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The Effects of Alignments:
Examining Group Faultlines, Organizational Cultures, and Performance
Katerina Bezrukova
Santa Clara University
Sherry M. B. Thatcher
University of Louisville
Karen A. Jehn
University of Melbourne
Chester S. Spell
Rutgers University
By integrating literature on group faultlines, organizational cultures, and value congruence, this research
presents a framework that explains how cultural alignment across organizational levels may influence the
relationship between faultlines and performance. The hypotheses were tested using representatively
sampled multisource qualitative and quantitative data on 138 teams from a Fortune 500 company. The
present findings demonstrate that although informational faultlines were detrimental for group perfor-
mance, the negative relationship between faultlines and performance was reversed when cultures with a
strong emphasis on results were aligned, was lessened when cultures with a weak emphasis on results
were aligned, and remained negative when cultures were misaligned with respect to their results
orientation. These findings show the importance of recognizing alignments not only within groups (group
faultlines) but also outside groups (cultural alignments between the group and departments) when
considering their implications for group performance.
Keywords: alignments, group faultlines, organizational culture, performance
Alignments are pervasive and take many forms in organizations.
We focus on two forms of alignment that are relevant when
considering group performance: within-group alignment and
group-organization alignment. The first form of alignment we
discuss is within-group alignment, also known as group faultlines.
Group faultlines form when group members’ individual attributes
(e.g., functional background, gender, work experience) align and
create subgroups (Lau & Murnighan, 1998). For instance, a fault-
line exists when all the engineers in a team are recent college
graduates and all the designers are just about to retire. Although
the faultline framework is based on the intuitively powerful idea of
demographic alignments in groups being inherently conflictual,
empirical studies have reported a variety of effects. Some studies
have demonstrated negative effects (e.g., Phillips, Mannix, Neale,
& Gruenfeld, 2004; Sawyer, Houlette, & Yealey, 2006); others
have shown positive effects of faultlines (e.g., Gibson & Vermeu-
len, 2003; Thatcher, Jehn, & Zanutto, 2003), suggesting contextual
environments should be considered when examining the effects of
faultlines.
The second form of alignment we discuss is the alignment of
contextual environments between a group and an organization.
One aspect of the contextual environment that is relevant for
employees in organizations is organizationally based cultures.
Culture refers to a set of norms and values that are widely shared
and strongly held by a group of people (Chatman & Barsade, 1995;
O’Reilly & Chatman, 1996). The dominant paradigm in culture
research has emphasized the homogeneous and undivided nature
of organizational culture (e.g., Chatman, Polzer, Barsade, & Neale,
1998; Chatman & Spataro, 2005). Yet, cultures are complex and
flexible enough to accommodate subcultures (Adkins & Caldwell,
2004; Chao & Moon, 2005; Martin & Siehl, 2002). These subcul-
tures have the potential not only to reinforce but also to deviate
from the values set by the higher level organizational unit (e.g.,
Mannix, Thatcher, & Jehn, 2001; Martin & Siehl, 1983). We,
therefore, merge the research streams on faultlines and organiza-
tional culture and ask our main research question: How do the
effects of faultlines on group performance vary with the extent to
which group culture aligns with the culture of the department in
which it is embedded?
We define cultural alignment as the extent to which a culture is
shared across different organizational levels. We argue that cul-
tural alignment (or misalignment) between a group and a depart-
ment (or any higher level organizational unit) may affect perfor-
mance in groups with faultlines. Group faultlines complicate work,
and reconciling faultline-based differences in members’ expertise,
This article was published Online First July 11, 2011.
Katerina Bezrukova, Department of Psychology, Santa Clara University;
Sherry M. B. Thatcher, Management Department, College of Business,
University of Louisville; Karen A. Jehn, Melbourne Business School,
University of Melbourne; Chester S. Spell, School of Business, Rutgers
University.
This research was funded by the Alfred P. Sloan Foundation and was
supported by the SEI Center for Advanced Studies in Management of the
Wharton School, and The George Harvey Program on Redefining Diver-
sity: Value Creation Through Diversity. We thank David Hofmann, Elaine
Zanutto, and Lawrence Ockhoo for their help with statistical analysis and
the OB seminar series working paper group of Rutgers-Camden for their
helpful comments.
Correspondence concerning this article should be addressed to Katerina
Bezrukova, Department of Psychology, Santa Clara University, 500 El
Camino Real, Santa Clara CA 95053. E-mail: ybezrukova@scu.edu
Journal of Applied Psychology © 2011 American Psychological Association
2012, Vol. 97, No. 1, 77–92 0021-9010/11/$12.00 DOI: 10.1037/a0023684
77
training, or work approaches may require considerable amounts of
time and energy (Jehn, Bezrukova, & Thatcher, 2008). Matters
may be further exacerbated in the case of cultural misalignment
when, for example, a faculty group has a culture that emphasizes
scholarly publications but gets little support from their department
that does not value scholarly achievements. Some theorizing about
the joint effects of faultlines and different contextual variables has
been done in the past (see e.g., Cramton & Hinds, 2005; Gibson &
Vermeulen, 2003; Mathieu, Maynard, Rapp, & Gilson, 2008);
however, a framework that considers alignments both within
groups (group faultlines) and outside groups (cultural alignments)
and their implications for group performance is still missing.
Consistent with the call by Griffin, Mathieu, and Jacobs (2001) for
more research examining “the role of contextual influences located
at different levels of analysis” (p. 576), our objective is to examine
how the alignment between multilevel cultures affects the relation-
ship between group faultlines and performance outcomes.
Faultlines in Groups
A substantial volume of research on group diversity has at-
tempted to explain how diversity influences workgroups (cf. Jack-
son, Joshi, & Erhardt, 2003; Mannix & Neale, 2005; Riordan,
2000; Williams & O’Reilly, 1998). This research has mainly
drawn upon the heterogeneity concept, or the dispersion view of
group composition, that emphasizes the degree of attribute distri-
bution among group members (Alexander, Nuchols, Bloom, &
Lee, 1995). One shortcoming of this stream of literature has been
that it assumes that the dispersions of each demographic attribute
are independent from one another. For example, in the case of race,
ignoring the effects of gender might lead one to erroneously
conclude that experiences of African American men are the same
as that of African American women in the same group (e.g., Roth,
Huffcutt, & Bobko, 2003). Lau and Murnighan (1998) questioned
this assumption of independence and introduced the concept of
faultlines, or the alignment view of group composition (Bezrukova,
Thatcher, & Jehn, 2007).
The faultline construct contributes to the group composition
literature by acknowledging that multiple demographic attributes
exist simultaneously and that there may be alignment across mul-
tiple attributes. On the basis of the principle of comparative fit
(defined as the extent to which a categorization results in clear
between-group differences and within-group similarities; Reyn-
olds & Turner, 2001; Turner, Hogg, Oakes, Reicher, & Wetherell,
1987), the alignment of similar attributes results in the formation
of subgroups (Lau & Murnighan, 1998). That is, alignment on
multiple dimensions increases the salience of subgroup categori-
zations (Bezrukova et al., 2007; Jehn et al., 2008). As a result of
this, faultline theorists argue that faultlines are a better predictor of
processes and performance than diversity variables based on dis-
persion models (Cramton & Hinds, 2005). This assertion has been
supported in empirical work; for example, studies have shown that
faultlines produce more direct and pervasive effects on conflict
and performance outcomes than traditional diversity variables re-
flecting simple dispersion of member differences (Bezrukova et
al., 2007; Lau & Murnighan, 2005; Li & Hambrick, 2005; Rico,
Molleman, Sanchez-Manzanares, & Van der Vegt, 2007).
