Computer mediated teamwork and the efficiency framework: Exploring the influence of synchrony and cues on media satisfaction and outcome success

Computers in Human Behavior 25 (2009) 1108–1119

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Computer mediated teamwork and the efficiency framework: Exploring the influence of synchrony and cues on media satisfaction and outcome success Kristine L. Nowak a,*, James Watt b, Joseph B. Walther c a

Communication Science Department, University of Connecticut, 850 Bolton Road, U-1085, Storrs, CT 06269-1085, USA Rensselaer Social and Behavioral Research Laboratory, Rensselaer Polytechnic Institute, USA c Department of Communication, Michigan State University, USA b

a r t i c l e

i n f o

Article history: Available online 21 June 2009 Keywords: Computer mediated teamwork Outcome success Media satisfaction Efficiency

a b s t r a c t There are many valid ways to evaluate collaborative systems. The efficiency framework argues that while user satisfaction and preference for systems are important, neither directly predict outcome success or the ability of a system to facilitate collaboration. Further, it points to the importance of distinguishing between user satisfaction and outcome success when evaluating collaborative systems. Despite users’ reported preferences for media requiring less effort and time, the efficiency framework predicts that the expenditure of effort better predicts outcome success than do user preferences. This study tests these predictions and extends the model by comparing synchronous and asynchronous media with varying levels of communicative cues. One hundred and forty-two participants in groups of 3 or 4 engaged in collaboration over a 5 week period. Groups were assigned to either interact face-to-face, or to one of four media conditions. There were no effects on actual group success that favored full-cue, synchronous communication. There was greater perceived group effectiveness for synchronous text and face-to-face interactions. Exploratory structural equation analysis showed that media characteristics predict copresence, which increases perceptions of group effectiveness, which in turn predict success. The results support the main assertion of the efficiency framework and explain some contradictions in prior research. Results are discussed with respect to their pertinence for theoretical and measurement issues in human computer interaction research. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction There is wide agreement that computer networked systems are able to facilitate remote collaboration, but there is much debate about how to assess system effectiveness. There are fundamental conflicts about how to discern the best interfaces and computer features for instrumentally successful or satisfying interactions and communication. Instrumentally speaking, it seems that people are able to adapt their communication behaviors to utilize almost any medium successfully regardless of variations in the range of cue systems that interactive media typically feature, even when interfaces require greater communicative effort to be effective (Walther & Bazarova, 2008). However, this does not address the question of which medium provides the most satisfactory connection or facilitates the most efficient way to successfully reach communication goals, ceteris paribus. While a lack of efficiency may reduce satisfaction with a medium, neither satisfaction nor efficiency directly predict outcome quality or success. With this in mind, the efficiency framework * Corresponding author. Tel.: +1 8604864080. E-mail address: [email protected] (K.L. Nowak). 0747-5632/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2009.05.006

(Nowak, Watt, & Walther, 2005; Walther, in press; Watt, Walther, & Nowak, 2002) argues that even though people can use any medium to accomplish their interaction goals, some media facilitate communication more efficiently, in less time and with less effort, than others. In fact, the efficiency framework predicts that the application of greater effort will lead to more successful collaborative outcomes even if it reduces satisfaction. This article presents the results of an experiment that explicitly tests the predictions of the efficiency framework by comparing group projects done using four differing computer mediated communication systems and face to face communication in terms of partner copresence, satisfaction with the medium and outcome success. It also explores some of the limitations of theoretical perspectives that have been used in previous research on computer mediated collaboration. 1.1. Successful and satisfactory collaborations using computer media A variety of theoretical perspectives have sought to predict and explain how different media features (audio, video, and text only) influence the success and satisfaction of interactions using computer media. Researchers comparing satisfaction levels of

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mediated and face-to-face interactions have generally found mediated interactions to be inferior (see Caldwell, Uang, & Taha, 1995; Schmitz & Fulk, 1991). To determine the extent to which a mediated interaction is successful, it is important to first define what one means by success and to recognize that increasing satisfaction does not necessarily increase success or outcome quality (Leavitt, 1951). There are a variety of ways researchers have evaluated success. Several theories – most notably social context cues hypothesis (Sproull & Kiesler, 1986) and media richness theory (Daft & Lengel, 1986) – have explicitly tied satisfaction and success are together and suggested that interactions using ‘‘lean media,” or computer media with fewer nonverbal cues (e.g., text based) are less satisfying than either face to face interactions or interactions using ‘‘rich media,” computer media with more cues (e.g., video conferencing systems) (Rice, 1993). These so-called deficit approaches (Thurlow, Lengel, & Tomic, 2004) suggest that computer mediated communication lacks the important qualities of face to face communication, which makes them less adequate and satisfying for many purposes. From that perspective, lean media are predicted to lead to a deficit in the quality of connection and distal outcomes. The richness and number of the communication cues a medium contains is frequently presumed to be predict the level of social presence between communication participants. The theoretical construct of social presence was originally conceptualized as the degree of cognitive salience of the interaction partner during the interaction as well as the salience and importance of the interpersonal relationship (Short, Williams, & Christie, 1976). While Short et al. (1976) predicted that the medium and number of cues would be a factor influencing outcomes and the level of efficiency in an interaction, they were very clear that the relevance and focus of social presence extended beyond the features of the medium and even beyond an individual interaction to include the salience of the person and the relationship. In fact, Short et al. (1976) explicitly predicted that more cues would not always be better than fewer cues. However, many researchers have interpreted social presence to be a theory about cues; in fact, the earliest of the ‘‘cues filtered out” theories of CMC (Culnan & Markus, 1987) encompassed this idea. The conclusion that social presence was defined in terms of a quality of the medium and the cues provided in a medium certainly contributed to this interpretation. This interpretation of social presence theory was also influenced by the way social presence was operationalized. The scale used by Short et al. (1976) includes items that relate more to the user’s perception of, or satisfaction with, a medium than to the salience of the mediated communication partner (see also Nowak & Biocca, 2003). For example, the scale includes items asking participants to rate the medium on criteria such as ‘‘a lot like face-toface” or ‘‘sociable/unsociable.” This operationalization of social presence may be related to, or even drive, espoused satisfaction with the medium. However, it lacks face validity as a measure of interpersonal salience. Nor is this a direct indicator of the potential success, efficiency, or capability of interaction using the medium in question. The efficiency framework argues that a successful interaction could be described by instrumental outcomes, and predicted by shared attention to the project, and/or by interest or involvement with the task or project. Depending on the goals of the interaction, success could even be affected by the salience of the partner or group, but not by perception about a medium’s resemblance to face-to-face interaction, or by satisfaction with the medium. Several other positions complement the efficiency framework, but do not explicate the basic processes in the same ways or offer the same predictions. For instance, social information processing theory argues that successful interactions using lean cue media take more time than using cue rich or face to face interactions (Walther, 1992). However, although its precepts are not inconsistent with effort issues, the theory did not explicitly consider the le-

