This is sure to eventually be a top tier publication ...
Consistency Matters! The Effects of Group and Organizational Culture on the Faultline-Outcomes Link
Katerina Bezrukova
Department of Psychology
Rutgers University
311 N. 5th Street
Camden, NJ 08102
Phone: 856-225-6120
Fax: 856-225-6602
E-mail: bezrukov@camden.rutgers.edu
Sherry M.B. Thatcher
Management Information Systems Dep't
University of Arizona
Tucson, AZ 85721
Tel: 520-621-2255 Fax: 520-621-2433
E-mail: sherryt@bpa.arizona.edu
Karen A. Jehn
Social and Organizational Psychology
Leiden University
Wassenaarseweg 52
Leiden 2333 AK
The Netherlands
E-mail: jehnka@fsw.leidenuniv.nl
Consistency Matters! The Effects of Group and Organizational Culture on the Faultline-Outcomes Link
ABSTRACT
We explain how two types of group and organizational culture (e.g., cultures about the importance of careers and acceptance of diversity) moderates the relationships between group faultlines and individual outcomes. Group faultlines are defined as hypothetical dividing lines that split a group into relatively homogeneous subgroups based on the group members’ alignment along one or more attributes (adapted from Lau & Murnighan, 1998). We extend the group composition literature by showing how different faultline bases (informational and social category) have different effects on employee’s performance and turnover rates under different cultural conditions (consistent vs. inconsistent). We test our hypotheses using data from 110 groups consisting of 671 individuals in a Fortune 500 information processing company. Our results revealed that members of groups with social category faultlines had lower levels of performance and a higher rate of turnover. Members of groups with informational faultlines were awarded higher bonuses in groups with a diversity-focused group culture, but lower levels of performance ratings and higher rates of turnover in groups with a career-focused group culture. Three-way interactions between faultlines, group cultures, and organizational cultures were also found and are discussed.
Consistency Matters! The Effects of Group and Organizational Culture on the Faultline-Outcomes Link
Recent research in the area of group composition has moved away from studying the direct effects of heterogeneity (or diversity) on outcomes due to the mixed and contradictory results relating to the effects of diversity and performance (Milliken & Martins, 1996; Williams & O’Reilly, 1998). Group composition researchers have responded to these findings in two ways. First, some researchers grouped diversity variables based on similar attributes (e.g., social category diversity and informational category diversity) (Jehn, Chadwick, & Thatcher, 1997; Jehn, Northcraft, & Neale, 1999; Polzer, Milton, & Swann, 2002) and investigated their relationship to outcomes. The second, and most recent, response has been to focus on the issue of demographic alignment as put forth in the group faultlines theory introduced by Lau & Murnighan (1998). A recent paper by Bezrukova, Thatcher and Jehn (2004) combined these two approaches and found that faultlines based on social categories did indeed predict different group processes and outcomes than faultlines based on informational categories.
Since group composition may interact with a variety of other group and organizational factors (Williams & O’Reilly, 1998), another way to explain the inconsistencies in empirical results is to look at the relationship between group composition and performance in a more complex framework and consider the role of contextual variables (Chatman, Polzer, Barsade, & Neale, 1998; Jehn, et al., 1999; Rousseau & Fried, 2001). According to Johns (2001), the context of individuals and groups often works in such a way as to encourage or impede behavior and attitudes in organizational settings. Recent research on diversity has suggested that group and organizational cultures may be of great importance when considering the effects of group diversity (Chatman et al., 1998; Jehn, 1994; Mannix, Thatcher, & Jehn, 2001). Furthermore, recent work on organizational climate has suggested that the strength of organizational cultures may also influence outcomes important to organizations (Schneider, Salvaggio, & Subirats, 2002).
There are three objectives of this paper. First, we will explain how two different types of group culture (e.g., cultures about career advancement and diversity) represent a contextual variable that moderates the relationships between group faultlines and individual outcomes. We propose that these group cultures may serve as moderators of the relationships in which a phenomenon at one level (e.g., group faultlines) has an impact at another level (e.g., individual performance, turnover rates) (Klein & Kozlowski, 2000; Rousseau, 1985). Second, we will contribute to theory on culture by investigating the cross-level effects of both group and organizational culture. In effect, we show that cross-level integration of culture enables us to understand culture consistency. Third, we show how group and organizational culture congruency (or cultural consistency) can be a strong contextual variable when understanding the effects of group faultlines on individual outcomes. We then test these relationships empirically.
Social Category and Informational Faultlines
We define group faultlines as hypothetical dividing lines that split a group into subgroups based on two or more characteristics (Lau & Murnighan, 1998). Most research on diversity in groups and organizations has looked at diversity as a composite of an individual’s various demographic characteristics (Thatcher & Jehn, 1998; Williams & O’Reilly, 1998). From this perspective diversity has been considered as a group-level variable defined as the degree to which there is dispersion of a particular demographic characteristic in a specific population (Blau, 1977). We advance the traditional understanding of diversity by utilizing a group faultlines approach (Lau & Murnighan, 1998; Thatcher, Jehn, & Zanutto, 2003) that takes into account more than one demographic characteristic at a time, the way the characteristics align, and the number of possible subgroupings that emerge.
Recent work has demonstrated how one can measure group diversity with a combination of characteristics. Jehn and her colleagues (1997; 1999) have stressed the value in distinguishing between forms of heterogeneity (e.g., those based on social categories and those based on informational categories). Bezrukova, et al. (2004) have applied the same logic to faultlines as described below.
Social category characteristics are observable attributes such as race/ethnic background, nationality, sex, and age (Cummings, Zhou & Oldham, 1993; Jehn, et al., 1999; 1997). Social category diversity is dispersion across members of a group on social category characteristics that are easily observed by others and used for categorization purposes. While social category diversity may not be relevant to the given task, it does shape people's perceptions and behaviors (Pelled, 1996) through mechanisms of categorization and prejudice. Age, race, and sex prejudice reflect the same categorization process of distinguishing between similarity and dissimilarity, leading to stereotyping and misinformation. These categorizations may or may not be accurate, and may lead to conflict if individuals are not living up to the expectations of others which coincide with these categories. The same logic used to develop social category diversity can be applied to faultlines. Thus, social category faultlines are hypothetical dividing lines that split a group into subgroups based on social category demographic characteristics (e.g., age, race, gender) (Bezrukova, et al., 2004).