We draw on this literature and, unlike past faultline research that
largely focused on faultlines formed around relationship-oriented
differences (e.g., race, gender, age, and nationality; e.g., Earley &
Mosakowski, 2000; Lau & Murnighan, 2005; Li & Hambrick,
2005; Molleman, 2005; Pearsall, Ellis, & Evans, 2008; Polzer,
Crisp, Jarvenpaa, & Kim, 2006), we focus on faultlines formed
around task-oriented, or informational, differences (e.g., educa-
tion, functional background, and tenure in a company) that are
more relevant to team-based projects. These attributes represent
distinct work experiences and are likely to be a source of different
task-related perspectives and information (Bezrukova, Jehn, Za-
nutto, & Thatcher, 2009; Horwitz & Horwitz, 2007; Jackson et al.,
2003). Like other deep-level attributes (i.e., personality traits,
values, attitudes, preferences, and beliefs), informational differ-
ences are not readily observable, yet they have been recognized as
critical for group processes and performance (Harrison, Price, &
Bell, 1998; Jehn, Chadwick, & Thatcher, 1997; Mohammed &
Angell, 2004; Webber & Donahue, 2001).
Following others who have stressed the value in differentiating
between types of diversity, we believe that informational faultlines
have important implications for performance in diverse work
groups (e.g., Jehn, Northcraft, & Neale, 1999; Molleman, 2005).
Research examining the effects of differences in education (e.g.,
Jehn et al., 1997; Schippers, Den Hartog, Koopman, & Wienk,
2003), tenure (e.g., Boeker, 1997; Carpenter & Fredrickson, 2001),
and functional background or expertise (e.g., Chattopadhyay,
Glick, Miller, & Huber, 1999; Van Der Vegt & Bunderson, 2005),
all indicate that the informational composition of teams has a
bearing on performance outcomes. Other group-level outcomes
could be influenced by faultlines (e.g., group processes, satisfac-
tion, creativity; Choi & Sy, 2010; Pearsall et al., 2008), but we
focus on group performance because it is a fundamental and
critical outcome for both groups and organizations.
Although group faultlines may potentially yield benefits in the
form of higher levels of learning and creative outcomes (Cramton
& Hinds, 2005; Nishii & Goncalo, 2008), Lau and Murnighan’s
(1998) original faultline model suggests that strong faultlines are
likely to be dysfunctional. According to social identity and cate-
gorization theories, alignment of individuals on similar informa-
tional attributes may lead to categorizing oneself and others as
members of subgroups (e.g., experienced designers and inexperi-
enced engineers) (Ashforth & Mael, 1989; Tajfel & Turner, 1986;
J. C. Turner, 1975). These categorizations make it easy for groups
with faultlines to divide into subgroups (Lau & Murnighan, 1998)
that disrupt behavioral integration (mutual and collective interac-
tion within a group) (Li & Hambrick, 2005). Because group
members depend and rely on information and knowledge of others
to meet the group’s goals (Milton & Westphal, 2005; Wageman,
1995), such fragmentation may limit group members’ access to
important informational resources. Valuable time may be used in
reconciling divisiveness between the subgroups, hence reducing
the time allocated to completing the task. Empirical research
further demonstrates that groups with faultlines often suffer from
communication breakdowns and limited information exchanges
(Dyck & Starke, 1999; Lau & Murnighan, 2005). We thus predict
that faultlines will be associated with reductions in group perfor-
mance.
Hypothesis 1 (H1): There will be a negative relationship
between informational faultlines and group performance.
78 BEZRUKOVA, THATCHER, JEHN, AND SPELL
Results-Focused Cultural Alignments in Organizations
Many studies on group composition have found that situational
features have a powerful impact on the extent to which group
composition enhances or detracts from performance (Chatman et
al., 1998; Jackson et al., 2003; Joshi & Roh, 2007; Mathieu et al.,
2008). Thus, whereas Lau and Murninghan (1998) emphasized the
disruptive dynamics of faultlines, the effects of faultlines may vary
across contexts. The context surrounding individuals and groups
creates situational opportunities and constraints that influence how
members act and treat others as a result of members’ informational
differences (Chatman & Flynn, 2001). In following the calls for
integrative research on faultlines within specific cultural contexts,
we focus on organization-based cultures (Gibson & Vermeulen,
2003; Mathieu et al., 2008). We believe it is critical to consider the
role of culture in understanding the relationship between faultlines
and outcomes for two reasons. First, culture has been conceptual-
ized as a “guide” (O’Reilly, 2001) in showing appropriate ways of
relating to others (e.g., valuing informational differences) (Schein,
1990). Second, culture is viewed as a normative order or collection
of central norms that motivate behavior in an organization or an
organizational grouping (O’Reilly, 2001; Ravasi & Schultz, 2006;
Whetten, 2003).
In conceptualizing organization-based cultures, it is important to
consider the content of a culture (Chatman & Jehn, 1994; O’Reilly,
Chatman, & Caldwell, 1991). Organization-based cultures and
their associated values reflect preferred ways to perform individual
and group tasks such as being innovative, career oriented, diversity
oriented, detail oriented, people oriented, or outcome oriented
(Chatman & Jehn, 1994; Jehn, 1994; Jehn et al., 1997; O’Reilly et
al., 1991). For example, a faculty group that has a results-based
culture emphasizing scholarly publications will have very different
values from a faculty group with a people-oriented culture empha-
sizing social support for colleagues (e.g., weekly lunches and
baseball games together). Although cultural variables are not mu-
tually exclusive (a faculty group could be high on both results and
people orientations, or vice versa), of particular interest for our
study is a culture with a results orientation that emphasizes the
extent to which accomplishing tasks, high expectations, and
achievement is valued in an organization (O’Reilly et al., 1991).
Early work described this cultural orientation as “task orientation”
(Cooke & Rousseau, 1988) or as organization-oriented values that
focus on tasks and are, in general, outcome oriented (Quinn &
Rohrbaugh, 1983). Prior research has established important asso-
ciations between results-focused cultural values and performance
(Jehn et al., 1997; Ostroff, Shin, & Kinicki, 2005; Tordera,
Gonza´lez-Roma´, & Peiro´, 2007), justifying the choice of our
variables.
Cultural Alignment
We further advance researchers’ understanding of results-
focused culture by recognizing its multilevel nature and including
the idea of cultural alignment (the extent to which results-focused
culture is shared across organizational levels). We build on orga-
nizational culture research in which the effects of culture on the
outcomes relevant to diversity research (e.g., performance, satis-
faction) have been studied. This research stream has primarily
focused on conceptualizing culture within one level of analysis
(e.g., Chatman et al., 1998; Chatman & Spataro, 2005). Yet, the
idea of multiple cultures and the fit between various cultures has
attracted a lot of attention in the stream of literature on the
congruency between an employee’s values and the culture of their
organization (value congruence research) (e.g., Arthur, Bell, Vil-
lado, & Doverspike, 2006; Chatman, 1989, 1991; Edwards &
Cable, 2009; O’Reilly et al., 1991). But, researchers have primarily
focused on understanding the main effects of culture fit (Edwards,
1994, 2002) and not on how culture at different organizational
levels may affect the relationship between faultlines and perfor-
mance. However, in line with past research on organizational
culture (Chatman & Flynn, 2001), cultural alignments may create
situations that affect how group members behave toward one
another based on their informational differences that may ulti-
mately affect group performance. We thus bring these two streams
of research together (culture as a moderator and fit as a multilevel
phenomenon) and suggest that the alignment between a results-
focused group culture and a higher level culture (e.g., departmental
culture) may act as a moderator of the faultline–performance link.
In addition, we contribute to the value congruence literature by
differentiating between the types of cultural alignments (strong-
strong vs. weak-weak emphasis on results across both group and
departmental levels, respectively) and by theorizing about the
implications of their interactive effects with faultlines for perfor-
mance. A substantial volume of research on value congruence has
emphasized that the alignment across cultures is associated with
more positive subjective experiences and improved interactions
(e.g., Elfenbein & O’Reilly, 2007; Kristof-Brown & Stevens,
2001). According to this literature, aligned values and expectations
reinforce feelings of predictability, coherence, and control for
individuals in groups (Edwards & Cable, 2009) and can attenuate
the negative effects of diversity on performance (Elfenbein &
O’Reilly, 2007). Overall, such cultural alignments are often
viewed as beneficial in comparison to misalignments; yet, as we
argue below, the extent to which they are beneficial will also
depend on how much the culture focuses on results. For instance,
having a low results orientation at both group and organizational
levels may overwhelm the effects of other factors (i.e., faultlines).
