9
INVESTIGATING EMOTIONS AND
AFFECT IN GROUPS
PURDUE UNIVERSITY
SOUTHERN ILLINOIS UNIVERSITY CARBONDALE
It is the middle of March and the NCAA basketball tournament for both women and men are down to the Sweet Sixteen. As any of those who have watched (or endured) their favorite team advance (or not!) through the first two rounds of the tournament, the affective nature of groups is quite apparent. The teams themselves show the thrill of victory or the agony of defeat visibly on their faces and in their demeanor. The fans, as well, reflect their team's fate in their expressions of elation or dejection. Quite clearly, many aspects of group process and performance are indexed by the emotional outcomes exhibited by their members.
In other domains as well, we find evidence that emotions are an important input to, context for, and outcome of group interaction. Certainly, friendship groups exist at least in part because they provide a context for interpersonal interaction and the pleasurable consequences of such interaction. And such groups also disintegrate when those consequences turn unpleasant. Members of task groups feel such emotions as pride and joy when their pet project has been accepted and has succeeded, and feel sadness or even anger when those projects are rejected or fail.
However, despite the essential emotional quality of groups, research into their more socio-emotional side has been lacking – until recently. For the past 25 years, individual-level researchers have been documenting the important influences that moods and emotions have on people's cognitive functioning. Moods and emotions have been found to influence people's judgments, evaluations, and style of information processing (Schwarz & Clore, 1996). Moods and emotions are also widely acknowledged to be interpersonal in context and character (Wallbott & Scherer, 1986). As a consequence of this, group researchers are also becoming interested in the causes and consequences of moods and emotions in small groups and there is growing recognition of the importance of these affective influences on group process and performance (Barsade & Gibson, 1998; George, 2002; Kelly, 2001; Kelly & Barsade, 2001; Kelly & Spoor, 2006, 2007; Spoor & Kelly, 2004; Tiedens, Sutton, & Fong, 2004).
A quick search of the literature shows a growing number of empirical studies examining affective influences in small groups. For example, a number of studies have examined emotional contagion in groups (e.g., Barsade, 2002; Bartel & Saavedra, 2000; Spoor & Kelly, 2009; Totterdell, 2000; Totterdell, Kellett, Teuchmann, & Briner, 1998), and others have investigated affect and group performance (e.g., Bramesfeld & Gasper, 2008; Forgas, 1992; Grawitch, Munz, Elliot, & Mathis, 2003; Grawitch, Munz, & Kramer, 2003; Jones & Kelly, 2009; Kelly & Spoor, 2007). Leadership is another growing area in which the role of affect is being explored (e.g., Damen, van Knippenberg, B., & van Knippenberg, D., 2008; Damen, van Knippenberg, D., & van Knippenberg, B., 2008; Lewis, 2000; Pescolido, 2002; Sy, Cote, & Saavedra, 2005), and yet, many theoretically important affective influences in groups and subsequent consequences for groups in terms of member interaction and performance remain uninvestigated. It is our hope that, as a result of this chapter, we will be able to suggest and bring to bear methods that will be useful in addressing this important gap in our knowledge of group behavior.
General Description of the Method
Types of affective experiences
Many researchers have described the variety of affective states that individuals may experience (Forgas, 1995; Isen, 1984; Kelly & Barsade, 2000). For example, individuals have affective dispositions, or their characteristic ways of affectively responding to situations (Lazarus, 1991). Individuals may experience diffuse positive or negative low-intensity mood states (Forgas, 1992; Isen, 1984), or higher intensity emotions that are generally of shorter duration than moods and that are directed toward specific targets (Fridja, 1994). As individual members of interacting groups, we are all likely to experience any of these types of affective states during group interaction. But as group members interact over time, these individual affective states may be influenced by other members’ states, and thus a group-level affect may also develop that ultimately shapes how the group operates and performs.