Informational characteristics are underlying attributes of individuals (such as work experience and education) which, although not immediately detectable, are important in the completion of a task (Jehn et al., 1997). The information/decision-making perspective suggests that diversity will have positive implications on workgroup outcomes, since the group will have access to a wider array of views, skills, and information (Gruenfeld, Mannix, Williams, & Neale, 1996). Educational background, functional background, and industry experience are all linked to the set of skills one employs when undertaking a task. However, different backgrounds can lead to fundamentally different preferred ways of completing a task. Therefore, informational faultlines are hypothetical dividing lines that create subgroups based on informational demographic characteristics (e.g., work experience, tenure) (Bezrukova, et al., 2004).
Group and Organizational Cultures
The essential core of culture consists of traditional ideas and especially their attached values and the extent to which these ideas and values are accepted by a group (Kroeber & Kluckhohn, 1963). There are different levels of analysis from which to study culture. For example, culture exists at societal, national, and regional levels (DiMaggio & Powell, 1983). Within organizations, culture can exist at an organizational, business unit, department, or group level (Chatman & Jehn, 1994; Mannix et al., 2001).
Group culture is defined as the extent to which group members have consensus on values, norms, and appropriate behaviors related to work (adapted from Chatman & Jehn, 1994; Mannix, et al., 2001; Rousseau, 1990; Triandis & Suh, 2002). Group culture is an important variable to look at when investigating group composition because one of the most often studied moderators in group composition research is group values (Jehn, 1994; Probst, Carnevale, & Triandis, 1999). Group values refer to the individuals’ fundamental beliefs regarding the desirability of behavior choices (Enz, 1988; Rokeach, 1973). They reflect, for example, preferred ways to perform individual and group tasks such as being innovative, task-oriented, or career-oriented (Jehn, 1994; Jehn, et al., 1997; O'Reilly, Chatman, & Caldwell, 1991). Two primary concerns become relevant when researchers conceptualize group values: (1) the extent to which members care about values (value strength), and (2) the extent to which these values differ across settings (value content) (Flynn & Chatman, 2001; Mannix, et al., 2001). The content of values, and sequentially, the norms and the behaviors they support (thus, group culture), vary widely across groups in an organization (Bettenhausen & Murnighan, 1991; Jehn, 1994). The content of values we are interested in revolves around those important for work groups (i.e. values about career advancement and diversity). Thus, we examine how different group cultures shape the way in which group faultlines affect performance. In fact, past research on diversity suggests that strong group cultures may be “a powerful way for managers to use informational and social influence processes to encourage solidarity rather than divisiveness” (Williams & O'Reilly, 1998).
Following Reichers and Schneider’s (1990) definition of organizational culture, we define organizational culture as a common set of shared meanings or understandings about an organization. As in our discussion of group culture, the impact of organizational culture comes from the content and the strength of the shared meanings. Previous research has found that organizational culture affects group-level actions (O’Reilly, Williams & Barsade, 1998; Thomas & Ely, 1996). O'Reilly, et al. (1998) found that organizational cultures that supported ethnic diversity reported positive effects on performance. Similarly, Thomas and Ely (1996) found that organizations that have cultures in which diversity is viewed as an opportunity to learn rather than as a legal requirement tend to perform better. However, we argue that it is not merely the content or strength of the organizational culture that influences group-level relationships; it is the resulting impact of shared group values and organizational culture (cultural consistency) that influences the relationship between group faultlines and individual outcomes.
Group- and Organizational Culture Consistency
By exploring how group and organizational cultures simultaneously interact in moderating the relationships between group faultlines and individual outcomes, we are essentially talking about a congruency or consistency effect between group and organizational cultures. The general notion of congruency, or fit, has attracted much research attention in psychology and organizational behavior (Nadler & Tushman, 1980). This body of research draws on the interactional psychology literature; both an individual and the situation combine to influence an individual’s response in a given situation (Chatman, 1991). Although the majority of person-environment fit literature has focused on the fit between an individual and the individual’s job, researchers recently suggested that this focus is no longer appropriate due to the changes in the nature of work, organizations, and employer-employee relationships (Carson & Stewart, 1996; Kulik, Oldham & Hackman, 1987).
More recent studies have attempted to look at the fit between an individual and some measure of the organization’s environment (Werbel & Gilliland, 1999). Original person-environment fit literature proposed that good matches between environments and individuals would result in high performance, high levels of satisfaction, and low levels of stress (Pervin, 1968). Individuals who have a poor person-environment fit were more likely to be injured on the job than those who were more suitably matched to their environment (Sherry, 1991). Some of the organizational components that are used when conducting a study of person-organization fit are expectations or demands as well as the climate, culture, norms, values and strategic needs (Chan, 1996). Previous research on person-organization fit has focused on employee variables such as values, goals, interests and cognitive styles (O’Reilly, et al., 1991; Vancouver & Schmitt, 1991; Holland, 1985; Chan, 1996). O’Reilly, et al. (1991) found that person-organization fit on values was a predictor of satisfaction, commitment, and turnover. Vancouver and Schmitt (1991) found that person-organization goal congruence was negatively correlated with intent to leave and Chan (1996) found that cognitive misfit predicted actual turnover.
Person-workgroup fit is the most recent addition to this body of literature but as teams become more of a workplace reality, workgroups are becoming an increasingly important part of the workplace context (Chan, 1996; Werbel & Gilliland, 1999; Kristof, 1996; Judge & Ferris, 1992). Jehn (1994) examined the fit between group values and supervisor values and found that more conflict existed when there was a misfit between the values of the groups and the values of the supervisor. Person-group fit is especially important in organizations where teams play a strong role in the organization and employees interact with their team members on a regular basis. What has not been examined thus far is the impact of the “fit” between group and organizational cultures on group and individual behavior.
Although research on organizational culture views the “strength” of the culture as important, it is a difficult concept to measure. Recent work in the area of organizational climate (Schneider, et al., 2002) adapted and tested a conceptualization of culture strength from Chan (1998). This conceptualization of culture strength, in effect, measured the consistency of the culture (reliable and stable) within a group by looking at within-group variability in climate perceptions. We are attempting to bridge work in the areas of culture, congruence, and organizational climate by showing how the congruence of group and organizational cultures (the similarity across these two levels of culture) represents cultural consistency (a measure of strength as determined in the climate literature). We argue that cultural consistency is important because it can create extremely positive effects (in the case where the group and organizational cultures align) or extremely negative effects (in the case where group and organizational cultures do not align).
Therefore, we add to the literature on culture and fit in two ways. First, we look at group-organization culture consistency which extends the literature by examining the fit between two entities that has not been previously examined. Very little research has studied culture in settings involving multiple group boundaries, a serious gap when one considers the frequency of organizational decisions involving the interests of multiple groups (Polzer, 2002). Second, we draw from recent organizational climate literature to show how group-organization cultural consistency provides a strong context under which individuals and groups work.