Cultural Alignment as a Moderator
Although our first hypothesis suggests that faultlines will be
negatively associated with performance, we predict that cultural
alignment with a strong emphasis on results across both group and
departmental levels (e.g., everyone in our group and department
agree that results are highly valued) can reverse this effect. Fault-
lines typically create problems because group members tend to act
in ways consistent with the behavior of their particular subgroup at
the expense of the overall group (Lau & Murnighan, 1998). How-
ever, in groups in which cultures have a strong emphasis on results
across levels, people may act in ways consistent with salient
aligned cultural values instead of focusing on problems associated
with faultline-based subgroups. This is consistent with the princi-
ple of functional antagonism (I. G. Turner, Oakes, Haslam, &
McGarty, 1994; J. C. Turner et al., 1987), which implies that when
cultural alignment is salient, a group of people will focus less on
their informational differences than on their similarities; that is,
they will be more likely to acknowledge and act in accordance
with the factors that tie them together. Serving as a common frame
79
FAULTLINES AND CULTURES
of reference (Boisnier & Chatman, 2003), the focus on results
might be seen as both a uniting factor and a superordinate goal
implicit in the values and norms related to performance that are
common to both group and department (Aronson & Bridgeman,
1979; Sherif, 1958).
Cultural alignment on the superordinate goal of results orienta-
tion may reduce tension and necessitate cooperative activities
across faultline subgroups (Aronson & Bridgeman, 1979; Sherif,
1958) by directing the allocation of effort toward accomplishing
the task (DeShon, Kozlowski, Schmidt, Milner, & Wiechmann,
2004; Payne, Youngcourt, & Beaubien, 2007). Furthermore, di-
versity researchers have long argued that information flows and
cooperation tend to be localized within subgroups of similar mem-
bers (Milton & Westphal, 2005), and differences across them can
broaden the network of external contacts through which a team
gains access to valuable resources (Beckman & Haunschild, 2002;
Mohrman, Tenkasi, & Mohrman, 2003). When cultures have a
strong emphasis on results across organizational levels, faultlines
may thus operate as “healthy divides” that stimulate effective
decision making and tap into more resources as subgroups are
more motivated to work together (Cramton & Hinds, 2005; Gibson
& Vermeulen, 2003). Thus, the overall group becomes more
efficient in managing and capitalizing on members’ differences.
Therefore, in results-focused, culturally aligned (strong-strong em-
phasis on results) situations, the negative relationship between
faultlines and performance will be reversed (will become positive).
We predict that the negative relationship between faultlines and
performance will be lessened in groups in which cultural align-
ment has a weak emphasis on results across both group and
departmental levels (e.g., everyone in our group/department agrees
that a focus on results is not the most important thing in our
group/department). Simply having cultures that are aligned may be
sufficient for unifying faultline groups and minimizing destructive
subgroup dynamics as the focus is now redirected from problems
associated with faultline-based subgroups to something that they
have in common—shared expectations that come with alignment.
This can lead to fewer group process losses (Bezrukova et al.,
2009; Jehn & Bezrukova, 2010), weakening the negative relation-
ship between faultlines and performance. However, unlike the
condition in which cultures have a strong emphasis on results
across both group and departmental levels, when the goals and
expectations are not dedicated to performance, there is little im-
petus for group members to leverage their informational differ-
ences and expend effort toward performance (Locke & Latham,
1990, 2002). For instance, returning to our earlier example of a
faculty group, regardless of whether faultlines are weak or strong,
if there is alignment but little support for research at both group
and departmental levels, it would seem unlikely that this group
would produce a prolific publications record.
Finally, we expect that the negative relationship between fault-
lines and performance will remain negative in groups in which
cultures are misaligned on results orientation across group and
departmental levels. Misaligned cultures send mixed messages that
do not provide clear emphasis on task accomplishment and shared
goals (Gilboa, Shirom, Fried, & Cooper, 2008; LePine, Podsakoff,
& LePine, 2005; Rizzo, House, & Lirtzman, 1970) and will not
unify faultline-based subgroups. Such cultural misalignments also
do not provide a common frame of reference that is necessary for
bringing group members together (Chatman & Spataro, 2005;
Chattopadhyay, Tluchowska, & George, 2004). That is, without
common, shared, and consistent goals and expectations, people
will pay attention to their differences and categorize themselves
and others into subgroups on the basis of differences, thus ampli-
fying destructive subgroup dynamics. Under this condition, mem-
bers may be unwilling to engage in a thorough and intensive
elaboration of the problem, will not freely express their ideas or
collaborate across a faultline, and ultimately will not be able to
take advantage of their informational differences (Cramton &
Hinds, 2005). Thus, situations in which results-focused cultures
are misaligned across organizational levels, the negative relation-
ship between faultlines and performance should remain. Therefore,
we predict the following hypothesis:
Hypothesis 2 (H2): Cultural alignment will moderate the
negative relationship between faultlines and group perfor-
mance. The specific form of the proposed interaction will
depend on the type of cultural alignment; that is,
(a) the negative relationship between faultlines and performance
will be reversed (will become positive) in groups in which cultures
have a strong emphasis on results across both group and depart-
mental levels,
(b) the negative relationship between faultlines and performance
will be lessened in groups in which cultures have a weak emphasis
on results across both group and departmental levels,
(c) the relationship between faultlines and performance will
remain negative in groups in which cultures are misaligned on
results orientation across group and departmental levels.
Method
Research setting. The hypotheses were tested in a multim-
ethod field study conducted at a Fortune 500 company in the
information-processing industry. The organization is well estab-
lished in North American and Western European markets and is a
leading outsourcing provider for corporate and governmental
e-mail services involved in the creation, development, and mar-
keting of technology products for information processing and mail
outsourcing. The company also has a very informationally diverse
workforce (specific characteristics described below) providing
variation on the informational faultline construct. For these rea-
sons, this company was well suited to test the present predictions.
Qualitative data from multiple sources (company documents and
archived questionnaires) using different data collection methodol-
ogies were obtained to measure group and departmental level
results-focused cultures. Quantitative measures of employees’ in-
formational characteristics (to measure group faultlines) and group
performance were obtained directly from organizational archives.
The population for the present study was composed of employ-
ees working in 156 middle-level management groups nested in 54
departments. These departments served distinct markets and con-
stituted all higher level units within this organization. Eighteen
groups were dropped from the present analysis because they did
not have complete information (necessary for the calculations of
faultlines) on all employees. Seven departments did not provide
responses to the Gallup poll (which was used to create the
department-level culture variable), and hence were not included,
leaving a final sample of 757 employees organized into 138 groups
(88% of the group-level population) working in 47 departments
80 BEZRUKOVA, THATCHER, JEHN, AND SPELL
(87% of the department-level population). In summary, all groups
included in the analysis had complete data on faultlines, culture,
and performance variables. The average group size was 8.18
(SD ⫽3.13); the number of employees in each group ranged from
4 to 14. The average number of groups per department was 2.27
(SD ⫽2.51), and the number of groups in each department ranged
from 1 to 13. The obtained sample thus comprised a diverse and
representative set of middle-level management groups in a variety
of departments.
The work groups and departments using a reporting system
developed by the company and supplementary organizational
charts provided by key senior staff were identified. That these
groups were actual working groups were verified by interviews
and observation (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 work groups; Williams & O’Reilly, 1998).
These groups consisted of middle-level managers who were re-
sponsible for various complex and nonroutine tasks; the tasks
included product development, sales, marketing, and distribution
of the company’s products in their respective markets. The age of
employees ranged from 27 to 68 years (M⫽45.89 years, SD ⫽
7.99). Seventy-four percent of the employees were men. The
majority of employees (87.45%) were Caucasian; 6.64% were
African American, 2.15% were Asian, and 3.76% were Hispanic.
