217

British Journal of Psychology (2015), 106, 217–234 © 2014 The British Psychological Society www.wileyonlinelibrary.com

The magic of collective emotional intelligence in learning groups: No guys needed for the spell! Petru L. Cursßeu1*, Helen Pluut1, Smaranda Borosß2 and Nicoleta Meslec1 1 2

Department of Organisation Studies, Tilburg University, The Netherlands Vlerick Business School, Brussels, Belgium Using a cross-lagged design, the present study tests an integrative model of emergent collective emotions in learning groups. Our results indicate that the percentage of women in the group fosters the emergence of collective emotional intelligence, which in turn stimulates social integration within groups (increases group cohesion and reduces relationship conflict) and the associated affective similarity, with beneficial effects for group effectiveness.

Groups are social systems with cognitive and emotional emergent properties. Although traditionally emotions have been conceptualized as a hallmark of individuals (Frijda, 1986; Mayer, Roberts, & Barsade, 2008), during the last decades substantial conceptual work has been devoted to understanding group emotionality as an emergent group-level property (Barsade & Gibson, 1998; Walter & Bruch, 2008). In the group emotions literature, of particular importance are the emergence of collective emotions (i.e., affective similarity) and collective emotional competencies (i.e., collective emotional intelligence [CEI]). Group cognition research shows that gender diversity is conducive for the emergence of group rationality (Cursßeu, Jansen, & Chappin, 2013) and group cognitive complexity (Cursßeu, Schruijer, & Borosß, 2007), while the percentage of women in the group is positively correlated with collective intelligence (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). In this context, as group scholars devoted substantial conceptual work to explore emotional intelligence as an emergent group-level competence (Ayoko, Callan, & H€artel, 2008; Druskat & Wolff, 2001; Jordan & Troth, 2004; Koman & Wolff, 2008), it becomes important to better understand the role of gender differences in groups for the emergence of CEI. Collective emotional intelligence is defined as the ability of a group to develop a set of norms that promote awareness and regulation of member and group emotions (Druskat & Wolff, 2001). Therefore, CEI is a group-level competence that emerges from the integration of individual competencies through interpersonal interactions. Due to their higher social sensitivity and relational orientation, women promote the emergence of collective competencies by facilitating the coordination of individual competencies during social interactions (Cursßeu et al., 2013; Woolley et al., 2010). Thus, the first aim of our study is to test the relation between the percentage of women group members and the emergent CEI in a group.

*Correspondence should be addressed to Petru L. Cursßeu, Department of Organisation Studies, Tilburg University, Room 2.105, Warandelaan 2, PO box 90153, 5000 LE Tilburg, The Netherlands (email: [email protected]). DOI:10.1111/bjop.12075

218

Petru L. Cursßeu et al.

Studies on collective emotions have focused mainly on the valence of group affect rather than the level of affective similarity within the group. In trying to explain the causal relations between the emergence of affective similarity and group dynamics, recent models portray an affective spiral (Walter & Bruch, 2008). On the one hand, high quality of social interactions in groups is conducive to positive affective similarity, or the degree to which group members align their positive individual affects. This effect is explained by processes such as interaction synchrony (the non-conscious process by which one individual’s behaviour is adjusted to coordinate or synchronize with another) and emotional contagion (the process where the affective states of one individual are transferred to nearby individuals) (Kelly & Barsade, 2001; Walter & Bruch, 2008). On the other hand, affective similarity generates interpersonal attraction and as such is conducive to positive interpersonal relations. Emergent group emotions are therefore conceptualized both as antecedents and consequences of interpersonal interactions (Walter & Bruch, 2008). Thus, a second aim of our research is to answer the call for further research on affective similarity in groups (Collins, Lawrence, Troth, & Jordan, 2013) and test these two proposed causal directions and specify more clearly the dynamics between affective similarity and teamwork quality (as indicated by cohesion and relationship conflict). To conclude, the current article aims to test a comprehensive socio-emotional model of groups that integrates both collective emotional competencies and affective similarity as they relate to quality of interpersonal relations and effectiveness in groups and as such uncovers the mechanisms through which the percentage of women in groups contributes to group effectiveness.

Theory and hypotheses Collective emotional intelligence is defined as the ability of a group to develop a set of norms that encourage expression, awareness, and regulation of the affective dynamics within the group, improving the ability of group members to work together effectively (Druskat & Wolff, 2001). Currently there are two main perspectives in the approach to CEI: one sees CEI as the sum of the individual emotional intelligence resources members bring to the table (Jordan & Troth, 2004), while the other refers to “the degree of emotional intelligence group members appear to use when they interact with each other [. . .], a set of norms or patterns about the way people behave with each other” (Elfenbein, 2006, p. 166). While the first perspective focuses on the individual resources brought by group members, the second capitalizes on the style of interacting that a group uses (i.e., the emotional quality of interactions in the group context). It focuses on how much emotional intelligence is displayed and actually used in interactions among group members, rather than the fixed individual attributes of teammates, as a predictor of group performance (Druskat & Wolff, 2001; Elfenbein, 2006). Previous research systematically points towards gender differences in emotional experience (Wegge, van Dick, Fisher, West, & Dawson, 2006), as well as emotional expression, awareness, and regulation (Ciarrochi, Caputi, & Mayer, 2003), with women scoring higher in emotional intelligence (Mandell & Pherwani, 2003). With respect to the style of interacting used in groups, women have higher social sensitivity than men (Hall, 1978). Recent studies indicate that due to women’s higher social sensitivity, the proportion of women in the group has a positive influence on the emergence of collective intelligence (Woolley et al., 2010). Women also consistently score higher on measures of communal traits. They are caring, emotionally aware, oriented towards maintaining good relations (Abele, 2003; Twenge, 1997), and display less direct aggressive reactions

Collective emotional intelligence in groups

219

(Lawrence & Hutchinson, 2014), which makes them focus on maintaining a good group atmosphere and catalyses the formation of norms that foster emotional awareness and regulation in the group. Hence, the heightened communal orientation in women is likely to be conducive to the creation of emotionally intelligent norms in the groups they belong to. We contend that women’s participation in a group (i.e., the percentage of women in the group) improves CEI through the emotional resources they bring to the group (developed individual emotional intelligence) as well as the interaction norms they help to develop in the group that support the awareness and regulation of group affect (due to their social sensitivity and other communal traits). In line with the arguments of women’s contribution to both the compositional and interactional aspect of CEI, we hypothesize that the proportion of women in the group is positively related to the emergence of CEI. Hypothesis 1:

The proportion of women in the group has a positive effect on the emergence of collective emotional intelligence.