The Effects of Information and Social Category Faultlines on Outcomes
Much research has been done to investigate the effects that different group composition variables have upon group performance (Milliken & Martins, 1996; Williams & O’Reilly, 1998). However, the reality of work life is that an employee works in a group but tends to be rewarded individually (Beersma & De Dreu, 2002). Thus, we hypothesize the effects of social category and informational faultlines on individual-level outcomes. Following Hackman’s (1987) model of effectiveness, we consider several performance outcomes: (1) individual performance ratings as determined by a group manager based on performance standards set by the company, (2) stock options and bonuses as drawn from individual, group, business unit, and company performance history, and (3) turnover rates, defined as the extent to which individuals leave their job.
We argue that faulty group processes, emerging from the negative categorization across subgroups formed by social category faultlines, may lead to severe losses in individual performance outcomes. The alignment of demographic attributes based on similarity of group members on gender, race and age is a sufficient condition for divisive social categorizations to come to play (Jehn, et al., 1999). The negative effects of stereotyping, in-group favoritism and out-group hostility can further sharpen the boundary salience around emerging subgroups, cause conflict and dislike to surface, and lead to decreased cohesion and social integration (Mackie, Devos, & Smith, 2000; Tajfel & Turner, 1986; Webber & Donahue, 2001). This lessens the frequency of interactions and information exchanges between members of different subgroups formed by social category faultlines, which in turn, may lead to individual productivity losses because both the amount of information and the access to the resource pool is reduced (Clement & Schiereck, 1973; Freidman & Podolny, 1992).
Hypothesis 1 (H1): Members of groups with strong social category faultlines will have lower levels of individual performance outcomes.
Literature on minority influence suggests that information sharing in diverse groups depends on the extent to which group members are provided with social support (c.f. Allen & Levine, 1971; Bragg & Allen, 1972). When a group has strong informational faultlines, its members may find support and validation for their knowledge (e.g., opinions, assumptions, information) in their subgroups due to mutual liking, shared experiences and perceived similarity of aligned members (Phillips, Mannix, Neale, & Gruenfeld, 2003). In such groups with strong informational faultlines, members may freely express their ideas and actively engage in open discussion of divergent perspectives across subgroups because they have support from within their own subgroup (Lau & Murnighan, 1998; Phillips, 2003). We further propose that in common-goal groups with informational faultlines, members are forced to integrate the divergent opinions into their view of the decision problem. As a result, synthesis of ideas that are superior to the individual solutions themselves (Schweiger & Sandberg, 1989; Schwenk, 1990) may emerge and thus reinforce individual performances of its members. Similar processes are highlighted by the literature on minority influence: minorities who argue consistently and flexibly are shown to promote a thorough, intensive elaboration of the problem (De Dreu & West, 2001; Moscovici, 1980; Phillips, 2003). Thus, we predict,
Hypothesis 2 (H2): Members of groups with strong informational faultlines will have higher levels of individual performance outcomes.
We further argue that dissatisfaction arising from either conflict or faulty processes (social category faultlines) or debating divergent opinions (informational faultlines) (Amason, 1996; Jehn, 1997) may impact an employee’s decision to leave his or her group. More specifically, the “us versus them” mentality of subgroups formed by social category or informational faultlines is likely to make it easy for one subgroup to blame the other subgroup for mistakes and create stressful and unpleasant environments. Tension and personal attacks within a group resulting from these processes can further escalate dissatisfaction among group members (e.g., Amason & Schweiger, 1994; Jehn, 1994) causing employees to leave. Thus, even though the two types of faultlines may have differential performance effects, we argue that the tension and conflict caused by having strong faultlines may lead to an environment that makes some individuals unhappy and uncomfortable. This, in turn, may lead to high levels of turnover.
Furthermore, once the boundaries around subgroups become salient, some subgroups can be automatically placed on the status hierarchy relative to the dominant subgroup (Asante & Davis, 1985). The status differentials between subgroups resulting from social category and informational faultlines may restrict access to important resources, deprive lower-status subgroup members (Mannix, 1993; Sherif, 1967) and create pay dispersion. Because pay dispersion influences an employee’s decision to leave or to stay with a group (Lazear & Rosen, 1981), we further propose that members in groups with strong social category and informational faultlines may feel less satisfied and thus, have stronger desire to leave.
While both types of faultlines may trigger an employee to quit his or her job, we predict that informational faultlines will contribute to employees’ turnover to a lesser degree than will social category faultlines. Members of groups with strong informational faultlines differ on demographic attributes that are directly job-related and pertinent to the task in hand. Members of such groups tend to have a stronger impact on perceptions of work group tasks and exhibit stronger attachment to their group as they know that the tension that may exist has positive implications for performance. These feelings are crucial determinants of people’s willingness to collaborate and stay with the group (Mitchell & Lee, 2001). Thus, we hypothesize:
Hypothesis 3 (H3): While individuals in groups with strong social category and informational faultlines will both have higher rates of turnover, social category faultlines will have a stronger effect on turnover rates than will informational faultlines.
The Moderating Effects of Group Culture and Group-Organization Culture
There are a number of facets of group and organizational culture that are relevant for individuals working in a group. We focus here on two of these facets: career advancement, and diversity (Jehn, 1994; O’Reilly, Chatman & Caldwell, 1991). We define career-focused group culture as the degree to which group members believe that career advancement opportunities are important to in their group (adapted from Riordan & Shore, 1997). Career-focused group culture provides a sense of career opportunities by sending messages to employees about design and implementation of group-level initiatives, such as selection, socialization and training (Higgins, 2001). Diversity-focused group culture refers to the extent that group members value differences (with regard to experiences, backgrounds, and work) and believe that diversity is important in their group (Richard & Johnson, 2001). Diversity-focused group culture sends messages to employees about the group’s values regarding diversity (Nemetz & Christensen, 1996) such as the acceptance and accommodation of various religious practices in the workplace (e.g., allowing days off for various religious holidays and special times for prayer).
