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What's the Difference? Diversity Constructs as Separation, Variety, or Disparity in Organizations


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Research on organizational diversity, heterogeneity, and related concepts has prolif- erated in the past decade, but few consistent findings have emerged. We argue that the construct of diversity requires closer examination. We describe three distinctive types of diversity: separation, variety, and disparity. Failure to recognize the meaning, maximum shape, and assumptions underlying each type has held back theory devel- opment and yielded ambiguous research conclusions. We present guidelines for conceptualization, measurement, and theory testing, highlighting the special case of demographic diversity
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Academy of Management Review - Forthcoming 2007
Smeal College of Business, Pennsylvania State University
The Wharton School, University of Pennsylvania
Management research on diversity, heterogeneity, dissimilarity, and related concepts of
within unit differences in organizations has proliferated in the past decade. However, few
clear or consistent findings have emerged. We argue that the nature of these difference-based
constructs requires closer examination. Using diversity as an overarching term, we contend
that it has three distinctive types:
, or
. Failure to recognize the
unique meaning, maximum shape, and assumptions underlying each type has held back
theory development and contributed to mismatched operationalizations and research design.
After presenting our diversity typology, we present guidelines for conceptualization,
measurement, and theory testing, highlighting the special case of demographic diversity.
Differences are a challenge.
Organizations have
struggled to embrace and manage them successfully.
Researchers have struggled to conceptualize and study
them effectively. Theories predict differing effects of
such differences: that they will spark integrative
insights, creativity, and innovation (e.g., Finkelstein &
Hambrick, 1996; Hoffman & Maier, 1961), or that they
will provoke conflict, division, and dissolution (e.g.,
Chatman, 1991; Tajfel & Turner, 1979). Guided by
these theories, research on differences of many kinds,
including demographic variables, values, skills,
personality and pay, has burgeoned. The volume of
such research has nearly doubled every five years, with
a joint ABI-Inform - PsycINFO search yielding 19
“diversity" investigations in 1988, 45 in 1993, 66 in
1998, and 134 in 2003.
Yet, as noted by many authors, the payoff from this
profusion of research has been disappointing (e.g.,
Harrison, Price, & Bell, 1998; Pitcher & Smith, 2001).
Cumulative findings about the consequences of within-
unit differences have been weak, inconsistent, or both.
A recent meta-analysis of the effects of task-related
(e.g. tenure) and nontask-related (e.g., ethnic and
gender) diversity, revealed no dependable effects on
performance or cohesiveness (Webber & Donahue,
2001). Summarizing a narrative review of the same
Author order was determined by the flip of a coin.
We are grateful to the Special Issue Editor (Terry Mitchell),
and two anonymous reviewers for supportive and
constructive suggestions. Paul Goodman, Don Hambrick,
David Hofmann, Susan Jackson, Charles O'Reilly, Ben
Schneider, and Jonathan Ziegert also provided helpful
feedback and recommendations on earlier drafts of this
manuscript. We further appreciate the assistance of several
current and former doctoral students in background research
for this manuscript: Ellen Godfrey, Andrew Knight, Jessica
Saltz, Amy Nicole Salvaggio, and Hock-Peng Sin.
domain, Jackson, Joshi, and Erhardt (2003: 810)
concluded, "Our examination of these studies yielded
few discernible patterns in the results. For most
diversity dimensions, the findings across studies were
mixed." Beyond demographic diversity, Bloom and
Michel (2002: 33) reached a similar conclusion:
"Research has yielded mixed results about what amount
of pay dispersion is optimal. In some cases, more
dispersed pay distributions have been positively related
to performance outcomes … In other cases, greater
dispersion has been negatively related to performance
In response to these disappointing cumulative
results, organizational scholars have refined their
theories and analyses, typically by elaborating possible
mediators and moderators of connections between
within-unit diversity and unit outcomes (e.g., Bloom &
Michel, 2002; Carpenter, 2002; Chatman & Flynn,
2001; Simons, Pelled, & Smith, 1998; Williams &
O'Reilly, 1998). We applaud these efforts. But, we
suggest that the very construct of diversity requires
closer examination and refinement. Authors frequently
and casually use the term "diversity" and synonyms
such as heterogeneity, dissimilarity, and dispersion.
Yet, the precise meaning of diversity is not clear.
Casting a deliberately broad net, we seek to clarify the
meaning of differences within organizational units. We
use the term diversity to describe the distribution of
differences among the members of a unit with respect to a common
attribute, X, such as tenure, ethnicity, conscientiousness,
task attitude, or pay. Diversity is a unit-level,
compositional construct. Thus, in describing the
diversity of a given attribute within a unit (e.g., a group
or organization), one describes the unit as a whole, not
a focal member's differences from other members –
which is the subject of most relational demography
research (e.g., Tsui & O'Reilly, 1989). Diversity, as we
use the term, is also attribute-specific. A unit is not
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diverse per se. Rather, it is diverse with respect to one
or more specific attributes of its members.
Diversity, we propose, is not one thing, but three
things. The substance, pattern, operationalization, and
likely consequences of those three things differ
markedly. Diversity within an organizational unit may
be indicative of separation: differences in position or
opinion among unit members. Such differences reflect
disagreement or opposition – horizontal distance along
a single continuum representing dissimilarity in a
particular attitude, or value, for example. Alternatively,
within-unit diversity may be indicative of variety:
differences in kind or category, primarily of
information, knowledge, or experience among unit
members. And finally, within-unit diversity may
indicate disparity: differences in concentration of valued
social assets or resources such as pay and status among
unit members – vertical differences that, at their
extreme, privilege a few over many. We use this insight
– implicit in some of the diversity literature, but neither
widely recognized across domains nor fully understood
– as the starting point for an analysis of the construct
definition, theoretical substance, and methodological
implications of each diversity type. We present this
typology in the hope of bringing greater theoretical
insight, as well as empirical rigor, to the study of
organizational diversity of all forms.