Emotions are at the core of social interactions in groups and therefore influence the way members work together. Studies indicate that groups developing norms to better deal with affective dynamics (i.e., develop CEI) are more effective in the long run (Wolff, Druskat, Koman, & Messer, 2006). Quality of social interactions tends to improve when emotions are managed appropriately (Lopes, Salovey, C^ ote, & Beers, 2005), and emotionally intelligent groups are therefore more effective due to improved teamwork (Druskat & Wolff, 2001). Two salient indicators for the quality of the intragroup relationships are group cohesion (as a positive indicator) and relationship conflict (as a negative indicator). Group cohesion reflects the social integration in small groups, in other words the force that keeps the group together (Evans & Jarvis, 1980). At the individual level, studies have shown that emotional intelligence of group members promotes group cohesion (Prati, Douglas, Ferris, Ammeter, & Buckley, 2003; Troth, Jordan, & Lawrence, 2012), which is further beneficial for group effectiveness (Beal, Cohen, Burke, & McLendon, 2003). However, studies investigating emotional intelligence as a group property and its impact upon social cohesion are rather scant. We posit that CEI is likely to enhance the cohesion in a group, as the development of norms that encourage awareness and regulation of affective dynamics within a group creates an atmosphere of trust and psychological safety which further enriches the social capital of the group (Druskat & Wolff, 2001). At the same time, CEI may reduce the likelihood that groups suffer from relationship conflict (Yang & Mossholder, 2004). Relationship conflict refers to interpersonal frictions and disagreements experienced by group members, and it has been systematically linked to impaired group performance (De Dreu & Weingart, 2003). Collective emotional intelligence enables groups to devise creative solutions to disagreements and avoid escalating these conflicts (George, 2000) as well as the transformation of task into relationship conflict (Cursßeu, Borosß, & Oerlemans, 2012; Van Den Berg, Cursßeu, & Meeus, 2014). Appraising the various emotions occurring in the group and understanding their full meaning allows group members to recognize in time the downward emotional spirals of conflict and act to prevent their potential damage (Yang & Mossholder, 2004). Furthermore, when conflict occurs, these groups are more successful in comprehending the emotions arising from it (Ayoko et al., 2008), and hence not giving way to the misattributions that lead to the escalation and transformation of conflict (Simons & Peterson, 2000). Thus, we expect CEI

220

Petru L. Cursßeu et al.

to positively impact on group effectiveness through increased group cohesion and reduced relationship conflict. Hypothesis 2a:

Cohesion mediates the influence of collective emotional intelligence on group effectiveness.

Hypothesis 2b:

Relationship conflict mediates the influence of collective emotional intelligence on group effectiveness.

Building on previous conceptualizations of emergent group emotions (George, 1990; Kelly & Barsade, 2001), Walter and Bruch (2008) put forward the positive group affect spiral model centred on the reciprocal interdependence between the quality of interpersonal relationships in the group and positive affective similarity. On the one hand, they argue, group relationship quality promotes affective sharing through emotional contagion, empathy, and emotional comparison. On the other hand, by promoting mechanisms of similarity-attraction, positive affective similarity reduces the incidence of conflict and promotes cooperation and trust, ultimately fostering social integration. The question that we raise in our study is whether both mechanisms are valid. In other words, we set out to explore whether affective similarity is an accurate predictor for the quality of interpersonal relations in groups or whether the quality of social interactions leads to emergent affect similarity. Group affect is a rather transient state (Smith, Seger, & Mackie, 2007; Watson, 2000) that can fluctuate with individual moods and emotions. We therefore argue that the emergence of affective similarity varies with the quality of intragroup relationships (in our case indicated by group cohesion and relationship conflict) rather than determining it. Due to the high degree of social interdependence in highly cohesive groups, social influence processes at play in these groups (e.g., conformity pressure, contagion, etc.) are likely to enforce affective similarity. If group members are dependent on each other and motivated to stick to the group, as is the case in highly cohesive groups (Evans & Jarvis, 1980), they will engage in mood scrutinizing activities and will be motivated to adapt their emotional experience to the other group members (Bartel & Saveedra, 2000). Therefore, we expect that members of cohesive groups are more likely, as compared to members of non-cohesive groups, to recognize each other’s emotions, and more motivated to preserve a positive emotional climate in the group. On the other hand, when group members are engaged in relational frictions, they are not motivated to adapt their emotional experiences to the other group members and therefore we expect relationship conflict to inhibit the alignment of positive emotions within groups. The affective similarity that subsequently does or does not emerge has been found to positively impact on group performance (Barsade, Ward, Turner, & Sonnenfeld, 2000). Hypothesis 3a:

Group cohesion is positively associated with affective similarity, which in turn positively impacts on group effectiveness.

Hypothesis 3b:

Relationship conflict is negatively associated with affective similarity, which in turn positively impacts on group effectiveness.

To conclude, we set out to test an integrative model of group emotions in which we hypothesize that the percentage of women in the group (as a configural group property) is conducive for the emergence of CEI, which in turn fosters social integration within groups (stimulates the emergence of group cohesion and reduces the likelihood of relationship

Collective emotional intelligence in groups

221

conflict) and the associated affective similarity, with beneficial effects for group effectiveness.

Methods Sample and procedure Our study uses a cross-lagged design, variables being evaluated at two distinct moments in time. The sample consists of 528 (45.9% women) students enrolled at various courses (Organizational Behaviour, Organization Development, Organization Theory, and Research in Organizations) at a Dutch university. The respondents had an average age of 21.1 years old, and the vast majority of the students were Dutch (86.9%). All courses required students to form small workgroups for the entire duration of the course (i.e., group membership remained fixed), resulting in a total of 100 student groups ranging from three (we excluded groups with less than three members) to seven members (average group size of 5.0). Our data were collected as part of a collaborative research project on social networks of teams. In two different course workshops, students were asked to fill out questionnaires evaluating the variables included in the study. We developed both Dutch and English versions of the questionnaires and used back-translation to ensure that the Dutch version is accurate. Individual group members filled out the questionnaires, and grouplevel scores were aggregated from individual evaluations. The first questionnaire (T1) measured collective emotional intelligence, group mood, group cohesion, and relationship conflict. Also, to be able to control for the effects of interpersonal familiarity, at T1 each individual group member was asked to report with whom they had a friendship tie within their group. The within-group sum of friendship ties (all reported ties were included, bi-directional ties were coded as two uni-directional ties) at T1 was further used as a control variable. The second questionnaire (T2) assessed the same set of variables as well as group effectiveness. In addition, both questionnaires contained items related to demographical characteristics (age, gender, and nationality). The period between the two evaluation moments covered 4 weeks. This cross-lagged design allows us to test the causal sequencing of the study variables.