There are a number of existing group-level constructs that have affective characteristics, including group cohesion (Hogg, 1992; Mullen & Copper, 1994) and emotional contagion (Hatfield, Cacioppo, & Rapson, 1994). However, a number of more recent group-level affective constructs have been proposed. George (1990, 1996), for example, proposed that some groups develop a “group affective tone” or a group's characteristic level of positive or negative affect. By definition, a group affective tone only exists when group members report similarity in their affective reactions in the group. That is, a group has a group affective tone to the extent that consistency exists in the affective responses of group members, such as when interacting in a group setting leads members to all feel positive. Bartel and Saavedra (2000) demonstrated that groups can be characterized as a whole by mood states. Their investigation showed that both members of a group and outside observers can agree in their judgment of a group's mood. In addition to general mood states, emotions have been demonstrated at the group level as well. For example, Duffy and Shaw (2000) described the effects of group envy. Group envy led to reduced group performance as a consequence of social loafing, less cohesiveness, and diminished confidence in the group's ability to succeed.
As described, members of groups and perhaps groups themselves, experience moods and emotions, and these moods and emotions influence and are shaped by group performance and interaction. That is, moods and emotions can serve as inputs into the group experience, can be a measure of group process, and can serve as an outcome of a group experience. We examine next how affect serves in each of those roles in a group context, and describe how researchers manipulate, observe, and measure moods and emotions in groups.
Manipulating group members’ moods and emotions
to serve as inputs
As mentioned previously, a growing number of studies have examined the effect of member affective states on group performance (e.g., Bramesfeld & Gasper, 2008; Grawitch, Munz, Elliot et al., 2003; Grawitch, Munz, & Kramer, 2003; Jones & Kelly, 2009). In each of these studies, mood states served as an input to the group process. That is, group members’ mood states were manipulated prior to group interaction in order to demonstrate how those mood states affected group process and performance.
In empirical research that explores these effects, researchers have primarily relied on traditional and individual-level methods of inducing mood (e.g., Forgas, 1992), such as exposing participants to films or music with sad or uplifting content (Hertel, Neuhof, Theuer, & Kerr, 2000), having participants read a series of increasingly positive or negative statements (e.g., Velten statements; Velten, 1968), or having participants recall and vividly describe recent happy, sad, or neutral events (Grawitch, Munz, Elliot et al., 2003; Grawitch, Munz, & Kramer, 2003).
Grawitch, Munz, and Kramer (2003), for example, had individuals imagine an event from their recent past that put them into a good mood or into a bad mood. Participants in a neutral condition imagined a neutral scenario (wandering through a grocery store). Hertel et al. (2000) showed participants video tapes in order to induce positive or negative moods. In the positive mood condition, participants watched a humorous video clip about ostriches roaming through the African steppe. Participants in the negative mood condition watched a sad video clip about a cruel killing of a tiger. Positive and negative music played in the background to support the intended emotional effects. Similarly, Sy et al. (2005) had participants in a positive mood condition view a humorous clip of David Letterman, whereas participants in the negative mood condition viewed part of a TV documentary about social injustice and aggression.
In our own recent research, we have often used film clips as a source of mood induction. We use a cover story such that participants are asked to rate various qualities of the video clip, including its vividness, memorability, and quality, to help us for an upcoming study. Then participants watch a series of short video clips and fill out rating forms in between each clip. For example, in Spoor and Kelly (2009; see also Jones & Kelly, 2009), participants watched a total of three clips (approximately seven minutes each). Everyone first watched a neutralizing clip from The Mosquito Coast, because we have found that the mood induction is more effective if all participants first watch a neutral movie scene. Using a neutral clip also serves to make the purpose of the procedure (to induce positive or negative moods) less obvious. Participants in the positive mood condition then watched humorous clips from Good Morning, Vietnam and Ferris Bueller's Day Off. Participants in the negative mood condition watched sad clips from Sophie's Choice and Terms of Endearment. The factors that participants rate each clip on can also be used to rule out possible alternative explanations for some mood results. For example, depending on one's research question, it might be important to show that the positive and negative clips were equally attended to or were equally arousing, and so the rating forms for the clips can include questions concerning attention and arousal. Finally, we follow the video clip rating task with a brief questionnaire, purportedly about impressions of the upcoming group interaction, in which we embed manipulation check questions designed to assess the participants’ current mood states (see section on ‘Dependent measures’ later in this chapter).
The preceding manipulations are generally conducted at the individual level. Even though group members may experience the manipulation in the same room, each group member individually recalls a recent happy experience or watches clips of happy or sad movie scenes. For some research questions, it might be desirable for group members to enter the group situation in different moods, and therefore individual mood inductions are required. For example, Spoor and Kelly (2009) deliberately paired together dyad members where one person was in a happy mood and one was in a sad mood in order to examine mood convergence. Tiedens et al. (2004) have suggested that creativity may be higher in groups where there is diversity in affect across members, and to test this hypothesis empirically, affectively diverse group members would need to be created.
