How Demographic Faultlines and Impression Management Behaviors Influence Group Affective Tone

Abstract

Drawing on the social categorization perspective, we theorized that team demographic faultlines increase negative group affective tone (NGAT) through reduced group identification, while team member positive impression management behaviors enhance positive group affective tone (PGAT) via enhanced group identification. Data were collected from 523 members of 101 newly formed student teams. Consistent with our hypotheses, team demographic faultlines were positively predicted NGAT via reduced group identification, while team self-promotion and ingratiation behaviors were positively associated with PGAT through group identification. Importantly, team self-promotion and ingratiation behaviors also mitigated the social categorization processes triggered by team demographic faultlines.

Keywords

group affective tone diversity impression management group identification faultlines

Responding to pressures from rapidly changing environments and fierce competition, organizations have widely used team-based work design to strengthen their flexibility and innovation (Kozlowski & Bell, 2003; West, 2004). Since team members may experience various affective states during team interactions, both researchers and practitioners have paid increasing attention to how affective experiences among team members influence team functions (e.g., Collins et al., 2013; To et al., 2017).

To clarify the effects of team-level affect, George (1990) proposed a meaningful team-level construct that captures members’ homogeneous affective states within teams: group affective tone (GAT). GAT is defined as “consistent or homogenous affective reactions within a group” (p. 108) and is composed of two dimensions: positive group affective tone (PGAT) and negative group affective tone (NGAT). Previous studies have indicated that PGAT and NGAT are independent dimensions with distinct antecedents and consequences, that influence team outcomes in unique ways (for reviews, see Barsade & Knight, 2015; Collins et al., 2013).

Although studies have provided insightful findings that have enabled construction of the nomological network of GAT (e.g., Barsade et al., 2000; Chi et al., 2011; Sy et al., 2005), these studies have not systematically explored how naturally differentiated and preexisting demographic compositions influence GAT in newly formed teams. In newly formed teams, members have little information regarding other members’ personality and only have limited opportunities for interaction (Fransen et al., 2018). Thus, members in newly formed teams tend to make initial social categorizations based on other members’ visible, observable, and immutable attributes, such as age, educational background, and gender (Harrison et al., 1998; Thatcher & Patel, 2012). Hence, it is plausible that diversity in members’ surface-level and demographic characteristics leads team members to perceive the distinctiveness of their fellow team members and become hostile towards others, thereby enhancing NGAT in newly formed teams (Garcia-Prieto et al., 2003; Lawler, 2007; Phillips & Lount, 2007). Since organizations form new teams to solve emergent problems (Kozlowski & Bell, 2003) and GAT influences critical team outcomes, it is theoretically important to clarify whether and how team members’ demographic characteristics increases NGAT in newly formed teams.

Moreover, when the team is newly formed (i.e., the forming stage in small group development; Tuckman, 1965), members tend to form initial impressions of each other and construct positive images since they want to be accepted by other team members (Priestley, 2019; Tuckman & Jensen, 1977). Therefore, when team members engage in favorable behaviors toward other members and display desired characteristics (competence, likability, and conscientiousness) during early interactions, other members may not only form positive emotions towards them, but also replace their initial social categorization or stereotypes with deeper-level information obtained during the interactions (Harrison et al., 1998). Thus, team members’ impression management behaviors, the behaviors that team members perform to influence the images of others have of them (Nguyen et al., 2008) in early interactions, play important roles in determining their subsequent positive affective reactions within teams. Again, these possibilities have not been systematically tested under an integrated theoretical framework.

This study is designed to answer these theoretical questions and contribute to the GAT literature in three important ways. First, drawing on the stage of group development model (Tuckman, 1965; Tuckman & Jensen, 1977), team members are likely to form initial social categorizations and impressions of other members based simultaneously on members’ observable demographic characteristics and impression management behaviors in early interactions. Thus, we integrate both team demographic faultlines (hypothetical dividing lines that split a team into several subgroups based on multiple demographic attributes, such as age, educational background, and gender; Lau & Murnighan, 1998; Thatcher & Patel, 2012) and team members’ impression management behaviors into our theoretical model, and simultaneously examine their main as well as interactive effects on PGAT and NGAT in newly formed teams. By doing so, we clarify how these two factors differentially influence PGAT and NGAT when teams are newly formed, and investigate whether team members’ impression management behaviors alleviate the negative social categorization and NGAT triggered by demographic faultlines.

Second, we further theorize that team demographic faultlines and impression management behaviors may influence PGAT and NGAT indirectly via group identification, but through distinct theoretical mechanisms. Group identification refers to the extent to which team members define themselves in terms of their group membership and the emotional significance attached to that membership (Tajfel, 1978). During initial interactions, team members’ differences in visible and observable characteristics (i.e., demographic faultlines) trigger the social categorization process (i.e., the social categorization pathway) automatically and intuitively (Tajfel & Turner, 1986), thereby developing NGAT through reducing members’ identification with the whole team (Lau & Murnighan, 2005; Smith et al., 2007). However, members in newly formed teams are motivated to perform positive impression management behaviors to create positive images and seek acceptance from other members (i.e., the image construction pathway). Thus, in order to promote PGAT in newly formed teams, team members must devote effort to constructing positive images during subsequent interactions (Tuckman & Jensen, 1977). Such intentional and deliberate behaviors not only increase members’ group identification, but also mitigate the detrimental effects triggered by the initial social categorization processes.

Finally, as noted above, we also clarify whether team members’ positive impression management behaviors can attenuate the negative effects of demographic faultlines on group identification as well as NGAT. As the impact of team members’ differences in demographic attributes on social categorization is weakened when team members obtain more information via observing other members’ behaviors (Harrison et al., 1998), we explore whether team members’ positive impression management behaviors (displaying behaviors perceived as competent, likable, and conscientious) help to mitigate the social categorization process activated by team demographic faultlines. Our theoretical framework is presented in Figure 1.

Figure 1.

The theoretical model of the current study.