Measures Collective emotional intelligence Collective emotional intelligence was measured with a scale comprising two dimensions: emotional awareness and emotion regulation. Three items assessed emotional awareness in the group (e.g., ‘We knew how everyone felt just by looking at each other’) and five items were used to assess group emotion regulation (e.g., ‘We made each other feel better when we were down’). We selected these items from scales on group emotional awareness and emotion regulation that evaluate groups’ competence of identifying and regulating collective emotions (Cursßeu et al., 2012). Answers were recorded on a 5-point Likert scale ranging from 1 (=totally disagree) to 5 (=totally agree). The reliability analysis pointed to two problematic (emotion regulation) items, which were therefore excluded from the scale. The final scale consisted of six items in total (three for each dimension) and had Cronbach’s alphas of .736 and .741 for Time 1 and Time 2 respectively. The items were answered by each individual group member and aggregated at the group level for further analyses.

222

Petru L. Cursßeu et al.

Affective similarity To assess affective similarity in the group, we first measured group mood using a selfreport circumplex model (see e.g., Russell, 1980). This model categorizes mood along two dimensions: hedonic valence (pleasant – unpleasant) and arousal (high activation – low activation). Respondents were asked to rate the atmosphere in the group during their work together on a scale from 5 to +5 for both hedonic valence (5 = unpleasant, +5 = pleasant) and arousal (5 = low energy, +5 = high energy). Respondents reported only positive values on both dimensions of group mood, which correlate significantly and positively with q = .53 (p = .0001) at Time 1 and q = .32 (p = .001) at Time 2. This supports aggregation and we therefore took the mean of the ratings on the two dimensions to evaluate group mood. Affective similarity was then assessed by computing the within-group agreement index (Rwg) for this mean by using the following formula: h i rwgðJÞ h

J 1s2xj =r2E

i 

J 1s2xj =r2E þ s2xj =r2E

, where s2x is the mean of item variance within groups and r2EU is j

computed as: r2EU ¼ ðA2  1Þ=12 for an assumed uniform distribution (A is the number of intervals on the Likert scale used to evaluate each dimension in the circumplex). Rwg is an index developed for assessing agreement among the judgments made by a group of people with respect to a target, the estimators being sensitive to the similarity (among judges) on the rank orderings of the target ratings as well as to the differences in the level of each judge’s ratings (James, Demaree, & Wolf, 1993).

Group cohesion For the measurement of group cohesion, we selected two items on group integration related to the task (e.g., ‘Our team is united in trying to deliver high quality assignments’) and three items referring to social group integration (e.g., ‘The team members feel they belong to this team’) from the Group Environment Questionnaire (Blanchard, Poon, Rodgers, & Pinel, 2000). Answers were recorded on a 5-point Likert scale ranging from 1 (=totally disagree) to 5 (=totally agree) at an individual level and group means were further used for the analyses. Cronbach’s alphas for the scale were .682 and .739 for Time 1 and Time 2 respectively.

Relationship conflict Four items from the intragroup conflict scale (Jehn, 1995) were used to measure relationship conflict. Individual members were asked about their experience of relationship conflict in the group (e.g., ‘How often are personality conflicts evident in your team?’). Answers were recorded on a 5-point Likert scale (1 = very rarely/little, 5 = very often/much) at an individual level and group means were further used for the analyses. The Cronbach’s alpha for the scale was .720 at Time 1 and .781 at Time 2.

Group effectiveness Following Hackman (1986), we conceptualize group effectiveness as a composite measure of group performance, group viability, and group (member) satisfaction. We selected 18 items from a scale developed by Whelan (2007) for organizational settings and adapted these to a higher education setting. Using a 5-point Likert scale (1 = totally

Collective emotional intelligence in groups

223

disagree, 5 = totally agree), five items measured performance (e.g., ‘All team members participate in the process of goal setting for my team’), seven items measured viability (e.g., ‘Team members are willing to be flexible and perform different roles and jobs within the team’), and five items measured satisfaction (e.g., ‘Team members are generally satisfied to be working in this team’). The items were collected at an individual level and aggregated at a group level using the group mean. The Cronbach’s alpha for the scale was .877.

Reflections on emergent states and preliminary data analyses In our study, we focused on two emergent states (cohesion and relationship conflict) and one emergent group-level competence (CEI) and in line with the non-linearity often implied by emergence (Cursßeu, 2006), a simple additive aggregation of individuallevel scores into group-level variables is questionable. In line with this non-linearity argument, previous research indeed shows that a single negative interpersonal relation in a team shatters cohesion (De Jong, Cursßeu, & Leenders, 2014) and generates relationship conflict (Chen, Sharma, Edinger, Shapiro, & Farh, 2011). One bad apple can often spoil the whole barrel! In operational terms, it is therefore important to make sure that our measures accurately capture the higher order (team level) emergent states and competencies. The notion of emergent states in team research originates in Marks, Mathieu, and Zaccaro (2001) and it was further extended by Kozlowski in various instances (Kozlowski & Chao, 2012; Kozlowski, Chao, Grand, Braun, & Kuljanin, 2013). In the original conceptualization (Marks & Mathieu, 2001), emergent states were introduced to distinguish state-like team attributes from team processes and later on Kozlowski and colleagues distinguished between emergent states as compositional (convergent, homogeneous) and as compilation (divergent, heterogeneous) phenomena (Kozlowski & Chao, 2012; Kozlowski et al., 2013). In a compositional sense, emergent states at the group level reflect the way in which individual perceptions/cognitions/behaviours ‘coalesce or diverge to create meaningful higher level patterns’ (Kozlowski et al., 2013, p. 586). In line with this compositional view on emergence and to use the group-level means for further analyses, we have to first show that within groups, individual scores are homogeneous rather than heterogeneous. To explore the consistency of within-group individual evaluations of the variables considered in our study, we used the group-size-corrected intraclass correlations (Bliese & Halverson, 1998) and the within-group agreement index (James et al., 1993). We computed the ICC(1) using Bliese and Halverson’s (1998) formula, based on the one-way MSb MSw random effects analysis of variance: ICCð1Þ ¼ MSb þððN , where: MSb is mean square g 1ÞMSw Þ between subjects, MSw is mean square within subjects and Ng is the arithmetic mean of group sizes. ICC (2) reflects group-mean reliability and it is computed based on the ICC(1) N ðICCð1ÞÞ values using the formula: ICCð2Þ ¼ 1þðNgg 1ÞICCð1Þ In our sample, these two aggregation statistics exceed the generally accepted cut-off points, showing a substantial within-group clustering of individual scores. The ICC(1) values presented in Table 1 show that between 24% (CEI T1) and 38% (group cohesion T1) of the score variance of our variables is explained by group-level factors. Moreover, the Rwg scores higher than .70 show that group members report very similar ratings for each of the variables considered in our analyses. In other words, combining the insights revealed by the ICC(1) and Rwg values, we can conclude that individual ratings display a

224

Petru L. Cursßeu et al.

