Download - Performance patterns in face-to-face and computer-supported teams

Performance patterns inface-to-face and

computer-supported teamsPilar Pazos

Department of Engineering Management and Systems Engineering,Old Dominion University, Norfolk, Virginia, USA, and

Mario G. BeruvidesIndustrial Engineering Department, Texas Tech University, Lubbock,

Texas, USA

Abstract

Purpose – This paper presents a longitudinal experimental study on teams with the purpose ofinvestigating the impact of communication media on decision-making teams. The authors aims toachieve that by comparing face-to-face (FTF) and computer-supported (CS) teams over a series of threesessions on three response variables: performance, cohesiveness, and synergy.

Design/methodology/approach – A total of 24 teams, each of five students, participated in threeseparate decision-making sessions in which they solved a survival simulation scenario. Each team wasrandomly assigned to either face-to-face (FTF) or computer-supported (CS) communication condition.The analysis compared overall means and mean patterns over time on the three response variablesacross the two communication media.

Findings – Results suggest that there were no differences in overall performance between CS andFTF teams and no differences in performance changes over time between the two media; there were nooverall differences in overall synergy or synergy changes over time; and FTF teams reported higheraverage cohesiveness than CS teams, but cohesiveness improved at a faster rate in CS teams than inFTF teams. Overall these results suggest that the CS communication did not reduce the group’s abilityto work together. Moreover, the higher increase in cohesiveness reported by CS teams suggests thatthe ability to build relationships can increase over time.

Practical implications – Given the prominence of information technologies as a communicationmechanism, the question of how team members in remote locations perform over time is of greattheoretical and practical importance.

Originality/value – This study provides some preliminary evidence that computer communicationdoes not significantly reduce the group’s ability to perform over time for decision-making tasks. CSteams report lower overall levels of cohesiveness which could indicate that some communicationbarriers might still limit the group’s ability to build relationships.

Keywords Team working, Group work, Decision making, Information media, Team performance,Feedback

Paper type Research paper

IntroductionThere is broad agreement that teams are instrumental for companies’ success intoday’s competitive environment. In particular, teams play a key role indecision-making processes. For years now, many companies have been usinginformation technology to support collaborative tasks (Davidow and Malone, 1992;

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Performancepatterns in teams

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Received July 2010Revised November 2010

Accepted December 2010

Team Performance ManagementVol. 17 No. 1/2, 2011

pp. 83-101q Emerald Group Publishing Limited

1352-7592DOI 10.1108/13527591111114729

Jarvenpaa and Ives, 1994; Martins et al., 2004) through the use of virtual teams.However, our understanding of how virtual teams use technology to communicateduring decision making is very limited.

For the purpose of this study, we define a virtual team as one whose members areworking towards a common goal by crossing geographic boundaries (Martins et al.,2004) and using information technology (e.g. e-mail, videoconference) to communicate.For the remaining of the document, we will refer to computer-supported teams as aspecific type of virtual team who uses a computer interface to support theircommunication.

Given the prominence of information technologies as a communication mechanism,the question of how effective team members are in making decisions from remotelocations is of great theoretical and practical importance. Our understanding of howvirtual teams perform is limited by the fact that most studies evaluated those teams bycomparing them with face-to-face teams at only one instance (e.g. Lea and Spears, 1991;Straus, 1997). Such studies did not account for the dynamic and complex nature ofgroups. Studies investigating group performance over time have just recently been theobject of more attention, but there is still much we do not know about how computersupported-groups learn and perform over time. With this research, we attempt to shedsome light into this area by comparing the performance patterns ofcomputer-supported groups and face-to-face groups over time. Knowing howdifferent media impacts group performance and interpersonal relationships iscritical to team managers in today’s work environments. The theoretical foundation forthis investigation was established through the analysis and integration of applicabletheories and concepts that are presented next.

We begin by providing a brief overview of relevant findings related to decisionmaking and computer-supported groups. Next, we present the theoretical model andthe research methodology that we used to test the hypotheses. We conclude byproviding results and implications for team leaders and team members.

Decision-making tasksA majority of experts in team research (Hackman and Morris, 1975; Marks et al., 2001;McGrath and Arrow, 2000) would agree that the performance of a group cannot bestudied generically without specific regard to the task. This recommendation is basedin the notion that performance is strongly influenced by the type and characteristic ofthe task on hand. As a result, the type of task has been reported as a critical variable toconsider when studying team-related processes and outcomes (McGrath, 1984). Themost accepted and cited typology of group tasks was developed by McGrath (1984),and it describes the main types of tasks that teams may encounter. Based on thattypology, decision-making tasks are defined as follows (McGrath, 1984):

. those for which a right answer can not be identified with certainty; and

. those for which the group needs to select an answer by consensus.

This study focused on teams solving decision-making tasks.

Virtual teams and computer supportThe increasing presence of virtual teams has resulted in abundant research examiningvarious aspects of virtual team adoption, use, and performance. Previous studies

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investigating the impact of information technologies on group outcomes are notconclusive and often times contradictory. Our understanding of group performanceand media use is limited by the fact that most research has been conducted usingad-hoc groups, that is, groups that met only once for the purpose of the research studywith no prior history of working together. Moreover, data collected in those studieswas frequently limited to one point in the life of the team. Such studies did not accountfor temporal changes inherent to the dynamic nature of groups. Only a small number ofstudies have addressed temporal issues in decision-making groups (Alge et al., 2003;Arrow and McGrath, 1993; Kelly and Karau, 1999; Marks et al., 2001; McGrath andO’Connor, 1996). Alge et al. (2003) examined temporal issues and identified type andamount of experience as important factors that may enable virtual groups to overcomethe limitations of electronic media. Scholars have called for additional researchexamining temporal scope, to account for the dynamic nature of teams (Alge et al.,2003; Baltes et al., 2001; Bell and Kozlowsky, 2002; McGrath and Arrow, 2000; Markset al., 2001).