Theory and Hypotheses

Group Affective Tone in Newly Formed Teams

Tuckman (1965) proposed four distinct stages of small group development: forming, storming, norming, and performing. In the context of newly formed teams, the patterns of member interactions in the forming stage may help to identify the potential antecedents of GAT in this stage.

In the forming stage (when the team is newly formed), members come together for the first time and get to know each other (Tuckman, 1965). To build relationships and reduce uncertainty, members share information about themselves explicitly through discussions or implicitly through non-verbal expressions (Furst et al., 2004). During this stage, members are cautious with their behaviors since they want to be accepted by other members of the team (Priestley, 2019). At the same time, members also observe other members’ visible attributes and behaviors to develop initial stereotypes and impressions (Harrison et al., 1998). These perceived stereotypes, impressions, and differences between team members in turn influence members’ relationship-building, as well as subsequent affective states (Furst et al., 2004).

In line with the aforementioned discussions, we propose that team demographic faultlines (members’ dissimilarity in multiple observable characteristics) and impression management behaviors (members’ displays of desirable behaviors) influence members’ shared affective states (GAT) in newly formed teams. Drawing on social categorization theory (Tajfel & Turner, 1986), we further theorize that the effects of team members’ demographic faultlines and impression management behaviors on GAT can be explained by the social categorization and image construction processes, respectively. Theoretical mechanisms and explanations are described below.

Team Demographic Faultlines and Negative Group Affective Tone

The social categorization perspective suggests that team members tend to use visible or relatively observable attributes to categorize themselves and other members into in-group or out-group members (Tajfel & Turner, 1986; van Knippenberg & Schippers, 2007). In early interactions, the alignment of demographic attributes based on the dissimilarity in members’ observable characteristics will lead team members to easily perceive the distinctiveness among team members and trigger social categorization processes (Harrison et al., 1998; Lau & Murnighan, 1998) such as creating subgroups within teams, forming hostility and biases toward out-group members, disfavoring out-group members over in-group members (e.g., Hogg & Terry, 2000; van Knippenberg & Schippers, 2007; Williams & O’Reilly, 1998), and creating tensions and negative affective reactions between subgroups (Bezrukova et al., 2009; Li & Hambrick, 2005).

Demographic faultlines refer to hypothetical dividing lines that split a team into relatively homogeneous subgroups based on the team members’ demographic alignment along multiple attributes (Bezrukova et al., 2009; Lau & Murnighan, 1998), such as age, gender, and educational background (Thatcher & Patel, 2012). An example of a team with strong demographic faultline is a four-person team composed of two 19 year old male students majoring in engineering. and two 23 year old female students 23 years old majoring in business administration. In this team, the demographic faultline is clear since two homogeneous subgroups would emerge based on members’ similarities in age, gender, and educational background (Bezrukova et al., 2009). Based on Lau and Murnighan’s (1998) definition, this team has strong demographic faultlines.

Demographic faultlines provide the impetus for team members with different demographic attributes (i.e., age, gender, educational background) to differentiate themselves and separate into several competing subgroups within a team (Bezrukova et al., 2009). Demographic faultlines are particularly salient in determining NGAT in newly formed teams, as the alignment of differences in such detectable characteristics are more likely to trigger social categorization processes in early interactions, which may result in hostility and anxiety when interacting with them (Li & Hambrick, 2005; Pelled, 1996). Such social categorization processes between subgroups may in turn enhance the levels of NGAT within the team. Hence,:

H1: Demographic faultlines are positively related to NGAT in newly formed teams.

Building on social categorization theory, we further argue that team members’ faultlines based on the alignment of explicit or visible attributes (i.e., age, gender, educational background) enhance NGAT via decreasing group identification. Specifically, high levels of demographic faultlines lead to the formation of subgroups within teams and activate members’ biases towards out-group members (Lau & Murnighan, 2005), which reduces members’ identification with the team as a whole (Hogg & Terry, 2000).

Group identification refers to the extent to which team members define themselves in terms of their group membership and the emotional significance attached to that membership (Tajfel, 1978). When strong demographic faultlines exist, team members are more likely to perceive themselves as being a member of a subgroup (rather than their entire group). The subgroup then becomes part of who they are, and their target of identification shifts from the entire team to the subgroup (Lau & Murnighan, 2005; Tajfel, 1981; Tajfel & Turner, 1986). When members strongly identify with their subgroup than the original team, they only form an emotional attachment to such subgroup membership, which leads them to form negative attitudes towards other subgroups within the original team (Li & Hambrick, 2005), thereby reducing identification with the original team.

Specifically, when demographic faultlines exist within newly formed teams, the alignment of differences in age, gender, and educational background may accentuate social categorization processes (Garcia-Prieto et al., 2003; Pelled, 1996; Thatcher & Patel, 2012). For example, demographic faultlines based on age, gender, and educational background may simultaneously trigger: (a) perceptions of status/experience differences among members (Harrison et al., 1998; Kearney et al., 2009), (b) perceptions of gender stereotypes towards members of the opposite gender (Ely, 1994; Joshi & Roh, 2009), and (c) different ways of information processing due to members’ divergent educational background (e.g., Dahlin et al., 2005; Herrmann & Datta, 2005). Overall, for teams with strong demographic faultlines based on age, gender, and educational background differences, members are more likely to create subgroups that align with these attributes and form negative evaluations towards out-group members within the same team. These in turn reduce group identification.

Finally, when team members do not identify with their entire team, they are more likely to disagree with other members and thus make communicating within teams more difficult (Garcia-Prieto et al., 2003). The disagreement and anticipated uncertainty during team interactions would lead members to experience negative moods such as nervousness and anxiety or display hostility toward others (Lawler, 2007; Phillips & Lount, 2007), resulting in high levels of NGAT. Taken together, we propose the following hypothesis:

H2: Group identification mediates the positive relationship between demographic faultlines and NGAT in newly formed teams.