Because group members’ differences in informational characteristics are directly related to the jobs that they perform, we argue that groups with informational faultlines may be more impacted by having a career-focused culture than groups with social category faultlines. Hence, we are specifically interested in looking at the effects of career-focused group culture with respect to informational faultlines. We argue that a career-focused group culture will have a positive influence on groups with informational faultlines because it reinforces members’ desire to succeed professionally. Kozlowski and Farr (1988) emphasized that work environment characteristics such as beliefs about career opportunities are key determinants of employees’ interest, engagement in career exploration, and rate of participation in development activities. Development opportunities include courses, workshops, seminars, and challenging assignments that enhance self–efficacy and influence professional and personal growth (London, 1989; Noe & Wilk, 1993). Self–efficacy includes employees’ beliefs that they can successfully cope with challenging situations (Bandura, 1977), and is positively related to task performance (Gist, Schwoerer, & Rosen, 1989) and the increased perceptions of career-related benefits (Noe & Wilk, 1993). As previously discussed, members of groups with strong informational faultlines enhance their self – efficacy due to social support provided by their subgroup members (Phillips et al., 2003). Thus, we argue that such a double effect of self-efficacy in groups with strong informational faultlines and career-focused group culture will produce positive effects.
Hypothesis H4 (H4): Career-focused group culture will moderate the relationship between informational faultlines and outcomes (performance and turnover) such that members of group with strong informational faultlines will have higher levels of performance and lower levels of turnover when their group culture emphasizes career advancement opportunities.
Groups with informational and social category faultlines and a culture of diversity will see the value of having members from different backgrounds, experiences, or demographic groups despite the difficulties. These groups should be able to focus on appreciating their members’ differences rather than focusing on differences in a negative way. Diversity group cultures may foster cooperation and a desire to solve problems collectively, thereby creating norms of tolerance and open communication (Hopkins & Hopkins, 2002). Employees in such environments may consider diversity as a valuable asset of their workgroup and embrace differences that can enhance effectiveness through creativity and innovation (Richard & Johnson, 2001). Workgroup environments in which employees believe that their group fairly values each group member’s contribution, may eventually result in their greater commitment and productivity due to common fate, shared values, and a sense of in-group membership facilitated by such cultures (Hicks-Clarke & Illes, 2000). Therefore, groups with cultures of diversity will have a moderating influence on the relationship between social category and informational faultlines and outcomes.
Hypothesis H5 (H5): Diversity-focused group culture will moderate the relationship between informational and social category faultlines and outcomes such that members of group with strong informational or social category faultlines will have higher levels of performance and lower levels of turnover rates when their group culture emphasizes diversity.
High congruence between group culture and organizational culture suggests that there is a consistent view of the culture across organizational levels. When cultures are consistent their effects are strong and thus, group-organizational career culture consistency should merely strengthen the results predicted in Hypothesis 4. More specifically, high consistency between shared members’ beliefs about career advancement opportunities existed within their group and within their organizational department should promote feelings of predictability, coherence and control (Schneider, Salvaggio, & Subirats, 2002). Such consistency between career-oriented group values and a higher-level organizational culture should produce positive responses because members of groups with strong informational faultlines will perceive uncertainty as more threatening to their relative stability and reliability. Thus, we argue that individuals in groups with strong informational faultlines will have high performance outcomes but this relationship will be strengthened in groups with strong group cultures on career achievement, and will be strengthened even further when there is strong group-organizational culture on career achievement. Thus,
Hypothesis H6 (H6): A three-way interaction between informational faultlines, career-focused group culture, and career-focus organizational culture is expected, such that members in groups with strong informational faultlines and career-focused group culture will have higher levels of performance and lower levels of turnover rates in departments that emphasize career advancement opportunities than in departments without such emphasis.
Similar to the rationale for Hypothesis 6, diversity-focused group-organization cultural consistency should strengthen the results predicted in Hypothesis 5. Individuals in groups with strong social category and informational faultlines will have higher levels of performance and lower turnover rates when they have strong diversity-focused group cultures and this relationship will be strengthened even further when there is strong group-organizational culture consistency on diversity. Thus,
Hypothesis H7 (H7): A three-way interaction between informational and social category faultlines, diversity-focused group culture, and diversity-focused organization culture is expected, such that members in groups with strong informational or social category faultlines and diversity-focused group culture will have higher levels of performance and lower levels of turnover in organizations that focus on diversity than in departments without such emphasis.
METHODS
Research Site
Our sample includes 110 groups from a Fortune 500 Corporation within the information processing industry. This company is truly global with business facilities in 130 countries and a World Headquarters in the United States. We identified the workgroups using a reporting system developed by the company, and information about the structure of the divisions and departments provided by key senior staff. We verified that these were actual working groups (i.e., they interacted on a day-to-day basis, were task interdependent, identified each other as group members, and were seen by others as workgroups) by interview and observation. Our groups included top- and middle-level managers who were responsible for monitoring the development and production, sales, marketing and distribution of the company’s products in their respective markets. Many groups were cross-functional and included representatives from corporate administration, finance, sales and marketing, product development and manufacturing divisions. We were informed by key senior staff and employees that “groups” of one or two employees or groups with over fourteen employees were not actual working groups. This is consistent with our definition of a group and with group process theories regarding group size. Therefore, we eliminated such “groups” from our analysis leaving a sample of 110 groups and 671 individuals with complete demographic and performance data. The age of employees ranged from 27 to 68 years with a mean of 45.6 years. Seventy two percent of the employees were male. The majority of employees (86.4%) were white; 7% were African American, 2.4% Asian, 3.9% Hispanic. The level of education ranged from grade school to the Ph. D. level; the modal level was a Bachelor’s degree. Tenure with the company ranged from less than one year to 44 years with a mean of 14 years. Work functions included four distinct categories (i.e., administrative, customer service, finance, and marketing).
Measures
Faultlines. We used the company’s personnel records and other archival data to locate employees’ demographics on age, gender, race, function, education, and tenure. Gender was a categorical variable coded as female = 0 and male = 1. Race was a categorical variable coded as white = 1, black = 2, Asian/Pacific Islander = 3, Hispanic = 4, Native American = 5. Age and tenure were continuous variables measured in years. Level of education was a continuous variable ranging from some grade school to doctorate degree on a 1-8 scale. Functional background was a categorical variable coded as administrative = 1; marketing and customer service = 2; finance = 3; operations = 4.
As past research showed the importance of distinguishing between the effects of faultline strength (how cleanly a group splits into subgroups) and faultline distance (how far apart subgroups are from each other), we operationalize group faultlines in terms of faultline strength and faultline distance. We use a faultline algorithm and rescaling procedure to calculate faultline strength and faultline distance scores for each work group (Bezrukova, Jehn, & Zanutto, 2004; Thatcher, et al., 2003).
Faultline strength was measured along informational (level of education, tenure with the company, and functional background) and social category (race, age, gender) faultlines using a faultline algorithm and a rescaling procedure developed by Thatcher, et al. (2003). This faultline strength measure calculates the percent of total variation in overall group characteristics accounted for by the strongest group split, in other words, the faultline strength score indicates how a group splits cleanly into two subgroups.