Most studies exploring the performance of groups when using computer-supportedplatforms based their results on groups lacking previous contact (zero-history groups).This research attempts to shed some light on the topic by using a longitudinal studythat evaluates groups over a period of time.

Understanding performance in computer-supported groupsAn abundance of research can be found in the area of computer-supported groups(Alge et al., 2003; Baltes et al., 2001; Burke and Chidambaram, 1995; Poole andDeSanctis, 1990). Several theories have attempted to explain the impact of computersupport on groups. The three theories more relevant for the current study are: mediarichness theory (MRT), social information processing theory (SIP), and adaptivestructuration theory (AST). These theories will be presented to describe the influenceof computer-supported communication on team outcomes and will be utilized tosupport the research hypotheses.

Media richness theory (MRT) (Daft and Lengel, 1986) describes the fit betweenspecific types of communication media and the task on hand. MRT suggests thatcomputer-mediated group communication has lower social presence and less taskfocus than face-to-face communication (Daft et al., 1987; Sproull and Kiesler, 1986). Thesupporters of media richness theory suggest that leaner media (e.g. instant message,e-mail) are better suited for non-equivocal tasks such as communicating information,whereas richer media (video conferencing) are considered more appropriate for moreequivocal tasks (where different interpretations of the data are possible). Mediarichness suggests that limitations in lean media such as instant message or text-basedcommunication are expected to persist over time. However, some studies have reportedthat media richness theory has not been able to successfully predict media choice inmanagers (Lee, 1994; Markus, 1994). Chidambaram (1996) outlines the limitations ofmedia richness theory as a deterministic model that generally seeks to explain groupperformance through the inability of certain media (like computer-supportedcommunication, e-mail, etc.) to convey visual and auditory cues. As a result, MRTdoes not fully account for the complexity of the interaction between teams andcommunication technology.


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Social information processing (SIP) theory takes a different stance by arguing thatmedia effects on group outcomes and processes are likely to be transitory or change asteams use the technology (Walther, 1992a, b, 1996). Rather than suggesting that certainmedia is constrained by static limitations, SIP theory suggests that the impact of mediaon group outcomes will likely evolve over time, allowing for social processes and groupoutcomes to be enhanced as users gain familiarity with each other and gain expertisewith the media and their features. Social information processing (SIP) theory providessome explanation for how a medium may, over time, allow groups to engage inincreased relational communication. The theory suggests that team members aredriven to develop social relationships and that those relationships can also be built incomputer-mediated groups (Walther, 1996, p. 10). Walther (1992a, b) suggests that,over time, users will adapt to the communicative cues that the media offers them;therefore, the role of time should not be ignored in the study of group communicationand behavior. We attempt to address this gap by evaluating groups on a series ofsessions. Using a similar argument, Chidambaram (1996) proposes that the users ofcomputer media can adapt the medium to meet their relational needs. Such adaptationencompasses alternative uses of the media or innovative ways of overcoming inherentcommunication or structural barriers.

A third theory describing the impact of communication technologies on teamoutcomes is adaptive structuration theory (AST). AST was first proposed by Poole andDeSanctis (1990) and suggests that teams communicating through technology developstructures and methods of communication through time, such that if given adequatetime, they can reach a relational level similar to that of face-to-face groups. It describesthe adjustment process when appropriation of a new technology occurs. In particular,the theory focuses on how actions of social members of a collective create thestructures that enable and constrain future actions.

To sum up, MRT is a deterministic theory suggesting that lean media such as chator IM will lead to lower levels of performance for equivocal tasks such asdecision-making tasks. The theory suggests that media effects and constraints remainstatic over time and disregards the dynamic and complex nature of teams and theadaptation to the environment and to the media. As a result, MRT offers limited insightabout the actual media impact on group outcomes over time. This study will evaluateoutcome patterns over time and explore a team’s adaptation to the media.

Team cohesivenessCohesiveness has been a variable of interest in the study of teams. Festinger (1950, p.274) defined group cohesiveness as “the resultant forces which are acting on themembers to stay in a group”. Prior studies revealed a significant relationship betweengroup cohesiveness and group performance (Mullen and Copper, 1994; Zaccaro andLowe, 1988). Zaccaro and Lowe (1988) also found that cohesiveness is directly relatedto task commitment (Zaccaro and Lowe, 1988). A study by Warkentin et al. (1997)using student teams indicated that face-to-face teams reported higher levels ofcohesiveness than did VTs. A more recent study reported a positive relationshipbetween task cohesiveness and team effectiveness for dispersed teams (Gonzalez et al.,2003). On the other hand, Aiello and Kolb (1995) found that higher levels ofcohesiveness did not result in increased productivity for VTs when working on asimple task.

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The relationship between cohesiveness, satisfaction, and performance has beenpointed out as a critical one to understand in workgroups (Hackman and Morris, 1975;Zaccaro and Lowe, 1988). Burke et al. (1999) suggest that media characterized by lesscapacity limits the exchange of verbal and visual cues facilitating a type of interactionthat has more task-focused and less relational-focus. Their results also indicate thatalthough team cohesiveness can be lower for leaner media, computer-supported groupshave the potential to increase their cohesiveness over time.

Since cohesiveness is mostly a relational type of variable, we expect averagecohesiveness to be lower in computer-supported groups than in face-to-face groups.However, we expect group cohesiveness to increase over time in both settings, withhigher rate of increase for computer-supported groups as group members adapt to thetechnology and to each other.

