technological me-

diation impacts team interaction. However, as is

often the case, there is value in noting what we

do not know at this point. Speciﬁcally, further

research is needed to examine the effects of

technological mediation on other processes,

other outcomes, and other moderators.

Other Processes

We have discussed in some detail the effects

of technological mediation on cohesiveness,

status processes, counternormative behavior,

and communication. There are a number of

other team processes of interest such as leader-

ship, decision making, cooperation, and confor-

mity that have not been fully examined in this

literature. For example, one process that may be

particularly relevant given the nature of the

computer-mediated environment is social loaf-

ing. Social loaﬁng refers to the tendency for

individuals to become less motivated to exert

full effort when working on a collective group

task than when working independently (Latane,

Williams, & Harkins, 1979). In the early part of

this century, Ringelmann (1913) found that

when research participants performed a rope-

pulling task as a group, less force was exerted

than would be expected by combining the forces

of each individual. Furthermore, as the size of

the group increased, the difference between ex-

pected and actual productivity became greater.

Subsequent research has shown that social loaf-

ing is most likely to occur when the individual

task performer is immersed in a larger group,

when the task contributions of others cannot be

easily identiﬁed, when the work group is com-

posed of strangers or lacks cohesiveness, and

when group members are less likely to perceive

the direct contributions of their own efforts to

the group outcome (Karau & Williams, 1993;

Mullen & Baumeister, 1987). These conditions

reﬂect those that occur in a virtual team envi-

ronment. Members of virtual teams work to-

gether but individual outputs are often difﬁcult

to distinguish or monitor. Members of virtual

teams work with others who are dispersed over

a wide geographic area and with whom they

have little direct contact, and they may be one

“node”of a network that may contain a large

group of members. Thus, a virtual team mem-

ber, who may see ﬁve other group members

represented on a computer screen (conferencing

software typically places an image of each other

member in a separate window on the screen),

may become lost in the crowd and devote less

effort to the task. Although a virtual team set-

ting may be a prime environment for social

loaﬁng to occur, very little research has exam-

ined this topic.

Other Outcomes

We limited our interest to the effects of tech-

nological mediation on team performance,

broadly deﬁned. There are clearly more speciﬁc

indicators of team performance that should be

examined, including performance accuracy,

318 DRISKELL, RADTKE, AND SALAS

speed, and quality of solutions, among other

measures. There are also important outcome

variables other than performance, including

team member satisfaction, the longevity or via-

bility of the team, and organizational outcomes.

Other Moderators

There are a number of other potential mod-

erators that warrant further scrutiny, including

the size of the team. Steiner (1972) noted that

team size may impact performance in several

ways. As a group gets larger, the diversity of its

members increases, thus increasing the variety

of viewpoints and potentially increasing the

number of problem solutions available to the

team. However, larger teams do not always

perform better than smaller ones. Increasing

team size makes coordination requirements

more difﬁcult, and as groups increase in size

and complexity, more group structure (role dif-

ferentiation, etc.) is required to coordinate

group activity. It is well established that

whereas total group performance may increase

as the size of the group increases, group mem-

ber performance (i.e., performance per person)

and satisfaction decrease as a function of group

size (e.g., Mullen, 1991; Mullen & Baumeister,

1987).

Separating team members from one another,

as in a virtual team environment, may serve to

“reduce”the size of the group in a perceptual

sense. In other words, we may be able to alle-

viate the negative consequences of being part of

a larger team by having team members work

together but remotely over a communications

network. Thus, it is possible that technological

mediation may increase team member satisfac-

tion in large teams by making a large group feel

smaller, yet it may decrease satisfaction in small

teams by disrupting the closer interpersonal

bonds forged by smaller groups. However, there

has been little research conducted to examine

potential social advantages of technological

mediation.

In summary, some differences that have been

observed between face-to-face and virtual

teams are likely to be temporary. For example,

the mechanical friction and user frustrations

inherent in using a relatively crude audio–visual

interface will dissipate as advances in hardware

and software are achieved. Some differences

that have been noted may be illusory: The ob-

servation that members of virtual teams ver-

bally disagree more than members of face-to-

face teams may simply reﬂect the fact that vir-

tual team members are less able to disagree

nonverbally. However, some differences are

more fundamental. We believe that one such

difference is the distinction between the imme-

diacy of a physically present team member and

the remoteness of a virtual team member. How-

ever, a social psychological analysis of the con-

cept of presence is lacking.