Nevertheless, for other research questions, it might be desirable for the group as a whole to experience a mood induction. For example, it may be particularly important for all group members to be in a similar or shared mood state if one is interested in questions of how group mood might affect task strategies or information exchange. Kelly (2003) proposed a group-level manipulation in which group members discussed neutral or uplifting topics before engaging in the primary task. Specifically, we had groups complete a ranking task, in which they rank ordered a series of humorous insurance claims (e.g., “The pedestrian had no idea which way to go, so I ran over him”) in order of their humorousness, a task which generally results in much laughter by group members. This group-level manipulation produced smaller but reliable differences in participants’ perceptions of the group's affective state. That is, when asked about their perception of how their group felt, rather than how they themselves felt, members of groups who had engaged in the uplifting task (rank ordering the humorous statements) perceived their group to be in a more positive mood than did members of groups who engaged in a neutral task (rank ordering neutral statements).
Barsade (2002) used a group-level manipulation of mood in an experiment exploring emotional contagion processes in groups. Specifically, she introduced a confederate into a group who was trained to portray pleasant or unpleasant, high- or low-energy emotions. For example, in the pleasant/high-energy condition, the confederate's behavior was characterized as acting pleasant, happy, warm, and optimistic in an energetic, active, and alert way. In contrast, the confederate in the unpleasant/low-energy condition was characterized as acting unpleasant and unhappy in a low-energy way. The mood of the groups in which they interacted was altered due to the transfer of mood (emotional contagion) from the confederate to other group members.
Other potential group-level manipulations of affect could be developed. Groups could be asked to engage in fun or boring tasks. For example, groups could engage in a game of charades or Pictionary, both of which are generally enjoyable, in order to induce a positive mood in the group as a whole. Neutral mood might be induced by engaging the group in a more serious task, such as recalling all US Presidents in chronological order. Groups with a history might be asked to discuss recent or negative events that have impacted the group as a whole. For example, task groups might be asked to recall a recent success or failure, and friendship groups might be asked to recall a recent pleasurable outing or falling out within the group. It should be noted, however, that group-level manipulations of negative mood may be complicated by the fact that group interaction often leads to increases in positive mood (Hinsz, Park, & Sjomeling, 2004). Manipulations of group-level emotions may also be somewhat difficult, as it might be challenging to create an intense emotional reaction in a laboratory setting. However, an emotional reaction might be produced by providing the group with a success or failure experience, or false feedback indicating success or failure. In addition, intergroup emotions, such as group pride, envy, or shame, might be induced by introducing some kind of intergroup competition or conflict.
How to observe mood and emotional processes in groups
The moods and emotions that group members experience can also be an index of group process. That is, the manner in which group members experience moods and emotions can mediate the relationship between group inputs and outcomes. One such process is emotional contagion, which is thought to occur in two stages. First, emotional expressions, including facial, postural, and vocal features, are automatically mimicked by interacting partners. Second, afferent feedback from facial, postural, or vocal expression produces the emotional experience in the mimicking partner (Duclos et al., 1989; Strack, Martin, & Stepper, 1988). The mimicry processes of the first stage have been observed in a number of studies including Chartrand and Bargh's (1999) study of smiling, Lakin, Chartrand, and Arkin's (2008) study of foot tapping in response to social exclusion, and Neumann and Strack's (2000) study of vocal mimicry.
Consistent with the notion of moods and emotions as a process, Spoor and Kelly (2004) suggested that mood convergence within groups can improve their ability to coordinate efforts and therefore improve performance. For example, Sy, Cote, and Saavedra (2005) found that group members who had positive mood leaders experienced more positive moods (through contagion processes), and group members who had negative mood leaders experienced more negative moods. More importantly, positive group affective tone fully mediated, and negative group affective tone partially mediated, the relationship between leader mood and group coordination. Barsade (2002) also found that positive emotional contagion was associated with improved cooperation, decreased conflict, and increased perceived task performance in groups.