Conceptual modelMost real teams have a shared history; thus, their interaction and their previousperformances will likely shape the team dynamics and future performance. This studyuses a longitudinal approach that collected data on teams over three sessions, each ofthem taking place two weeks apart. The goal was to examine teams’ dynamic patternsof performance, synergy, and cohesiveness over the three sessions in two differentsettings, face-to-face (FTF) and computer supported (CS). This study incorporates anexploration into performance dynamics by evaluating teams over time using threeseparate sessions in which they solved independent tasks. Figure 1 depicts theproposed conceptual model with the relationships that this study seeks to investigate.The independent variables are communication mode (FTF versus CS) and sessionnumber (1, 2, 3). The right side represents the response variables: performance,cohesiveness, and synergy, which will be collected using a longitudinal study approachthroughout three sessions.

Research hypothesesBased on media richness theory and the reported limitations of computer-supportedmedia to transmit information when compared to face-to-face media (Daft and Lengel,1986), we hypothesize that FTF groups will initially surpass CS groups in the averagevalue of all three outcome variables: performance, cohesiveness, and synergy (H1, H2and H3). Nevertheless, we have presented supporting theories (AST and SIP) thatindicated that over time, teams are likely to adapt to the use of computer-supportedmedia and become more efficient at using it as they gain familiarity and experience. As

Figure 1.Proposed conceptual

model


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a result, we predict that the initial superiority in response variables for FTF teams willlikely diminish over time.

Based on our review of relevant theories and with support from SIP theory andAST, we hypothesize that CS groups will show different over time patterns in theresponse variables than FTF groups (H4, H5 and H6). Evidence suggests that overtime users go through an adaptation to the communication media and, as they becomemore familiar with each other and comfortable with new media, are able to use it moreeffectively (Walther, 1992a, b, 1996). Based on the support from these findings, webelieve that teams using computer support will adapt to using this medium tocommunicate with fellow team members, and this will result in a higher rate of increasein the three response variables for CS teams when compared to FTF teams (H4, H5 andH6). That is, we believe that even though CS groups might initially have lower levels ofthe response variables (due higher richness of FTF communication), they will improveat a higher rate over time as group members adapt to the CS media. Below are thehypotheses in the alternative form:

H1. There are differences in overall mean performance between FTF and CSgroups.

H2. There are differences in overall mean cohesiveness between FTF and CSgroups.

H3. There are differences in overall mean synergy between FTF and CS groups.

H4. Group performance in CS groups will increase at a higher rate than FTFgroups.

H5. Group cohesiveness in CS groups will increase at a higher rate than FTFgroups.

H6. Group synergy in CS groups will increase at a higher rate than FTF groups.

Research methodologyThis study was conducted at a large public university in Texas. We used a controlledexperiment methodology to isolate the impact of computer support on team outcomes.Sample selection was carefully performed to have a balanced sample across treatment(CS groups) and control (FTF groups) and including all the possible randomizations ofthe three tasks. The design required a sample multiple of six as there are six possibleorderings of the three tasks. This protocol for determining sample size aligns withrecommended practices of sample selection in controlled experiments (Montgomery,2009).

ParticipantsWe used a total of 120 subjects grouped in 24 teams of five that met three times. A totalof 24 five-person teams were recruited to participate with the incentive of a prize for thehighest-scoring group. The sample comprised 46 percent females and 54 percent males.Regarding class level, 34.5 percent of the participants were seniors, 31 percent juniors,15.9 percent sophomores, and 17.7 percent freshmen. Additional backgroundinformation was collected by means of a questionnaire. Participants were asked toassess their level of experience working in groups using a five-point Likert-type scale

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anchored with “not experienced” to “very well experienced”. A total 72 percent ofparticipants rated themselves as experienced or well-experienced working in groups,16 percent as somewhat experienced, and 11 percent as very well experienced. Post hoctests indicated no significant differences in demographic variables between the twoconditions.

Experimental designEach participant was randomly assigned to a five-person team, and each team wasrandomly assigned to one of two treatments, FTF or CS. Teams remained the same forthe duration of the study. Every team went through an identical training session tointroduce the communication interface, get group members acquainted with eachother, and introduce the decision-making structure. The length of the training sessionwas 20 minutes. After the training, each team went through the three experimentalsessions. Each session was conducted two weeks apart, and no formal interactionsoccurred among team members between sessions. Computer teams used a “text-based”web interface (chat) to communicate from remote stations whereas face-to-face teamsmet around a table. Note that for the remaining section of this paper the term groupand team will be used interchangeable. Each team attended three sessions. During eachsession, teams solved a randomly assigned task, that is, the order in which the threetasks were solved was random. The tasks portrayed three simulation scenarios (A, B,C). The following section describes the task scenarios. To ensure that differential taskcomplexity did not account for differences in outcomes between FTF and CS teams, weused a balanced experimental design with a number of groups multiple of six toinclude all six possible orderings of the three tasks (ABC, ACB, BAC, BCA, CAB, CBA).This design provides a balanced representation of all the possible task orderings(randomizations) across experimental conditions for non-identical tasks.

Each session consisted of a structured decision-making process includingidentification of objective, analysis of the situation, generation of alternativesolutions, and selection of preferred solution. The process was facilitated using apretested script identical in both conditions so that the facilitator would not have animpact on the team outcomes. The maximum time available for each session was90 minutes for a total contact time of 270 minutes for all sessions. Each session wasseparated by two weeks. The data collection period took place over approximatelythree months.