The level of education ranged from grade school to the doctoral
level; the modal level was a bachelor’s degree. Fifty percent of
employees were in administration, 12% were in marketing, 13%
were in finance, and the remaining 25% were in engineering.
Tenure with the company ranged from less than 1 year to 44 years
(M⫽14.44 years, SD ⫽9.48).
Measures.
Faultlines. The company’s personnel records were used to
locate information on employees’ level of education, functional
background (i.e., administrative, marketing, finance, and engineer-
ing), and tenure with the company. The choice of these attributes
was based on prior research on informational diversity (e.g., Jehn
et al., 1997, 1999; Polzer, Milton, & Swann, 2002), suggesting that
differences in education, tenure, and functional background or
expertise may have bearing on performance outcomes for team-
based projects (e.g., Carpenter & Fredrickson, 2001; Chattopad-
hyay et al., 1999; Schippers et al., 2003). To measure the faultline
construct, the faultline algorithm developed by Thatcher et al.
(2003) and used in faultline research by others (e.g., Bezrukova,
Spell, & Perry, 2010; Lau & Murnighan, 2005; Molleman, 2005)
was used in the present analysis. This measure was adopted from
multivariate statistical clustering analysis (e.g., Jobson, 1992;
Morrison, 1967; Sharma, 1996). It takes into account cumulative
proportions of variance across variables, which allows for estima-
tions of how well the variability within the group can be explained
by the presence of different clusters (members’ alignments on
multiple attributes) within the group.
As recommended by scholars (Bezrukova et al., 2009; Zanutto,
Bezrukova, & Jehn, 2011), the strength of faultline splits, or Fau,
was first measured, which indicates how cleanly a group splits into
two subgroups by calculating the percent of total variation in
overall group characteristics accounted for by the strongest group
split. More specifically, this is accomplished by calculating the
ratio of the between-group sum of squares to the total sum of
squares in a two-step process. The first step is to calculate:
Faug⫽
冢
冘
j⫽1
p
冘
k⫽1
2
nk
g共x
䡠jk ⫺x
䡠j䡠兲2
冘
j⫽1
p
冘
k⫽1
2
冘
i⫽1
n2
2
共xijk ⫺x
䡠j䡠兲2
冣
g⫽1,2,. . .,S,
where xijk is the value of the jth characteristic of the ith member of
subgroup k,x
䡠j䡠is the overall group mean of characteristic j,x
䡠jk
is the mean of characteristic jin subgroup k, and nk
gis the number
of members of the kth subgroup (k⫽1, 2) under split g. The second
step is to calculate the maximum value of Faugover all possible
splits g⫽1,2,. . .S (to avoid splits involving subgroups consisting
of a single member, we maximize over all splits where each
subgroup contains at least two members). Fau is always larger than
zero and less than or equal to one, with larger values indicating
greater faultline strength. The values of faultline strength in our
data set ranged from .333 (weak faultline strength) to .961 (very
strong faultline strength).
Second, faultline distance was measured (Bezrukova et al.,
2009), which indicates the degree of difference between faultline
subgroups that adds to the overall effect of faultline strength and is
calculated as the distance between subgroup centroids (the Euclid-
ean distance between the two sets of averages):
Dg⫽
冑
冘
j⫽1
p
共x
䡠j1⫺x
䡠j2兲2,
where the centroid (vector of means of each variable) for subgroup
1⫽共x
䡠11,x
䡠21,x
䡠31,...,x
䡠p1兲, and the centroid for group 2 ⫽
共x
䡠12,x
䡠22,x
䡠32,...,x
䡠p2兲. Faultline distance can take on values
between 0 and ⴥ, with larger values indicating a larger distance
between the resulting subgroups. Possible values of faultline dis-
tance in our data set ranged from .534 (small faultline distance) to
22.853 (very large faultline distance).
Finally, in line with the recommendations of Zanutto et al.
(2011) and because there are differences in ranges, scores of
strength and distance were standardized (see Schaffer & Green,
1996). Their scores were then multiplied to account for the joint
effect of faultline strength and distance (a multiplier form, Zanutto
et al., 2011, p. 708), and this overall group faultline score was used
in the present analyses (it ranged from .01 to .88, with M⫽0.35
and SD ⫽0.20 at the group level). The faultline measure showed
good psychometric properties in prior research. Faultlines were
correlated with a conceptually related measure of active faultlines
(y⫽.26, p⫽.026; Zanutto et al., 2011), providing evidence for
convergent validity, and faultlines were unrelated to the concep-
tually different constructs of morale or group size (b⫽.16, ns;b⫽
⫺.08, ns; Thatcher et al., 2003), providing evidence for discrim-
inant validity (see Zanutto et al., 2011, for more details about
specific calculations).
Results-focused cultures. Following O’Reilly et al. (1991),
results-focused culture was operationalized as the extent to which
groups or departments in the company value and support achieve-
ments, have high demands and expectations, and are, in general,
outcome oriented. This operationalization is also consistent with
Cooke and Rousseau’s (1988) “task” cultural orientation and with
values related to what Quinn and Rohrbaugh (1983) described as
organization-oriented values that emphasize accomplishing tasks.
81
FAULTLINES AND CULTURES
Group results-focused culture. To generate measures of
group-level results-focused culture, archival questionnaire data
that were collected as part of a human resources department
sponsored program were used. This program was designed to
monitor objectives and task values of the work group environment,
and all 757 employees participated in the process. Employees were
given a company guide providing a set of 19 values (e.g., “com-
municator,” “competitive thinker,” “results driven,” “people fo-
cused”) described in terms of specific behaviors. For example, a
“results-driven” value included the following behaviors: “drives
others and self for results,” “works through obstacles to achieve
objectives,” “meets deadlines,” and “keeps people focused on the
most critical and important work.” Participants then were asked to
pick only those values they thought were most representative of
their group when they submitted online responses to these ques-
tions. These selections comprised the data that were used to arrive
at our measure of group culture.
Similar to Jehn and Bezrukova’s (2004) approach, the company
guide was content analyzed with the 19 values to identify specific
results-focused values. The number of times a results-focused
value was mentioned as being representative of the group was
counted. More specifically, following a content analysis procedure
used in prior research (e.g., Abrahamson & Hambrick, 1997;
Kabanoff, 1997), two raters blind to the hypotheses and purpose of
the study independently reviewed the company guide describing
the behavioral values. The raters then identified the values (e.g.,
“competitive thinker,” “results driven”) that represented a results-
focused culture based on theory (e.g., Cooke & Rousseau, 1988;
O’Reilly et al., 1991). Cohen’s kappa, which measures interrater
reliability (Cohen, 1960), was statistically significant at .88 (p⬍
.01). Discrepancies were discussed and resolved. The raters then
summed all the occurrences of relevant values describing work-
group results-focused culture in each employee report (range ⫽
0 – 4) and recorded respective scores into the spreadsheet.
Following the suggestions of Bliese (2000), the validity of this
group-level construct was tested using intraclass correlation coef-
ficients (ICC[1] and ICC[2]) analysis. ICC(1) represents the reli-
ability of a single rating of the team mean, or the statistical
agreement among team members regarding a rated variable.
ICC(2) represents the reliability of the average across team mem-
ber responses. A one-way analysis of variance was conducted, and
between-groups variance for this variable was found to be signif-
icant at the .001 level. ICC[1] and ICC[2] measures were accept-
able with the values of .34 and .72, respectively, suggesting that
much of the variance in the results-focused culture was due to
group membership; thus, aggregation was justified.