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vel of effort required to use the medium, which is an important part of the efficiency threshold and it adds another contingency with regard to the principle of least effort. Korzenny’s (1978) theory of electronic propinquity suggests that users compensate for lower bandwidth (relative reduction in nonverbal cues a system supports) with greater communicative effort, and this assertion has been supported in a recent study involving videoconferencing, audio, and text-based CMC (Walther & Bazarova, 2008). However, electronic propinquity focuses only on affective outcomes, and is silent with respect to medium effects on task success. It would not be expected from that theory’s perspective that task-related communication could be, at the same time, lower in satisfaction and higher in quality, a possibility that is explicated in the following. 1.2. Considering the efficiency framework The efficiency framework recognizes that there are some things that are most easily done face to face and that people report a preference for multichannel and face-to-face communication (e.g. Gale, 1991; Rice, 1993; Tang & Isaacs, 1993; van den Berg & Watt, 1991). Further, it maintains that mediated interactions are different from face-to-face interactions, and that some mediated interactions are different from others, particularly in the sense that systems require different levels of time and effort to accomplish similar outcomes, and they therefore differ in efficiency (Walther, Loh, & Granka, 2005). However, this does not mean that one mode of communication is inherently better, or able to provide a more successful interactions than others, because people adapt their communication behavior. Research indicates that the preference for face-to-face, or multichannel media, is not based on what is able to provide better, or more successful, interactions. Mühlfelder, Klein, Simon, and Luczak (1999) found no differences in interpersonal trust between videoconferencing and face-to-face communication. Hinds (1999) found that real-time videoconferencing overloaded the cognitive processing of coworkers performing a complex task and biased their perceptions of one another compared to those using a text-based conferencing system. Matarazzo and Sellen (2000) similarly found participants using a lower quality videoconferencing system not only preferred the system, but also completed their tasks more quickly than those using a higher quality system. Less dense information about the other participants apparently was less distracting compared to a high-resolution but less useful focus on each other’s faces. Human evolution has yielded a capability to transmit and receive substantive and affective information simultaneously through multiple channels (i.e., numerous verbal, vocal, and visual physical behaviors that are simultaneously perceived via visual and aural sensory systems and decoded at the same time). For example, people are able to process information simultaneously in the channels of verbal and nonverbal communication with little conscious effort when information is redundant or consonant. Nonverbal behavior frequently adds qualification, clarification, or even contradiction to verbal messages (Ekman & Friesen, 1969). When nonverbal and verbal behaviors are not completely redundant, processing both channels requires more effort. Similarly, multichannel communication facilitates the transmission of multiple messages, such as content and affective or relational messages, simultaneously (Watzlawick, Beavin, & Jackson, 1967). Processing both substantive and affective messages simultaneously is possible, even in cue lean systems like text-based communication. Again, this requires more effort. Therefore, the transmission of affective and relational behavior requires greater effort and more time in cue lean media as compared to cue rich media, or in face to face interactions.

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This presents a limitation of social information processing theory (Walther, 1992) in that the theory does not consider effort in its assumptions about relational motivation. That is, the theory does not take into account the potential influence of efficiency or consider the potential effect of the increased thought and effort potentially required when users translate affect from nonverbal to verbal symbols (for a review, see Walther & Parks, 2002). Thus, if successful mediated interactions should require more effort, and if users are not sufficiently motivated to expend greater-than-normal effort to pursue social goals via an inefficient medium, the eventual parity of relational outcomes between media over time that the theory predicts should not in fact be expected to occur. However, this premise has not yet been tested. From this perspective, claims that synchronous multichannel communication devices such as video-based systems provide certain functionality that non-visual communication cannot provide should be examined using criteria germane to specific functions. For instance, Whittaker (1995), Whittaker and O’Conaill (1997) argued that visual information in communication is more than just information about communication partners. Video provides a shared workspace and allows communicators to sense environmental cues that contextualize and add meaning to the references being discussed. Optimally, video may allow for the observation of specific artifacts, rather than a focus on the users themselves (Geisler, 1999). This may allow users to adapt their language to even greater efficient by facilitating the ability to use referential, rather than descriptive, speech (Gergle, Kraut, & Fussell, 2004). Videoconferencing thus provides particular communicative advantages to geographically and/or temporally dispersed groups that text-based communication cannot, at least when visible objects are involved. The efficiency framework suggests that video offers potential to achieve these advantages more rapidly and naturally than non-visual mediated alternatives. However, it recognizes that other systems can achieve the same benefits if their users have experience with them and are willing to expend the required effort to adapt their behaviors to the features of the medium. If communicators use a medium that does not convey their personal physical appearance or their immediate physical surroundings and the objects in it, they can, and indeed must, adapt their communication behaviors to accommodate for the absence of this information (see Clark & Brennan, 1991). To do so they may use more deictic language, i.e. tell one another explicitly what object or feature they may be discussing (‘‘now let’s discuss the tip of the widget from its back” or ‘‘the third paragraph on the second page from the end” rather than pointing and saying ‘‘this one;” Gergle et al., 2004). Similarly, partners will likely adapt their behaviors to use features available in the medium if the goal of the interaction is to communicate emotional and affective reactions. With text, these behaviors may include any number of strategies in which people readily substitute verbal for nonverbal expressions to convey affect (e.g., ‘‘I really like that”) or other overt statements of emotion, rather than a smile (Walther et al., 2005). But this adaptation requires more effort than smiling. It is possible that more effortful expression from senders and attention to these messages from receivers will result in fewer misunderstandings, and improve the success of interactions, while at the same time possibly reducing satisfaction with the interaction as the cognitive load increases. At the same time, the multichannel media provide more consistent engagement, which includes both relevant and irrelevant information, during an interaction. This may be particularly true during synchronous interactions. For example, in a teleconferencing interaction one can actually see a partner during lulls in the conversation whereas one is left to stare at an empty text box or re-read previous messages while waiting for a partner to respond