Decision-making task: survival simulationsThe three tasks chosen for the sessions portray survival simulation situations(Survival Simulation Series, 2002, 2003a, b). The tasks locate the group in hypotheticalsituations occurring in isolated areas. The group members must figure out a strategyand plan for survival that makes the best use of the resources available. Thesimulations occur in three different settings: a yacht that shipwrecks (reef simulation),a fire in the forest (bushfire simulation), and a helicopter crash (cascades simulation).The tasks required participants to make a decision about the best survival strategyand rank a set of 12 items according to their importance for survival. These types oftasks have been extensively used for research purposes as well as for team training(Silberman, 1990; Szumal, 2000).


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Sessions protocolBoth FTF and CS sessions followed identical protocols. A trained facilitator withextensive experience in team facilitation conducted all the meetings using a scriptedprotocol. The protocol guided all teams through a rational decision-making processconsisting of analysis of the situation, identification of alternatives, evaluation ofalternatives, and selection of preferred solution.

Prior to the first session, teams received a training session addressing the followingareas: ground rules and structure of the decision-making process, getting groupmembers acquainted with each other, and introducing the communication media.During the training session, participants also filled out a questionnaire to collect theirdemographic information as well as data on their attitudes and experience with groupdecision making.

During each of the three sessions, the groups learned about the simulation situation,and each individual was asked to make a decision about the best strategy to survive inthe proposed scenario. Based on their strategy, they had to decide the relativeimportance of a set of 12 items in relation to their usefulness in surviving the describedscenario. As a result, each participant created a ranked list of the 12 items. Once theyhad finished, they met as a group. During the group process, groups followed astructured decision-making process through the following phases: analyzing thesituation, setting objectives, considering alternatives, analyzing consequences, anddeciding on a final solution. Next, participants received feedback about their strategyselection during the decision-making process and how it compared to the experts’suggested strategy. The specific stages used during the decision-making process areshown in Figure 2.

The previous section described the general procedure followed in all sessions. Theonly difference between FTF and CS sessions was that FTF groups sat around aU-shaped table while CS groups sat at their individual workstations andcommunicated using a chat room.

Relevant variablesTwo categories of variables will be described: independent and response variables.Since the main purpose of the study was to investigate group performance, the unit ofanalysis was the group. Table I summarizes the variables considered in the design.

Figure 2.Sequence of thedecision-making processwithin each session

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Communication platform, session number, and task scenario are the independentvariables whereas group performance, cohesiveness, and synergy are the responsevariables.

Type of task: decision-making tasks were selected based on the classificationcreated by McGrath (1984). Decision-making tasks are defined as tasks that requirereaching consensus on a preferred answer. The selected tasks can be scored against arecommended expert solution. In order to use comparable and consistent sessions,three tasks from the same simulation series were selected (Survival Simulation Series,2002, 2003a, b). The tasks have very similar structure; however, they take place underdifferent circumstances. They have been described as “content-free” situations that arelikely to be outside the sphere of expertise of the group but that are designed to attractthe attention to overall team problem-solving processes (Potter and Balthazard, 2002).

Next we present the main response variables investigated in this study along withtheir operational definitions:

. Individual performance was defined as the quality of the decision made by anindividual in the group according to the expert criteria. Note that individualsdecide their solution to the task before they meet as a group to decide on thegroup solution. The tasks chosen for the experiment have a pre-determinedexpert rating criteria based on the ranking of a list of items that assigns a scoreto any decision made (Survival Simulation Series, 2003a). Individualperformance was measured using the same scale, ranging from 0 to 72. Lowervalues of the score corresponded to better individual performance.

. Group performance was defined as the quality of the final decision made by thegroup according to expert criteria. Group performance was measured bycalculating the difference between the group score at the end of the meeting andthe expert score. The larger the score, the further it is from the expert ranking,thus reflecting the quality of the final decision. The range of values for groupperformance also ranged from 0 (perfect score) to 72 (worst score).

. Cohesiveness is a widely used measure of group interaction. It has been definedas the degree of closeness that group members feel to one another and theirattraction to the group (Miller, 1964). Although this measurement ofcohesiveness is based on perceptions, it is not expected to reduce validity ofthe measure since this study is primarily focused on the actual feeling of eachindividual member towards others within the same group. A set of five items inthe post-session questionnaire was used to assess perceived group cohesiveness(see the Appendix for items). These items, adapted from Seashore’s Index (Miller,1964), measured perception of the group cohesiveness after each session. Theinstrument, originally created to be used in work teams, was later adapted andvalidated to be used by student groups resulting in high levels of reliability(Chidambaram et al., 1990). The scores from the five items were summed for each

Independent variables Response variables

Communication platform Objective group performanceSession number CohesivenessTask scenario Group synergy

Table I.Main variables in the

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individual, resulting in the cohesiveness index for each subject. Finally, thecohesiveness index for the group was calculated as the average value of theindividual average scores. The index ranges from 5 to 25, with the lower scorescorresponding to more cohesive groups. Post-hoc analysis revealed that theinstrument had a reliability estimate of a ¼ 0:84. We also conducted anexploratory factor analysis using Varimax rotation to evaluate the underlyingfactor structure in order to determine if in fact the five items were part of thesame cohesiveness construct. The analysis was conducted using SPSS 16. Boththe scree plot (see Figure 3) and the results of the analysis confirm the existenceof only one factor that incorporates all five items. The total variance explained bythe five factors was 61 percent. These results help us support the constructvalidity of the cohesiveness measure.

. Group synergy was defined as the potential ability of groups to perform better asa result of being part of a group as compared to average individual performance.For the purpose of this study, the term synergy refers to the improvement on thequality of the decision due to interaction between individuals. Synergy wasinvestigated as the difference between the average individual score and thegroup score (Survival Simulation Series, 2003b).

Group size was held constant at five members per team along with facilitator effectsthat were also controlled. All sessions had a facilitator who assisted with theexperiment but did not influence the content of the discussions. In both settings, thefacilitator followed a pre-tested script. Post-hoc analysis of individual responsesrevealed no significant differences in facilitator role or impact between groups in thetwo treatment conditions.