Table 1. Aggregation statistics

CEI (T1) Relationship conflict (T1) Group cohesion (T1) Relationship conflict (T2) Group cohesion (T2) Team effectiveness (T2)

ICC (1)

ICC (2)

Mean Rwg (SD)

Range Rwg

.24 .27 .38 .31 .37 .26

.56 .60 .71 .63 .69 .58

.97 (.01) .93 (.04) .95 (.03) .92(.04) .94 (.03) .98 (.00)

[.90, 1.00] [.78, 1.00] [.82, .99] [.77, 1.00] [.73, 1.00] [.90, 1.00]

Note. CEI = collective emotional intelligence; T1 = time 1; T2 = time 2.

high degree of within-group homogeneity. Also the ICC(2) values were higher than .56, showing sufficient team level inter-rater reliability. Based on these aggregation statistics, we can conclude that using the group-level mean for further analyses provides an accurate estimate of the group-level phenomenon investigated.

Results We started out by testing the bi-directional causal claims put forward by Walter and Bruch (2008) on the interrelation between affective similarity on the one hand and group cohesion and relationship conflict (as indicators of relationship quality) on the other hand. The cross-lagged data on affective similarity (AS) and relationship quality (RQ) allow us to explore the temporal sequencing of these variables. Table 2 shows the correlational matrix for affective similarity and relationship quality, evaluated at two moments in time. The correlations show that AS at Time 2 and RQ at Time 2 are significantly related. The correlation between AS at Time 1 and RQ at Time 2, however, is not significant. Also, the correlation between AS at Time 1 and AS at Time 2 is not significant. This set of correlations shows that affective similarity is a rather volatile and transient state, as it is not persistent over time and does not influence relationship quality at a later point in time. A different pattern of correlations can be found for relationship quality. RQ at Time 1 correlates significantly with both AS at Time 1 and AS at Time 2. Moreover, a strong correlation exists between RQ at Time 1 and RQ at Time 2. This indicates that relationship quality is not a transient state but rather reflects a stable pattern of interpersonal interactions in the group. To conclude, the correlational findings show that (1) affective similarity is a transient state; (2) relationship quality is persistent over time; and (3) relationship quality is more likely to drive affective similarity rather than the other way around. Looking at the partial correlations, we find that the association between RQ at Time 1 and AS at Time 2 drops to a not significant relation when controlling for RQ at Time 2 (q = .02; p = .88 for the correlation involving group cohesion and q = .03; p = .77 for the correlation involving relationship conflict), implying that the association between relationship quality at Time 1 and affective similarity at Time 2 is fully explained (mediated) by relationship quality at Time 2. Based on these findings, we conclude that affective similarity is an associated outcome of relationship quality within the group. To further explore this interpretation, we tested a comprehensive model that builds on the notion of affective similarity as an emergent state associated with group cohesion and relationship conflict. In this comprehensive model, we also controlled for the number of friendship ties within groups by adding this variable as a covariate for all mediators included in the model. The hypothesized path model was tested with the AMOS software,

46.16 8.45 3.53 1.58 3.70 .94 1.72 3.71 .93 3.66

31.72 6.03 .31 .37 .42 .05 .47 .45 .05 .28

SD 1 .11 .31** .24* .26** .09 .00 .13 .08 .11

1

2 1 .50** .06 .58** .03 .15 .55** .08 .19

Note. CEI = collective emotional intelligence; T1 = time 1; T2 = time 2. *p < .05; **p < .01.

1. Percentage of women 2. No. of friendship ties (T1) 3. CEI (T1) 4. Relationship conflict (T1) 5. Group cohesion (T1) 6. Affective similarity (T1) 7. Relationship conflict (T2) 8. Group cohesion (T2) 9. Affective similarity (T2) 10. Group effectiveness (T2)

M

Table 2. Means, standard deviations, and correlations

1 .36** .69** .19 .28** .55** .19 .43**

3

1 .35** .23* .64** .30** .24* .36**

4

1 .22* .25* .71** .27* .37**

5

1 .07 .02 .18 .07

6

1 .45** .36** .53**

7

1 .40** .62**

8

1 .46**

9

Collective emotional intelligence in groups 225

226

Petru L. Cursßeu et al.

version 19 using Structural Equation Modelling. The fit summary of this model as well as the standard path coefficients are presented in Figure 1. The chi-square value provides a statistical test for global model fit and shows that the model is not significantly different from the data, v2 (8) = 8.84, p = .35. Two categories of fit indices can be distinguished: (1) absolute fit indices, which illustrate how well the data are reproduced by the theoretical model and (2) incremental fit indices, which compare the tested model with the baseline model (Widman & Thomson, 2003). We have focused on the Root Mean Square Error of Approximation (RMSEA = .03) as an absolute fit index and we use the Tucker–Lewis Index (TLI = .98), the Normed Fit Index (NFI = .95), and the Comparative Fit Index (CFI = .99) as incremental fit indices. All values of the fit indices point to a good fit between the model and the data and to the fact that the model cannot be substantially improved (Hu & Bentler, 1999). We find that the percentage of women in the group positively influences CEI (b = .25, p = .004). Collective emotional intelligence, in turn, has strong effects on cohesion (b = .36, p = .001) and relationship conflict (b = .26, p = .02). In line with the argument that affective similarity is a group state that concurrently emerges from relationship quality within the group, the data show that cohesion is positively associated (b = .40, p = .001) and relationship conflict is negatively associated (b = .24, p = .023) with affective similarity measured at the same moment in time. Cohesion, relationship conflict, and affective similarity had significant associations with group effectiveness. As hypothesized, cohesion (b = .41, p = .001) and affective similarity (b = .23, p = .007) had a positive influence, while relationship conflict (b = .26, p = .002) had a negative influence on group effectiveness. The number of friendship ties had significant positive associations with CEI (b = .48, p = .001) and with cohesion (b = .37, p = .001), whereas the associations with relationship conflict (b = .02, p = .83) and affective similarity (b = .16, p = .14) were not significant. SEM is an advantageous analytical approach because it allows the simultaneous test of multiple mediators and more complex mediating chains (as we intended to test in our study) and also provides global model fit indices useful to estimate the general accuracy of comprehensive models (Tomarken & Waller, 2005). Nevertheless, to further check the robustness of our analyses, we used a resampling procedure and individually tested hypotheses 2 and 3 using bootstrapping as recommended by Preacher and Hayes (2004).