Team Positive Impression Management Behaviors and Positive Group Affective Tone

In addition to the social categorization processes, we propose that team members’ impression management may influence PGAT via the image construction processes. Impression management is the process by which people attempt to influence the perceptions that others have of them (Turnley & Bolino, 2001). Within the team context, team impression management behavior is defined as the behaviors that team members perform to influence the images that other members have of them (Nguyen et al., 2008). As noted earlier, when a team is newly formed and individuals are assigned to or join a team, they are motivated to construct positive images (Priestley, 2019; Tuckman & Jensen, 1977) in order to obtain acceptance and social approval from others (Baumeister & Leary, 1995; Roberts, 2005; Schlenker & Weigold, 1992). Thus, in order to affiliate with groups and to be positively valued by other members, individuals are motivated to display certain behaviors that help to create positive images and impressions, such as engaging in favorable behaviors toward other members or displaying desired characteristics such as competent, likable, and conscientious (Leary & Kowalski, 1990; Roberts, 2005). Yet, in early interactions, team members also carefully observe other members’ behaviors and expressions to obtain more information about their future teammates (Furst et al., 2004). Hence, when most members engage in impression management behaviors during the early interactions, other members are more likely to give their team members positive evaluations and identify with their teams (Roberts, 2005).

According to the in-group positivity principle of social identity theory (Brewer, 2001), when team members identify themselves with their current team, they are more likely to form in-group favoritism (i.e., positive attitudes and emotions reserved for the in-group members; Brewer, 1999) and harmonious interpersonal relationships with other members (Bezrukova et al., 2009). These in turn produce positive feelings and affect towards their teams (Brewer, 2001; Tajfel & Turner, 1986). Thus, high levels of group identification may enhance members’ shared PGAT. Team members’ positive impression management behaviors are therefore beneficial for enhancing group identification, which may lead to greater PGAT in newly formed teams.

Researchers have primarily examined the effects of three types of positive IM behaviors within a team setting (Nguyen et al., 2008; Rozell & Gundersen, 2003): (1) ingratiation: team members praise or compliment other members in order to be seen as likeable; (2) self-promotion: team members make other members aware of their abilities or accomplishments to be seen as competent; and (3) exemplification: team members go above and beyond the call of duty to appear dedicated (Jones & Pittman, 1982; Turnley & Bolino, 2001). According to the image construction perspective, team members’ positive impression management behaviors can enhance group identification. First, research has shown that ingratiation is the most effective impression management behavior, since such behavior carries little risk to the ingratiator and is generally accepted in social situations (Koopman et al., 2015). In addition, ingratiation is less likely to be viewed as manipulative or insincere when directed towards others, since people tend to instantly accept positive statements about themselves without considering the motives of the ingratiators (Vonk, 2002). Finally, Gordon (1996) found that individuals form favorable evaluations and attitudes towards the ingratiator when the ingratiation behaviors are directed towards them, as such behaviors bolster their self-esteem (Vonk, 2002). Thus, when team members consistently engage in ingratiation behaviors (e.g., praise or compliment other members to be regarded as a nice person), other members may perceive these team members as friendly and likable (Gordon, 1996; Nguyen et al., 2008). These positive evaluations may activate members’ identification with and positive affect towards teams (Smith et al., 2007).

Although no known study has directly examined the relationship between team ingratiation and group identification, Nguyen et al. (2008) found that team members’ ingratiation behaviors are positively related to perceived liking toward other members, providing initial evidence for our argument. Furthermore, Tanghe et al. (2010) found that group identification is positively related to PGAT. Thus, we propose the following hypotheses:

H3a: Members’ ingratiation behaviors are positively related to PGAT in newly formed teams.

H3b: Group identification mediates the relationship between members’ ingratiation behaviors and PGAT in newly formed teams.

Second, in the forming stage (Tuckman, 1965), team members seek out information regarding other members’ abilities and dependability to judge their trustworthiness and reduce potential risk (Furst et al., 2004; Leary & Kowalski, 1990; Mayer et al., 1995). In addition, individuals often desire to be viewed as competent and professional in a newly formed team (Lester et al., 2002). Thus, team members are motivated to create positive images of themselves and demonstrate their knowledge, abilities, and experience, since this can influence other members’ attitudes toward themselves and the evaluations of the team (Roberts, 2005). Therefore, when team members consistently display self-promotion behaviors to promote their professional image (Roberts, 2005), other members are more likely to perceive the team members as competent (Turnley & Bolino, 2001), thereby forming positive attitudes towards them.

Gibson and Earley (2007) proposed that when team members are aware of other members’ abilities, they will develop a feeling of certainty that they can achieve the team goals (i.e., group efficacy). In an experimental study, van Zomeren et al. (2010) further observed that team members’ beliefs in their abilities to perform tasks can trigger the team’s shared tendency for collective action, thereby strengthening group identification. Previous studies also suggest that self-promotion can successfully establish the perceptions of competence when targets have little opportunity to verify the claims of competence (Higgins et al., 2003). In the context of newly formed teams, team members have limited chances to verify other members’ abilities within initial team interactions. Hence, team members’ self-promotion behaviors are more likely to enhance other members’ group identification and positive affect within newly formed teams. Based on the foregoing discussion, the following hypotheses are proposed:

H4a: Members’ self-promotion behaviors are positively related to PGAT in newly formed teams.

H4b: Group identification mediates the relationship between members’ self-promotion behaviors and PGAT in newly formed teams.

Finally, when team members display exemplification behaviors (e.g., work hard on team tasks; give extra effort for the team) in early interactions, other members often perceive their members as dedicated and conscientious (Bolino & Turnley, 1999) and are more likely to identify with their team (Roberts, 2005). This may lead to higher levels of positive group affect in teams (Garcia-Prieto et al., 2007; Smith et al., 2007). Although no known studies have directly tested these associations, Turnley and Bolino (2001) found that team member exemplification is positively related to members’ perceived dedication. Moreover, Rozell and Gundersen (2003) indicated that leaders’ exemplification is associated with members’ positive affect such as team cohesion and satisfaction. Based on these arguments, we propose the following hypotheses:

H5a: Members’ exemplification behaviors are positively related to PGAT in newly formed teams.

H5b: Group identification mediates the relationship between members’ exemplification behaviors and PGAT in newly formed teams.