[pic]
where [pic]denotes the value of the [pic]characteristic of the [pic]member of subgroup k, [pic] denotes the overall group mean of characteristic j, [pic] denotes the mean of characteristic j in subgroup k, and [pic]denotes the number of members of the [pic]subgroup (k=1,2) under split g. The faultline strength is then calculated as the maximum value of [pic] over all possible splits [pic] Possible values of faultline strength scores ranged from .369 (weak faultline strength) to 1 (very strong faultline strength) for informational faultlines and .427 (weak faultline strength) to .996 (very strong faultline strength) for social category faultlines.
We measured how far apart the two subgroups are from each other (faultline distance) on informational (level of education, tenure with the company, and functional background) and social category characteristics (race, age, gender,). The faultline distance measure was adapted from multivariate statistical cluster analysis (e.g. Morrison, 1967; Jobson, 1992; Sharma, 1996) and calculated as a distance between centroids (the Euclidean distance between the two sets of averages): [pic], where centroid (vector of means of each variable) for subgroup 1 = ([pic]), centroid for group 2 = ([pic]). Faultline distance can take on values between 0 and ∞, with larger values indicating larger distance between the resulting subgroups. Possible values of faultline distance in our dataset ranged from .439 (weak faultline distance) to 3.536 (very strong faultline distance) for informational faultlines and .375 (weak faultline distance) to 3.250 (very strong faultline distance) for social category faultlines.
The faultline strength and distance measures take into account multiple characteristics of group members by calculating scores for both continuous and categorical variables simultaneously. Since there is no theoretical guidance on what differences would become noticeable, meaningful and reasonable, we assumed an equal importance of all demographic attributes when calculating these scores (see Thatcher et al., 2003, for more detail on the rescaling procedure). To account for the joint effect of faultline strength and distance we standardized both scores and then calculated an overall group faultline score representing the interaction between the two scores.
Performance. In this study, we used merit-based performance ratings (individual level) and bonuses – the most frequently used pay plans for performance in contemporary organizations (Lowery, Beadles, Petty, Amsler, & Thompson, 2002) – as performance outcomes variables. Performance ratings are the codes associated with an employees' performance review (e.g., 5 refers to employee’s outstanding performance, and 1 refers to his or her unsatisfactory performance). Trained supervisors in this company conduct performance appraisals using pre-defined criteria and rating scales to gauge actual behavior and worker performance (Drazin & Auster, 1987). Bonus amounts are the actual bonus amounts paid out for the year. The yearly bonus is calculated on total base salary for the year and includes multiple performance indicators determined by the company. Another important outcome variable is turnover, which includes both employee termination as well as transfer.
Control Variables. We included employees’ gender and tenure as individual-level control variables and group size as a group-level control variable. Extensive body of literature has identified the effect of gender on patterns of interaction and status (Ferdi & Wheelan, 1992) and indicated its substantial impact on various performance outcomes. The effects of tenure (work experience and competence) have also been shown to affect intragroup communication (O’Reilly, Snyder & Boothe, 1993) and various performance outcomes (Williams & O’Reilly, 1998). Group size has been shown to be of a great importance for group processes and outcomes (Goodman, Ravlin & Argote, 1986). All controls were obtained from the archival file data provided by the company.
Qualitative Data Analysis
Group Culture. To generate measures of our group culture variables, we content-analyzed company documents that were part of a human resources-sponsored program designed for managers and supervisors of workgroups to assess employee competencies (i.e., values, goals, skills, and knowledge). In order for managers and supervisors to complete these assessments, they are provided with a guide that describes multiple competencies. These competencies define the scope of management’s objectives and values regarding critical aspects of the workgroup environment. According to Doty, Glick, and Huber (1993), managers and supervisors translate managerial objectives into the actual context of their departments and workgroups. Thus, we believe that these data are appropriate to use for specifying the workgroup culture variables because the competencies assessed in the supervisor reports can serve as indirect evidence of current group environments regarding certain cultures.
We content-analyzed these supervisor reports based on the following procedure established in prior research (Abrahamson & Hambrick, 1997; Kabanoff, 1997). First, two raters blind to the hypotheses and purpose of the study independently reviewed the guide provided by the company describing each competency. They then sorted the competencies into seven key phrase lists based on relevant organizational theories regarding group cultures, as well as the concepts used in the company’s rhetoric (see Appendix 1). The level of initial agreement between the two raters was 84%. Second, the two raters together reviewed the descriptions and phrase lists of the context variables for each competency, discussed each definition and phrase list until they had a common understanding of it, and then refined the key phrase list for each variable studied. Third, when the key phrase lists were complete, the data were organized by group. Fourth, these data were searched for the words from the key phrase lists using the program MonoConc Pro 2.0 (Barlow, 2000) to obtain frequencies of context variable phrase occurrence. Finally, to arrive at a score for each group culture variable, the percentage of total relevant hits for a particular variable representing each group were summed. This procedure allowed us to make direct quantitative comparisons of groups within various workgroup environments using established computer-aided text analysis techniques successfully employed in past organizational research (e.g., Abrahamson & Hambrick, 1997; Doucet & Jehn, 1997; Kabanoff, 1997). We rated two group cultures (career-focused and diversity-focused) using the above procedure. See Appendix 1 for the examples of selected key phrases for each group culture variable.
Organization Culture. To generate measures of organizational culture, we content-analyzed company documents that were part of a human resource-sponsored program. In this organization, the business unit defined the largest entity of culture relevant to the employees; therefore, we measured the business-unit culture. Employees submitted information regarding their business unit culture directly over the corporate intranet or via the internet. This information is confidential and available only to the employee, his or her direct manager and a selected group of human resource personnel.
We rated these documents by first developing lists of key words characterizing each variable under study based on relevant group and organizational theories, as well as the concepts used in the company’s rhetoric. We ran computer-aided text analysis on the company’s textual data using the program MonoConc Pro 2.0 (Barlow, 2000) and created frequency lists with the terms mentioned most to least often. To arrive at the key word lists for each variable, three raters first independently considered all terms from the frequency lists and selected the key words representing each variable under study. They then discussed their respective lists of key terms and composed the final lists containing only the words that they agreed upon. Following the method of Jehn and Werner (1993), two independent raters further conducted the key word searches on all individual responses, reviewed the surrounding context and coded the text for each variable of interest as defined by theory. The inter-rater agreement ranged from 89% to 97% on the variables and was determined by checking the number of times that the raters agreed upon the score which they assigned to an individual response. When raters rated a response farther than 1 point apart, they discussed the response until they reached an agreement and then, they refined their coding rules.