Group cohesion (T2) 0.41**

0.36** 0.40**

% of women in groups

0.25*

Collective emotional intelligence (T1)

–0.26**

–0.40**

Affective similarity (T2)

0.23**

Group effectiveness (T2)

–0.24*

Relationship conflict (T2)

–0.26**

Figure 1. A comprehensive model of collective emotional intelligence. Note. Model fit: Chisquare = 8.84 (df = 8; p = .35); TLI = .98; CFI = .99; NFI = .95; RMSEA = .03. Standardized path coefficients are shown. We also controlled for the number of friendship ties within groups by adding it as a covariate to all mediators in the model, yet for parsimony reasons, this control variable is not depicted in the figure. *p < .05; **p < .01.

Collective emotional intelligence in groups

227

First, we tested hypotheses 2a and 2b using bias-corrected bootstrapping with 5000 resamples and to account for the covariance between relationship conflict and cohesion we entered both mediators simultaneously. We used the sum of friendship ties as a control variable, CEI evaluated at T1 as independent variable, cohesion and relationship conflict evaluated at T2 as mediators and group effectiveness as dependent variable. The indirect effect of CEI through cohesion is significant (effect size = .17) and the 95% confidence interval does not contain zero [.08, .31]. Moreover, the indirect effect of CEI, mediated by relationship conflict is also significant (effect size = .06) and the 95% confidence interval does not contain zero [.01, .19]. The total indirect effect of CEI, mediated by cohesion and relationship conflict is also significant (effect size = .24) and the 95% confidence interval does not contain zero [.11, .39]. As the direct effect of CEI is only marginally significant (b = .18, p = .05), we can conclude that hypotheses 2a and 2b were fully supported by the data and that the effect of CEI on group effectiveness is mediated by cohesion and relationship conflict (both reflecting relationship quality in groups). Further on, we independently tested hypotheses 3a and 3b using the same bootstrapping procedure with 5000 resamples. We controlled for the sum of friendship ties and modelled group cohesion evaluated at T2 as independent variable, affective similarity evaluated at T2 as mediator and group effectiveness as dependent variable. The indirect effect of cohesion through affective similarity is significant (effect size = .07) and the 95% confidence interval does not contain zero [.02, .15]. The direct effect of cohesion on group effectiveness remains significant (b = .64, p = .001), therefore we can conclude that the effect of cohesion on group effectiveness is only partially mediated by affective similarity. We then tested the mediated effect of relationship conflict on group effectiveness, controlling for sum of friendship ties and using as mediator affective similarity. The indirect effect of relationship conflict on group effectiveness through affective similarity is significant (effect size = .07) and the 95% confidence interval does not contain zero [.14, .03]. The direct effect of relationship conflict on group effectiveness stays significant (b = .32, p = .001), therefore we can conclude that the effect of relationship conflict on group effectiveness is partially mediated by affective similarity. To cross-check the robustness of our findings, we ran the same bootstrapping procedure using cohesion and relationship conflict evaluated at T1 as independent variables, affective similarity evaluated at T2 as mediator and group effectiveness as dependent variable. The results were robust, supporting the same (partial) mediating patterns as the ones obtained using the T2 evaluations of relationship quality (cohesion and relationship conflict). Because the results of the bootstrapping procedure are perfectly aligned with the results obtained in SEM, we can conclude that all of our hypotheses are supported by the data.

Discussion Our study tested a comprehensive model of CEI and its impact on group effectiveness in gender diverse groups. We found strong evidence that the percentage of women in the group improves the CEI. As such, we provide support for the claim that gender composition is an important driver of group-level competencies. Also, we uncover a mechanism for the impact of gender composition on group effectiveness and provide insights into the group dynamics that explain this effect. More specifically, we looked at the interrelation between interpersonal interactions and affective dynamics in the group. Our findings align with existing evidence that groups with better emotional competencies

228

Petru L. Cursßeu et al.

– due to a compositional effect of members’ emotional intelligence (Barsade & Gibson, 1998; Elfenbein, Polzer, & Ambady, 2007) or the development of norms that actively tend to the group’s affective dynamics (Ayoko et al., 2008; Druskat & Wolff, 2001; Wolff et al., 2006) – are more cohesive and experience less conflicts than less emotionally intelligent groups. This increased quality of intragroup relationships fosters the emergence of affective similarity within groups. An emergent result that supports this claim is also the positive association of the number of friendship ties with the emergence of CEI. Our results show that groups composed of friends are more cohesive and in turn display more similar emotions. The positive strong association between the number of friendship ties in groups and CEI needs further attention. It is likely that friends can anticipate each other’s emotional reactions in group settings and as such, groups composed of friends are more likely to be effective in regulating group emotions than groups composed of members with no previous interpersonal experience. A possible venue for future research opened by this emergent result is the composition of groups based on relational preferences. Previous empirical results show that groups composed by maximizing the reciprocated relational preferences of their members, experience higher teamwork quality and are more effective than groups composed by maximizing diversity (Cursßeu, Kenis, Raab, & Brandes, 2010). Future research could explore whether CEI is the mediating factor that explains the positive effect of reciprocated relational preferences on group dynamics and effectiveness. An important contribution of our article is that it reports an initial empirical test of the positive affect spiral model. Testing the claims by Walter and Bruch (2008) through a correlational analysis, we conclude that intragroup relationship quality (cohesion and relationship conflict) drives affective similarity rather than the other way around. That is, we did not find an effect of affective similarity on cohesion and relationship conflict at a later point in time. Rather, we find that emotional intelligence (as a collective competence) and not emotional similarity drives the quality of intragroup relations. We posit that this is not surprising given the transient nature of group affective states (Watson, 2000). Our data indeed provide support for the transient nature of affective similarity in groups, and we therefore tested the relationships cross-sectionally as well. We find that cohesion is conducive to and relationship conflict is detrimental for affective similarity in groups. We therefore contribute to the literature on emergent states in groups and argue that affective similarity is an emergent result of social integration in groups. Literature on emergent cognitive competencies in groups is yet unclear as to whether these collective competencies are compositional or compilation phenomena. Collective intelligence for example, reflects consistent collective performance across a variety of group tasks and was shown to be unrelated to individual intelligence (Woolley et al., 2010). Woodley and Bell (2011) argued that collective intelligence is a group-level manifestation of the General Factor of Personality (GFP) as the GFP could account for the correlates of collective intelligence (i.e., social sensitivity). Group rationality reflects a collective set of cognitive competencies associated with making decisions aligned with a normative ideal and it has been shown to be strongly related to individual rationality and the decision rule used to guide individual interactions in groups (Cursßeu et al., 2013). Because emotional intelligence is related to GFP (Woodley & Bell, 2011) as well as to the quality of interpersonal interactions in groups (Cursßeu et al., 2012), future research should explore the extent to which CEI explains the association between group composition and emergent group-level cognitive competencies. For example, in line with the claim that gender composition is an important driver of emergent collective competencies, future research should extend our focus on collective emotional