Data analysis procedureThis study sought to investigate the differences in performance, synergy, andcohesiveness for groups that met repeatedly over time. The analysis was twofold:assessing the between-group effect by comparing average values between FTF and CS

Figure 3.Scree plot

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and the within-group effect by comparing patterns over the three sessions for bothmedia.

The type of analysis chosen was based on the characteristics of the data collected.Data points in the response variables come from three observations, one per session,for each of the 24 groups. The analysis was conducted using the PROC MIXEDprocedure for repeated measures from SAS/STAT statistical package. This procedureprovides a very flexible modeling environment for handling repeated measuresproblems that include a hierarchical structure such as the one in this study in whichindividuals are nested in groups as well as modeling of repeated measures type of data(Singer, 1998). Mixed-effects model is a term used for statistical models with fixed (e.g.treatment) and random effects, covariance pattern models, and combinations of them.Numerous authors have indicated mixed-effects approach has important advantagesover traditional methods of repeated-measures analysis (Littell et al., 1987; Guerin andStroup, 2000). Mixed models have several benefits including using all available data oneach subject, reducing the impact from missing data and increased flexibility inmodeling. This type of analysis allows for modeling data at the team and individuallevel in the same analysis, situation that is often relevant in team-level research.

Data in this study come from individuals that interact with a team over a period oftime, so data from individuals within the same team are likely to be correlated (suchthat students from the same team are more likely to show similarity in some variables).With these types of data, classical methods such as OLS regression would not producecorrect standard errors. Hierarchical linear modeling (HLM) has been suggested as abetter approach as it takes the issue of correlated errors into consideration andprovides more realistic and conservative statistical testing. The PROC MIXEDprocedure in SAS/STAT provides an approach to modeling repeated measures datathat combine both ANOVA and HLM (Wallace and Green, 2002).

Before applying PROC MIXED, the normality assumption was evaluated using QQplots, and data suggested the assumption was reasonably met. The analytical methodsfocused on comparing changes over time in both platforms (FTF vs CS). Threeresponse variables were investigated: performance, cohesiveness, and synergy. Weobserved and analyzed the patterns in the values of the response variables over threesessions, under two different platforms (CS and FTF).

ResultsA summary of the results of the hypotheses tests are shown in Table II.

Three separate analyses using PROC MIXED were conducted for each of thedependent variables. Table III shows the overall means and standard deviation of theresponse variables.

Hypothesis 1Results of the analysis revealed that the main effect of platform on averageperformance was not significant (F1;22 ¼ 0:95, p ¼ 0:3397). In other words, data areconsistent with the hypothesis stating no overall difference in average performancebetween FTF and CS teams.


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Hypothesis 2Results of the analysis revealed that the main effect of platform on cohesiveness wassignificant (F1;22 ¼ 8:59, p ¼ 0:008). That is, CS teams reported significantly lowerlevels of cohesiveness than FTF teams. Average levels of cohesiveness for eachcommunication media are shown in Table III.

Hypothesis 3Results of the analysis revealed that the main effect of platform on synergy was notsignificant (F1;22 ¼ 0:05, p ¼ 0:819). In other words, we found no differences inaverage synergy between FTF and CS teams.

Hypothesis 4The difference on performance patterns between both FTF and CS is a key element ofthis study. In particular, such patterns will reveal information concerning groups’ useof the communication platform and how performance changes over time in bothplatforms. For this purpose, the effect of the interaction term platform*session onperformance was investigated. The interaction was not significant (F2;36 ¼ 1:24,p ¼ 0:3), suggesting that the alternative hypothesis H4 was not supported. Resultsindicate both platforms show similar performance profiles.

Hypothesis 5This hypothesis evaluated the cohesiveness profile over the three sessions. Resultsindicate a significant interaction between platform and session in the cohesiveness

Alternative hypothesis Result

H1 There are differences in average performancebetween FTF and CS groups

Not supported

H2 There are differences in average cohesivenessbetween FTF and CS groups

Supported

H3 There are differences in average synergy betweenFTF and CS groups

Not supported

H4 Group performance in CS groups will increase at afaster rate than FTF groups

Not supported

H5 Group cohesiveness in CS groups will increase at afaster rate than FTF groups

Supported

H6 Group synergy in CS groups will increase at a fasterrate than FTF groups

Synergy: not supportedTable II.Summary of hypothesestests

Performancea Cohesivenessb SynergyCommunication medium Mean Std error Mean Std error Mean Std error

FTF 32.33 3.683 9.8625 0.33935 2.50 3.636CS 34.67 4.788 11.0708 0.34687 0.50 3.230

Notes: a Performance scores ranged from 0 to 72 with 0 being the best and 72 the worst possible score;b Cohesiveness scores range from 5 to 25 with 5 being the highest and 25 the lowest

Table III.Overall means andstandard deviations perexperimental condition

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variable (F2;40 ¼ 3:39, p ¼ 0:0431). In other words, there were significant differences inthe cohesiveness profiles of FTF and CS groups. Differences in profiles were furtherevaluated using the output of the LSMEANS statement in SAS/STAT. The analysisprovided the mean cohesiveness estimates shown in Figure 4. Multiple comparisons ofthe different levels were conducted to identify where the significant differences existed.Results indicated that CS groups showed a higher increase in cohesiveness than FTFgroups from session 2 to session 3 (p ¼ 0:01). The increase is represented in Figure 4by the slope between the last two points in the FTF and CS cohesiveness lines.