Department-level results-focused culture. A measure of
department-level culture was generated by content analyzing 5,844
pages of archived questionnaire data. It was important to us that
the measures of results-focused culture at two different levels were
from different sources to avoid common method bias and to ensure
that we independently captured the culture at the sources specific
to the level of focus (e.g., group, department). The procedure
developed by Gibson and Zellmer-Bruhn (2001) and Zellmer-
Bruhn and Gibson (2006) in their content analysis of organiza-
tional culture was adapted. Unlike group-level culture data (based
on a list of behavioral values defined by the company), the
department-level culture data were comprised of unlimited text.
The data were part of a human resources-sponsored program and
included employee responses to a question from a Gallup poll:
“What do you (the employee) like most about working for your
department?” The answers were anonymous (employees submitted
information regarding their department directly over the corporate
Intranet or via the Internet) and were grouped together by depart-
ment. There were 47 text files (composed of 757 employee re-
sponses), each representing one department—next to the organi-
zational level, the largest entity of culture relevant to the
employees in this company.
Following the procedure of Doucet and Jehn (1997), computer-
aided text analysis of these data was conducted using the program
MonoConc Pro 2.0 (Barlow, 2000), and a frequency list was
created with the terms mentioned most to least often. Then, two
raters who were not familiar with the specific hypotheses were
asked to independently consider all terms from the frequency list
and select the keywords representing a results-focused culture
based on our definition (Cohen’s ⫽.84, p⬍.01). They then
discussed their respective lists of key terms and composed the final
list containing only the words that they agreed on. Examples of the
keywords for a results-focused culture at the department were high
expectations,demanding,results,productive,environment, and
culture (e.g., Gibson & Zellmer-Bruhn, 2001).
Second, the raters conducted “in-context verification” to ensure
that the words were used in the way suggested by our results-
focused culture definition. They performed keyword searches on
archival questionnaire data from all 47 departments and reviewed
the surrounding context (several lines of text occurring before,
during, and after a search term). Next, they read every excerpt and
removed excerpts that were inconsistent with our definition of a
results-focused culture. For instance, Responses 1 and 2 below
were dropped from our analysis because environment referred to
something other than a results-focused culture. Response 3 was
dropped because results referred to a compensation program (a
case of a specific human resources practice) and not a results-
focused culture.
(Response 1) “The ‘extended family’ environment has been a
real joy to be a part of I hope that hasn’t come to an end.”
(Response 2) “When placed in a non-work environment, the
people are fantastic, everybody gets along very well and enjoys
spending time together.”
(Response 3) “Very, very poor communication regarding bonus
results, bonus calculations, all-star results, all-star calculations and
the gathering of this critical information is also very poor. We get
excited about the program and then receive little information about
the items listed above.”
Responses that demonstrated evidence of a results-focused de-
partmental culture were included in the analysis (see examples
below, keywords are italicized):
(Response 4) “It’s about the culture. Targets are challenging,
but as a whole the department offers a lot of support on achieving
those objectives. This makes for a productive and exciting envi-
ronment. It is a wonderful environment to work in, and we are a
strong team working towards being the best!”
(Response 5) “Self-directed, result-oriented, good goal setting,
not micro-managed. A productive environment with opportunities
for professional growth. I like the fact that we are trying to do new
things. It provides an awesome working environment.”
Following the procedure of Gibson and Zellmer-Bruhn (2001),
the raters scored each employee response included in the analysis
82 BEZRUKOVA, THATCHER, JEHN, AND SPELL
by counting the number of search terms that occurred in the
response. To control for differences in the number of words across
responses (the greater the length, the more likely that a given term
would appear), they further divided the number of occurrences by
the total number of words in the response. For instance, for
Response 4, provided above, the score is .10 because five of the 49
words in the excerpt are search terms. In Response 5, the score is
.16 or five of the 32 words. Examples of weak results-focused
departmental culture included statements such as “It is so difficult
to do my job when nobody in my department takes accountability
for their actions” or “There are a lot of problems with office gossip
and petty bickering. Only a few of us are willing to go the extra
mile for our department and our customers.” Finally, to test for the
validity of the department-level construct, the suggestions of
Bliese (2000) were followed. Between-department variance for
this variable was significant at the .001 level, and the ICC[1] and
ICC[2] measures were acceptable with the values of .35 and .67,
respectively. On the basis of these results, it was concluded that
aggregation to the departmental level was justified.
Cultural alignment. Measuring cultural alignment can be
accomplished in several ways. One is to create a separate “align-
ment” variable using difference scores (see Chatman, 1989). This
method, however, is prone to numerous methodological problems
(see Edwards, 1994, for details). An alternative procedure involves
the use of polynomial regression equations containing the compo-
nent measures composed of the difference and higher order terms
such as the squares and product of these measures (Edwards, 1994;
Edwards & Parry, 1993). Although this approach allows examin-
ing more complex, primarily curvilinear and unconstrained rela-
tionships, its exclusive focus on congruence indices as independent
variables limits the examination of congruence as a moderating
variable (Edwards, 1994, p. 91, 2002, p. 395). Following recent
work in fit research (e.g., Dickson, Resick, & Hanges, 2006; Zohar
& Luria, 2005), a moderation analysis was therefore used, and the
hypothesized Faultlines ⫻Alignment effect was tested via a
three-way interaction. The cultural alignment variable was created
via the interaction term of Group Results-Focused Culture ⫻
Department Results-Focused Culture, where both culture variables
were continuous. This approach has the following benefits: It is
free from most of the problems surrounding congruence indices
(Edwards, 1994, p. 88); it employs the most efficient analytical
strategy; it keeps all data in their original format (preserves the
data structure, nothing is lost); and it tests all constructs at their
respective levels of analysis.
Dependent variables. Group performance was measured us-
ing group stock options and group bonuses—the most frequently
used pay plans for performance in contemporary organizations
(Lowery, Beadles, Petty, Amsler, & Thompson, 2002). These
measures are consistent with those used in prior diversity research
(Ely, 2004; Jehn & Bezrukova, 2004; Joshi, Liao, & Jackson,
2006). Group stock options refer to the number of options awarded
yearly based on recommendations provided by senior management
as part of a performance appraisal procedure. According to this
procedure, groups were formally recognized through a grant nom-
ination process as eligible for group discretionary stock option
awards based on team performance. Group bonuses were deter-
mined on the basis of goal attainment set at the corporate level as
a function of the group’s performance. Group bonuses were a
one-time adjustment to employees’ base pay distributed to all
employees in the group according to their grade level.
Control variables. The job level of an employee was con-
trolled for because it is often tied to the level of compensation
within a company. This continuous variable was based on the
company’s assigned values that ranged from 1 to 74 (M⫽28.01,
SD ⫽9.09), with 1 indicating the lowest job level and 74 indicat-
ing the highest job level. Employee salary (ranging from $24,000
to $300,000) was included, as it may affect merit raises (Elvira &
Graham, 2002). Group size was further controlled for because it
plays an important role in linking diversity, culture, and outcomes
(Carrol & Harrison, 1998); for example, larger groups may exhibit
less integration and consensus, and hence more performance losses
than smaller groups. To isolate the unique effects of faultlines
associated with a specific member alignment, the recommenda-
tions of Bezrukova et al. (2007) and Lau and Murnighan (2005)
were closely followed, and diversity effects were controlled for.
Blau’s (1977) heterogeneity index was used to measure group
heterogeneity for the functional background categorical variable.
The coefficient of variation was used to measure group diversity
for continuous variables (e.g., education and tenure) (Allison,
1978). Following the procedure suggested by Jehn et al. (1999),
tenure, functional background, and educational heterogeneity vari-
ables were averaged to arrive at the informational heterogeneity
control variable. These demographic characteristics were chosen
on the basis of previous research on group diversity (Williams &
O’Reilly, 1998) and their respective match with the faultline
variable. Last, the company’s personnel records were used to
locate employee information on age, gender, and race. Because the
results were essentially the same with the demographic control
variables as without them, they were not included in subsequent
analyses.