in a text based interaction. The latter condition might make such periods of inactivity more boring and could lead to less engagement and satisfaction with the task. People are more likely to enjoy interactions mediated by systems that keep them occupied and consistently stimulated than interactions with relatively long periods of inactivity (Shaw, 1954). 1.3. The influence of synchrony on communicative efficiency While researchers have paid much attention to the number of cue systems, relatively little attention has been paid to the difference between synchronous and asynchronous media. One way in which asynchronous computer media may be able to increase communicative efficiency is by removing temporal constraints and facilitating interactions which might otherwise be impossible or impractical. Asynchronous systems can provide time and space independence and allow users to write and respond independent of geographic location, time zones, or other impediments to mutual availability (Johansen & O’Hara-Devereaux, 1994). This overcomes many of the problems associated with competing demands for attention and time that make synchronous and face-to-face meetings difficult (Hesse, Werner, & Altman, 1988; Walther, 1996). Concurrently, asynchronous media have been designed with capabilities that increase efficiency, some of which are not possible in synchronous interactions. For example, a communicator can revise messages before sending them. Users can take all the time they need, without ‘‘costs” associated with atypical response latencies that can be present in synchronous interactions (Clark & Brennan, 1991). Asynchrony allows sorting and archiving the content of interactions so both current members and those who join a group later can review past interactions (see Turoff, 1991). These features turn temporal independence into a utility that allows users to prevent interactions from becoming disorganized and undecipherable. But at the same time, communicators may find such effective interactions less satisfactory because of time lags in the exchange or a lack of a sense of real time connection with interaction partners. 1.4. Differentiating satisfaction and outcome success The efficiency framework is not unique in its proposal that single-cue systems require more effort for complex information processing compared to multichannel systems. In this respect the framework is consistent with other approaches in arguing that multichannel (rather than minimal-cue) systems result in greater communication efficiency (see Daft & Lengel, 1984; Korzenny, 1978). However, the framework is unique in its assertion that the greater effort required by minimal-cue systems nevertheless leads to greater objective success despite their potential inefficiency. This is consistent with Leavitt’s (1951) separation of group efficiency from the satisfaction of group members with the experience. User satisfaction is generally driven more by level of effort than by outcome quality or level of efficiency (see Leavitt, 1951; Olaniran, 1996). Research has shown that differences in satisfaction across media often arise even when outcome quality is no different (e.g., Gale, 1991; Rice, 1993; Tang & Isaacs, 1993; van den Berg & Watt, 1991). Consistent with this, the efficiency framework predicts that groups using media that require more effort to communicate will have less satisfaction with the experience or the medium, but this does not mean the less satisfied groups will be any less successful. Thus, the efficiency framework emphasizes the importance of differentiating success (outcome quality) and satisfaction with the partner, medium, or interaction when evaluating collaborative work and media. At the same time, these factors are related and likely influence one another. The level of efficiency and satisfaction with a medium and the interaction will be

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influenced by varying characteristics of media. However, objective success will not be determined by the features of the medium as long as participants are familiar with the medium and have sufficient time on a project to overcome potential reductions in efficiency. While people can have instrumentally successful outcomes regardless of efficiency or perceived group effectiveness, this is not to say that they will like the process equally as well. Thus, the efficiency framework predicts that the preference for face to face or multichannel media may be related to the relatively greater level of effort required to adapt communication to the features of the computer media. Research suggests that restricted-cue media require more effort to achieve the same ‘‘relative effectiveness” as face to face or cue-enhanced media (Walther & Bazarova, 2008; Walther & Parks, 2002). Such an increase in effort may entail more focus and attention to the partner and the task and thus could potentially improve the quality of the outcome. To investigate this, we propose the following research question: 1.5. RQ1: Do different levels of cue system availability or synchrony/ asynchrony affect objective outcome success? A byproduct of the effort required to adapt to media is predicted to yield an increase in the sense of connection, or copresence, with the interaction partner. Copresence is achieved when communicators are ‘‘uniquely accessible, available, and subject to one another” (Goffman, 1963, p. 22). Copresence differs from related concepts such as involvement and social presence in its reciprocal nature, such that it is not enough that a person reports being involved, one must also perceive that their partner is involved (Goffman, 1963). In addition, as discussed above, social presence has operationally included the perception of, and satisfaction with, the medium rather than the interactants. These factors make copresence meaningfully different from both the conceptualization and operationalization of social presence (see also Nowak, 2001; Nowak & Biocca, 2003). The efficiency framework predicts that people need to increase effort in their interactions to adapt and overcome the potential reduction of presence produced by inefficiencies in mediated technologies. The increase in effort required to construct and decode mediated messages may have the compensatory effect of leading to higher levels of achieved copresence and group effectiveness. Therefore, we predict: H1: Groups using media with fewer cues have higher copresence than groups using media with more cues. H2: Groups using media with fewer cues have greater perceived group effectiveness than groups using media with more cues. The influence of synchrony on efficiency is difficult to predict. Asynchronous media are convenient and make collaboration among people with busy schedules across geographic locations and time zones less onerous. Alternatively, synchronous systems provide a real time connection and may seem more efficient because there is not a long delay between sending a message and receiving a response. Although most sources consider asynchronous CMC to be the more difficult of the two versions (e.g., McGrath, 1990), it can also be argued that asynchronous interaction may reduce the effort one needs to communicate effectively, relative to synchronous interaction: One can, for instance, attend to the stored content at a time and opportunity that is discretionary and convenient, reducing distractions, and facilitating entrainment (Walther, 1996). Synchronous communication, on the other hand, requires turn-taking efforts, and when there are multiple partners,