Hypothesis 6This hypothesis investigated group synergy profiles of both platforms over the threesessions. The statistical analysis revealed no significant differences in theplatform*session interaction term for the synergy variable (F2;40 ¼ 0:54,p ¼ 0:5852). In other words, synergy profiles did not significantly differ betweenFTF and CS groups.

Discussion and implicationsResults from this study add to the body of knowledge on computer-supported teamsthrough an exploration on how response variables change over time in FTF and CS.The findings revealed no significant differences in overall performance between bothplatforms and no differences in performance patterns over time. That is, CS groupsperformed at similar levels than FTF groups. Synergy changes over time did not differacross platforms indicating that CS groups were as effective building synergy as FTFgroups. This is a significant result in itself because virtual teams facilitate savings intravel and accommodation for organizations, provide flexibility, and allow for a lesslimited pool of participants.

Results also indicate that CS groups showed significantly lower levels ofcohesiveness than FTF groups. Prior research suggests that high levels ofcohesiveness can reduce communication barriers and be instrumental in promotingcollaboration (Powell et al., 2004). This result can have implications for team leadersand virtual team members. Training methods focused on increasing cohesivenesslevels in virtual teams during the team formation process might help enhance teamcommunication and performance. Results also suggest a faster increase incohesiveness in CS groups between session 2 and 3, which could indicate that CS

Figure 4.Cohesiveness profiles from

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teams might become more effective at establishing relationships as time passes. Thisresult is consistent with social information processing theory, which suggests that theimpact of media on group outcomes will likely evolve over time, allowing for socialprocesses and group outcomes to be enhanced as users gain experience with thecommunication media and their features (Walther, 1992a, b, 1996). These data supportthe argument that the impact of computer support on team outcomes is not one ofdeterministic nature that consistently leads to lower level outcomes when compared toface-to-face teams.

ConclusionsIn conclusion, the results obtained allow us to draw reasonable conclusions about theimpact of computer support in group performance, synergy, and cohesiveness. To doso required an understanding of CS and FTF by comparing patterns over time in theresponse variables. Results suggest that the use of computer media to communicate didnot negatively impact overall group performance for the type of tasks used in thisstudy. However, we did find that FTF groups reported higher levels of cohesivenessthan CS groups. Differences in media richness between FTF and CS communicationcould have contributed to those differences as participants reported feeling lessattached to team members when their interaction was via computer. However, we alsofound that from session 2 to 3 cohesiveness increased at a higher rate in CS than in FTFteams. This significant increase in cohesiveness over time suggests that teams mightbe able to overcome some of the barriers of CS communication. This result is consistentwith theory of adaptive structuration, which suggests that the users of computer mediawill adapt the medium to meet their relational needs (Walther, 1992a, b, Chidambaram,1996).

The particular characteristics of the population used may limit the potential forgeneralization of our findings. Participants belong to a generation characterized bytheir familiarity with technology and extensive experience communicating andestablishing relationships via chat and other electronic communication methods. Weexpect that this familiarity with the platform will likely differ by generation, butresults remain relevant to understand the future workforce and how differentgenerations might interact and communicate.

One limitation of this study is a side effect of its longitudinal nature. Studies usingrepeated measures designs are subject to potential undetected interactions betweensubjects outside the laboratory setting. However, subjects were asked not to discusstheir experiences during sessions with other subjects, did not know each other ahead oftime, and had no other formal opportunities to interact as a group.

One factor that may limit the external validity of the results relates to teammembership stability. Based on membership stability, teams can be classified as adhoc (purposively assembled to participate in a project) or intact (members with ashared history of working together within an organization) (Salas et al., 2008). Theobjective of this study was to isolate the impact of communication technology in theoutcome variables. A controlled experimental setting facilitated the accomplishment ofthat goal. However, the teams used in this study differ from traditional teams in thatthe later ones have a shared history as a result of having worked together in the past.This shared history has the potential to influence future team performance. Intactteams are more traditionally used in field study research and usually result in higher

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external validity. However, the large number of confounding variables usually presentin field research reduces internal validity. As with most experimental designs, thisstudy resulted in higher internal validity than a field study to the expense of decreasedexternal validity.

Some general guidelines to increase the external validity of research using ad hocgroups were followed (Walther, 1996). First, group members were given a trainingsession to help establish relationships before the sessions. The training alsofamiliarized teams with the communication media and general information on theirrole as team members. They were told that they would meet in their intact groupstogether over several sessions. Second, following McGrath (1984), “concocted” alliancesrecommendations, teams were presented with a real incentive tied to the outcome oftheir task accomplishment. Accordingly, participants were informed that teams withhigher levels of performance would be given a monetary award based on the quality ofthe decisions reached by their team. While the use of student subjects offersquestionable generalization to professional teams, this study was intended as alongitudinal experiment to complement previous studies while contributing to ourunderstanding of team decision-making (see also McGrath, 1984).

We believed this study addressed gaps in the literature that ignored the longitudinaleffect of communication media on team outcomes. By controlling for confoundingfactors, we were able to isolate the impact of media to better understand how teamsadapt to the technology and how team performance changes over time as a result ofthat adaptation.

Future researchResults of the current study suggest the need to further investigate how both platformscompare when teams are engaged in more complex tasks or for longer periods of time.In addition, an important question that remains is how individual perceptions aboutthe communication platform and about the other group members change over time andwhat factors might trigger those changes. We also suggest further studies exploringthe dynamics of team performance using intact teams to complement the resultsobtained in the present study.

It is known that the current generation of students, also known as the millennialgeneration, is more familiar with computer communication than previous generations.Future research can investigate the generational differences and their impact on virtualteam performance and adoption of information technologies.

References

Aiello, J.R. and Kolb, K.J. (1995), “Electronic performance monitoring and social context: impacton productivity and stress”, Journal of Applied Psychology, Vol. 80, pp. 339-53.