Analytic strategy. Given the hierarchical structure of the data
with observations at one level of analysis (individuals) nested
within a second level of analysis (groups) and within a third level
of analysis (departments), three-level hierarchical linear modeling
(HLM3; Bryk & Raudenbush, 1992; Raudenbush, Bryk, Cheong,
& Congdon, 2000) was used. A series of linear analyses (HLM3)
was performed for both the continuous dependent variables (stock
options and bonuses). The null models (with no predictors in-
volved) and random coefficients regression models (with Level 1
control variables) were estimated for the outcome variables, and
significant Level 2 and Level 3 variances in these variables were
found, confirming the appropriateness of using HLM3 for testing
the cross-level relationships. Each HLM3 analysis was then con-
ducted in a hierarchical fashion that included adding Level 2
controls (Step 1), main effects to test H1 (Step 2), two-way
interactions (Step 3), and three-way interactions to test H2 (Step 4)
(Bryk & Raudenbush, 1992; Hofmann, Griffin, & Gavin, 2000).
The deviance index (⫺2⫻log-likelihood of a maximum likeli-
hood estimate) was used to assess model fit, and a series of
chi-square tests were performed to examine which models pro-
vided superior fit (Bryk & Raudenbush, 1992). These two statistics
allow us to determine the explanatory value of a particular model
and the effect size associated with the addition of specific param-
eters. Following the recommendation of Enders and Tofighi
(2007), grand mean centering was used in Models 1 and 2 (pre-
dictor and same-level interactions) and group mean centering in
83
FAULTLINES AND CULTURES
Model 3 (cross-level interactions) while controlling for respective
variable means at Level 3 (Hofmann & Gavin, 1998).
Results
All descriptive data and zero-order correlations are presented in
Table 1.
Hypothesis testing. Table 2 presents the results of the HLM
analyses testing the main effects of faultlines on the performance
outcomes (see Model 1). In support of H1, the relationship be-
tween faultlines and group performance was negative and signif-
icant (stock options: y⫽⫺.278, p⬍.001; group bonuses: y⫽
⫺.284, p⬍.05). A chi-square test of the change in the deviance
statistic from the control model to the model with faultlines con-
firmed that including faultlines significantly improved the model
fit for stock options and bonuses,
2
(7) ⫽9.049, p⬍.05;
2
(7) ⫽
5.793, p⬍.05, respectively.
We conducted a series of HLM analyses to test the relationship
between faultlines, alignment across results-focused group- and
department-level cultures, and performance outcomes as predicted
in H2. In support of H2 (see Table 2, Model 3), the three-way
interactions (which capture the cultural alignment between groups
and departments) were significant for both of our outcome vari-
ables: group stock options (y⫽.265, p⬍.05) and group bonuses
(y⫽.515, p⬍.001). A chi-square test of the change in the
deviance statistic confirmed that including the three-way interac-
tion of faultlines, results-focused group cultures, and results-
focused department cultures improved the model fit for group
options and bonuses,
2
(1) ⫽1.282, p⬍.1;
2
(1) ⫽4.110, p⬍
.05, respectively. To advance further interpretations of findings
predicting group performance outcomes (H2), we plotted the in-
teraction effects for the two levels of results-focused cultures at
one standard deviation above and below the mean (see Figure 1a–
1d), performed a simple slope analysis as recommended by Aiken
and West (1991), and recently extended to the case of multilevel
modeling (Bauer & Curran, 2005; Preacher, Curran, & Bauer,
2006).
In support of H2, predicting that (a) the negative relationship
between faultlines and performance would be reversed in aligned
cultures with a strong emphasis on results, we found that the
relationship between faultlines and group performance was posi-
tive and significant: bonuses: y⫽.912, p⫽.001; stock options:
y⫽.121, p⫽.029 (see Figure 1a and 1c). H2 further predicts that
(b) the negative relationship between faultlines and performance
would be lessened in groups in aligned cultures with a weak
emphasis on results across both group and departmental levels.
Figure 1b and 1d show that the negative and significant relation-
ship between faultlines and group performance was lessened and,
in fact, became nonsignificant: bonuses: y⫽⫺.305, ns; stock
options: y⫽⫺.187, ns, when both groups and departments had
cultures with a weak emphasis on results (culturally aligned). As
evidenced from Figure 1b and 1d, performance was lowest for all
groups in conditions in which both groups and departments had a
weak cultural emphasis on results. Finally, in line with H2, pre-
dicting that (c) the relationship between faultlines and performance
would remain negative in groups in which cultures are misaligned
(see Figure 1a–1d), the relationship between faultlines and perfor-
mance was negative but did not reach statistical significance for
bonuses (y⫽⫺.491, ns;y⫽⫺.098, ns), and negative and
significant for stock options (y⫽⫺.428, p⫽.001; y⫽⫺.220,
p⫽.047), when results-focused group and department cultures
were misaligned (strong-weak or weak-strong focus on results).
Discussion
In this article, we contribute to the literature on diversity by
examining a specific type of group split, informational faultlines
(members’ alignments based on skill, knowledge, and expertise),
and by theorizing about its effects on group performance. Unlike
most faultline research that has focused on workgroups with
relations-oriented faultlines (e.g., gender- and race-based align-
ments) (e.g., Lau & Murnighan, 2005; Polzer et al., 2006), we
investigate attributes that, although not immediately detectable, are
important for task completion. Whereas some research has theo-
rized about the benefits of informational faultlines (Bezrukova et
al., 2009), our results revealed that groups with strong informa-
tional faultlines had performance problems (i.e., lower group stock
options and bonuses). Our findings corroborate a central argument
of the faultline perspective (Lau & Murnighan, 1998); that is, the
alignment of attributes based on member similarity, even those that
are not easily visible, may elicit negative categorization processes
to the extent that groups’ tangible outcomes such as group bonuses
or options would be affected. Thus, we show that informational
faultlines can be detrimental for performance.
Understanding the direct effects of faultlines on performance is
certainly important, but our main contribution sheds light on
Table 1
Means, Standard Deviations, and Zero-Order Correlations Among Variables
Variable MSD123456789
1. Job level 28.01 9.09 —
2. Salary 94,559.92 39,220.78 .73
ⴱⴱ
—
3. Group size 8 3 .25
ⴱⴱ
.19
ⴱⴱ
—
4. Informational heterogeneity 0.32 0.10 ⫺.08
ⴱ
.02 ⫺.13
ⴱⴱ
—
5. Informational faultlines 0.35 0.20 .16
ⴱⴱ
.19
ⴱⴱ
.27
ⴱⴱ
.29
ⴱⴱ
—
6. Results-focused group culture 0.38 0.33 .01 ⫺.03 ⫺.10
ⴱⴱ
⫺.08
ⴱ
.08
ⴱ
—
7. Results-focused dept. culture 0.01 0.01 .07 .11
ⴱⴱ
⫺.14
ⴱⴱ
.23
ⴱⴱ
.22
ⴱⴱ
.00 —
8. Group stock options 2,274.08 3,730.91 .69
ⴱⴱ
.74
ⴱⴱ
.14
ⴱⴱ
.09
ⴱ
.09
ⴱ
.21
ⴱⴱ
.06 —
9. Group bonuses 12,567.29 21,393.80 .61
ⴱⴱ
.75
ⴱⴱ
.15
ⴱⴱ
.03 .08
ⴱ
.16
ⴱⴱ
.05 .84
ⴱⴱ
—
Note.N⫽757. dept. ⫽department.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
84 BEZRUKOVA, THATCHER, JEHN, AND SPELL
identifying a condition that may alter the direct effect of faultlines.
Little is known about how cultural environments may shape the
relationships between group faultlines and outcomes. We thus
integrate the literature on faultlines and organization-based culture
by theorizing about the specific content of cultures (results-
focused), recognizing the multilevel nature of organizational cul-
tures, and showing how cultural alignment across organizational
levels may operate as a moderating variable that influences per-
formance in groups with faultlines. Our findings demonstrate that
although informational faultlines were detrimental for group per-
formance, the negative relationship between faultlines and perfor-
mance was reversed when cultures with a strong emphasis on
results were aligned, was lessened when cultures with a weak
emphasis on results were aligned, and remained negative when
cultures were misaligned with respect to results orientation. Be-
yond these findings, our study revealed additional results about the
relative performance levels of groups with weak and strong fault-
lines. We discuss these findings next.