Buffering Effects of Team Member Impression Management Behaviors

The social categorization theory and social identity theory support the notions that team members’ categorization of others is based on visible and observable attributes in initial encounters (Harrison et al., 2002; Lau & Murnighan, 2005). However, when team members acquire more information through subsequent interactions, they are more likely to modify these stereotypes based on other members’ observed behaviors and the effects of these visible and observable attributes on social categorization processes will become less important (Harrison et al., 1998, 2002). Following this line of reasoning, when team members obtain more information regarding other members based on their positive impression management behaviors, the detrimental effects of demographic faultlines on group identification and NGAT may be mitigated.

As noted by Furst et al. (2004), team members’ identifiable and favorable actions (e.g., showing friendliness, demonstrating abilities, or devoting more effort to team tasks) not only signal that they are agreeable and competent, but also alleviate the negative impacts of mistaken stereotypes and presumed differences between members on group identification. Thus, when most team members display positive impression management behaviors such as ingratiation, self-promotion, and exemplification behaviors, team members’ negative attitudes and hostility towards out-group members would be alleviated since team members have more opportunities to realize and observe the positive aspects of out-group members (e.g., friendly, competent, and dependable). Thus, we propose the following hypotheses:

H6a: Members’ ingratiation behaviors attenuate the negative indirect effects of demographic faultlines on NGAT by reducing group identification.

H6b: Members’ self-promotion behaviors attenuate the negative indirect effects of demographic faultlines on NGAT by reducing group identification.

H6c: Members’ exemplification behaviors attenuate the negative indirect effects of demographic faultlines on NGAT by reducing group identification.

Method

Sample and Procedure

Undergraduate students (n = 562) working on tasks in 101 newly formed teams were recruited from eight undergraduate management courses at a university in northern Taiwan. Teams were chosen from management courses for two reasons. First, the course schedules, materials, requirements, and the teaching methods were standardized across the eight management courses, which helped to rule out exogenous variables that may have influenced the research findings (Schwab, 2005). Second, the management courses were elective courses open to all students across different concentrations/majors. As a result, students from different educational backgrounds and ages could form teams together.

As part of the class requirements, members of each team were asked to discuss management cases and prepare group presentations every 2 weeks, and to finish a term project by the end of the semester. The final grades were based on the teams’ outputs such as their performance on the final group presentation and term project. Thus, members of different teams must compete with each other to gain better grades and have to regularly organize meetings, outline task schedules, and assign weekly tasks. These characteristics fit the definition of self-managed teams (Sy et al., 2005).

In the first week of the course (time 1), the undergraduate students were randomly assigned to teams of 5 to 7 members (M team size = 5.64 persons, SD = 1.77). Following the approach of Tasa et al. (2007), instructors were asked to form teams by randomly drawing names from the student lists in sequence. After forming the teams, the instructors collected data regarding the age, gender, and educational background of all team members (to estimate the demographic faultlines). At this time (time 1), class instructors gave all team members about 1 hour to introduce themselves and get to know each other (some members may engage in impression management behaviors in this initial interaction). One week after the teams were formed (time 2), data were collected about team members’ impression management behaviors, group identification, GAT, and the control variables before the first case presentations to avoid the possibility that their performance on presentations may change their perceptions of GAT and group identification. Since all teams were required to prepare for the first case presentation at T2, team members had to organize meetings, discuss the cases and prepare for presentations within the week between T1 and T2. On average, team members met about two times a week (M = 1.72 times/week) and spent approximately 5 hours together. Therefore, team demographic faultlines and impression management behaviors should have been able to influence group identification and GAT during initial team interactions.

In total, data from 101 newly formed teams (523 members; M team size = 5.64 persons, SD = 1.77) were used in the subsequent analyses. Team members were predominantly female (56 %). Team member mean age was 20.38 years old (SD = 1.58). Finally, team members’ educational background is composed of 18 different concentrations/majors, which include: management (17%), accounting (14%), international business (12%), financial management (12%), information systems (10%), statistics (5%), education (3%), English (3%), Chinese (2%), history (3%), diplomacy (3%), psychology (3%), advertising (3%), journalism (2%), political science (2%), economics (3%), public affairs (2%), and law (1%).

Measures

Following Brislin’s (1980) suggestion, we first translated the original English version of the questionnaire into Chinese, and then two bilingual foreign-language experts translated it back from Chinese to English. Three organizational-behavior scholars reviewed this translation for appropriateness to ensure the validity of our measures.

Positive and Negative Group Affective Tone

To assess PGAT and NGAT, we followed previous studies (Chi & Huang, 2014; George, 1990, 1995; Tsai et al., 2012) and asked team members to evaluate their positive and negative moods during team meetings, and then tested the within-group agreement of each group on team members’ positive and negative moods to determine the suitability of aggregation to the group level.

Team members’ moods were measured using the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988). To match the 1-week time frame used in past studies (e.g., George, 1990, 1995), team members were asked to indicate the extent to which each of the adjectives (e.g., excited, enthusiastic, hostile, nervous) described their feelings during team meetings in the past week. Responses were made on a 5-point Likert scale (1 = very slightly or not at all to 5 = extremely). Cronbach’s α for PGAT was 0.89, for NGAT, 0.84.

Team demographic faultlines

Although there are several possible ways of measuring faultlines (Li & Hambrick, 2005; Shaw, 2004; Thatcher and Patel, 2012), Meyer and Glenz’s (2013) review suggested that the average silhouette width (ASW) measure is the most robust method (Meyer et al., 2014). Specifically, the ASW measure is a measure of the quality of the team’s within-subgroup homogeneity and the between-subgroup separation, which are perfectly aligned with the definition of a faultline.

Hence, we followed the approach of Meyer et al. (2016) and calculated the strength of demographic faultlines using the asw.cluster package developed by Meyer and Glenz (2013) in R software. As mentioned earlier, the most commonly used attributes in demographic faultlines are age, educational background, and gender; these three attributes when calculating the demographic faultlines (Meyer et al., 2016). We entered the team members’ ages, their genders (male, female), and members’ concentrations/majors. Then the asw.cluster package generated the values of demographic faultlines for all teams. In the present study, the values of demographic faultlines ranged from 0 to 1 (M = 0.69, SD = 0.30), suggesting the teams have moderate levels of demographic faultlines with acceptable variances.