Two different types of organizational culture (career-focused and diversity-focused) were identified by content coding the company’s textual data. Examples of career-focused organizational culture and diversity-focused organizational culture are shown below, respectively.
Career-focused: “In the last 3 years I have experienced several positive opportunities. Much of which as to do with people I reported to (name), for example, is a great manager/mentor. Additionally, this is carried over in the [name of the department] which I am now a part of. This is an environment in which efforts are recognized. It is intellectually and personally satisfying.”
Diversity-focused: “I like the diversity of my job and the work place. Not only the ethnic, gender etc., but my job gives me the opportunity to deal with a wide range of cross functional fields. I like people and learning new things.”
Quantitative Data Analysis
Given that informational and social category faultlines are multilevel phenomena, with observations at one level of analysis (individuals) nested within another level of analysis (groups), we employed two-level hierarchical linear modeling (Bryk & Raudenbush, 1992; Hofmann, 1997). The basic two-level HLM model is depicted in equation form as follows:
Level 1: yij = β0j + β1jx1ij + rij (1)
Level 2: β0j = γ00 + γ01zj + U0j (2)
β1j = γ10 + U1j (3)
where yij is an individual-level outcome measure for person i in group j, x1ij represents an individual-level independent variable, β0j and β1j are random coefficients representing a within-group intercept and a within-group slope, respectively, rij is an individual-level error term and is assumed to be independent and normally distributed with a mean 0 and a variance of σ 2. zj represents a group-level variable, γ00 and γ10 are between-group intercepts, γ01 is a between-group slope, U0j, and U1j are group-level error terms that represent the residual variance for each equation and are assumed to be normally distributed with mean 0 and variance in intercepts (τ 00) and slopes (τ 10).
We performed a sequence of models using the HLM 5.04 statistical package (Bryk, Raudenbush, Cheong, and Congdon, 1994). Each HLM analysis was conducted in a hierarchical fashion that included six steps (Bryk & Raudenbush, 1992; Hofmann, Griffin & Gavin, 2000). Model 1 estimated within- and between-group variance in our dependent variables. Model 2 included individual-level controls (gender and tenure), model 3 added group-level controls (group size), and model 4 included the main effects of variables under study. Finally, to test the moderating effects we specified model 5 which included two-way interactions (e.g., informational faultlines x career-focused group culture) and model 6 which included hypothesized three-way interactions (e.g., social category faultlines x career-focused group culture x career-focused organizational culture). In addition, because the predicted value of our turnover variable can only take on one of two values (0 = active employee; 1 = terminated/transferred employee), and therefore cannot be normally distributed, we conducted a series of nonlinear analyses.
RESULTS
Table 1 displays the means, standard deviations, and correlations, respectively, among all variables. Social category faultlines were negatively and significantly associated with bonuses. Informational faultlines were positively and significantly correlated with both career- and diversity group and organizational cultures while social category faultlines were negatively related to diversity-focused group culture. We examine the relationships between informational and social category faultlines, group and organizational cultures, performance and turnover using hierarchical linear modeling analyses.
---------------------INSERT TABLE 1 ABOUT HERE --------------------
Faultlines, Performance and Turnover
The results of null models for bonuses, performance ratings, and turnover (τ00 = 2.92, df = 85, χ2 = 781.18, p = .000; τ00 = .07, df = 85, χ2 = 156.91, p = .000; τ00 = .71, df = 85, χ2 = 119.55, p = .008, respectively) show that there is systematic between-group variance in bonuses and performance ratings. The results of random coefficients regression models show significant variance in the intercept parameters for bonuses (τ00 = 3.13, df = 67, χ2 = 557.85, p = .001), performance ratings (τ00 = .07, df = 67, χ2 = 106.95, p = .002), and turnover (τ00 = .55, df = 85, χ2 = 65.80, p = .087) models confirming the appropriateness of testing the cross-level relationships.
Table 2 presents the HLM analyses testing the main effects of informational and social category faultlines on individual-level outcomes. In support of hypothesis 1, members of groups with strong social category faultlines had lower levels of performance ratings (γ03 = -.05, p = .09) and bonuses (γ03 = -.21, p = .07). Hypothesis 2, predicting that members of groups with strong informational faultlines will have higher levels of individual performance outcomes was not supported. Hypothesis 3, predicting that while individuals in groups with strong social category and informational faultlines will both have higher rates of turnover, social category faultlines will have a stronger effect on turnover rates than will informational faultlines, was partially supported. Members of groups with strong social category faultlines had higher rates of turnover (γ03 = .36, p = .001).
---------------------INSERT TABLE 2 ABOUT HERE --------------------
The Moderating Effects of Group Culture
We further conducted HLM analyses to test hypotheses 4 and 5 predicting the moderating effects of career- and diversity-focused group cultures on the relationship between group-level faultlines and individual-level outcomes. Hypothesis 4 predicted that career-focused group culture will moderate the relationship between informational faultlines and outcomes (performance and turnover) such that members of group with strong informational faultlines will have higher levels of performance and lower levels of turnover when their group culture emphasizes career advancement. Opposite to what was expected, members of groups with strong informational faultlines and a career-focused group culture had lower levels of performance rating (γ05 = -.17, p = .029) and higher rates of turnover (γ05 = .77, p = .004) than groups that did not have this group culture. Hypothesis 5, predicting that diversity-focused group culture will moderate the relationship between informational and social category faultlines and outcomes such that members of group with strong informational or social category faultlines will have higher levels of performance and lower levels of turnover rates when their group culture emphasizes diversity, was partially supported. Members of groups with strong informational faultlines and a diversity-focused group culture were awarded higher amounts of bonuses (γ05 = .27, p = .09).
---------------------INSERT TABLE 3 ABOUT HERE --------------------
The Moderating Effects of Group and Organizational Culture
Finally, we conducted HLM analyses to test a more complex relationship between faultlines, group- and organizational culture, and outcomes (H6 and H7). The main effects HLM model included faultlines and culture variables (e.g., informational faultlines, diversity-focused group- and organizational culture). The first interaction HLM model included all the two-way interactions (e.g. informational faultlines x diversity-focused group culture; informational faultlines x diversity-focused organizational culture; diversity-focused group culture x diversity-focused organizational culture). The second interaction HLM model included a three-way interaction between faultlines, specific group-, and organizational culture.