Alge, B.J., Wiethoff, C. and Klein, H.J. (2003), “When does medium matter? Knowledge-buildingexperiences and opportunities in decision making teams”, Organizational Behavior andHuman Decision Processes, Vol. 91, pp. 26-37.

Arrow, H. and McGrath, J.A. (1993), “Membership matters: how membership change andcontinuity affect small group structure, process and performance”, Small Group Research,Vol. 24 No. 3, pp. 334-61.


97

Baltes, B.B., Dickson, M.W., Sherman, M.P., Bauer, C.C. and LaGanke, J.S. (2001),“Computer-mediated communication and groups decision making: a meta-analysis”,Organizational Behavior and Human Decision Processes, Vol. 87 No. 1, pp. 156-79.

Bell, B.S. and Kozlowsky, S.W. (2002), “A typology of virtual teams: implications for effectiveleadership”, Group and Organization Management, Vol. 27, pp. 14-49.

Burke, K. and Chidambaram, L. (1995), “Developmental differences between distributed andface-to-face groups in electronically supported meeting environments: an exploratoryinvestigation”, Group Decision and Negotiation, Vol. 4, pp. 213-33.

Burke, K., Aytes, K., Chidambaram, L. and Johnson, J. (1999), “A study of partially distributedwork groups: the impact of media, location, and time on perceptions and performance”,Small Group Research, Vol. 30 No. 4, pp. 453-90.

Chidambaram, L. (1996), “Relational development in computer supported groups”, MISQuarterly, Vol. 20 No. 2, pp. 143-65.

Chidambaram, L., Bostrom, R.P. and Wynne, B.E. (1990), “A longitudinal study of the impact ofgroup decision support systems on group development”, Journal of ManagementInformation Systems, Vol. 7 No. 3, pp. 7-25.

Daft, R.L. and Lengel, R.H. (1986), “Organizational information requirements, media richness andstructural design”, Management Science, Vol. 32 No. 5, pp. 554-71.

Daft, R.L., Lengel, R.H. and Trevino, L.K. (1987), “Message equivoquality, media selection, andmanager performance: implications for information systems”, MIS Quarterly, Vol. 11 No. 3,pp. 355-66.

Davidow, W.H. and Malone, M.S. (1992), The Virtual Corporation, HarperCollins, New York, NY.

Festinger, L. (1950), “Informal social communication”, Psychological Review, Vol. 57, pp. 271-82.

Gonzalez, M.G., Burke, M.J., Santuzzi, A.M. and Bradley, J.C. (2003), “The impact of groupprocess variables on the effectiveness of distance collaboration groups”, Computers inHuman Behavior, Vol. 19, pp. 629-48.

Guerin, L. and Stroup, W.W. (2000), “A simulation study to evaluate PROC MIXED analysis ofrepeated measures data”, Proceedings of the 12th Kansas State University Conference onApplied Statistics in Agriculture, April 30-May 2, 2000, Manhattan, KS.

Hackman, J.R. and Morris, C.G. (1975), “Group tasks, group interaction process, and groupperformance effectiveness: a review and proposed integration”, in Berkowitz, L. (Ed.),Advances in Experimental Social Psychology, Vol. 8, Academic Press, New York, NY,pp. 45-99.

Jarvenpaa, S.L. and Ives, B. (1994), “The global network organization of the future: informationmanagement opportunities and challenges”, Journal of Management Information Systems,Vol. 10 No. 4, pp. 25-57.

Kelly, J.R. and Karau, S.J. (1999), “Group decision making: the effects of initial preferences andtime pressure”, Personality and Social Psychology Bulletin, Vol. 25, pp. 1342-54.

Lea, M. and Spears, R. (1991), “Computer-mediated communication, de-individuation and groupdecision making”, International Journal of Man-machine Studies, Vol. 34 No. 2, pp. 283-301.

Lee, A.S. (1994), “Electronic mail as a medium for rich communication: an empirical investigationusing Hermeneutic interpretation”, MIS Quarterly, Vol. 18 No. 2, pp. 143-57.

Littell, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D. (1987), SAS System for MixedModels, SAS Institute, Cary, NC.

TPM17,1/2

98

McGrath, J.E. (1984), Groups: Interaction and Performance, Prentice-Hall, Englewood Cliffs, NJ.

McGrath, J.E. and Arrow, H. (2000), “The study of groups: past, present, and future”, Personalityand Social Psychology Review, Vol. 4 No. 1, pp. 95-105.

McGrath, J.E. and O’Connor, K.M. (1996), “Temporal issues in work groups”, in West, M.A. (Ed.),Handbook of Work Group Psychology, John Wiley & Sons, Chichester, pp. 25-52.

Marks, M.A., Mathieu, J.E. and Zaccaro, S.J. (2001), “A temporally based framework andtaxonomy of team processes”, Management Review, Vol. 26 No. 3, pp. 356-76.

Markus, M.L. (1994), “Electronic mail as the medium of managerial choice”, Organization Science,Vol. 5 No. 4, pp. 502-27.

Martins, L.L., Gilson, L.L. and Maynard, M.T. (2004), “Virtual teams: what do we know andwhere do we go from here?”, Journal of Management, Vol. 30 No. 6, pp. 805-36.

Miller, D.C. (1964), Handbook of Research Design and Social Measurement, Davic MckayCompany, New York, NY.

Montgomery, D.C. (2009), Design and Analysis of Experiments, 7th ed., Wiley, New York, NY.

Mullen, B. and Copper, C. (1994), “The relation between group cohesiveness and performance: anintegration”, Psychological Bulletin, Vol. 115 No. 2, pp. 210-27.