How can the affective processes of group members be observed and recorded? A number of systems exist for coding nonverbal behavior in humans; see Harrigan, Rosenthal, and Scherer (2005) for a comprehensive review. It is fairly straightforward to train observers to code simple nonverbal behaviors exhibited by group members to an adequate degree of reliability (see Hall, 2010). For example, generally observers can accurately code the degree of smiling among group members. More complicated systems for coding facial expressions exist (see e.g., the Facial Action Coding System; Ekman & Rosenberg, 1997), but such systems are probably too cumbersome and time consuming for use in group research.
Alternatively, Bartel and Saavedra (2000) have shown evidence that group mood can be assessed more globally and can be reliably measured by those outside of the group through observation. They have developed a “Guide to Work Group Mood” that consists of a set of behavioral indicators of eight mood categories that comprise the mood circumplex (see further description of the mood circumplex in the later section on dependent measures). In their study, observers familiarized themselves with the guide, observed a work group actively engaged in a task, and indicated when any of the behavioral indicators of mood were exhibited by group members. Teams of observers then discussed to consensus the degree to which groups experienced each of the eight mood categories on scales from 1 (not at all) to 6 (a great deal). Bartel and Saavedra (2000) reported significant positive correlations between group members’ self-reports and observer ratings of work group mood, validating their group mood assessment instrument as an accurate indicator of the group's mood.
Barsade (2002) also used observers to record emotional contagion processes in groups. Barsade first trained coders to recognize affective states using the list of behaviors that indicate work group mood provided by Bartel and Saavedra's (2000) work group mood system described above. Barsade's coders then recorded the emotional contagion process in her groups by watching participants’ facial expressions, body language, and tone of voice throughout the experimental session and rating the level of each participant's pleasant mood every two minutes (at the sound of a beep) on a five-point scale ranging from 1 (not at all) to 5 (very much). Consistent with an emotional contagion process, coders rated the participants who interacted with a pleasant confederate as more positive than those who interacted with an unpleasant confederate.
In addition to the growing research that involves observations of nonverbal behavior that are indicative of affective states, there is the older tradition of interaction process analysis that has been used to assess social–emotional aspects of group interaction (Bales, 1950; Kelly, 2000; Ridgeway, 1994). Interaction Process Analysis is a technique whereby observers are trained to code the verbal acts of group members during interaction. These verbal acts are then coded into categories that are theoretically meaningful for the research question at hand. In its traditional form as proposed by Bales (1950), verbal acts are coded into categories reflecting task activities (e.g., gives suggestions, asks for orientation) or socio-emotional activities (e.g., seems friendly, disagrees). Bales (1950) proposed that socio-emotional communication serves to support task activities and that the two forms of communication operate in equilibrium. In a more recent examination of process that focused more specifically on socio-emotional (relational) communication, Keyton (2009) examined the role of relational communication in a cancer support group. In particular, she examined the type of communication that preceded and followed relational communication in order to understand more fully the function of the relational act (e.g., agreement to confirm, agreement to signal identification, disagreement). These acts can change the communication atmosphere of a group, both positively and negatively.
How to measure moods and emotions as an outcome or DV
The field of group research seems to be focused on task outcomes such as productivity and efficiency. With a few exceptions (e.g., Grawitch, Block, & Ratner, 2005), little research to date has looked at affective outcomes to group processes. Different group processes (e.g., conflict, cooperation, coordination failures or successes) probably lead to different emotions, and these emotions may vary across group members. Imagine how conflict during a group meeting can make members angry, or how a complete group failure can lead to group members feeling miserable and depressed. Furthermore, groups characterized by different affective states may approach future tasks differently. For example, Grawitch and Munz (2005) argue that positive emotions lead groups to adopt a promotion focus, while negative emotions lead groups to assume a prevention focus. At the individual level, emotions such as anger can lead people to feel stronger and more powerful, resulting in riskier behavior (Lerner & Keltner, 2001). These individual tendencies combined with other group dynamics could dramatically shape group processes.