One feature that characterizes much of the

research on virtual teams is an emphasis on

developing advanced technological environ-

ments for virtual team interaction. One disad-

vantage of this technology-focused approach is

that key social and psychological variables may

be overlooked or ignored, as Hollingshead and

McGrath (1995) observed. If ever there was an

essential need and role for social and psycho-

logical research, this is it. Although research on

virtual teams may span the disciplines of human

factors, communication, and human computer

interaction, the knowledge of group dynamics is

central to understanding performance in virtual

teams.

References

Ambady, N., Hallahan, M., & Rosenthal, R. (1995).

On judging and being judged accurately in zero-

acquaintance situations. Journal of Personality

and Social Psychology, 69, 518–529.

Ambady, N., & Rosenthal, R. (1992). Thin slices of

expressive behavior as predictors of interpersonal

consequences: A meta-analysis. Psychological

Bulletin, 111, 256–274.

Anderson, A. H., O’Malley, C., Doherty-Sneddon,

G., Langton, S., Newlands, A., Mullin, J., et al.

(1997). The impact of VMC on collaborative prob-

lem solving: An analysis of task performance,

communicative process, and user satisfaction. In

K. E. Finn, A. J. Sellen, & S. B. Wilbur (Eds.),

Video-mediated communication (pp. 133–155).

Mahwah, NJ: Erlbaum.

Ashford, S. J., & Tsui, A. S. (1991). Self-regulation

for managerial effectiveness: The role of active

feedback seeking. Academy of Management Jour-

nal, 34, 251–280.

Back, K. W. (1951). Inﬂuence through social com-

munication. Journal of Abnormal and Social Psy-

chology, 46, 9–23.

Baron, R. M., & Kenny, D. A. (1986). The modera-

tor–mediator variable distinction in social psycho-

logical research: Conceptual, strategic, and statis-

319VIRTUAL TEAMS

tical considerations. Journal of Personality and

Social Psychology, 51, 1173–1182.

Berger, J. (1992). Expectations, theory, and group

processes. Social Psychology Quarterly, 55, 3–11.

Berger, J., Webster, M., Ridgeway, C., & Rosen-

holtz, S. J. (1986). Status cues, expectations, and

behavior. In E. Lawler (Ed.), Advances in group

processes: Theory and research (Vol. 3, pp. 1–22).

Greenwich, CT: JAI Press.

Blanton, H., & Christie, C. (2003). Deviance regula-

tion: A theory of action and identity. Review of

General Psychology, 7, 115–149.

Bos, N., Olson, J. S., Gergle, D., Olson, G. M., &

Wright, Z. (2002). Effects of four computer-medi-

ated communications channels on trust develop-

ment. In Proceedings of CHI 2002 (pp. 135–140).

New York: ACM Press.

Boyle, E., Anderson, A., & Newlands, A. (1994). The

effects of visibility on dialogue and performance in

a cooperative problem solving task. Language and

Speech, 37, 1–20.

Burgelman, J. C. (2000). Traveling with communica-

tion technologies in space, time and everyday life:

An exploration of their impact. Retrieved from http://

www.ﬁrstmonday.dk/issues/issue5_3/burgelman/

Butler, D. L., & Winne, P. H. (1995). Feedback and

self-regulated learning: A theoretical synthesis.

Review of Educational Research, 65, 245–281.

Cairncross, F. (1997). The death of distance: The

trendspotter’s guide to new communications. Bos-

ton: Harvard Business School Press.

Cappel, J. J., & Windsor, J. C. (2000). Ethical deci-

sion making: A comparison of computer-supported

and face-to-face groups. Journal of Business Eth-

ics, 28, 95–107.

Carver, C. S., & Scheier, M. F. (1981). Attention and

self-regulation: A control-theory approach to hu-

man behavior. New York: Springer-Verlag.

Chapanis, A., Ochsman, R. B., Parrish, R. N., &

Weeks, G. D. (1972). Studies in interactive com-

munication: I. The effects of four communication

modes on the behavior of teams during cooperative

problem-solving. Human Factors, 14, 487–509.

Clark, H. H., & Brennan, S. E. (1991). Grounding in

communication. In L. B. Resnick, J. M. Levine, &

S. D. Teasley (Eds.), Perspectives on socially

shared cognition (pp. 127–149). Washington, DC:

American Psychological Association.