Team member impression management behaviors

Turnley and Bolino’s (2001) scale was used to assess team members’ positive impression management behaviors (i.e., ingratiation, self-promotion, and exemplification) during team interactions. To match the time-frame of GAT, team members were asked to indicate on a 5-point Likert scale (e.g., 1 = strongly disagree to 5 = strongly agree) whether they engaged in the described behaviors during the team meeting in the past week.

To assess different types of team member impression management behaviors, a four-item subscale was used to measure members’ ingratiation behaviors (e.g., “Praise your group members for their efforts so that they will consider you a nice person”; “Take an interest in other group members’ personal lives to show them that you are friendly”). Self-promotion was measured using the four-item subscale (e.g., “Make other group members aware of your unique skills and abilities”; “Speak proudly about your past accomplishments that may help to make the term project successful”). Exemplification was assessed by a five-item subscale (e.g., “Let others know that you have been putting in a lot of time on the project.” “Let other group members know how hard you have been working on this project”). Cronbach’s α values for the three scales were 0.81, 0.76, and 0.77, respectively.

We expect that team members engage in similar impression management behaviors within teams for two reasons. Theoretically, as all members are assigned to join a new team, members have strong motivations to display positive impression management behaviors in order to construct positive images (e.g., competent, likable, conscientious) and gain social approval from new members (Leary & Kowalski, 1990; Roberts, 2005). Empirically, Mulvey et al. (1998) found that team members’ defensive impression management behaviors are highly consistent within a group. Thus, we expect members of each team would display similar impression management behaviors.

Finally, in their framework, Jones and Pittman (1982) also discussed two negative impression management behaviors: supplication (i.e., team members advertise their shortcomings to be viewed as needy) and intimidation (i.e., team members seek to appear threatening to be seen as dangerous). Although we believe that team members are less likely to engage in these two types of negative impression behaviors (since they want to create positive impressions to be accepted by other members), these two negative impression management behaviors have rarely been studied in the team context (Nguyen et al., 2008). Hence, team members’ supplication and intimidation behaviors were included in the model and examined their relationships with group identification and GAT in an exploratory vein.

Data about members’ supplication and intimidation behaviors were collected using Turnley and Bolino’s (2001) scale. Both supplication and intimidation behaviors were assessed using five-item subscales. Responses were made based on a 5-point Likert scale (e.g., 1 = strongly disagree to 5 = strongly agree). Cronbach’s α values for the two scales were 0.82 and 0.78, respectively.

Group identification

Group identification was measured using Cameron’s (2004) 12-item scale. Team members were asked to evaluate their feelings towards other members during the past week (e.g., “I feel strong ties to other group members”; “I have a lot in common with other group members”; “I don’t feel a sense of being connected with other group members” [reverse coding]). Responses were made on a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree). Cronbach’s α for this scale was 0.83.

Control Variables

In order to rule out alternative explanations, we controlled for the effects of potential confounding variables. As George (1990) noted, team members’ averaged positive and negative affective traits can influence PGAT and NGAT, respectively. To control for the effects of member personality composition (George, 1990), data on team members’ mean-level extraversion and neuroticism were collected using Gosling et al.’s (2003) scale (1 = strongly disagree to 7 = strongly agree).

In addition, given that some members may know other members before they were assigned to the same team (e.g., they were friends prior to the beginning of the course), this familiarity among team members may influence the socialization process and the group affect within teams. Thus, we developed one item that asked members to evaluate the extent to which they were familiar with their team members (0 = not at all; 100 = very familiar; M = 72.92; SD = 14.98). The responses from the same team members were aggregated into a team-level construct referred to as member familiarity, and controlled for it in the subsequent analyses. Third, since members’ interaction frequency can influence emotional contagion within teams (Bartel & Saavedra, 2000), we controlled for team members’ interaction frequency during the week between time 1 and time 2 (M = 1.72, SD = 0.84) to partial out its effect.

Validity of Measures

In order to evaluate the discriminant and convergent validity of the study measures, several confirmatory factor analyses (CFA) were conducted using LISREL 8.54 with maximum likelihood estimations, and compared the fit indices between the proposed eight-factor model² (i.e., three positive impression management behaviors, PGAT, NGAT, group identification, and two negative impression management behaviors) and two alternative models: a seven-factor model and a six-factor model. For the seven-factor model, since group identities and PGAT capture team members’ positive feelings and experiences during team interactions, group identification and PGAT into one factor. As for the six-factor model, we combined items that may reflect negative feelings during team meetings (i.e., NGAT and the two negative impression management behaviors).

The CFA results show that the proposed eight-factor model provided a better fit to the data (GFI = 0.90, CFI = 0.97, NFI = 0.96, SRMR = 0.04) than the seven-factor model (GFI = 0.86, CFI = 0.95, NFI = 0.78, SRMR = 0.05) and the six-factor model (GFI = 0.86, CFI = 0.95, NFI = 0.77, SRMR = 0.05). The Chi-square tests show that the χ² decrements among the proposed 8-factor model, 7-factor model, and 6-factor model were statistically significant (Δχ² = 532, Δdf = 8, p < .01; Δχ² = 642, Δdf = 15, p < .01, respectively).

Moreover, a confidence interval test was performed to assess the discriminant validity between the study variables by calculating the confidence interval of plus or minus two standard errors around the correlations among these factors. If the confidence interval does not include 1.0, then discriminant validity is demonstrated (Anderson & Gerbing, 1988). The results show that the confidence interval of each pair of factors did not include 1, which indicated good discriminant validity among these variables. Finally, the factor loadings of all items were significant (p < .01), suggesting that the convergent validity of measures was acceptable (Bagozzi et al., 1991).