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efforts at tracking who it is that has said what (Black, Levin, Mehan, & Quinn, 1983). Therefore, based on the efforts entailed in synchronous communication described above, we predict: H3: Synchronous media produce greater copresence than asynchronous media. H4: Synchronous media produce greater perceived group effectiveness than asynchronous media. The measurement of users’ affective reactions to media and each other is complicated by artifacts in the scales that are often used to make such comparisons. In tests of media richness theory, efficiency has largely been operationalized by asking people to report how appropriate a particular medium is for a given task (Rice, 1993) instead of which medium they used, or would prefer to use, in different situations. Likewise, in social presence research, media are evaluated on the extent to which people feel they could use them to persuade someone, and whether participants perceive the medium as similar to face-to-face (Short et al., 1976). These measures are likely tapping into a construct related to people’s perceptions of socially appropriate media usage or their satisfaction with the medium, and not to what people can actually do with the medium or their psychological reactions to their communication partners. Thus, media that are perceived to provide an experience that is closer to face-to-face interaction (e.g. more cues or synchronous) will likely be rated higher on media satisfaction due to structural features without regard to their functional dynamics or the perception of the salience of users’ interaction partners. Further, those in face-to-face interactions will report higher levels of communication satisfaction than those using media, regardless of actual task outcomes or perceptions of partners. In other words, traditional measures of social presence are actually measuring media satisfaction or similarity to face to face and not the sense of a connection to partners. This means that these measures assess only the extent to which a mediated experience differs from face-to-face communication on people’s satisfaction with the medium. We argue that these items are not indicators of partner salience, satisfaction, or success, although media perception is important and influential. If we are correct, media satisfaction will be related to, and predicted by, the features of the medium (cues, synchrony) rather than perceived or actual project quality. A previous study mirrors these patterns: Honeycutt (2001) compared students’ satisfaction and performance using synchronous and asynchronous CMC in online peer evaluations. Whereas students were more satisfied with the synchronous chat-type system, the quality of their work, and the economy of their language indicated that asynchronous CMC was superior to synchronous CMC as far as performance outcomes were concerned. This finding reinforces our prediction that: H5: Groups using media with more cues have greater media satisfaction than groups using media with fewer cues. H6: Groups using synchronous media have greater media satisfaction than groups using asynchronous media. H7: Groups interacting face-to-face have greater media satisfaction than mediated groups. To test these predictions, we examine the independent and combined effects of synchrony and cues on the sense of copresence, perceived group effectiveness, media satisfaction (using the construct previously called ‘‘social presence”), and objective outcome success (the collaboration task outcome). In order to address these issues, research was conducted employing groups using technology that varied in synchrony, cues, and mediation.

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the best presentation was to receive a $100 prize in addition to the course grade.

2. Method 2.1. Participants

2.4. Procedure Participants were 142 students enrolled in a communication course at a large public university in the eastern United States. These students were randomly assigned to 39 groups of 3 or 4 members each. These groups, in turn, were assigned to collaborate over a 5-week period either in face-to-face meetings, or by using one type of computer medium to complete a class project. 2.2. The media To disentangle the impact of cues and synchrony on outcome quality, five different group collaboration conditions were used. This included face-to-face groups and four types of mediated groups that were factorial combinations of time mode and cue multiplicity, including synchronous high-cue (synchronous CUSeeMe video conference), synchronous low-cue (synchronous WebBoard Chat), and asynchronous low-cue (WebBoard textbased conference), and asynchronous high cue. All of the media conditions utilized commercially available systems, except for the asynchronous audio-visual system. This cell was filled with the Time Independent Collaboration system (TIC), the development of which is detailed in Watt et al. (2002). TIC uses Logitech cameras with microphones for recording messages that are stored on a local server and played back with the click of a button by clicking on the thread or an individual message with a mouse and then selecting either play, reply pause or delete as shown in Fig. 1. 2.3. The task Students were assigned to a group of 3–4 students in their class to prepare a 12–15 min oral report that was worth 20% of their overall grade in a course. All students completed the same task, which was to prepare a mock oral presentation to Congress. Students were to read relevant sections of the Patriot Act and make policy recommendations to Congress about how to balance Internet privacy and national security. The final oral report required each group member to present a portion of the arguments, thus preventing ‘‘social loafing.” The group that gave

All participants came to different research labs at a large public University for an hour at the same time and day each week (e.g., Monday at 3 pm) for five weeks to complete the task described above. All participants in assigned to a media condition used the same computers that were connected via broadband Intranet connections. Only face to face groups were in the same room at the same time. In synchronous conditions, all group members were participating at the same time in different research labs and the interaction did not begin until all members of the group were present and ready. Asynchronous participants were also assigned a particular day and time to come into the lab and participate. This ensured that the computers were available and free for their use, that teammates would have responses from all members of the team every time they arrived in the lab, that all members of all teams were engaged in working on this task on a weekly basis and that team members did not arrive in the lab either at the same, or adjacent, times. Asynchronous teammates would review their teammates’ messages during the appointed time and respond as desired. No time limits were given to participants, although the amount of time they used each medium was similar in all conditions (one hour per week, maximum), including face to face. Participants were videotaped as they gave their mock presentation to congress. A week later, participants returned to a lab at their assigned time and viewed the videotape of their presentation. Participants completed a paper and pencil self-report questionnaire assessing the media satisfaction, perceived group effectiveness, and copresence after viewing the videotape of the presentation. 2.5. Measurement instruments Items were selected from previously published scales and adapted to the task. Item analysis based on Cronbach’s alpha was used to construct the final measures, with the exception of the outcome success measure. When initial reliabilities did not attain alpha of.70, single items that most decreased alpha were iteratively deleted until the final scale achieved at least a 0.70 alpha. See Appendix A for items included in the final measures.

Fig. 1. The time independent collaboration system (TIC) used for the asynchronous audio–visual condition.

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Outcome success. Presentations were evaluated for outcome success by three Ph.D. students in Communication and Rhetoric. The evaluators examined the final 10–12 min oral presentations with a fixed checklist that required evaluation of argument structure, articulation of main points, conclusions, content, and delivery/style on a series of scales that yielded an overall evaluation of the quality of the group’s presentation on a 0- to 36-point scale. The mean of the most reliable pair of scores provided by the evaluators of each group was used as that group’s measure of Group Success. Interrater reliability achieved a Pearson product–moment correlation of 0.66 between judges on the group performance evaluations, indicating a reasonable level of agreement between coders on the overall Group Success variable. As the bivariate correlation does not correct for chance agreement in ratings, the more stringent Krippendorff alpha reliability coefficient was computed for the two judges, using the assumption of interval level measurement of the Group Performance measure. This value was 0.50, again indicating moderate agreement between judges on overall performance of the groups, but sufficiently divergent to argue for use of raters’ means as the final measure. Media satisfaction. These measures were based on Short et al.’s (1976) social presence scale. These items ask participants to consider the medium’s usefulness in persuasive communication, their sense that the medium contributed to the successful completion of the project, and perceived similarity to face to face interactions. On their face, these items measure media satisfaction. Reliability analysis yielded an inter-item Cronbach’s alpha of 0.91. Copresence. Copresence was operationalized as two separate scales (self-reported involvement with others in the group and perception of the others’ involvement). Both scales were created by modifying items from Burgoon and Hale’s (1987) involvement scale as detailed fully by Nowak and Biocca (2003). All items were Likert type items on a 7 point scale. Self reported copresence was measured using 3 items with an inter-item alpha reliability of 0.71. Partner copresence was measured with 10 items with an inter-item alpha of 0.91. Perceived group effectiveness. This was measured using 9 Likert type items with an inter-item alpha of 0.83 from Canary and Spitzberg’s (1987) conversational effectiveness scale. Items were revised so that the meeting replaced the word conversation in the initial scale to make items relevant to this project. Correlations among measures indicate a fairly good discriminant validity among the measures (i.e., they are only moderately correlated and are thus not likely redundant measures of the same underlying construct), as Table 1 shows.