Collective emotional intelligence in groups

229

competencies (i.e., CEI) to collective cognitive competencies such as group rationality (Cursßeu et al., 2013) and explore whether the emergence of collective emotional competencies mediates the impact of gender composition on group rationality. It is reasonable to argue that prosocial and emotionally intelligent behaviours influence group rationality through the social integration and affective similarity they generate within groups. In addition to its contributions, our article has also a few limitations. First, we did not use an experimental design, and causal claims in our model should therefore be interpreted with caution. Our independent variable is a group composition feature based on a (relatively) immutable individual trait, therefore the issue of reversed causation is a problem for the relations between our mediators and the dependent variable only. We tried to correct for this by using a cross-lagged design and separating in time the evaluations of these variables (i.e., the evaluation of CEI preceded the evaluation of the other emergent states and group effectiveness), yet the reversed causation cannot be fully refuted based on only temporal sequencing. Second, we used self-report data and the common method could have influenced our results, although we tried to reduce the concerns about common method bias by separating our evaluations in time (Podsakoff, MacKenzie, & Podsakoff, 2011). The use of self-report data was the most suitable method for our purposes because our aim was to explore individual perceptions of group-level emergent properties and their reports of affective reactions. More problematic is our reliance on self-reports for evaluating group effectiveness. Nevertheless, our multidimensional evaluation of group effectiveness is theoretically grounded (Hackman, 1986) and previous research reported moderate positive correlations between self-reports of the three dimensions included in the effectiveness measure and objective performance indicators (Cursßeu & Schruijer, 2010) or effectiveness ratings of external raters (Alper, Tjosvold, & Law, 1998). As meta-analytic evidence strongly supports the positive association between group cohesion and performance (Beal et al., 2003) and the negative association between relationship conflict and group performance (De Wit, Greer, & Jehn, 2012), it was not our main aim to further probe these relationships. Our main aim was to explore the dynamics of the emotional and emergent states in groups. Third, we argued for women’s advantage over men in building CEI, yet we did not explicitly evaluate the specific mechanisms that explain their superiority and we did not evaluate individual emotional intelligence. Although we build on extant empirical literature that documented gender differences in the understanding and regulation of emotions, this is a limitation of our study, and future research could explicitly evaluate these mechanisms and explore whether CEI really transcends individual emotional intelligence (evaluated as ability, see for details Austin, 2010). Finally, our respondents were students and future research should replicate these findings in groups working in other organizational settings. The benefit of using student groups, however, is that these groups are likely to be in the same stage of group development, an importantissuetobe controlled for when exploring the emergenceofCEI. As argued by previous research (Cursßeu et al., 2012), norms for group emotion regulation need time to develop. Having groups in different stages of group development might therefore bias the results, as they could exhibit different norms for emotion regulation. Our results have important practical implications. Due to EI’s predictive validity for individual performance across a wide variety of tasks, emotional intelligence scores are extensively used in personnel selection (Mayer et al., 2008). We extend these insights and show that emotional intelligence as an emergent group-level property is beneficial for group effectiveness in collaborative learning groups. It becomes therefore increasingly

230

Petru L. Cursßeu et al.

important to support groups to develop their emotional competencies. One way of fostering CEI is through group design, namely by finding those compositional variables that are conducive to the emergence of CEI. Gender composition is just one of these variables and it may be that in real organizational settings it is not always open to manipulation. Other compositional variables (e.g., individual emotional intelligence) could also play an important role in generating CEI. As CEI is an emergent competence, normative interventions that prescribe interpersonal interactions could also create the conditions for CEI to emerge (Cursßeu et al., 2012). Finally, we address the issue of women’s contribution to organizations. To date, research and interventions have focused primarily on the issue of women leaders, and paid less attention to the contribution that women bring to the workplace and the change in culture that comes with it. Our findings showed that the percentage of women in a group increases CEI and subsequently improves teamwork quality, ultimately enhancing group effectiveness. By contributing to our understanding of how women contribute to group effectiveness (Hoogendoorn, Oosterbeek, & Van Praag, 2013; Woolley et al., 2010), we help in the long run to support organizations and their teams to reap the benefits of gender diversity. The key practical implication of our findings is that women’s contribution to performance in the workplace will be maximized when women will not act counter-stereotypically, as many management and coaching gurus suggest when stressing the need to boost women’s assertiveness and task-orientation at the cost of their communal and emotional traits. We argue that women should rather build on their strengths and make those an active part of organizations’ cultures. With this statement, we align with the stream of research on servant leadership, which already recognized the difference women can make in leading positions and changing leadership cultures (Barbuto & Gifford, 2010). To conclude, on a more humorous note, we cannot but agree with Woolley and Malone (2011) that managers working with groups should seriously consider bringing in the women.

Acknowledgements We thank Jeroen de Jong, Jing Han, Steffie Janssen, Rob Jansen, and Gertjan Lucas for their help with data collection.