Browsing the existing literature on affect in groups, one can see that mood and emotions have been measured in a number of ways. Think of the last time someone asked you, “How do you feel?” Perhaps you answered with a simple and general statement such as “bad” or “happy.” Some research has used straightforward scales assessing the experiences of positive and negative feelings (e.g., Jones & Kelly, 2009). These scales ask group members to respond to a given statement (e.g., “What is your group's current mood?”) on a Likert-type scale anchored with a negatively valenced word on one end (e.g., negative, sad) and a positively valenced word on the other end (e.g., positive, happy). However, other measures of mood and emotions allow the experiences of positive and negative affect to be orthogonal or independent of one another. Consequently, positive and negative emotions are measured individually. For example, Grawitch et al. (2005) asked group members to recall experiences that produced a variety of different emotions (e.g., eager, excited, tense, worried). Likewise, when completing the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), participants rate the extent to which each of 20 words (e.g., upset, scared, excited, interested) reflect their feelings in the specified time frame (e.g., right now, today, in general). These ratings are indexed into scores for positive affect and negative affect, and are assumed to be independent.
The PANAS focuses only on emotions characterized by high arousal: those affective experiences characterized by high physiological stimulation. For example, imagine the increased heart rate associated with the excitement of your team successfully completing a challenging task or the increased blood pressure correlated with an angry feud with fellow group members. In contrast to the PANAS, other measures of affect differentiate between emotions on two independent dimensions: valence (positive vs. negative) and arousal (high vs. low). For instance, experiences of negative emotions are not all equal. In a bad economy, a work team may experience gloom as the financial health of their company declines or as they see fellow co-workers laid off. Although the work team's feelings are clearly negative, the physiological experience of gloom is more subdued. In contrast, consider a situation where the team members are all afraid that their team will be dismantled because the company is downsizing. Such an experience is not only negative, but also characterized by more physiological arousal as the team members feel stress and ruminate on their fears. Models of affect that differentiate between valence and arousal are called circumplex models, and such models have been proposed by Russell (1980) and Feldman Barrett (2004). The Brief Mood Introspection Scale (BMIS; Mayer & Gaschke, 1988) allows researchers to measure these two dimensions with only 16 questions.
We should note that, at the individual level, the arousal dimension of emotional states can be assessed through physiological measures. Typical measures include skin conductivity levels, facial electromyography, skin temperature, breathing frequency, and pulse frequency (Ekman, Levenson, & Friesen, 1983). However, we feel that it is unlikely that such measures will have much application for emotion assessment at the group level, given that the leads and connections that are used to monitor these responses are likely to interfere with interaction among group members.
When deciding which method to use for measuring moods and emotions, researchers should balance practical and theoretical concerns. Simple positive– negative scales may be best when brevity is important. In particular, when investigating moods and emotions as outcomes in field settings, researchers may face limits on the number of questions they can ask or may encounter time constraints for respondents. As previously described, to avoid tipping off their participants to the purpose of a laboratory study, researchers may also wish to use a few short scales embedded in other questions, rather than a whole series of questions related to moods and emotions. Conversely, theory may lead to predictions about differential outcomes for positive and negative affect or high and low affective arousal. Therefore, using longer scales that reliably measure these dimensions may be necessary. For example, Grawitch and Munz (2005) suggested that unsuccessful group procedures should lead to, above all, greater negative affective experiences. Moreover, one might be interested in the implications of groups characterized by high vs. low arousal or positive vs. negative affective states, which can differentially influence whether groups are promotion or prevention focused (Grawitch & Munz, 2005; Park & Hinsz, 2006). Although Barsade (2002) predicted that high-arousal emotions would transfer more easily than low-arousal emotions, no such differences occurred. Nevertheless, researchers may be interested in predicting contextual factors that could produce these differences, in which case using a scale that differentiated between high and low arousal affect would be necessary.
When measuring mood in groups, it is important to define what exactly constitutes group mood. At the individual level, the focus is on the emotional experience of one person. However, when groups are the unit of focus, one has to consider the emotional experience of multiple people. Imagine Group A whose three members report an emotional experience of 5, 5, and 5 on a seven-point scale. Consider Group B whose three members describe their individual emotional experiences as 3, 5, and 7. One way to conceptualize group mood is by calculating the mean level of its group members’ affective experiences (George, 1990). In the aforementioned Groups A and B, the mean level affect of both groups is 5. Nevertheless, a glance at the individual experiences of group members suggests a different climate in each group. In Group A, all group members shared the same emotional experience. However, in Group B, the emotional experiences of group members varied widely, with one group member experiencing elation (7) and another feeling relatively negative (3). As such, measures of agreement and disagreement of group members are an important consideration when calculating a group's mood and emotions.