Post hoc interpretation of results. Although our results
show that cultural alignment with a strong emphasis on results
reversed the negative relationship between faultlines and group
performance, groups with faultlines did not have the highest levels
of performance (see Figure 1a and 1c). One explanation is that
groups with strong faultlines in aligned results-oriented cultures
might be able to capitalize on their informational differences to
some extent (benefits of faultlines), yet, as our results show, they
might not be able to overcome process losses entirely due to
frictions between faultline subgroups (detriments of faultlines).
We theorized that the principle of functional antagonism ( I. G.
Turner et al., 1994; J. C. Turner et al., 1987) might explain how
frictions across faultline subgroups can be lessened by members
focusing on their similarities (common values focused on results)
rather than their differences (distinct subgroups). However, our
results suggest that this process only goes so far. In fact, recent
research has demonstrated a more complex phenomenon when
dual identities (e.g., faultline subgroups and culturally aligned
contexts) exist simultaneously (George & Chattopadhyay, 2005).
In this case, the benefits of aligned and highly results-oriented
faultline groups may not be fully realized because distinct sub-
group identities may lead to strong competition across subgroups
(Haslam, 2004; Tajfel & Turner, 1979). This might explain our
finding that groups with strong faultlines and aligned results-
oriented cultures do better than groups with weak faultlines and
aligned results-oriented cultures but still underperform relative to
groups with weak faultlines and misaligned results-oriented cul-
tures.
Groups with weak faultlines in misaligned cultures performed
roughly equivalent to or better than the groups with strong fault-
lines in aligned results-oriented cultures (shown in Figure 1a–1d).
In fact, the highest performing groups were those with weak
Table 2
Results of HLM Estimation
a
Model & variable
Stock options Bonuses
Model 1
(H1) Model 2
Model 3
(H2)
Model 1
(H1) Model 2
Model 3
(H2)
Control variables
Level 1
Job level .337 .323 .110 .178 .278 .160
Salary 2.406
ⴱⴱⴱ
2.959
ⴱⴱⴱ
3.408
ⴱⴱⴱ
3.356
ⴱⴱⴱ
3.750
ⴱⴱⴱ
3.872
ⴱⴱⴱ
Level 2
Group size .167
†
.133 .126 .162 .063 .103
Heterogeneity 1.576
ⴱⴱ
1.105
ⴱⴱ
1.155
ⴱⴱ
.728 .427 .524
Main effects
Level 2
Informational Faultlines (iFau) (H1)⫺.278
ⴱⴱⴱ
⫺.242
ⴱⴱ
⫺.254
ⴱ
⫺.284
ⴱ
⫺.266
ⴱⴱ
⫺.238
ⴱ
Res-fcsd gp culture (RgrC) .778
ⴱⴱⴱ
.948
ⴱⴱⴱ
.744
ⴱⴱⴱ
.917
ⴱⴱⴱ
Level 3
Res-fcsd dept. culture (RdptC) .040
ⴱⴱ
.022
Two-way interactions
Two-way Level 2 interactions
iFau ⫻RgrC .261 .219 .665
ⴱ
.632
†
Two-Way Level 2 ⫻Level 3 interactions
iFau ⫻RdptC .051
ⴱ
.028
RgrC ⫻RdptC ⫺.025 ⫺.203
ⴱⴱ
Three-way interactions
Three-Way Level 2 ⫻Level 2 ⫻Level 3
interactions
iFau ⫻RgrC ⫻RdptC (H2) .265
ⴱ
.515
ⴱⴱⴱ
Model deviance
b
2,216.690 2,194.536 2,192.180 2,869.11 2,848.284 2,844.003
Note.N(Level 1) ⫽757; N(Level 2) ⫽138; N(Level 3) ⫽47. HLM ⫽hierarchical linear modeling; H1 ⫽Hypothesis 1; H2 ⫽Hypothesis 2; Res-fcsd
gp ⫽Results-focused group; Res-fcsd dept. ⫽Results-focused department.
a
HLM3 analysis was used. Entries corresponding to the predictors are estimations of the fixed effects, y
s
, with robust standard errors.
b
Deviance is a
measure of model fit; it equals ⫺2⫻the log-likelihood of the maximum likelihood estimate. A smaller model deviance means a better fit.
†
p⬍.10.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
85
FAULTLINES AND CULTURES
faultlines in misaligned cultures (strong–weak emphasis on results
at the group and departmental levels, respectively; see Figure 1a
and 1c). One explanation is that weak faultline groups may have
cross-cutting ties (Gibson & Vermeulen, 2003) that enable effec-
tive information sharing and are unlikely to have strong subgroups
that can potentially cause process losses. When cultures are mis-
aligned, the combination of a weak faultline group, a strong group
results-focused culture, and a weak department results-focused
culture leads to the highest level of group performance. We think
this is because with little in the way of subgroup dynamics, group
members focus on the norms and values of the culture that is most
relevant or proximal to them (Edwards & Cable, 2009)—the
group-level culture with a strong emphasis on results. In our data,
groups that are highly focused on achievement may see themselves
as an elite high-performing entity within their department; thus,
motivational gains may result from a contrast effect (Liao, Liu, &
Loi, 2010). A sense of efficacy may come as a result of these
perceptions and is likely to fuel performance. This phenomenon is
known in high-tech companies as “skunk works” (Rich & Janos,
1996), which describes high-performing groups that are culturally
distinct from larger organizational units.
Surprisingly, our results further show that groups with weak
faultlines in aligned cultures with a strong emphasis on results had
low levels of performance. These teams perform poorly because
they might be overly cohesive, with strong cultures and similar
members. Another plausible explanation for this effect is that
whereas cultural alignment focusing on performance should ide-
ally stimulate cooperation in activities leading toward a common
goal (Aronson & Bridgeman, 1979; Sherif, 1958), this type of
alignment might also create too much pressure and associated
stress to perform (Karasek, 1979; Latham & Locke, 2006). In the
absence of faultlines, group members might lack social support
and cooperation that comes from being in a subgroup of similar
others. Recent research has identified this as a benefit of faultlines;
faultlines can operate as “healthy divides” by providing a safe
harbor for group members in response to a variety of stressors
(Bezrukova et al., 2010). Our findings demonstrate that although
members of groups with weak faultlines might not experience the
detriments of faultlines (faultline subgroups that are inherently in
a state of friction), they might also be missing the benefits of
faultlines (alleviation from stress caused by pressure to achieve
results). One response from our data that demonstrates that groups
with weak faultlines in aligned cultures with a focus on strong
results were facing this type of pressure to perform is provided
below:
I resent so much pressure is being put on us to “produce,” we are
reminded constantly of our responsibility to the shareholders, to
(a)
(b)
Group culture (strong emphasis on results)
0
1
0.2
0.4
0.6
0.8
1.2
1.4
1.6
1.8
weak faultlines strong faultlines
standardized group bonuses
dept culture-weak emphasis on results
dept culture-strong emphasis on results
Group culture (weak emphasis on results)
0
0.2
0.4
0.6
0.8
1.2
1
1.4
1.6
1.8
weak faultlines strong faultlines
standardized group bonuses
dept culture-weak emphasis on results
dept culture-strong emphasis on results
(c)
(d)
Group culture (strong emphasis on results)
0
0.2
0.4
0.6
0.8
1
1.2
weak faultlines strong faultlines
standardized group stocks
dept culture-weak emphasis on results
dept culture-strong emphasis on results
Group culture (weak emphasis on results)
0
0.2
0.4
0.6
0.8
1
1.2
weak faultlines strong faultlines
standardized group stocks
dept culture-weak emphasis on results
dept culture-strong emphasis on results
Figure 1. Interactive effects of informational faultlines, results-focused group culture, and results-focused
department culture on group level outcomes. dept ⫽department.