References Abele, A. E. (2003). The dynamics of masculine-agentic and feminine-communal traits: Findings from a prospective study. Journal of Personality and Social Psychology, 85, 768–776. doi:10.1037/ 0022-3514.85.4.768 Alper, S., Tjosvold, D., & Law, K. S. (1998). Interdependence and controversy in group decision making: Antecedents to effective self-managing teams. Organizational Behavior and Human Decision Processes, 74, 33–52. doi:10.1006/obhd.1998.2748 Austin, E. J. (2010). Measurement of ability emotional intelligence: Results for two new tests. British Journal of Psychology, 101, 563–578. doi:10.1348/000712609X474370 Ayoko, O. B., Callan, V. J., & H€artel, C. E. (2008). The influence of team emotional intelligence climate on conflict and team members’ reactions to conflict. Small Group Research, 39, 121– 149. doi:10.1177/1046496407304921 Barbuto, J., & Gifford, G. T. (2010). Examining gender differences of servant leadership: An analysis of the agentic and communal properties of the Servant Leadership Questionnaire. Journal of Leadership Education, 9, 4–21. doi:10.12806/V9/I2/RF1

Collective emotional intelligence in groups

231

Barsade, S. G., & Gibson, D. E. (1998). Group emotion: A view from top and bottom. In D. Gruenfeld, E. Mannix & M. Neale (Eds.), Research on managing groups and teams (pp. 81–102). Stanford, CT: JAI Press. Barsade, S. G., Ward, A. J., Turner, J. D. F., & Sonnenfeld, J. A. (2000). To your heart’s content: A model of affective diversity in top management teams. Administrative Science Quarterly, 45, 802–836. doi:10.2307/2667020 Bartel, C. A., & Saveedra, R. (2000). The collective construction of work group moods. Administrative Science Quarterly, 45, 197–231. doi:10.2307/2667070 Beal, D. J., Cohen, R. R., Burke, M. J., & McLendon, C. L. (2003). Cohesion and performance in groups: A meta-analytic clarification of construct relations. Journal of Applied Psychology, 88, 989. doi:10.1037/0021-9010.88.6.989 Blanchard, C., Poon, P., Rodgers, W., & Pinel, B. (2000). Group Environment Questionnaire and its applicability in an exercise setting. Small Group Research, 31, 210–224. doi:10.1177/ 104649640003100204 Bliese, P. D., & Halverson, R. R. (1998). Group size and measures of group-level properties: An examination of eta-squared and ICC values. Journal of Management, 24 (2), 157–172. Chen, G., Sharma, P. N., Edinger, S., Shapiro, D. L., & Farh, J. L. (2011). Motivating and demotivating forces in teams: Cross-level influences of empowering leadership and relationship conflict. Journal of Applied Psychology, 96, 541–557. doi:10.1037/a0021886 Ciarrochi, J., Caputi, P., & Mayer, J. D. (2003). The distinctiveness and utility of a measure of trait emotional awareness. Personality and Individual Differences, 34, 1477–1490. doi:10.1016/ S0191-8869(02)00129-0 Collins, A. L., Lawrence, S. A., Troth, A. C., & Jordan,P. J.(2013). Group affective tone: A review andfuture research directions. Journal of Organizational Behavior, 34, S43–S62. doi:10.1002/job.1887 Cursßeu, P. L. (2006). Emergent states in virtual teams. A complex adaptive systems perspective. Journal of Information Technology, 21, 249–261. doi:10.1057/palgrave.jit.2000077 Cursßeu, P. L., Borosß, S., & Oerlemans, L. A. G. (2012). Task and relationship conflict in short-term and long-term groups: The critical role of emotion regulation. International Journal of Conflict Management, 23, 97–107. doi:10.1108/10444061211199331 Cursßeu, P. L., Jansen, R. J. G., & Chappin, M. M. H. (2013). Decision rules and group rationality: Cognitive gain or standstill? PLoS ONE, 8, e56454. doi:10.1371/journal.pone.0056454 Cursßeu, P. L., Kenis, P., Raab, J., & Brandes, U. (2010). Composing effective teams through teamdating. Organization Studies, 31, 873–894. doi:10.1177/0170840610373195 Cursßeu, P. L., & Schruijer, S. G. L. (2010). Does conflict shatter trust or does trust obliterate conflict? Revisiting the relationship between team diversity, conflict and trust. Group Dynamics: Theory, Research and Practice, 14, 66–79. doi:10.1037/a0017104 Cursßeu, P. L., Schruijer, S., & Borosß, S. (2007). The effects of groups’ variety and disparity on groups’ cognitive complexity. Group Dynamics: Theory, Research, and Practice, 11, 187–206. doi:10. 1037/1089-2699.11.3.187 De Dreu, C. K. W., & Weingart, L. R. (2003). Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis. Journal of Applied Psychology, 88, 741–749. doi:10.1037/0021-9010.88.4.741 De Jong, J. P., Cursßeu, P. L., & Leenders, R. T. A. (2014). When do bad apples not spoil the barrel? Negative relationships in teams, team performance, and buffering mechanisms. Journal of Applied Psychology, 99, 514–522. doi:10.1037/a0036284 De Wit, F. R., Greer, L. L., & Jehn, K. A. (2012). The paradox of intragroup conflict: A meta-analysis. Journal of Applied Psychology, 97, 360. doi:10.1037/a0024844 Druskat, V. U., & Wolff, S. B. (2001). Collective emotional intelligence and its influence on group effectiveness. In C. Cherniss & D. Goleman (Eds.), The emotionally intelligent workplace: How to select for, measure, and improve emotional intelligence in individuals, groups, and organizations (pp. 132–155). San Francisco, CA: Jossey-Bass. Elfenbein, H. A. (2006). Team emotional intelligence: What it can mean and how it can affect performance. In V. U. Druskat, F. Sala & G. Mount (Eds.), Linking emotional intelligence and

232

Petru L. Cursßeu et al.