Connolly, T., Jessup, L. M., & Valacich, J. S. (1990).

Effects of anonymity and evaluative tone on idea

generation in computer-mediated groups. Manage-

ment Science, 36, 97–120.

Costanzo, M. (1992). Training students to decode

verbal and nonverbal cues: Effects on conﬁdence

and performance. Journal of Educational Psychol-

ogy, 84, 303–313.

Craig, T. Y., & Kelly, J. R. (1999). The effects of

task and interpersonal cohesiveness on group cre-

ativity. Group Dynamics: Theory, Research, and

Practice, 3, 243–256.

Cramton, C. D. (2001). The mutual knowledge prob-

lem and its consequences for dispersed collabora-

tion. Organizational Science, 12, 346–371.

Culnan, M. J., & Markus, M. L. (1987). Information

technologies. In F. M. Jablin, L. L. Putnam, K. H.

Roberts, & L. W. Porter (Eds.), Handbook of or-

ganizational communication: An interdisciplinary

perspective (pp. 420–443). Newbury Park, CA:

Sage.

Curtis, R. C., & Miller, K. (1986). Believing another

likes or dislikes you: Behaviors making the beliefs

come true. Journal of Personality and Social Psy-

chology, 51, 284–290.

Daft, R. L., & Lengel, R. H. (1984). Information

richness: A new approach to managerial behavior

and organization design. Research in Organiza-

tional Behavior, 6, 191–233.

Daft, R. L., & Lengel, R. H. (1986). Organizational

information requirements, media richness, and

structural design. Management Science, 32, 554–

571.

DePaulo, B. M., Rosenthal, R., Eisenstat, R. A.,

Rogers, P. L., & Finkelstein, S. (1976). Decoding

discrepant nonverbal cues. Journal of Personality

and Social Psychology, 36, 313–323.

Devine, D. J. (2002). A review and integration of

classiﬁcation systems relevant to teams in organi-

zations. Group Dynamics, 6, 291–310.

Doherty-Sneddon, G., Anderson, A., O’Malley, C.,

Langton, S., Garrod, S., & Bruce, V. (1997). Face-

to-face and video-mediated communication: A

comparison of dialogue structure and task perfor-

mance. Journal of Experimental Psychology: Ap-

plied, 3, 105–125.

Driskell, J. E., Hogan, R., & Salas, E. (1987). Per-

sonality and group performance. In C. Hendrick

(Ed.), Review of personality and social psychology

(Vol. 9, pp. 91–112). Newbury Park, CA: Sage.

Driskell, J. E., & Mullen, B. (1990). Status, expec-

tations, and behavior: A meta-analytic review and

test of the theory. Personality and Social Psychol-

ogy Bulletin, 16, 541–553.

Driskell, J. E., Olmstead, B., & Salas, E. (1993). Task

cues, dominance cues, and inﬂuence in task

groups. Journal of Applied Psychology, 78, 51–60.

Driskell, J. E., & Radtke, P. H. (in press). The effect

of gesture on speech production and comprehen-

sion. Human Factors.

Dubrovsky, V., Kiesler, S., & Sethna, B. (1991). The

equalization phenomenon: Status effects in com-

puter-mediated and face-to-face decision-making

groups. Human-Computer Interaction, 6, 119–146.

Erickson, B., Lind, E. A., Johnson, B. C., & O’Barr,

W. M. (1978). Speech style and impression forma-

320 DRISKELL, RADTKE, AND SALAS

tion in a court setting: The effects of “powerful”

and “powerless”speech. Journal of Experimental

Social Psychology, 14, 266–279.

Evans, C. R., & Dion, K. L. (1991). Group cohesion

and performance: A meta-analysis. Small Group

Research, 22, 175–186.

Festinger, L. (1950). Informal social communication.

Psychological Review, 57, 271–282.

Frank, M. G., & Ekman, P. (1997). The ability to

detect deceit generalizes across different types of

high-stake lies. Journal of Personality and Social

Psychology, 72, 1429–1439.

Fussell, S. R., & Benimoff, N. I. (1995). Social and

cognitive processes in interpersonal communica-

tion: Implications for advanced telecommunica-

tions technologies. Human Factors, 37, 228–250.