Data Aggregation

To investigate the team-level properties of measures and the appropriateness of the data aggregation, we first examined inter-rater agreement by calculating the r_wg values (James et al., 1984) of main study variables. Following LeBreton and Senter’s (2008) suggestion, r_wg values were calculated using uniform, triangular, and skewed distributions, respectively. The results show that all r_wg values ranged from 0.71 to 0.97. In addition, all ICC (1) values ranged from 0.13 to 0.27, while ICC (2) values ranged from 0.47 to 0.65. Although our ICC (2) values were slightly lower than the conventionally accepted value of 0.70 (Bliese, 2000), Chen and Bliese (2002) have noted that the aggregation should be based on high r_wg values and significant between group variance (i.e., ICC[1] values). Therefore, aggregating individual members’ responses into the team-level was deemed to be appropriate.

Data Analysis

To test the proposed hypotheses, we used the PROCESS program (Hayes, 2012) to compute the confidence intervals (CIs) of the indirect effects of demographic faultlines and team members’ impression management behaviors on GAT via group identification. To test H6a to 6c, PROCESS was used to compute the conditional indirect effects of demographic faultlines on NGAT via group identification under different levels of team ingratiation, self-promotion, and exemplification behaviors. Mean-centered the variables were used to construct the interaction terms (Aiken & West, 1991).

Results

Table 1 presents the means, standard deviations, reliabilities, and correlations among the study variables. As can be seen in Table 1, team members’ mean levels of extraversion and interaction frequency were positively related to PGAT (r = 0.23 and 0.23, all p values < .05). In addition, members’ mean levels of neuroticism, supplication, and intimidation were positively related to NGAT (r = 0.24–0.32, all p values < .05). These findings suggest that controlling for the effects of the aforementioned variables was meaningful.

Table 1.

Means, Standard Deviations, Reliabilities, and Correlations Among Study Variables.

Variables	Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13
1. Extraversion (mean)	4.49	0.72	(0.77)
2. Neuroticism (mean)	3.43	0.66	−0.15	(0.60)
3. Member familiarity	72.92	14.98	0.02	0.04	–
4. Interaction frequency	1.72	0.84	0.11	0.06	0.43**	–
5. Supplication	2.27	0.32	−0.29**	0.18	0.02	0.13	(0.82)
6. Intimidation	2.17	0.42	−0.17	0.18	0.16	0.03	0.20*	(0.78)
7. Demographic faultlines	0.69	0.29	−0.01	0.10	−0.17	−0.01	−0.07	−0.02	–
8. Ingratiation	3.55	0.36	0.06	0.17	0.23*	0.07	0.07	−0.03	0.11	(0.81)
9. Self-promotion	3.52	0.38	0.17	0.05	0.32**	0.07	−0.12	0.09	0.13	0.55**	(0.76)
10. Exemplification	3.31	0.36	0.08	−0.16	0.19	0.01	0.02	0.14	0.08	0.28**	0.39**	(0.77)
11. Group identification	4.38	0.44	0.31**	−0.15	0.57**	0.32**	−0.10	−0.10	−0.30**	0.36**	0.47**	0.21*	(0.83)
12. PGAT	2.80	0.36	0.23*	−0.22*	0.16	0.23*	0.04	−0.07	−0.03	0.20*	0.44**	0.25*	0.42**	(0.89)
13. NGAT	1.41	0.28	−0.21*	0.25*	−0.01	0.01	0.24*	0.32**	0.26*	−0.21*	−0.17	−0.10	−0.30**	−0.15*	(0.84)

Note. 1: Cronbach’s alpha coefficients are presented in boldface on the diagonal; *p < .05. **p < .01. N = 101. 2: PGAT, NGAT and team member IM behaviors were measured using a 5-point Likert scale. Group identification was measured using a 6-point Likert scale. Extraversion and neuroticism were measured using a 7-point Likert scale.

Hypothesis Testing

Relationships between team demographic faultlines, group identification, and NGAT

The results of hypothesis testing are presented in Table 2. Model 5 of Table 2 shows that after controlling for the effects of control variables and other predictors, team demographic faultlines are positively related to NGAT (β = 0.20, p < .05). Hence, H1 was supported. In addition, Model 1 of Table 2 indicates that team demographic faultlines had negative associations with group identification (β = −0.20, p < .01), and group identification was negatively related to NGAT (β = −0.21, p < .05; see Model 6). To test the significance of the proposed mediating effect, we used Hayes’ (2012) PROCESS program to calculate the 95% confidence intervals (95% CIs) of this mediating effect. The results of PROCESS indicated that team demographic faultlines significantly and positively predicted NGAT via reducing group identification (indirect effect = 0.05, 95% CI [0.01, 0.16]). Hence, H2 also received support.

Table 2.

Results of Hierarchical Regression Analyses.

Variables	Group identification		PGAT		NGAT
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Control variables
Member familiarity	0.40**	0.37**	−0.15	−0.14	0.03	0.09
Interaction frequency	0.15	0.14	0.16	0.16	−0.01	0.02
Mean-level extraversion	0.20**	0.19*	0.18†	0.18†	−0.07	−0.04
Mean-level neuroticism	−0.13	−0.12	−0.09	−0.08	0.17†	0.13
Main effects
Demographic faultlines (DF)	−0.20**	−0.19*	−0.11	−0.10	0.20*	0.18†
Team ingratiation	0.22**	0.21*	0.02	0.02	−0.14	−0.13
Team self-promotion	0.21**	0.17†	0.34**	0.33**	−0.06	−0.03
Team exemplification	−0.03	−0.04	0.22**	0.20*	−0.08	−0.08
Team supplication	−0.06	−0.07	0.08	0.08	0.16†	0.14
Team intimidation	−0.11	−0.06	−0.16†	−0.15	0.33**	0.32**
Group identification				0.22**		−0.21*
Interaction terms
DF*team ingratiation		0.31**
DF*team self-promotion		0.21*
DF*team exemplification		−0.12
DF*team supplication		−0.14
DF*team intimidation		−0.18†
Total R²	0.55**	0.64**	0.29**	0.36**	0.30**	0.36**
ΔR², each step	–	0.09**	–	0.07*	–	0.06*

Note. 1: Standardized regression coefficients (β) are shown in each equation. N = 101 teams; †p < .10; *p < .05; **p < .01 (two-tailed). 2: The results were unchanged if all control variables were removed. Similarly, the results were unchanged if we removed supplication and intimidation from the model.