Hypothesis 6, predicting a significant three-way interaction between informational faultlines, career-focused group culture, and career-focus group-organizational culture, such that members in groups with strong informational faultlines and career-focused group culture will have higher levels of performance and lower levels of turnover rates in departments that emphasize career advancement opportunities than in organizations without such emphasis, was not supported. Hypothesis 7, predicting a significant three-way interaction between informational and social category faultlines, diversity-focused group culture, and diversity-focused group-organization culture consistency, such that members in groups with strong informational or social category faultlines and diversity-focused group culture will have higher levels of performance and lower levels of turnover in organizations that focus on diversity than in organizations without such emphasis, was partially supported. Members of groups with strong informational faultlines and diversity-focused group culture had higher levels of performance ratings (γ10 = 11.32, p = .014) and lower levels of turnover (γ10 = -68.73, p = .001) in departments that focus on diversity than in departments without such emphasis. Furthermore, opposite what was expected, members of groups with strong social category faultlines and diversity-focused group culture had lower levels of performance ratings (γ10 = -12.69, p = .056) and higher levels of turnover (γ10 = 81.69, p = .014) in departments that focus on diversity than in departments without such emphasis.
DISCUSSION
Discussion of Results
Consistent with the predictions made by Lau and Murnighan (1998), our results revealed significant and negative relationships between social category faultlines and outcomes. Members of groups with social category faultlines had lower levels of performance and a higher rate of turnover. As theory predicts and our results strongly support, the alignment of demographic attributes based on similarity of group members on gender, race and age may fire up negative categorization processes to the extent to which individuals’ tangible outcomes such as bonuses or performance ratings become affected. Furthermore, in groups with strong social category faultlines, individuals must truly feel that "us vs. them" distinction negatively colors their group experience to the point when they are willing to leave.
Consistent with our predictions, we found that members of groups with informational faultlines were awarded higher amounts of bonuses in groups with an emphasis on diversity. Our findings suggest that when groups with informational faultlines have a strong diversity-focused group culture, members of such groups may become more inclusive to different opinions that arise across subgroups. They may shift their focus from a subgroup towards the larger group and make their overall information and communication exchanges more inclusive, thereby minimizing process losses. This also suggests that members of groups with informational faultlines and a culture of diversity may consider diversity as a valuable asset of their workgroup and embrace differences that can enhance effectiveness through creativity and innovation (Richard & Johnson, 2001).
Some more puzzling results were obtained with respect to the moderating effects of career-focused group cultures. Contrary to what was expected, members of groups with informational faultlines had lower levels of performance rating and a higher rate of turnover in groups with an emphasis on career advancement. One possible explanation is that such culture in groups with informational faultlines places an emphasis on individual career achievement, which may interfere with some patterns of social interactions within subgroups and lead to the decline in mutual helping behaviors. For example, members of group with strong informational faultlines and a career-focused culture may suspect that their career advancement opportunities are mutually exclusive and thus, refuse to assist their peers. Another explanation is that career-focused group culture may create an environment in which members are constantly under pressure of “producing” and are judged based on their career achievements. Termination or transfer may become a feasible solution and one of the possible responses to such frustrating situations.
We found that while members of groups with informational faultlines and diversity-focused group culture had higher levels of performance and a lower rate of turnover in departments that focused on diversity, members of groups with social category faultlines had lower levels of performance and a higher rate of turnover in such departments. In both cases, the emphasis on diversity highlights people to think about the way they are different. In the case of informational faultlines it highlights peoples differences regarding task-related issues (consistent with what we expected) and suggests that these differences are good. For social category faultlines the emphasis on diversity highlights that people are different on gender, age, and race. Although the culture may emphasize that diversity is good, individuals still must deal with subgroups of people who are very different from themselves.
Limitations of the Study
The strengths of the current research (e.g., data collected from an actual workplace setting, multiple methodologies) are accompanied by potential weaknesses. Some limitations of this study are common in demography studies that use archival file data. For instance, while we were able to construct reliable measures of group culture variables using content analysis of company documents, no direct measures of these variables were available. Future research should use employee survey data and interviews that allow a more thorough understanding of how various group cultures shape effects of faultlines and affect employee behavior. We also realize that our performance measures may have different antecedent predictors. For example, bonuses can be based on “hard” performance numbers (e.g., sales or customer satisfaction), while individual performance ratings may indicate a more subjective perception of an employee’s performance by her or his supervisor. This might be one reason we obtained different levels of significance in effects when testing our hypotheses.
Contributions of the Study
In this field study, we investigate a more complex relationships between contextual variables and their effects on groups. Many researchers have proposed to employ a configurational framework to the study of context and group processes. For example, Richard (2000) believes that future multilevel research should investigate the backdrop of business strategies, human resource practices, and cultures as a system of combined contextual factors. Jehn and Chatman (2000) suggest that the recognition that not one type of conflict but rather that the composition of all conflicts (the type of conflict present in a group relative to the other types of conflict present) within a group matters. In our study, we draw upon and extend past research on organizational culture and climate by examining the effects of different workgroup environments and more importantly, by seeking the most effective alignment of group and organizational cultures for group diversity to be beneficial. Our findings suggest that we should look not only at workgroup environments alone where a group operates (organizational cultures), but also consider the immediate context of groups (group cultures) and the interaction between the two.
In this study, we further extend the theory of group faultlines (Lau & Murnighan, 1998) and make our predictions about group interactions based on multiple member demographic characteristics and their alignment within the group. Past diversity studies have often ignored individuals’ multiple demographic characteristics (e.g., gender, race, age) and the alignment of these characteristics across group members. This alignment can be crucial for understanding the effects of group and organizational context on the group composition-performance relationship. Based on faultline theory (Lau and Murnighan, 1998), the alignment construct we introduce conceptually accounts for the interdependence among multiple demographic characteristics. It suggests that the effects of diversity are most likely a complex function of the various demographic characteristics aligning rather than working separately and recommends a more sophisticated consideration of all the potential dynamics that many different characteristics when aligned can activate (Lau and Murnighan, 1998).
In this study, we also attempt to further the theoretical understanding of group faultlines in demographically diverse organizations by theorizing about separate effects for social category and informational faultlines. Recent work has stressed the value in distinguishing between forms of heterogeneity (e.g., those based on social categories and those based on informational categories) (Jehn, 1997; Jehn et al., 1999), thus we suggest that the same logic can be applied to faultlines.
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Appendix 1. Selected key phrases and descriptions of competencies for group culture variables.
1. Career-focused group culture:
Examples of key phrases (competencies):
Coach and Develop
Change Champion
Continuous Learner
Description of a competency:
Definition
Provides challenging assignments and opportunities for development.