Golembiewski, R. T. (1962). The small group: An

analysis of research concepts and operations. Chi-

cago: University of Chicago Press.

Grifﬁth, T., & Neale, M. A. (1999). Information

processing and performance in traditional and vir-

tual teams: The role of transactive memory (Re-

search Paper No. 1613). Stanford, CA: Stanford

University Graduate School of Business.

Hackman, J. R., & Morris, C. G. (1975). Group tasks,

group interaction process, and group performance

effectiveness: A review and proposed integration.

Advances in Experimental Social Psychology, 8,

45–99.

Heath, C., & Luff, P. (1992). Media space and com-

municative asymmetries: Preliminary observations

of video-mediated interaction. Human-Computer

Interaction, 7, 315–346.

Hiltz, S. R., Johnson, K., & Turoff, M. (1986). Ex-

periments in group decision making: Communica-

tion process and outcome in face-to-face versus

computerized conferences. Human Communica-

tion Research, 13, 225–252.

Hiltz, S. R., Turoff, M., & Johnson, K. (1989). Ex-

periments in group decision making: 3. Disinhibi-

tion, deindividuation, and group process in pen and

real name computer conferences. Decision Support

Systems, 5, 217–232.

Hollingshead, A. B., & McGrath, J. E. (1995). Com-

puter-assisted groups: A critical review of the em-

pirical research. In R. A. Guzzo, E. Salas, I. L.

Goldstein, et al. (Eds.), Team effectiveness and

decision making in organizations (pp. 46–78). San

Francisco: Jossey-Bass.

Hollingshead, A. B., McGrath, J. E., & O’Conner,

K. M. (1993). Group task performance and com-

munication technology: A longitudinal study of

computer-mediated versus face-to-face work

groups. Small Group Research, 24, 307–334.

Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979).

Consequences of individual feedback on behavior

in organizations. Journal of Applied Psychol-

ogy, 64, 349–371.

Jarvenpaa, S. L., & Leidner, D. E. (1999). Commu-

nication and trust in virtual teams. Organizational

Science, 10, 791–815.

Jecker, J., Maccoby, N., Breitrose, H. S., & Rose,

E. D. (1964). Teacher accuracy in assessing cog-

nitive visual feedback from students. Journal of

Applied Psychology, 48, 393–397.

Karau, S. J., & Williams, K. D. (1993). Social loaf-

ing: A meta-analytic review and theoretical inte-

gration. Journal of Personality and Social Psy-

chology, 65, 681–706.

Kiesler, S., Zubrow, D., Moses, A., & Geller, V.

(1985). Affect in computer-mediated communica-

tion: An experiment on synchronous terminal-to-

terminal discussion. Human-Computer Interac-

tion, 1, 77–104.

Krauss, R., & Fussell, S. (1990). Mutual knowledge

and communicative effectiveness. In J. Galegher,

R. E. Kraut, & C. Egido (Eds.), Intellectual team-

work: Social and technical bases of collaborative

work (pp. 111–146). Hillsdale, NJ: Erlbaum.

Latane, B., Williams, K., & Harkins, S. (1979). Many

hands make light work: The causes and conse-

quences of social loaﬁng. Journal of Personality

and Social Psychology, 37, 822–832.

Lea, M., O’Shea, T., Fung, P., & Spears, R. (1992).

“Flaming”in computer-mediated communication:

Observations, explanations, and implications. In

M. Lea (Ed.), Contexts of computer-mediated com-

munication (pp. 89–112). Hertfordshire, England:

Harvester Wheatsheaf.

Levine, J. M., & Moreland, R. L. (1990). Progress in

small group research. Annual Review of Psychol-

ogy, 41, 585–634.

Linville, J. M., Liebhaber, M. J., Obermayer, A. H.,

& Fallesen, J. J. (1991). Computer-mediated group

processes in distributed command and control sys-

tems: Supervised shared work (Tech. Rep. 926).

Alexandria, VA: U.S. Army Research Institute for

the Behavioral and Social Sciences.

Lott, A. J., & Lott, B. E. (1961). Group cohesiveness,

communication level, and conformity. Journal of

Abnormal and Social Psychology, 62, 408–412.

Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas,

E., & Cannon-Bowers, J. A. (2000). The inﬂuence

of shared mental models on team process and

performance. Journal of Applied Psychology, 85,

273–283.