Relationships between team impression management, group identification, and PGAT

For H3a, H4a, and H5a, Model 3 of Table 2 reveals that team self-promotion and exemplification behaviors were positively related to PGAT (β = 0.34 and 0.22, all p values < .01), whereas ingratiation behaviors were not. Therefore, H4a and H5a were supported, but Hypothesis 3a was not.

As for H3b, H4b, and H5b, Model 1 of Table 2 indicates that team ingratiation and self-promotion were positively related to group identification (β = 0.22 and 0.21, all p values < .01), while exemplification behaviors were not. In addition, group identification had a positive relationship with PGAT (β = 0.22, p < .01, see Model 4). The results of PROCESS showed that both team members’ ingratiation and self-promotion behaviors predicted PGAT indirectly via increased group identification (indirect effects = 0.08 and 0.06, 95% CIs are [0.02, 0.21] and [0.01, 0.17], respectively). Thus, Hypotheses 3b and 4b were supported, whereas H5b was not.

Team positive impression management behaviors as boundary conditions: A moderated mediation model

To test H6a, H6b, and H6c, we first examined whether the three types of positive impression management behaviors moderate the association between team demographic faultlines and group identification. As revealed in Model 2 of Table 2, team ingratiation and self-promotion behaviors positively and significantly moderated the team faultlines-identification relationship (β = 0.31 and 0.21, all p values < .05), whereas exemplification behaviors did not. Thus, H6c was not supported. To clarify the forms of moderation, we followed Aiken and West’s (1991) approach and plotted the team faultlines-identification relationship under high (1 SD above the mean) and low (1 SD below the mean) levels of team ingratiation and self-promotion behaviors. As shown in Figure 2, the negative relationship between team demographic faultlines and group identification was strengthened when members display low levels of ingratiation behaviors (simple slope = −0.18, p < .05). However, this relationship became non-significant when the level of team members’ ingratiation behaviors was high (simple slope = 0.02, ns). Similarly, Figure 3 reveals that the negative team faultlines-identification relationship was strengthened when the level of self-promotion behaviors was low (simple slope = −0.17, p < .05). However, this negative relationship became non-significant when team members display high levels of self-promotion behaviors (simple slope = 0.01, ns).

Figure 2.

The moderating effect of team ingratiation behaviors on the relationship between team demographic faultlines and group identification.

Figure 3.

The moderating effect of team self-promotion behaviors on the relationship between team demographic faultlines and group identification.

Finally, we applied Hayes’ (2012) PROCESS program to calculate the 95% CIs of the proposed indirect effect of team demographic faultlines on NGAT via group identification under high/low levels of team ingratiation and self-promotion behaviors. The results show that the positive indirect effect of team demographic faultlines on NGAT via group identification became stronger when the level of team ingratiation behaviors was low (indirect effect = 0.10, 95% CI [0.01, 0.24]). However, this indirect effect became non-significant when team members display high level of ingratiation behaviors (indirect effect = 0.02, 95% CI [−0.03, 0.13]). Hence, H6a was supported. Similarly, the results of PROCESS indicate that the positive indirect effect of team demographic faultlines on NGAT via group identification was enhanced when the level of team self-promotion behaviors was low (indirect effect = 0.11, 95% CI [0.02, 0.27]). However, this positive indirect effect was not significant when team members displayed high level of self-promotion behaviors (indirect effect = 0.02, 95% CI [−0.08, 0.17]). These patterns were consistent with H6b.

Additional analyses

Although most results were consistent with our hypotheses, two methodological concerns need to be addressed. First, as all study variables were rated by team members, the common method variance (CMV) problem may have influenced the findings. To address this issue, additional analyses were conducted whereby we randomly selected half of the members from each team as sources for IM measures and the other half as sources for group identification (identical to the approach described above for group identification and GAT). If team members’ agreement in terms of the study variables is high enough (i.e., r_wg values), it is appropriate to randomly separate the teams into two parts and obtain measures from the two parts (Podsakoff & Organ, 1986). Since 23 teams were composed of 3 members each, it was inappropriate to separate them into two parts. Thus, these 23 teams were removed when performing the analyses. The additional analyses showed that our findings were unchanged, which suggested that the CMV problem did not adversely influence our findings.

Finally, we also examined the relationships between two negative impression management behaviors (i.e., supplication and intimidation) and group identification/GAT in an exploratory vein. Model 5 of Table 2 shows that team members’ supplication behaviors had a marginally significant relationship with NGAT (β = 0.16, p < .10), while intimidation behaviors had a strong and negative association with NGAT (β = 0.33, p < .01). However, both negative impression management behaviors were unrelated to group identification.

Discussion

Theoretical Implications for Group Affect Literature

Based on the similarity attraction, emotional contagion, and social influence perspectives, previous GAT studies have primarily investigated how team members’ personality compositions, leader/members’ moods and behaviors influence GAT (Barsade & Knight, 2015; Collins et al., 2013). In the present study, team demographic faultlines positively predicted NGAT through reduced group identification. Compared with existing studies, our findings take a further step by highlighting how differences in team members’ observable attributes (i.e., age, gender, and educational background) may serve as the basis for forming demographic faultlines and triggering social categorization processes. These findings also echo previous researchers’ propositions that team diversity may increase members’ negative affective states within teams (e.g., Garcia-Prieto et al., 2003; Phillips & Lount, 2007).

Moreover, team members’ exemplification behaviors were positively related to PGAT, and team members’ ingratiation and self-promotion behaviors positively predicted PGAT via enhancing group identification. These findings supported the argument of the image construction process. GAT researchers often use the socialization or social influence perspective to theorize the antecedents of GAT (e.g., Bartel & Saavedra, 2000; George, 1990, 1996): team members observe and learn adequate behaviors and norms during the team interactions, which can influence their moods within teams (Bartel & Saavedra, 2000; Sy et al., 2005). However, both socialization and social influence processes are less likely to occur in initial interactions when the teams are newly formed. Our findings suggest that, in newly formed teams, team members can enhance PGAT by performing positive impression behaviors such as ingratiation, self-promotion, and exemplification in their early interactions.