Methods for assessing group mood have been recently developed. George (1990, 1996) argued that high levels of intermember consistency are necessary for group affective tone to exist. If such consistency exists, then individual-level reports of affect may be combined into a group average that reflects the group's affective tone. In the case of low intermember consistency, an affective tone does not exist for that particular group. To index within-group agreement on a single target (e.g., group mood), a measure of interrater agreement such as rWG should be used. To address limitations with rWG, Brown and Hauenstein (2005) proposed an alternative method of calculating interrater agreement called aWG [See LeBreton and Senter (2008) for instructions on calculating these indices and for a discussion of how these measures differ from each other and from measures of interrater reliability]. For the previously mentioned groups, Group A would have a high rWG value, whereas Group B would not.
Although researchers may be interested in group members’ shared affect, they may also wish to consider the dispersion or diversity of affective experiences of group members (e.g., Barsade, Ward, Turner, & Sonnenfeld, 2000). Members of Group A experienced a homogeneous emotional tone, but Group B's experience was more heterogeneous. Tiedens et al. (2004) discuss several reasons why group members, such as those in fictitious Group B, may not have similar emotional responses. They note that group characteristics such as low cohesiveness could reduce a group's shared affective tone. In addition, norms for expressed affective similarity may vary in strength and some groups may actually be explicit about a desire for emotional heterogeneity. A focus on affective diversity could be used to examine development of shared affective tone over time or to determine antecedents of homogeneous vs. heterogeneous affective experiences in groups. Moreover, dispersion in affective experiences has implications for subsequent group processes (Tiedens et al., 2004). Rather then the aforementioned indices of agreement, calculating SDX is more appropriate for dispersion composition models (Chan, 1998; LeBreton & Senter, 2008).
An alternative to considering dispersion composition may be to examine minimum and maximum levels of individual group members’ affect. Investigating personality composition, Halfhill, Nielsen, and Sundstrom (2008) showed that minimum scores on conscientiousness and agreeableness predicted group performance better than a group's mean level on these dimensions. Consequently, researchers may be interested in the maximum and minimum levels of group members’ affect, because of possible implications for later group interaction. For example, consider the effect that a single “toxic” group member with high negative affect and low positive affect could have on a group. Likewise, imagine the potential impact on a group's morale by a group member with high positive affect – bubbly and upbeat. Regardless of the group mean, groups with members at either extreme could greatly impact the group's overall performance and effectiveness.
Strengths and Weaknesses
Laboratory and field settings
To date, the research that has been conducted in this field consists of both laboratory and field-setting approaches. For example, in studies of emotional contagion or convergence, some have taken place in laboratory settings (e.g., Barsade, 2002; Spoor & Kelly, 2009; Sy et al., 2005; Chartrand & Bargh, 1999), whereas others have taken place in naturalistic settings (Totterdell, 2000; Totterdell et al., 1998). Given that there are strengths and weaknesses inherent in both settings, this multisetting approach is likely to provide a stronger set of research findings than if only one setting were utilized. It should be noted that our review of methods for manipulating mood tended to focus on techniques that would be used in laboratory settings. However, most of the process measures and dependent measures could be used equally well in laboratory and field settings.
The duration of affective experiences
One caveat that was mentioned previously concerning research in this area is that affective experiences, and especially intense emotional experiences, might be somewhat short in duration. As Hinsz, Park, and Sjomeling (2004) found, the simple experience of interacting in a group can alleviate negative moods. Therefore, when exploring questions of affect and group performance, and especially when using an approach where the mood states of group members are manipulated, the duration of the affective experience needs to be accounted for carefully. A host of mood-regulation processes can occur in groups (George, 2002; Kelly & Barsade, 2001; Kelly & Spoor, 2006) such that initial affective inputs can be quickly changed. Therefore, it is important to make periodic assessments of mood over time, or to include additional induction reminders in order to assure that mood states are still intact. For example, Bramesfeld and Gasper (2008) had participants engage in a mood-refresher task, where participants were asked to write for one minute about the most memorable aspect of the film clip that they were shown (the original mood manipulation) and then were asked to report how happy or sad they felt at this moment on a seven-point scale.
The dynamic nature of group affect
Another way of framing the duration question is in terms of the dynamics of affect in groups. In groups interacting over time, affective processes are likely to be dynamic and cyclical in nature. Affective inputs are altered by affect regulation processes in groups (e.g., emotional contagion, norms concerning affective display, etc.) resulting in new levels of affect or affective compositions. In addition, groups may experience sudden outcomes (e.g., success, failure) that can immediately alter the affect of a group. In order to understand affective processes in groups fully, future research will need to consider the dynamic nature of those processes.