McGrath, J. E. (1984). Groups: Interaction and per-

formance. Englewood Cliffs, NJ: Prentice-Hall.

McGrath, J. E. (1990). Time matters in groups. In J.

Galegher, R. E. Kraut, & C. Egido (Eds.), Intel-

lectual teamwork: Social and technical bases of

collaborative work (pp. 23–61). Hillsdale, NJ:

Erlbaum.

McGrath, J. E. (1997). Small group research, that

once and future ﬁeld: An interpretation of the past

321VIRTUAL TEAMS

with an eye to the future. Group Dynamics, 1,

7–27.

McGrath, J. E., & Hollingshead, A. B. (1994).

Groups interacting with technology: Ideas, issues,

evidence, and an agenda. Newbury Park, CA:

Sage.

McLeod, P. L. (1992). An assessment of the experi-

mental literature on electronic support of group

work: Results of a meta-analysis. Human-Com-

puter Interaction, 7, 257–280.

Meyerson, D., Weick, K. E., & Kramer, R. M.

(1996). Swift trust and temporary groups. In R. M.

Kramer & T. R. Tyler (Eds.), Trust in organiza-

tions: Frontiers of theory and research (pp. 166–

195). Thousand Oaks, CA: Sage.

Mullen, B. (1987). Self-attention theory: The effects

of group composition on the individual. In B.

Mullen & G. R. Goethals (Eds.), Theories of group

behavior (pp. 125–146). New York: Springer-Ver-

lag.

Mullen, B. (1991). Group composition, salience, and

cognitive representations: The phenomenology of

being in a group. Journal of Experimental Social

Psychology, 27, 297–323.

Mullen, B., Anthony, T., Salas, E., & Driskell, J. E.

(1994). Group cohesiveness and quality of deci-

sion making: An integration of tests of the

groupthink hypothesis. Small Group Research, 25,

189–204.

Mullen, B., & Baumeister, R. F. (1987). Group ef-

fects on self-attention and performance: Social

loaﬁng, social facilitation, and social impairment.

In C. Hendrick (Ed.), Review of personality and

social psychology (pp. 189–206). Beverly Hills,

CA: Sage.

Mullen, B., & Copper, C. (1994). The relation be-

tween group cohesiveness and performance: An

integration. Psychological Bulletin, 115, 210–227.

Mullen, B., Salas, E., & Driskell, J. E. (1989). Sa-

lience, motivation, and artifact as contributions to

the relation between participation rate and leader-

ship. Journal of Experimental Social Psychol-

ogy, 25, 545–559.

Nixon, H. L. (1979). The small group. Englewood

Cliffs, NJ: Prentice-Hall.

Oliver, L. W., Harman, J., Hoover, E., Hayes, S. M.,

& Pandhi, N. A. (1999). A quantitative integration

of the military cohesion literature. Military Psy-

chology, 11, 57–83.

Olson, G. M., & Olson, J. S. (2000). Distance mat-

ters. Human-Computer Interaction, 15, 139–179.

Poole, M. S., Holmes, M., Watson, R., & DeSanctis,

G. (1993). Group decision support systems and

group communication: A comparison of decision

making in computer-supported and nonsupported

groups. Communication Research, 20, 176–213.

Postmes, T., & Spears, R. (1998). Deindividuation

and antinormative behavior: A meta-analysis. Psy-

chological Bulletin, 123, 238–259.

Postmes, T., Spears, R., & Cihangir, S. (2001). Qual-

ity of decision making and group norms. Journal

of Personality and Social Psychology, 80, 918–

930.

Postmes, T., Spears, R., & Lea, M. (1998). Breaching

or building social boundaries? SIDE-effects of

computer-mediated communications. Communica-

tion Research, 25, 689–715.

Postmes, T., Spears, R., & Lea, M. (2002). Intergroup

differentiation in computer-mediated communica-

tion: Effects of depersonalization. Group Dynam-

ics, 6, 3–16.

Ridgeway, C. L. (1987). Nonverbal behavior, domi-

nance, and the basis of status in task groups. Amer-

ican Sociological Review, 52, 683–694.

Ridgeway, C. L., & Diekema, D. (1989). Dominance

and collective hierarchy formation in male and

female task groups. American Sociological Re-

view, 54, 79–93.