3. Results All statistical analyses involved hierarchical analyses that took into account the potential nonindependence of observations, given that data were obtained from subjects nested in groups nested in experimental conditions. None of the data involved interdepen-

Table 1 Pearson correlations among variables.

Partner copresence Self copresence Social presence Perceived group Effectiveness * **

p < .05. p < .01.

Outcome success

Partner copresence

Self copresence

Social presence / media satisfaction

0.145 0.113 0.155 0.174*

0.270** 0.041 0.422**

0.510** 0.568**

0.419**

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dence by definition (e.g., the way a group’s speaking time is allocated such that Member X’s time is a partial function of the whole time minus Member Y’s and Member Z’s). It is nevertheless possible that group members influence one another’s perceptions in ways that inflate between-group differences and obscure (or exaggerate) between-condition effects (see Kenny, Kashy, Mannetti, Pierro, & Livi, 2002; Malloy & Albright, 2001). The SPSS MIXED procedure facilitated the appropriate analyses by employing a random factor for subjects within groups (see Kenny, Kashy, & Cook, 2006). The hypotheses were first tested for the main and interaction effects for the number of cues and for synchrony with only the mediated groups, excluding the face-to-face groups. Where predictions were confirmed, a second analysis involved pairwise comparisons of all five conditions that contrasted each of the media conditions with the face-to-face condition and with each other in order to detect the relative impact of each on the dependent variable. 3.1. Test of the research question In this question, the impact of the number of cue systems and synchrony provided by the communication media on objective group outcome success was investigated. Although the efficiency framework predicts no media effects on objective outcomes, these tests resulted in a significant main effect of number of cue systems, F(1, 127) = 5.39, p = .02, and a significant interaction effect of cue systems and synchrony on the quality of final presentation, F(1, 127) = 18.08, p < .001. The asynchronous high-cue group (M = 18.72, SE = 1.27) was significantly higher (according to pairwise tests) on outcome success than both the asynchronous lowcue (M = 11.83, SE = 1.32, p = .002) and synchronous high-cue conditions (M = 14.91, SE = 1.06, p = .006). The face-to-face groups did not differ significantly in pairwise tests from any of the mediated groups. The synchrony factor’s effect on outcome success did not reach significance, F(1, 129) = 0.37, p = .55, and the asynchronous highcue condition did not differ significantly from the synchronous low-cue condition (M = 16.92, SE = 1.27) or the face-to-face condition (M = 17.37, SE = 1.42). The face-to-face groups did not differ significantly in pairwise tests from any of the mediated groups. These unanticipated results have valuable implications for the efficiency framework, as they contextualize the results that follow. 3.2. Hypothesis tests Hypotheses 1 and 3 both offered predictions for media effects on copresence. H1 posited that media presenting fewer-cue systems would produce greater copresence than media presenting more cues. H3 stated that asynchronous media would produce less copresence than those presenting more cue systems. The hypotheses were tested simultaneously in an omnibus mixed ANOVA test including the interaction of cue systems and synchrony. First, we tested the prediction for effects on the perceived partner copresence variable. Each of the hypothesized variables achieved significance; the main effect for number of cues was significant, F(1, 127) = 11.84, p = .001, as was the synchrony main effect, F(1, 128) = 6.55, p = .012. In support of H1, groups using media with more cues felt lower partner copresence (M = 51.26, SE = 1.37) than groups using media with fewer cues (M = 55.76, SE = 1.45). H3 found support, as synchronous media effected greater partner copresence (M = 55.03, SE = 1.37) than did asynchronous media (M = 51.80, SE = 1.45). Second, we examined self reported copresence. The number of cue systems failed to show a significant main effect, F(1, 134) = 0.365, providing no support for H1. However, the synchrony main effect produced significant differences, F(1, 134) = 27.58, p < .001. Those using synchronous media reported greater perceived partner

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copresence (M = 15.43, SE = 0.35) than those who used asynchronous media (M = 12.66, SE = 0.40). There was no significant interaction effect, F(1, 134) = 0.038. The next hypotheses predicted effects on perceived group effectiveness, with H2 predicting greater perceived effectiveness among groups using fewer-cue systems than higher-cue systems, and H4 predicting greater perceived effectiveness from synchronous media than from asynchronous media. The hypotheses were not supported in their independent form. Although a significant main effect for synchrony was obtained, F(1, 129) = 16.82, p < .001, providing partial support for H4, a significant interaction between cues and synchronicity, F(1, 129) = 6.84, p = .01, was also found. The synchronous low-cue group scored significantly higher on perceived group effectiveness (M = 53.43, SE = 1.50) than did any of the other three experimental conditions. In the other synchronous conditions, synchronous high-cue (M = 47.50, SE = 1.13) and faceto-face groups (M = 49.63, SE = 1.70) did not differ in perceived group effectiveness from the asynchronous conditions, the asynchronous low-cue (M = 43.85, SE = 1.56), or asynchronous highcue (M = 45.38, SE = 1.48). Hypotheses 5 and 6 concerned media effects on satisfaction, premised on the notion that satisfaction is greater when media offer less effortful interfaces, regardless of the actual quality of discussions or objective task outcomes. H5 predicted that media presenting more cue systems generate greater satisfaction than media offering fewer cues, whereas H6 predicted a synchrony effect such that synchrony produces more satisfaction than asynchrony. Both H5 and H6 were supported. The hypotheses were tested simultaneously to allow detection of any possible interaction effect of cues and synchrony to emerge that would otherwise obscure these main effects. No significant interaction emerged, F(1, 134) = 0.046, p = .83, allowing independent interpretation of the main effects. The number of cue systems significantly affected satisfaction, F(1, 134) = 5.77, p = .018, as predicted. Systems with a greater number of cue systems generated more satisfaction (M = 38.20, SE = 1.33) than did lower-cue systems (M = 33.33. SE = 1.53). Synchrony also affected satisfaction as predicted, F(1, 134) = 32.93, p < .001. Asynchronous media generated substantially less satisfaction (M = 29.95, SE = 1.53) than did synchronous media (M = 41.59, SE = 1.33). Hypothesis 7 predicted that groups interacting face-to-face would report greater media satisfaction than mediated groups. The omnibus test and pairwise comparisons indicated support for the hypothesis, F(4, 133) = 41.79, p < .001. Face to face communication (M = 57.68, SE = 1.89) was significantly more satisfying than any other condition. Asynchronous text (M = 27.30, SE = 1.70) was rated lower than any other medium. Indeed, each condition (including synchronous text, M = 39.37, SE = 1.62) was significantly different from every other, with the exception of synchronous video (M = 33.28, SE = 1.64) and asynchronous video systems (M = 32.60, SE = 1.62) which did not differ from each other.