Latest Innovations
Given that this is a relatively recent field of inquiry, the development of almost any technique for manipulating moods and emotions in groups, or for measuring affective group processes or outcomes can be considered an innovation. There are, however, a few innovative technologies that we have come across recently that we think could be adapted for use to study affective processes in groups.
One such technology involves a method for assessing online changes in mood over time. This technique has previously been used to assess moment to moment changes in people's attitudinal reactions to persuasive messages (Keim, 2008), and other moment to moment states. The technology involves a dial that can be turned up or down as the participant feels changes in some subjective affective state. In a recent application (Wirth, Wesselmann, Williams, & Mroczek, 2008), the technology was adapted so that it could be used to assess online changes in mood over time. In this new application, Wirth et al. trained participants to calibrate their use of the dial to changes in mood using short training exercises in which participants imagined themselves in various positive or negative scenarios. Participants turned the dial clockwise to record positive changes in mood and turned the dial counter-clockwise to indicate negative changes in mood. Participants then engaged in a game of Cyberball (see Wirth, Feldberg, Schouton, van den Hoof, & Williams, this volume), a virtual ball-toss game during which they were either included or excluded (ostracized) by two other (computerized) participants who also were supposedly playing the game. During the game, participants were instructed to turn the dial in accordance to how they felt during the Cyberball game. Dial values were converted to numeric form and could be graphically displayed. In Wirth et al. (2008), the dial output dramatically and graphically showed the tremendous negative impact of being ostracized, even under these relatively minimal conditions. We are particularly intrigued by the possibility of using such a dial technology to examine emotional contagion or mood convergence in groups.
A second device involves an existing piece of equipment called a video quadraplexor. A quadraplexor is a device that can take several video inputs and display them on a single screen. The most common Currently, use of the device is for surveillance, where input from several cameras focused on different areas can be viewed simultaneously on the same screen. We believe that this technology could be adapted for use in observing emotional contagion processes in groups, as well as other affective processes. For example, separate cameras could capture images of each of several group members working together on a task, and these images could be displayed (and recorded) together on a single screen. Trained coders could then make judgments about the level of affect displayed by the group members, or the degree to which group members displayed similar emotional expressions.
Finally, there is recent research that investigates the use of emoticons in computer-mediated interaction (Lo, 2008). This research demonstrates that Internet users use emoticons to enhance their communications, and specifically that emoticons are used similarly to other nonverbal cues to communicate emotions. In fact, emoticons have been referred to as quasi-nonverbal cues (Lo, 2008). An examination of the pattern of use of emoticons in computer-mediated communication may be an important clue to emotional exchanges in this medium.
Ethical Issues and Concerns
Although research on moods and emotions in groups has important implications for group processes and performance, there are important ethical issues to consider, especially when exploring moods and emotions as independent variables in laboratory research. The first issue has to do with how participants might react to a mood manipulation. The second issue concerns subsequent results of the mood manipulation after the experiment is complete. A final issue concerns the use of deception in some manipulations of affective states. Feedback from an institution's IRB may lead researchers to address these issues, but researchers should also actively consider the implications of their mood manipulations and use materials with which they feel comfortable. These concerns primarily deal with inducing negative mood.
First, researchers need to balance their desire to use a strong manipulation in order to test their theory with concerns about how participants might react to a particular mood induction. Clearly, a very strong negative mood manipulation will leave researchers with little doubt about the effectiveness of their manipulation, increasing the chances of finding significant results. However, even when mood inductions use mundane materials (e.g., Hollywood movies), it can be difficult to predict how any one person will respond. Each person comes with their own unique history. While a vast majority may not be affected by the mood manipulation in any extreme way, there is a chance that some individuals will. For example, a clip that shows the tragic outcomes of a traffic accident may be sad to a majority of participants, but traumatic for a participant who survived or caused such an accident (or knows someone else who did). On some of our consent forms, we have a statement to help in these circumstances: “If you experience anything during the experiment that disturbs you or makes you recall something that you would like to speak with someone about, there are services available at. …” It is also possible that particular individuals may be reluctant to show evidence of experiencing a strong emotion due to norms of emotional constraint in the situation or due to personal norms about emotional expression. Inducing a strong emotional experience in such a person may be a particularly negative experience if it violates personal or social standards.