Ringelmann, M. (1913). Research on animate

sources of power: The work of man. Annales de

l’Institut National Agronomique (2e serie, Tome

XII), 1–40.

Rocco, E. (1998). Trust breaks down in electronic

contexts but can be repaired by some initial face-

to-face contact. In Proceedings of the Conference

on Human Factors in Computing Systems (pp.

496–502). Los Angeles: ACM Press.

Rudman, C., Hertz, R., Marshall, C., & Dykstra-

Erickson, E. (1997). Channel overload as a driver

for adoption of desktop video for distributed group

work. In K. E. Finn, A. J. Sellen, & S. B. Wilbur

(Eds.), Video-mediated communication (pp. 199–

223). Mahwah, NJ: Erlbaum.

Saunders, C., Robey, D., & Vaverek, K. (1994). The

persistence of status differentials in computer con-

ferencing. Human Communication Research, 20,

443–472.

Schachter, S., Ellertson, N., McBride, D., & Gregory,

D. (1951). An experimental study of cohesiveness

and productivity. Human Relations, 4, 229–238.

Selfe, C. L., & Meyer, P. R. (1991). Testing claims

for online conferences. Written Communication, 8,

163–192.

Sellen, A. J. (1995). Remote conversations: The ef-

fects of mediating talk with technology. Human

Computer Interaction, 10, 401–444.

Shaw, M. E. (1973). Group dynamics: The psychol-

ogy of small group behavior. New York: McGraw-

Hill.

Short, J. (1974). Effects of medium of communica-

tion on experimental negotiation. Human Rela-

tions, 27, 225–243.

322 DRISKELL, RADTKE, AND SALAS

Short, J., Williams, E., & Christie, B. (1976). The

social psychology of telecommunications. New

York: Wiley.

Siegel, J., Dubrovsky, V., Kiesler, S., & McGuire, T.

(1986). Group process in computer-mediated com-

munication. Organizational Behavior & Human

Decision Processes, 37, 157–187.

Silver, S. D., Cohen, B. P., & Crutchﬁeld, J. H.

(1994). Status differentiation and information ex-

change in face-to-face and computer-mediated

idea generation. Social Psychology Quarterly, 57,

108–123.

Spears, R., & Lea, M. (1992). Social inﬂuence and

the inﬂuence of the “social”in computer-mediated

communication. In M. Lea (Ed.), Contexts of com-

puter-mediated communication (pp. 30–65). Hert-

fordshire, England: Harvester Wheatsheaf.

Sproull, L., & Kiesler, S. (1986). Reducing social

context cues: Electronic mail in organizational

communication. Management Science, 32, 1492–

1512.

Sproull, L., & Kiesler, S. (1991). Connections—New

ways of working in the networked organization.

Cambridge, MA: MIT Press.

Steiner, I. D. (1972). Group process and productiv-

ity. New York: Academic Press.

Straus, S. G. (1996). Getting a clue: The effects of

communication media and information distribution

on participation and performance in computer-me-

diated and face-to-face groups. Small Group Re-

search, 27, 115–142.

Straus, S. G. (1997). Technology, group process, and

group outcomes: Testing the connections in com-

puter-mediated and face-to-face groups. Human-

Computer Interaction, 12, 227–266.

Straus, S. G., & McGrath, J. E. (1994). Does the

medium matter? The interaction of task type and

technology on group performance and member

reactions. Journal of Applied Psychology, 79, 87–

97.

Terborg, J. R., Castore, C., & DeNinno, J. A. (1976).

A longitudinal investigation of the impact of group

composition on group performance and cohesion.

Journal of Personality and Social Psychology, 34,

782–790.

Thompson, L. F., & Coovert, M. D. (2003). Team-

work online: The effects of computer conferencing

on perceived confusion, satisfaction, and postdis-

cussion accuracy. Group Dynamics, 7, 135–151.

Tziner, A., & Vardi, Y. (1982). Effects of command

style and group cohesiveness on the performance

of self-selected tank crews. Journal of Applied

Psychology, 67, 769–775.

Wagner, D. G., & Berger, J. (1993). Status charac-

teristics theory: The growth of a program. In J.

Berger & M. Zelditch (Eds.), Theoretical research

programs: Studies in the growth of theory (pp.

23–63). Stanford, CA: Stanford University Press.