4. Discussion 4.1. Hierarchical analyses The results reveal some limitations of prior theory and generally support the efficiency framework. As predicted, people are able to successfully adapt their communication behaviors to utilize almost any medium to collaborate successfully. Further, there was an important difference between a medium’s ability to facilitate outcome success and the perceptions of group effectiveness and satisfaction with the medium reported by group members. Mediated interactions were perceived as different from face-to-face interac-

tions and as less satisfactory. However, face-to-face interactions did not lead to higher copresence among participants, or to more successful outcomes. In direct contrast to what would be predicted by media richness theory, the synchronous low-cue group achieved higher levels of perceived group effectiveness than any other mediated condition group. This revealed an unpredicted interaction between number of cues and synchrony which obfuscated the predicted independent main effects of either cue systems (H2), or synchrony (H4), on perceived group effectiveness. This is consistent with predictions based on the efficiency framework and suggests that multiple factors are at play. The most successful groups used either one of the most commonly used systems (synchronous low-cue technologies represented by text based instant messaging) or the most unique and unusual (asynchronous audio video). In this case both the familiar and the unique medium resulted in better rated presentations. First, the low-cue asynchronous groups (instant messaging) were familiar with their medium and found it easy to use to complete the task. This may indicate that people who use a medium frequently become more adept at adapting their communication behaviors to it and to using it effectively and perhaps more efficiently during their deliberations. Alternatively, the high-cue asynchronous medium was likely the least familiar to participants and may have required a higher level of effort to use. This expended effort and attention to the medium may have spilled over to effort toward the task, resulting in the higher objective outcome performance for those in this condition. In this way, either familiarity with medium or higher level of effort can predict outcome success. This is consistent with the idea that participants are able to accommodate their communication behaviors and utilize the features of any medium, even when it is assigned to them in an experimental context. Future research is needed to confirm this possibility. With this exception, when a medium offers a greater number of cues, satisfaction is greater than when a medium offers fewer cues (H5). The essential prediction that the features of the medium would not predict the actual success of the group was only partially supported. First, face-to-face groups were not rated significantly differently from mediated groups on either perceived group effectiveness or outcome success. The analysis also revealed that the asynchronous high-cue medium was superior in outcome success to one synchronous system (synchronous high-cue audio/ video) as well as to the other asynchronous medium (asynchronous text), although the asynchronous high-cue medium was rated near the bottom in satisfaction. Conventional social presence measures tap the perception of a medium’s ability to provide satisfactory interactions, not the perception of psychological connection between interactants. We found that satisfaction is greater when media are synchronous rather than asynchronous (H6) and that face-to-face communication was significantly more satisfying than any mediated condition (H7). This subjective assessment of the medium itself was the only variable where mediation made a significant difference. The tests of direct effects revealed that synchronous low-cue media had the highest perceived group effectiveness, though this group did not receive the highest outcome success rating. While people felt both more copresence and media satisfaction after using synchronous media, there was an interaction effect. Despite the potential convenience of asynchronous media, asynchronous interactions are generally not as involving as synchronous interactions, presumably because communicators are not engaged in real time exchanges. This is consistent with the prediction that synchronous communication requires more effort than asynchronous communication. Students like those in this sample spend a lot of time interacting both face-to-face and using synchronous text systems (such as Instant Messenger systems). This usage and

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familiarity may have added to their sense of copresence in these conditions. Neither synchronous nor asynchronous audio video were as high on copresence as synchronous text. However, once again the perception of group effectiveness associated with synchrony did not significantly increase actual outcome success in tests of direct effects. As discussed above, some of the results of the hierarchical analysis contradict predictions of earlier theories. This may be due to the fact that the hierarchical analysis makes several strong assumptions about the structure of effects: First, there is a direct link between the characteristics of the medium (synchrony and cues) and the outcome variables. Second, there is no important covariation between dependent variables that would confound the direct effects of the independent variables; and third, that there are no variables intervening between the technology differences and the outcomes. However, as Table 1 shows, some of the dependent variables have significant covariance and the interactions between media characteristics variables found in the above analyses suggest a more complicated process. It may be that it is not the lack of efficiency that drives perceived group effectiveness, but rather the level of copresence in the interaction. In other words, perceived group effectiveness may be indirectly influenced by the efficiency of the medium, but more directly affected by the level of copresence. To explore this conjecture, we conducted a structural path analysis using the AMOS package to test the implicit model assumed in the hierarchical analyses and to explore the possibility of an alternative structure involving mediating variables in the overall process. This might provide a possible explanation of some of the contradictory findings. 4.2. Exploratory structural analysis of results The direct effects results presented above assume synchrony and cues as the independent variables, which directly predict each of the separate dependent variables, which are unrelated to each other. Fig. 2 shows the structural test of these assumptions. The overall v2for this structural model was large and significant, v2 = 106.9, df = 10, p < .001. The goodness of fit to the observed data as measured by the Root Mean Square Error of Approximation (RMSEA) was 0.27, which is well above the unacceptable threshold value of 0.10 or greater and the chi-square Minimum per degree of freedom (CMinDF) was 10.7, far above the acceptable 2.0 level (see Arbuckle & Wothke, 1999). This means that the assumed structure of direct and independent effects of medium implied in the mixed ANOVA tests and shown in Fig. 2, while correct in isolation, do not actually fit the full set of observed data very well. Previous structural models of group collaboration have suggested that group participant involvement mediates the impact of media characteristics on ultimate effects like partner perception (Nowak et al., 2005). In this formulation, media characteristics directly affect involvement or copresence, which then produces the actual outcomes. This basic process was incorporated in the model shown in Fig. 3, using the self reported and perceived copresence as mediating variables. Analysis of this model improved goodness of fit to the observed data (RMSEA = 0.15; CMinPerDF = 4.8), but the fit error was still above acceptable levels. Overall, the implied coefficients in the model still differed significantly from those actually observed, v2 = 47.7, df = 10, p < .001, indicating this model is not a good fit to the observed data, either. To find a structure that fit the observed data, an exploratory inductive and iterative process of model construction was used. First, all non-significant relationships were deleted and the model coefficients recomputed. Then direct paths were introduced between variables where the implied (computed) coefficients were clearly lower than the observed correlation coefficients as indicated by the AMOS modification indices. This process was re-