Finally, it should be noted that though we controlled for the effects of previous theoretically-related variables in our analyses, such as variables related to similarity attraction (i.e., members’ similarity and team members’ personality traits), emotional contagion (i.e., interaction frequency for emotional contagion) and member socialization (i.e., member familiarity), we found that team members’ demographic faultlines and positive impression management behaviors differentially predict group identification and GAT in newly formed teams. These approaches help to highlight the unique effects of the social categorization and image construction processes in predicting GAT when the teams are in the forming stage.

Theoretical Implications for Diversity and Impression Management Literature

Although diversity researchers argue that diversity leads to higher NGAT through the social categorization process (Phillips & Lount, 2007; Williams & O’Reilly, 1998), relatively few studies have directly tested this assertion. The present investigation provides a direct test of this argument and highlights the important roles of team demographic faultlines in predicting NGAT. Furthermore, we also advanced the findings in diversity and faultline literature by showing when the detrimental effects of demographic faultlines can be alleviated. Specifically, we found that team members’ ingratiation and self-promotion behaviors help to mitigate negative indirect effects of team demographic faultlines on NGAT through reducing group identification. These suggest that team members can actively engage in positive impression management behaviors to modify or replace the incorrect stereotypes or social categorization activated by differences in demographic attributes (Harrison et al., 1998, 2002).

Interestingly, team exemplification behaviors did not buffer the social categorization processes triggered by team demographic faultlines. Given that members’ exemplification behaviors (i.e., putting extra effort into the project) may require a longer period of time to be noticed by other members, it is plausible that the limited interaction frequency among team members in our sample (M = 1.72) constrained the effects of exemplification behaviors. In addition, the present findings also offer theoretical implications for the impression management field. In their comprehensive review, Bolino et al. (2008) called for more studies to examine the effect of impression management behaviors using a multi-level perspective. Responding to their call, our findings indicate that team members’ ingratiation and self-promotion behaviors can lead to greater group identification and PGAT, whereas exemplification behavior can increase PGAT indirectly. In addition, the two types of negative impression management behaviors explored in this study can enhance NGAT. These findings suggest that impression management behaviors are meaningful constructs at the team-level that trigger different team outcomes.

Practical Implications

Since team members’ demographic faultlines enhance NGAT through decreasing group identification, it is important for managers to consider appropriate age, gender, and educational background composition when selecting members in newly formed teams. Specifically, managers should be aware of potential problems in teams with high age, gender, and background diversity, which can create strong faultlines within the teams, thereby enhancing team members’ negative reactions during the forming stage of the team’s development.

Second, as team members’ ingratiation and self-promotion tactics positively predict group identification and PGAT as well as buffering the indirect effects of team demographic faultlines on NGAT, organizations can provide team management training to teach members how to appropriately use positive behaviors to enhance other members’ positive impressions and facilitate team-building (Roberts, 2005). This suggestion may be particularly useful during initial interactions after the teams are formed.

Limitations and Directions for Future Research

Several limitations of our study should be noted. The first concerns the difficulty of making causal inferences from the cross-sectional design used. For the faultline-GAT associations, since team members’ gender, age, and educational background composition are objective facts that occurred before data collection, it is less reasonable to argue that NGAT influences team diversity. However, we encourage future researchers to re-examine the impression management behaviors-group identification-GAT linkage by employing experimental research design. For example, future researchers can manipulate team members’ impression management behaviors, and then measure the changes in group identification. After this, researchers can manipulate the group identification and identify the effects of group identification on PGAT (Van Kleef et al., 2006).

The second limitation is the potential problem of CMV. However, as noted in the results of the additional analyses, our major findings remained unchanged after we randomly selected half of the members from each team as the source for team impression management measures and the other half as the source for group identification (as was done for group identification and GAT). The results showed that CMV was not a serious problem in our study. Third, student teams were the sample. Thus, it is unclear whether our findings can be generalized to other types of teams. However, Langfred (2004) and Sy et al. (2005) suggested that student self-managed teams can control their work methods, schedules, meetings, and task assignments, which are similar to the interactions that are required in actual self-management teams. Thus, the findings of our study should be applicable to other types of self-managed teams such as production or project teams (Kozlowski & Bell, 2003).

Finally, our sample is composed of student teams. Thus, we are not able to examine whether the team- or individual-based reward and performance evaluation systems influence the effects of team faultlines and impression management behaviors on GAT via group identification. In real teams that emphasize team-based outputs and rewards (Chi et al., 2009), it is plausible that team members pay more attention to the characteristics and impressions of other members since this information is critical for team success. Hence, the effects of both social categorization and image construction pathways on GAT may be amplified when organizations implement team-based reward and performance evaluation systems. However, when organizations employ individually based reward and appraisal systems, other members may view individual members’ impression management behaviors as forms of image manipulation and pursuit of self-interest, which may create more conflicts between team members and between team sub-groups. Therefore, the beneficial effects of team impression management behaviors may be inhibited. Future researchers could re-examine our theoretical model in newly formed teams in the organizations, and investigate whether the level of team-based pay (Balkin & Montemayor, 2000) acts as a boundary condition of our findings.

Footnotes

Acknowledgements

We thank Chiung-Yi Huang for her useful suggestions about the calculation of demographic faultlines strength.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nai-Wen Chi

Author Biographies

Nai-Wen Chi is Distinguished Professor and the Director of Graduate Institute of Human Resource Management at National Sun-Yat Sen University. His research areas include group affective tone and emotional labor. His papers have been published at Journal of Applied Psychology, Journal of Management, and Personnel Psychology.

Wei-Chi Tsai is Professor of the Department of Business Administration at National Chengchi University. His research interests include affect at work, impression management, employment interviews, and personnel recruitment. His work appears in Journal of Applied Psychology, Journal of Management, and Journal of Organizational Behavior.

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