performance at work: Current research evidence with individuals and groups (pp. 165–184). Mahwah, NJ: Lawrence Erlbaum. Elfenbein, H. A., Polzer, J. T., & Ambady, N. (2007). Team emotion recognition accuracy and team performance. In N. M. Ashkanasy, W. J. Zerbe & C. E. J. H€artel (Eds.), Research on emotions in organizations (pp. 87–119). Amsterdam, NL: Elsevier. Evans, N. J., & Jarvis, P. A. (1980). Group cohesion: A review and reevaluation. Small Group Behavior, 11, 359–370. doi:10.1177/104649648001100401 Frijda, N. H. (1986). The emotions. London, UK: Cambridge University. George, J. M. (1990). Personality, affect, and behavior in groups. Journal of Applied Psychology, 75, 107–116. doi:10.1037/0021-9010.75.2.107 George, J. M. (2000). Emotions and leadership: The role of emotional intelligence. Human Relations, 53, 1027–1055. doi:10.1177/0018726700538001 Hackman, J. R. (1986). The design of work teams. In J. Lorsch (Ed.), Handbook of organizational behavior (pp. 315–342). Englewood Cliffs, NJ: Prentice-Hall. Hall, J. A. (1978). Gender effects in decoding nonverbal cues. Psychological Bulletin, 85, 845–857. doi:10.1037//0033-2909.85.4.845 Hoogendoorn, S., Oosterbeek, H., & Van Praag, M. (2013). The impact of gender diversity on the performance of business teams: Evidence from a field experiment. Management Science, 59, 1514–1528. doi:10.1287/mnsc.1120.1674 Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi:10. 1080/10705519909540118 James, L. R., Demaree, R. G., & Wolf, G. (1993). rwg: An assessment of within-group interrater agreement. Journal of Applied Psychology, 78, 306–309. doi:10.1037//0021-9010.78.2.306 Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Administrative Science Quarterly, 40, 256–282. doi:10.2307/2393638 Jordan, P. J., & Troth, A. C. (2004). Managing emotions during team problem solving: Emotional intelligence and conflict resolution. Human performance, 17, 195–218. doi:10.1207/ s15327043hup1702_4 Kelly, J. R., & Barsade, S. G. (2001). Moods and emotions in small groups and work teams. Organizational Behavior and Human Decision Processes, 86, 99–130. doi:10.1006/obhd. 2001.2974 Koman, E. S., & Wolff, S. B. (2008). Emotional intelligence competencies in the team and team leader: A multi-level examination of the impact of emotional intelligence on team performance. Journal of Management Development, 27, 55–75. doi:10.1108/02621710810840767 Kozlowski, S. W. J., & Chao, G. T. (2012). The dynamics of emergence: Cognition and cohesion in work teams. Managerial and Decision Economics, 33, 335–354. doi:10.1002/mde.2552 Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2013). Advancing multilevel research design: Capturing the dynamics of emergence. Organizational Research Methods, 16, 581–615. doi:10.1177/1094428113493119 Lawrence, C., & Hutchinson, L. (2014). The impact of non-aggressive behavior early in aggressive interactions: Sex differences in direct and indirect aggression in response to provocation. British Journal of Psychology, 105, 127–144. doi:10.1111/bjop.12020 Lopes, P. N., Salovey, P., C^ ote, S., & Beers, M. (2005). Emotion regulation abilities and the quality of social interactions. Emotion, 5, 113–118. doi:10.1037/1528-3542.5.1.113 Mandell, B., & Pherwani, S. (2003). Relationship between emotional intelligence and transformational leadership style: A gender comparison. Journal of Business and Psychology, 17, 387–404. doi:10.1023/A:1022816409059 Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26 (3), 356–376. Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human abilities: Emotional intelligence. Annual Review of Psychology, 59, 507–536. doi:10.1146/annurev.psych.59.103006.093646

Collective emotional intelligence in groups

233

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2011). Sources of method bias in Social Science research and recommendations on how to control it. Annual Review of Psychology, 63, 1–31. doi:10.1146/annurev-psych-120710-100452 Prati, L. M., Douglas, C., Ferris, G. R., Ammeter, A. P., & Buckley, M. R. (2003). Emotional intelligence, leadership effectiveness, and team outcomes. International Journal of Organizational Analysis, 11, 21–40. doi:10.1108/eb028961 Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36, 717– 731. doi:10.3758/BF03206553 Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178. doi:10.1037/h0077714 Simons, T. L., & Peterson, S. R. (2000). Task conflict and relationship conflict in top management teams: The pivotal role of intragroup trust. Journal of Applied Psychology, 85, 314–322. doi:10. 1037/0021-9010.85.1.102 Smith, E. R., Seger, C. R., & Mackie, D. M. (2007). Can emotions be truly group level? Evidence regarding four conceptual criteria. Journal of Personality and Social Psychology, 93, 431–446. doi:10.1037/0022-3514.93.3.431 Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: Strengths, limitations and misconceptions. Annual Review of Clinical Psychology, 1, 31–65. doi:10.1146/annurev. clinpsy.1.102803.144239 Troth, A. C., Jordan, P. J., & Lawrence, S. A. (2012). Emotional intelligence, communication competence, and student perceptions of team social cohesion. Journal of Psychoeducational Assessment, 30, 414–424. doi:10.1177/0734282912449447 Twenge, J. M. (1997). Changes in masculine and feminine traits over time: A meta-analysis. Sex Roles, 36, 305–325. doi:10.1007/BF02766650 Van Den Berg, W., Cursßeu, P. L., & Meeus, M. T. H. (2014). Emotion regulation and conflict transformation in multi-team systems. International Journal of Conflict Management, 25, 171– 188. doi:10.1108/IJCMA-05-2012-0038 Walter, F., & Bruch, H. (2008). The positive group affect spiral: A dynamic model of the emergence of positive affective similarity in work groups. Journal of Organizational Behavior, 29, 239– 261. doi:10.1002/job.505 Watson, D. (2000). Mood and temperament. New York: Guilford Press. Wegge, J., van Dick, R., Fisher, G. K., West, M. A., & Dawson, J. F. (2006). A test of basic assumptions of affective events theory (AET) in call centre work. British Journal of Management, 17, 237– 254. doi:10.1111/j.1467-8551.2006.00489.x Whelan, C. (2007). Team performance management in the Irish Health Service (Unpublished doctoral dissertation). Nottingham University Business School. Widman, K. F., & Thomson, J. S. (2003). On specifying the null model for incremental fit indices instructuralequationmodeling. PsychologicalMethods,8,16–37. doi:10.1037/1082-989X.8.1.16 Wolff, S. B., Druskat, V. U., Koman, E. S., & Messer, T. E. (2006). The link between group emotional competence and group effectiveness. In V. U. Druskat, F. Sala & G. Mount (Eds.), Linking emotional intelligence and performance at work: Current research evidence with individuals and groups (pp. 223–242). Mahwah, NJ: Lawrence Erlbaum. Woodley, M. A., & Bell, E. (2011). Is collective intelligence (mostly) the general factor of personality? A comment on Woolley, Chabris, Pentland, Hashmi and Malone (2010). Intelligence, 39, 79–81. doi:10.1016/j.intell.2011.01.004 Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330, 686–688. doi:10.1126/science.1193147 Woolley, A. W., & Malone, T. (2011). What makes a team smarter? More women. Harvard Business Review, 89, 32–33.

234

Petru L. Cursßeu et al.

Yang, J., & Mossholder, K. W. (2004). Decoupling task and relationship conflict: The role of intracollective emotional processing. Journal of Organizational Behavior, 25, 589–605. doi:10.1002/job.258 Received 9 January 2014; revised version received 1 May 2014

Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

The magic of collective emotional intelligence in learning groups: No guys needed for the spell!

Using a cross-lagged design, the present study tests an integrative model of emergent collective emotions in learning groups. Our results indicate tha...
231KB Sizes 2 Downloads 3 Views