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peated iteratively, one path at a time, until the model shown in Fig. 4 was obtained. This model contains only significant structural coefficients and has a very good overall RMSEA of 0.04, which is below the ‘‘good fit” value of 0.05 or less, and CMinDF of 1.3, which is also less than the 2.0 criterion for a good fit (see Arbuckle & Wothke, 1999). The v2 test indicates that the implied coefficients in this model do not differ significantly from those observed, v2 = 12.6, df = 10, p > .20.1 Given these excellent goodness-of-fit indices, this model can be considered a good representation of the observed data. Because the final model shown in Fig. 4 was inductively constructed with the iterative procedure, there is a possibility of capitalization on chance variation in inferring structural relationships. To examine this possibility, the data were randomly partitioned into two subsets, which were independently analyzed. The splithalf models, whose coefficients are also shown in Fig. 4 do not differ dramatically from the overall model, indicating that the overall model structure is stable and correctly represents the observed data. This procedure revealed the presence of covariance between the residuals of two endogenous variable pairs (self-reported copresence and partner copresence; and perceived group effectiveness and media satisfaction). These covariances indicate the presence of a common theoretical construct that overlaps both variables of each pair. In the case of the copresence variables, this is consistent with the prediction that copresence is a reciprocal construct. It also shows the importance of distinguishing between self reported and perceived copresence. Specifically, the model shows that synchrony increases both dimensions of copresence, but high-cue media actually decreases partner copresence and has no direct path to self reported copresence. Likewise, the covariance between perceived group effectiveness and media satisfaction is likely due to a common perception by individuals of the group’s success that included both perceived group effectiveness and media satisfaction. In this final model, media characteristics produce changes in copresence, which increase the perception of group effectiveness, which in turn predicts outcome success. Media characteristics (synchrony and number of cues) have only an indirect effect on outcome success, as they are mediated by copresence. Both synchrony and medium familiarity with low-cue media may be working together to predict copresence, which in turn affects the outcome variables. The characteristics of the medium did not directly produce changes in either of our measures of success (perceived group effectiveness and outcome success), but operated only indirectly via copresence. These structural relationships lead to two important inferences. First, the characteristics of the medium do not in themselves predict the success in achieving the task addressed by the group, as suggested by the efficiency framework. They are important only in producing copresence among group participants. Second, as perceived group effectiveness is the only direct predictor of outcome success, it should be given prominence in both designing and assessing mediated collaboration technologies. A final issue is related to the question of how best to measure people’s connection with one another in mediated groups. The direct relationships between medium characteristics and social presence measures used in earlier studies, and the lack of direct impact of this social presence variable on other variables seems to imply that these social presence items measure the mental model of what participants think makes up a successful collabo1 As a check on this causal chain, direct effects from all causally prior variables in the chain to the final outcome variables were introduced in a series of models. None of these new direct paths were significant, indicating that the model shown in Fig. 4 with copresence mediation best fits the observed data.

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resid

Self Copresence

.42

.05 .17

resid

Partner Copresence

Synchronous Medium

-.28 resid

.44 Social Presence: Media Satisfaction

.10 .18

resid

.31

High Cue Medium

Perceived Group Effectiveness

-.16 -.01

resid

Chi-Sq = 106.917 (df 10), p = .000 RMSEA = .266

Outcome Success

.13

Chi-S q Min p er df = 10.692

Fig. 2. Implied hierarchical analysis model. Structural coefficients not interpreted due to poor overall model fit.

ration medium and not the actual psychological coorientation that occurs when group members communicate. It thus appears to be an indirect and less effective measure of a medium’s likely effectiveness.

4.3. Limitations As with any experiment, there are some factors that limit the generalizability of the results. The fact that all participants had resid

Social Presence: Media Satisfaction

.53 .42 Synchronous Medium

-.10

Self Copresence

resid

.50

resid

.17

Perceived Group Effectiveness

.10 .05 resid

High Cue Medium

-.28

Partner Copresence

.30

.12 Chi-Sq = 47.745 (df 10), p = .000

.08

Outcome Success

RMSEA = .166 Chi-S q Min p er df = 4.774

resid

Fig. 3. Involvement mediation model. Structural coefficients not interpreted due to poor overall model fit.

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Social Presence: Media Satisfaction

.27 .39

(.27) [.26]

Synchronous Medium

.42 (.38) [.45]

Self Copresence

resid

(.09#) [.23]

-.29

.48 (.38) [.61]

(.09#) [.33]

.26 resid

(.16#) [.33]

Partner Copresence

(.-.36) [-.23]

resid

.26 .19

.17

High Cue Medium

(.52) [.24]

.30 (.09#) [.23]

Chi-Sq = 12.629 (df 10), p = .245

(.28) resid [.34]

Perceived Group Effectiveness

.18

(.20#) [.17#]

Outcome Success

RMSEA = .044 Chi-S q Min

p er df = 1.263

resid

Fig. 4. Final structural model with replications. All structural coefficients significant at p < .05. Replication on random split-half subsets of data: Random Split-half Subset 1 coefficients in parentheses. (N = 69). Model v2 = 14.5, df = 10, p > .15. RMSEA = 0.08, CMinDf = 1.45. Structural coefficients marked with # are n.s., all others significant at p < .05. Random Split-half Subset 2 in square brackets. [N = 69]. Model v2 = 6.3, df = 10, p > .75. RMSEA = 0.00, CMinDf = 0.63. Structural coefficients marked # are n.s., all others significant at p < .05.