Walther, J. B. (1997). Group and interpersonal ef-

fects in international computer-mediated collabo-

ration. Human Communication Research, 23, 342–

369.

Walther, J. B., Anderson, J. F., & Park, D. W. (1994).

Interpersonal effects in computer-mediated inter-

action: A meta-analysis of social and antisocial

communication. Communication Research, 21,

460–487.

Walther, J. B., & Burgoon, J. K. (1992). Relational

communication in computer-mediated communi-

cation. Human Communications Research, 19,

50–83.

Warkentin, M. E., Sayeed, L., & Hightower, R.

(1997). Virtual teams versus face-to-face teams:

An exploratory study of a web-based conference

system. Decision Science, 28, 975–996.

Webster, M., & Driskell, J. E. (1983). Processes of

status generalization. In H. Blumberg, A. P. Hare,

V. Kent, & M. Davies (Eds.), Small groups and

social interaction (pp. 57–67). New York: Wiley.

Weisband, S., & Atwater, L. (1999). Evaluating self

and others in electronic and face-to-face groups.

Journal of Applied Psychology, 84, 632–639.

Weisband, S. P., Schneider, S. K., & Connolly, T.

(1995). Computer-mediated communication and

social information: Status salience and status dif-

ferences. Academy of Management Journal, 38,

1124–1151.

Whittaker, S., & O’Conaill, B. (1997). The role of

vision in face-to-face and mediated communica-

tion. In K. E. Finn, A. J. Sellen, & S. B. Wilbur

(Eds.), Video-mediated communication (pp. 23–

49). Mahwah, NJ: Erlbaum.

Williams, E. (1977). Experimental comparisons of

face-to-face and video mediated communication.

Psychological Bulletin, 84, 963–976.

Zaccaro, S., & Lowe, C. (1986). Cohesiveness and

performance on an additive task: Evidence for

multidimensionality. Journal of Social Psychol-

ogy, 128, 547–558.

Zaccaro, S., & McCoy, M. C. (1988). The effects of

task and interpersonal cohesiveness on perfor-

mance of a disjunctive group task. Journal of Ap-

plied Social Psychology, 18, 837–851.

Received September 16, 2002

Revision received August 27, 2003

Accepted September 2, 2003 䡲

323VIRTUAL TEAMS

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Gesture recognition has gained significant traction in the fields of computer vision and machine learning, offering users a natural and intuitive means of interacting with robots and system interfaces without the need for additional equipment. The primary objective of gesture recognition within human-computer interaction (HCI) is to develop systems capable of identifying specific human gestures and utilizing them for conveying information or controlling devices. To accomplish this, hand gesture interfaces based on visual input necessitate swift and highly precise hand detection alongside real-time gesture recognition. This article presents a comprehensive solution utilizing machine learning algorithms that can be universally applied to a diverse range of real-time gesture recognition systems for human-computer interfaces. The proposed system facilitates controlling the LED light, enabling users to switch it on or off based on the recognition of hand gestures.

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Understanding the impact that an online work environment poses for the inclusion of migrants and other international workers has become a highly relevant issue due to increasing labor shortage in the global business environment. In this regard, international inclusiveness scholarship has focused on the role of cultural and linguistic differences for organizational integration of foreign nationality minorities but has rarely considered the extent to which this is taking place through computer-mediated interaction. Simultaneously, virtual work research has delved on the social disengagement that is prominent in online work but without explicit attention to its effect on inclusive organizational practices. We, therefore, integrate inclusiveness and virtual work theories to conceptualize how migrants’ integration into the MNC as a global workplace is affected by the online environment. These insights lead to theoretical and practical advances in the conversation about cultural and linguistic differences in international business research suggesting that the online environment can have both positive and negative consequences for organizational inclusion of migrants.

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In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. (46 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)

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Informal social communication.

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Leon Festinger

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S. Strauss

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Brian Mullen

Self-attention theory (Carver, 1979, 1984; Carver & Scheier, 1981; Duval & Wicklund, 1972; Mullen, 1983) is concerned with self-regulation processes that occur as a result of becoming the figure of one’s attentional focus. According to self-attention theory, there are three fundamental requirements for any self-regulation of behavior to occur. These requirements are: self-focused attention, a salient behavioral standard, and a sufficiently good outcome expectancy to warrent continued efforts. We will begin by delineating each of these three elements of self-attention theory.

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