86 BEZRUKOVA, THATCHER, JEHN, AND SPELL
everyone. People under too much pressure doing too many tasks
causes strain in obtaining others’ help when needed.
The absolute lowest levels of performance occurred when there
was alignment across group and department cultures, but the
culture reflected a weak emphasis on results as shown in Figure 1b
and 1d. This result occurred regardless of the strength of the
faultline. Thus, having an aligned and weak results orientation
creates a bottoming-out in both our dependent variables (group
bonuses and stock options). In fact, it seems possible that a floor
effect
1
weakens the relationship between faultlines and perfor-
mance in culturally aligned groups with a weak emphasis on
results. Although group members have consistent expectations that
might help to unite faultline factions and avoid some process
losses, there is ultimately “nothing to lose” because there are no
shared task goals necessary for performance. Under this condition,
faultlines seem to have little influence on performance.
Overall, our findings highlight some important implications of
cultural alignment for faultline-based teams. For instance, in light
of multilevel theory, one would assume that groups in aligned
cultures should have stronger effects than groups in misaligned
cultures (Kozlowski & Klein, 2000). In our case, however, in
environments in which result-focused messages are not strong
(aligned cultures with no emphasis on results) or come from only
one level (misaligned cultures), faultlines do not lead to increased
performance. Yet, when there is pressure to achieve results
(aligned cultures focused on results), faultlines are relatively ben-
eficial because they provide a social support mechanism for sub-
groups to deal with pressure to perform. The cases that reverse or
eliminate the effects of faultlines occur when there is congruence
between levels. Thus, our results inform multilevel theory by
revealing previously unidentified interactions in multilevel cultural
orientation alignment that may be beneficial for faultline groups.
Future research. Given the number of unexpected findings
(see the Post hoc section), there is a clear need to replicate the
findings and determine the extent to which they are robust. For
example, our findings suggest that the alignment of cultures may
not be universally good— having a consistent message to achieve
results may actually hurt performance if it results in too much
pressure and individuals lack trusted others (a subgroup) to help
them deal with the pressure. Yet, when there is a lack of encour-
agement for results at any level of culture, there is a floor effect on
performance. Both situations produce less than optimal perfor-
mance; however, the mechanisms behind those effects might be
different and even potentially beneficial when the content of the
culture is focused on something other than results. For instance,
researchers should examine the moderating effects of cultural
alignment when cultures are not necessarily positive or results
oriented. Socially oriented cultures may work in getting teams to
look past faultlines but may not adequately focus teams on sharing
the right type of knowledge that would generate high levels of
team-based results. However, an alignment of group-level and
department-level socially oriented cultures may lead to higher
performance levels as employees will be happy, as has been shown
by decades of job satisfaction research (Judge, Thoresen, Bono, &
Patton, 2001). The culture of Southwest Airlines, for instance, is
often referred to not only as “fun” but also as high performing.
Future research should examine various cultural contents (e.g.,
people, safety, or service-oriented cultures) as well as environ-
ments with multiple cultures (e.g., people and service-oriented)
and their alignments at different organizational levels within the
group faultline framework to understand their implications for
different outcomes.
Another way to extend research on cultural alignment might be
to examine how it develops over time. Although it is often as-
sumed that an organizational culture is relatively stable and con-
sists of values that are widely shared and have special relevance to
its members (O’Reilly & Chatman, 1996), recent analysis draws
attention to the evolving nature of culture (Choi, 2004; Young &
Parker, 1999). Despite the considerable attention that organiza-
tional culture has received in the past (e.g., Chatman et al., 1998;
O’Reilly, 2001), little is known about how cultural alignment
develops over time and what drives the process. For example, in
line with Gersick’s (1991) punctuated equilibrium model of dis-
continuities in how groups evolve, major events like mergers can
disrupt an existing cultural alignment and trigger a sudden shift in
cultural content. The merger of United and Continental Airlines,
according to industry experts, is a case for how a customer-service
cultural alignment across organizations might emerge. In addition,
consistent with Richard (2000), future research should adopt a
configurational framework and consider a change in cultural align-
ments simultaneously across different cultural contents (e.g.,
results-, social-, creative oriented) as a system of combined con-
textual factors. Thus, we hope that our study can stimulate more
research on cultural alignment.
Limitations and strengths of the study. Our data have
certain limitations that are common in demography studies that
use archival file data. Although we were able to construct reliable
measures of results-focused group and department-level culture
variables using content analysis of company documents, no direct
measures of these variables were available. Furthermore, assessing
group- and department-level cultures from different documents
and using different methods may account for the pattern of our
results. This study, however, benefited from qualitative data con-
cerning context that we were able to code (Johns, 2006) and our
qualitative approach to culture research, which is rare in archival
data collections (see Gibson & Zellmer-Bruhn, 2001, for an ex-
ception). We believe this type of research has the potential to
illuminate various contextual effects and more research should
adopt this dual-natured approach. Another limitation is that our
study design, as is common in field research, does not allow us to
determine the actual cause– effect relationships, yet we believe that
our model is feasible based on past research (e.g., Chatman et al.,
1998). Finally, our bonus data were unevenly distributed across
groups, which limited statistical power necessary to detect signif-
icant relationships (fewer groups received bonuses than stock
options). This is likely why the relationship between faultlines and
bonuses in misaligned conditions failed to reach statistical signif-
icance, even though the effects were in the predicted direction.
There are several very positive features inherent in our data.
First, we have multisource data (qualitative data on cultures and
quantitative data on employees’ characteristics and performance)
that is not plagued with percept-percept bias. Our approach for
assessing group and organizational culture is particularly compel-
ling as we use data from two different archival questionnaires and
1
We thank an anonymous reviewer for this insight.
87
FAULTLINES AND CULTURES
use rigorous content analysis techniques based on prior research
(e.g., Abrahamson & Hambrick, 1997; Kabanoff, 1997). Our quan-
titative data come from different data sets, and most importantly,
performance measures are not self-reports that have well-known
measurement limitations, such as unknown reliabilities and a po-
tential for response bias due to a participant’s lack of introspection
(Asendorpf, Banse, & Mucke, 2002) and motivation to distort
responses (Orne, 1962). Instead, we used objective performance to
advance our understanding of implications of cultural alignments
in faultline groups. Second, we used a representative sampling
design that captured data from nearly the entire population of
employees in a Fortune 500 company. We also only used data from
intact groups, which is superior to the multilevel data that has been
used in other studies. For all these reasons, we believe that the
nature of the data strengthens confidence in our findings.
Practical implications. Our findings provide some guidance
for managers in how organizations can take steps to ensure that
groups with faultlines operate in ways that are effective. For
instance, strong leadership may result in a cultural alignment
throughout various organizational levels that can help to leverage
demographic-based faultlines that are prevalent in the current
workforce. This can be accomplished either in formal ways (such
as letters or memos) or through socialization tactics—where group
members experience common learning with other groups in their
department. An illustration of this approach is found among suc-
cessful companies such as Google, which is well known for its
emphasis on commitment to creating a culture of “perfection”
(Hof, 2008). One way that Google ensures a results-focused cul-
ture exists across organizational levels is by encouraging engineers
to communicate across various project groups outside of their main
group. Thus, engineering groups that are notably diverse (have
faultlines) are exposed to many projects and ideas across organi-
zational levels, and they receive a common message about the
organization, roles, and appropriate responses. This is just one
example of how companies can achieve cultural alignment and
help groups capitalize on their group composition. That is, groups
with internal demographic alignments (group faultlines), often
thought of as detrimental, can potentially benefit from alignments
outside groups (cultural alignments) if there is a strong focus on
results. We thus shed light on the importance of considering the
combined effects of alignments in (faultlines) and out (cultures) of
a group in relation to a group’s performance.
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Received August 28, 2009
Revision received March 10, 2011
Accepted March 21, 2011 䡲
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