to report to the lab at a particular place and time to use some of the media examined in this experiment – even the asynchronous text and videoconferencing systems – eliminated a fundamental advantage of asynchronous media that would have come with being able to use the medium at any time or any place participants wished. The desire to control for computer speed, time on task, and order of participation in this study overrode the concern about the potential limitation introduced by this control. Future research should explore the possible effect of this limitation. Another potential problem occurred with the synchronous audio/visual groups. Even though all participants were on a broadband connection in university research labs, this condition was particularly sensitive to network congestion that caused delay in some interactions. Data were examined for the possibility that this might have produced the negative relationship between high-cue media and partner copresence but no such effect was found when contrasting groups that experienced more network latency with those whose video conferences were smoother. Other factors that were intentionally not controlled in this design may actually increase the external validity of the results. First, in this study students enrolled in a course where they saw each other several times a week and they did not limit their discussions or interactions about the project to their assigned medium. This is consistent with most real world group projects that generally span a variety of modes of communication (face to face, email, etc.). Even though participants in this study did not limit their projectrelated interactions to one medium, the assigned media still significantly affected outcomes and perceptions. It is also possible that our measure of outcome success (quality of rhetoric) is not the best measure of objective group success, although effective presentation was a central goal of the task addressed by each group. A final limitation is the reliance on a homogeneous student sample. Future research should replicate this design with differing populations, group tasks, measures, and group goals.

5. Conclusion While not all hypotheses were supported, the results support the basic premise of the efficiency framework and aspects of Korzenny’s (1978) theory of electronic propinquity, that users adapt their communication behavior to the medium they are using. Future research is needed to understand how this process works given that groups using the least familiar medium (asynchronous high-cue) had the highest scores on outcome success but much lower perceptions of the medium’s ability to be useful. Perhaps more importantly, the data show that measuring user satisfaction with a medium is not the same as measuring the ability of users to successfully utilize a medium. While it is true that participants felt the most satisfaction in face-to-face and the lowest in low-cue asynchronous media, media satisfaction only indirectly predicted either perceived or actual success. This contradicts the underlying assumption of some traditional approaches to media evaluation, which propose that people in synchronous and highcue groups are more likely to be satisfied and that more satisfied groups will be more successful than those in other groups. There are several possible explanations for why people rate some media as being less satisfactory when the medium is actually sufficient (and in some cases superior) for completing a task. It may simply be that users prefer what is familiar regardless of its capability, or that people do not know, or are not able to express, what they need for successful interactions. It is also possible that people are responding in ways that express their possibly naïve ideas of what an appropriate medium should be. If this is the case, asking how close a medium is to a face to face interaction or whether one would choose this medium to collaborate may be a misleading way to evaluate the medium. The surface affordances of mediated systems are not always useful in assessing the potential usefulness of a medium. If you are in the same time zone and geographic location, then a face to face interaction may be possible, practical, and desirable. However, practical factors such as time and distance at times make face to

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face interactions impossible. The goals of media designers should be to provide efficiencies that are only possible in mediated interactions, focusing on ways that media can augment and enhance the communication process in normal work and personal interactions. Mediated interactions do not, cannot, and will not feel the same as face-to-face interactions (Hollan & Stornetta, 1992). The reported preference of users for face-to-face contact may ignore what is practical, possible, needed, or even what will work best. With this viewpoint, researchers should probably cease using face-to-face interactions a benchmark for assessing the usefulness or quality of mediated communication, and focus instead on the efficiencies and qualities that are inherent in the match of particular kinds of mediated communication to specific kinds of communication settings.

Appendix A. Measurement instrument scale items All items within constructed indexes were measured on a 7point Likert-type scale. The minimum for each index is thus the number of items in the scale and the maximum is 7*number of items in the scale. Social presence/media satisfaction (range = 10–70, alpha = 0.9146):  To what extent did you feel you got a good enough idea of how people at the other end were reacting?  Do you think you got a ‘‘feel” for the people at the other end?  To what extent do you feel you were able to assess your partners’ reactions to what you said?  To what extent was this like a face -to-face meeting?  To what extent was this like you were in the same room with your group?  To what extent did you feel the medium facilitated your group’s ability to complete the project?  How likely is it that you would choose to use this system of interaction for a meeting in which you wanted to persuade others of something?  To what extent did you feel you could get to know someone that you meet only through this system?  To what extent did your group seem ‘‘real”?  To what extent did you feel the medium retracted from your group’s ability to complete the project? Self-reported copresence (range = 3–21, alpha = 0.7134):  Reverse: I was detached during the conversations.  I found the interaction stimulating.  I was intensely involved in our interactions. Partner copresence (range = 10–70, alpha = 0.9123):  Partner was willing to listen to me – group mean.  Partner was intensely involved in our interaction – group mean.  Reverse: Partner did not want a deeper relationship – group mean.  Partner seemed to find our interaction stimulating – group mean.  Reverse: Partner created a sense of distance between us – group mean.  Reverse: Partner seemed detached during our interactions – group mean.  Partner created a sense of closeness between us - group mean.  Reverse: Partner acted bored by our interactions – group mean.  Partner was interested in our interaction – group mean.  Partner showed enthusiasm for our interactions – group mean.

Perceived group effectiveness (range = 9–63, alpha = 0.8284):         

Our group meetings were very beneficial. Reverse: The group meetings were useless. I got what I wanted out of the meetings. I found the meetings useful and helpful. Reverse: I didn’t know what was going on during the meetings or the task. Reverse: Our meetings were generally unsuccessful. My group did a good job on the task given our constraints. I achieved everything I hoped in our group project. My contribution to the group was effective.

Outcome success (range = 0–36, inter-rater reliability = 0.66): Each of the 18 items was scored on a 0–2 scale (0 = poor; 1 = acceptable; 2 = good), and the 18 scales summed to provide the final index of Group Success.                  

Gains attention. States purpose. Previews topics. Introduces team. Main points identified. Support. Clear transitions. Recaps main points. Emphasizes central idea. Definite closing. Level of detail. Appropriate for audience. Organization of ideas. Evidence of preparation. Enthusiasm/energy. Vocal quality. Eye contact. Smooth hand/offs.

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