Behaviors
-Provides challenging assignments to facilitate individual development
-Shows interest in employees’ career
-Stimulates others to make changes and improvements
2. Diversity-focused group culture:
Examples of key phrases (competencies):
Valuing diversity
Ethics & Values
Global Perspective
Description of a competency:
Definition
Creates a work environment that reflects respect for everyone’s contributions; demonstrates and fosters respect for each person whatever that person’s background
Behaviors
-Values the talents and skills of others
-Recognizes and utilizes the contributions of people from diverse backgrounds
-Creates an environment in which people from diverse backgrounds feel comfortable
-Helps people from diverse cultures/backgrounds/lifestyles succeed
Table 1. Means, Standard Deviations, and Zero-Order Correlations Among Variables.
|Correlations |Mean |S.D. |Mean |
| |(N =671 ) |(N = 671) |(N = 110) |
|Model & Variable |Coefficient |Standard error |Coefficient |Standard error |Coefficient |Standard error |
|One-way ANOVA | | | | | | |
|τ 00 (Group variance) |2.924*** | |.071*** | |.706** | |
|σ 2 (Residual variance) |2.412 | |.551 | | | |
| | | | | | | |
|Control Variables | | | | | | |
|Random-coefficients regression | | | | | | |
|γ10 (Tenure) |-.005 |.127 |-.054 |.057 |.618*** |.139 |
|γ20 (Gender) |.383* |.157 |.001 |.067 |.464* |.220 |
|τ 00 (Group variance) |3.134*** | |.069** | |.553† | |
|σ 2 (Residual variance) |2.255 | |.549 | | | |
| | | | | | | |
|Intercepts-as-outcomes | | | | | | |
|γ10 (Tenure) |-.004 |.127 |-.075 |.058 |.618*** |.145 |
|γ20 (Gender) |.394* |.158 |.009 |.068 |.456* |.225 |
|γ01 (Group Size) |.993* |.487 |.302* |.117 |.343* |.313 |
|τ 00 (Group variance) |2.983*** | |.056* | |.545† | |
|σ 2 (Residual variance) |2.258 | |.544 | | | |
| | | | | | | |
|Main Effects | | | | | | |
|Intercepts-as-outcomes | | | | | | |
|γ10 (Tenure) |.007 |.127 |-.067 |.057 |-.709*** |.148 |
|γ20 (Gender) |.389* |.159 |-.011 |.068 |-.456* |.229 |
|γ01 (Group Size) |.943† |.484 |.287* |.118 |-.207 |.315 |
|γ02 (Info Faultlines) |-.055 |.177 |-.015 |.046 |-.013 |.094 |
|γ03 (Social Category Faultlines) |-.207† |.114 |-.054† |.036 |.357** |.106 |
|τ 00 (Group variance) |2.998 | |.058* | |.475 | |
|σ 2 (Residual variance) |2.256 | |.543 | | | |
| | | | | | | |
† p < .1; *p < .05; **p < .01; ***p < .001
Table 3. Results of HLM Estimation for Individual-Level Outcomes (moderated models).
| |Bonuses |Performance Ratings |Termination |
|Model & Variable |Coefficient |Standard error |Coefficient |Standard error |Coefficient |Standard |
| | | | | | |error |
|Career Group Culture | | | | | | |
|Main Effects | | | | | | |
|γ10 (Tenure) |.024† |.142 |-.076 |.056 |-.119 |.122 |
|γ20 (Gender) |.412* |.198 |.056 |.070 |-.411† |.234 |
|γ01 (Group Size) |.112† |.06 |.025† |.014 |-.066† |.038 |
|γ02 (Info Faultlines) |-.045 |.130 |-.023 |.035 |-.033 |.081 |
|γ03 (Career Group Culture) |-3.232*** |.784 |-.508** |.165 |-.033 |.591 |
|τ 00 (Group variance) |2.749*** | |.038† | |.611 | |
|σ 2 (Residual variance) |2.347 | |.498 | | | |
|Interactions | | | | | | |
|γ10 (Tenure) |.011 |.141 |-.071 |.056 |-.185 |.119 |
|γ20 (Gender) |.409* |.198 |.054 |.069 |-.331 |.241 |
|γ01 (Group Size) |.107† |.062 |.028† |.015 |-.061† |.036 |
|γ02 (Info Faultlines) |-.136 |.224 |.026 |.038 |-.285** |.099 |
|γ03 (Career Group Culture) |-3.654*** |.944 |-.465** |.157 |-.803 |.622 |
|γ05 (Info Fau x Career Gr Culture) |.236 |.366 |-.171* |.078 |.769** |.266 |
|τ 00 (Group variance) |2.758*** | |.038† | |.579 | |
|σ 2 (Residual variance) |2.346 | |.499 | | | |
|Diversity Group Culture | | | | | | |
|Main Effects | | | | | | |
|γ10 (Tenure) |.001 |.143 |-.097† |.057 |-.145 |.122 |
|γ20 (Gender) |.367† |.199 |.046 |.072 |-.424† |.234 |
|γ01 (Group Size) |.135* |.067 |.035* |.016 |-.059 |.038 |
|γ02 (Info Faultlines) |-.105 |.193 |-.053 |.037 |-.066 |.090 |
|γ03 (Soc Cat Faultlines) |-.200 |.126 |-.022 |.044 |.229† |.132 |
|γ04 (Diversity Group Culture) |.204 |.628 |.269† |.163 |.379 |.391 |
|τ 00 (Group variance) |3.409*** | |.049† | |.597 | |
|σ 2 (Residual variance) |2.352 | |.498 | | | |
|Interactions | | | | | | |
|γ10 (Tenure) |.005 |.143 |-.082 |.059 |-.187 |.137 |
|γ20 (Gender) |.356† |.198 |.044 |.071 |-.471* |.228 |
|γ01 (Group Size) |.149* |.067 |.033* |.016 |-.053 |.045 |
|γ02 (Info Faultlines) |-.389 |.294 |-.118† |.062 |.058 |.180 |
|γ03 (Soc Cat Faultlines) |.038 |.204 |-.083 |.065 |.340 |.219 |
|γ04 (Diversity Group Culture) |.072 |.665 |.143 |.205 |.654 |.506 |
|γ05 (Info Fau x Diversity Gr Culture) |.269† |.163 |.072 |.052 |-.137 |.140 |
|γ06 (Soc Cat Fau x Diversity Gr Culture) |-.473 |.356 |.128 |.133 |-.248 |.642 |
|τ 00 (Group variance) |3.393*** | |.050* | |.675 | |
|σ 2 (Residual variance) |2.354 | |.498 | | | |
† p < .1; *p < .05; **p < .01; ***p < .001
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