Poole, M.S. and DeSanctis, G. (1990), “Understanding the use of group decision support systems”,in Steinfield, C. and Fulk, J. (Eds), Organizations and Communication Technology, Sage,Newbury Park, CA.

Potter, R.E. and Balthazard, P.A. (2002), “Understanding human interaction and performance inthe virtual team”, The Journal of Information Technology Theory and Application, Vol. 4No. 1, pp. 1-25.

Powell, A., Piccoli, G. and Ives, B. (2004), “Virtual teams: a review of current literature anddirections for future research”, Database for Advances in Information Systems, Vol. 35No. 1, pp. 6-36.

Salas, E., DiazGranados, D., Klein, C., Burke, C.S., Stagl, K.C., Goodwin, G.F. and Halpin, S.M.(2008), “Does team training improve team performance?”, Human Factors, Vol. 50,pp. 903-33.

Silberman, M.L. (1990), Active Training: A Handbook of Techniques, Designs, Case Examples,and Tips, McGraw Hill, New York, NY.

Singer, J.D. (1998), “Using SAS PROC MIXED to fit multilevel models, hierarchical models, andindividual growth models”, Journal of Educational and Behavioral Statistics, Vol. 23,pp. 323-55.

Sproull, L.S. and Kiesler, S.B. (1986), “Reducing social context cues: electronic mail inorganizational communication”, Management Science, Vol. 32 No. 11, pp. 1492-512.

Straus, S.G. (1997), “Technology, group process, and group outcomes: testing the connections incomputer-mediated and face-to-face communication”, Human-computer Interaction,Vol. 12, pp. 227-66.

Survival Simulation Series (2002), Cascades Survival Simulation, Human Synergistics, Douglas.

Survival Simulation Series (2003a), Bushfire Survival Simulation, Human Synergistics, Douglas.

Survival Simulation Series (2003b), Reef Survival Simulation, Human Synergistics, Douglas.

Szumal, J.L. (2000), “How to use group problem solving simulations to improve teamwork”,in Silberman, M. and Philips, P. (Eds), The 2000 Team and Organization DevelopmentSourcebook, McGraw Hill, New York, NY.


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Wallace, D. and Green, S.B. (2002), “Analysis of repeated measures designs with linear mixedmodels”, in Moskowitz, D.S. and Hershberger, S.L. (Eds), Modeling IntraindividualVariability with Repeated Measures Data, Erlbaum, Mahwah, NJ, pp. 103-34.

Walther, J.B. (1992a), “Interpersonal effects in computer-mediated interaction: a relationalperspective”, Communications Research, Vol. 19 No. 1, pp. 52-90.

Walther, J.B. (1992b), “A longitudinal experiment on relational tone in computer-mediated andface-to-face interaction”, paper presented at the Hawaii International Conference onSystem Sciences, Los Alamitos, CA.

Walther, J.B. (1996), “Computer-mediated communication: impersonal, interpersonal, andhyperpersonal interaction”, Communications Research, Vol. 23 No. 1, pp. 3-43.

Warkentin, M.E., Sayeed, L. and Hightower, R. (1997), “Virtual teams versus face-to-face teams:an exploratory study of a web-based conference system”, Decision Sciences, Vol. 28 No. 4,pp. 975-96.

Zaccaro, S.J. and Lowe, C.A. (1988), “Cohesiveness and performance on an additive task: evidencefor multidimensionality”, The Journal of Social Psychology, Vol. 128 No. 4, pp. 547-58.

Further reading

Burke, K., Aytes, K. and Chidambaram, L. (2001), “Media effects on the development of cohesionand process satisfaction in computer-supported workgroups”, Information Technologyand People, Vol. 14, pp. 122-41.

McGrath, J.E. (1991), “Time, interaction and performance (TIP): a theory of groups”, Small GroupResearch, Vol. 22, pp. 147-74.

Pazos, P., Beruvides, M., Jian, J., Canto, A., Sandoval, A. and Taraban, R. (2007), “Structuringgroup decision making in a web-based environment by using the nominal grouptechnique”, Computers and Industrial Engineering, Vol. 52 No. 2, pp. 277-95.

Seashore, S.E. (1954), Group Cohesiveness in the Industrial Workplace, University of Michigan,Ann Arbor, MI.

Zack, M.H. and McKenney, J.L. (1997), “Social context and interaction in ongoingcomputer-supported management groups”, Organization Science, Vol. 6 No. 4, pp. 394-422.

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Appendix

About the authorsPilar Pazos is an Assistant Professor in the Department of Engineering Management andSystems Engineering at Old Dominion University. Before joining Old Dominion she worked inthe areas of quality control, team learning and consulting. Most recently, she was a ResearchAssociate at Northwestern University with a joint position for the VaNTH Engineering ResearchCenter and the Searle Center for Teaching Excellence. Her research interests include: knowledgemanagement, organizational learning, collaborative learning, group decision making andperformance, virtual teams and team dynamics. Pilar Pazos holds a BSc in IndustrialEngineering from the University of Vigo, Spain, an MS in Systems and EngineeringManagement from Texas Tech University and a PhD from Texas Tech University (2005) inIndustrial Engineering with a focus on engineering management and a minor in AppliedStatistics from the Rawls College of Business. Pilar Pazos is the corresponding author and can becontacted at: [email protected]

Mario G. Beruvides is AT&T Professor of Industrial Engineering and Director of theLaboratory for Systems Solutions at the Department of Industrial Engineering in Texas TechUniversity. He is a Registered Professional Engineer from the state of Texas. Mario G. Beruvidesholds a BS in Mechanical Engineering and an MS in Industrial Engineering from the Universityof Miami and a PhD in Industrial Engineering from Virginia Polytechnic Institute.

Figure A1.Questionnaire items

assessing cohesiveness


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