Second, at the completion of the experiment, the manipulated mood could continue to have an effect on the participant. Having experienced negative moods and emotions, participants may be more easily provoked or more likely to interpret ambiguous stimuli in a negative way. Being in a negative mood could also be a burden for someone who has had a bad day. For these reasons, before they leave the lab, all of our participants who experienced a negative mood induction complete a task designed to lift their mood. Sometimes, we have participants complete the insurance ranking task that we described in the manipulation section. Other times, we have had participants watch a segment from Jay Leno involving a phony photo booth. In this clip, a photo booth makes bizarre requests of people at an amusement park before they have a free picture taken. For example, the photo booth may be concerned about someone's drink and therefore says that the liquid is interfering with its camera. The person places the drink outside the booth, but the photo booth says the liquid is still too close. This continues a few more times until the person places the drink in the middle of the street. At that point, the photo booth says the liquid is too far away. The person looks down in dismay and the picture is taken. Our participants laugh out loud at these vignettes and it gives the experimenter a mood induction of their own to see the groups of participants laughing so hysterically. Finally, we should also note that some of the techniques that we have recommended for manipulating moods and emotions involve the use of deception. For example, we suggested that the mood of a group could be manipulated by providing the group with false feedback concerning their success or failure on a task. As with all research, the researcher must carefully weigh the necessity of the deception for the purposes of the research against the subsequent potential for resentment and anger on the part of research participants when they are informed of the deception. Deception should be avoided when possible, but if used, it is the responsibility of the researcher to debrief the participants carefully on the necessity of the procedures that were used.
Recommendations
When conducting research on mood and emotion, researchers may want to consider several issues when designing their research, to maximize the effectiveness of manipulations and the knowledge gained from their studies. First, one challenge is for researchers to manipulate moods that last the entire duration of the experiment. As previously mentioned, group interactions tend to increase positive affect (Hinsz, Park, & Sjomeling, 2004), which poses a challenge to maintaining a group's negative mood. Moreover, the task itself can interfere with the experienced mood. For instance, we developed a task where groups watched either a positive or negative movie clip and then ordered still images from the movie in sequence (Jones & Kelly, 2008). One challenge when creating this task was to find a proper task difficulty. The task needed to be difficult enough to allow for variability on performance scores, which was the main dependent measure. However, it could not be so difficult that groups were frustrated or bored and failed to notice the humorous or disturbing imagery in the stills.
Second, the use of proper control groups in research on mood and emotion will provide information about whether it is positive mood, negative mood, or both moods that impact group processes and performance. In research at the individual level, a neutral mood, generally defined as a mood state where the mean falls in between positive and negative moods, is often induced as a control condition (e.g., Hirt, Melton, McDonald, & Harackiewicz, 1996), although one might use a no-induced mood condition as a control instead. When examining mood and emotions in groups, given the importance of consensus when aggregating individual group members’ responses to calculate group affective tone, it is strongly recommended that a neutral mood is induced, similar to negative and positive conditions (e.g., Grawitch et al., 2003), so that all group members are in similarly neutral moods.
Third, researchers may wish to not only consider how mood and emotion influence group processes themselves, but also how they can change the relationship between individual and group performance. For instance, research on social loafing shows that individuals working separately often outperform the same number of individuals working together as a group (Latane, Williams, & Harkins, 1979). Similarly, interacting groups often recall fewer words (Basden, Basden, Bryner, & Thomas, 1997) and are less creative (Mullen, Johnson, & Salas, 1991) than nominal groups. Different moods and emotions may exacerbate or attenuate these performance differences between individuals and groups (e.g., Jones & Kelly, 2009).
Conclusion
We have shown that moods and emotions can be studied in a variety of settings, such as the field and laboratory, and can be examined in a variety of ways, including as an independent variable, a mediator, and a dependent variable. Our hope is that this chapter will provide a set of techniques for future researchers who are interested in the many ways in which moods and emotions can influence groups.
It is exciting to participate in a growing area of research such as this one. As research findings reach a critical mass, more sophisticated theories can be developed to describe and predict the influence of moods and emotions on group process and performance. We hope that this chapter will serve to stimulate such future research.
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