Below, we first provide a brief overview of several
examples of diversity theory and research, noting
conceptual confusion in definitions of diversity and
some resulting ambiguities in the literature. We then
offer, in the second section of the paper, a detailed
presentation of our typology, highlighting differences in
the meaning, maximum, and major theoretical streams
involved in the separation, variety, and disparity types.
We explore the typology's implications for the special
case of demographic diversity, showing that the same
demographic differences within units may be
conceptualized as separation, variety, or disparity. In
the third section of the paper, we describe
methodological consequences of our diversity typology.
We emphasize the importance of matching a specific
operationalization of diversity to a specific
conceptualization of diversity. In the fourth and final
section of the paper, we describe new directions for
diversity theory and empirical investigation, enabled
and motivated by the diversity typology we propose.
For many reasons, the organizational literature on
diversity is confusing – difficult to understand and
difficult to synthesize. It is difficult to synthesize in
part because consistent findings and cumulative
insights have not emerged, but also simply because the
diversity literature itself is so diverse. Much of it
addresses within-unit differences in demographic
variables: gender (e.g., O'Reilly, Williams, & Barsade,
1997), race and ethnicity (e.g., Riordan & Shore, 1997),
age (Pelled, 1999), tenure (e.g., Hambrick, Cho, &
Chen, 1996), education (e.g., Jackson, Brett, Sessa,
Cooper, Julin, & Peyronnin, 1991), functional
background (e.g., Jehn, Northcraft, & Neale, 1999), and
marital status (e.g., Harrison et al., 2002). Further,
researchers have investigated differences in many non-
demographic variables including values (e.g., Jehn et al,
1999), attitudes (e.g., Harrison et al., 1998),
conscientiousness (e.g., Barrick, Stewart, Neubert &
Mount, 1998), affect (e.g., Barsade, Ward, Turner, &
Sonnenfeld, 2000), dress (e.g., Rafaeli & Pratt, 1993),
network ties (e.g., Beckman & Haunschild, 2002),
individual performance (e.g., Doerr, Mitchell,
Schriesheim, Freed, & Zhou, 2002), and pay (e.g.,
Pfeffer & Langton, 1988).
Synthesis is also made difficult by the varied
theoretical perspectives used to guide diversity
research. Often, these perspectives suggest contra-
dictory effects. And, each perspective has received
some (albeit mixed) support in empirical studies. One
such perspective draws on similarity-attraction (Byrne,
1971) and social categorization (Tajfel, 1981; Turner,
1987) theories to suggest that diversity of attributes
such as age, values, and personality limits within-unit
behavioral and social integration, fosters conflict and
turnover, and diminishes morale, cohesion, and
performance (Williams & O'Reilly, 1998). A second
theoretical perspective draws from ecological and
cognitive models of variation, selection, and retention
(e.g., Campbell, 1970) and the cybernetic principle of
requisite variety (Ashby, 1956) to highlight the benefits
of heterogeneity in information resources. This
perspective suggests that diversity of attributes such as
functional background, tenure, and range of network
ties may enrich the supply of ideas, unique approaches,
and knowledge available to a unit, enhancing unit
creativity, quality of decision making, and complex
performance (Williams & O'Reilly, 1998). And, a third
perspective builds on distributive justice theory (e.g.,
Deutsch, 1985; Adams, 1963), tournament theory (e.g.,
Lazear, 1995; Lazear & Rosen, 1981), and stratification,
status hierarchy or status characteristics theories (e.g.,
Blau, 1960; Berger, Fisek, Norman, & Zelditch, 1977).
This perspective highlights relative comparisons among
unit members to suggest that within-unit diversity,
typically of pay but also of other valued assets or
resources that connote prestige or power, leads to
within-unit competition, suppression of voice, reduced
(quality of) communication, and interpersonal
Finally, synthesis is made difficult because, as a
term, "diversity" has a taken-for-granted quality in the
organization literature; it is seldom explicitly defined.
Researchers use a variety of labels, often inter-
changeably, to refer to diversity – including dispersion,
heterogeneity, dissimilarity, disagreement, divergence,
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variation, and inequality, or their opposites,
homogeneity, similarity, agreement, consensus,
convergence, and equality. But, the more serious
problem is that diversity investigators offer only spare
or generic definitions of the principal construct. For
example, Hambrick and his colleagues (1996: 662)
defined top management team heterogeneity as
"variation in team members' characteristics." Pelled et
al. (1999: 1) defined demographic diversity as "the
extent to which a unit (e.g., a work group or
organization) is heterogeneous with respect to
demographic attributes." Jehn et al. (1999: 745)
defined value diversity by noting that it "occurs when
members of a workgroup differ in terms of what they
think the group's real task, goal, target, or mission
should be."
None of these definitions is inappropriate. Indeed,
our definition of diversity above is quite similar.
However, even the most exacting of these definitions
ties diversity to differences and goes no further. The
definitions do not pinpoint and substantiate the nature
of those differences, nor do they specify the collective
distribution – the compositional pattern of differences
within a unit. Readers may discern from such
definitions the meaning of minimal diversity: it occurs
when there are no differences on attribute X among the
members of a unit. But, the distribution, shape, and
meaning of maximal diversity are less clear. We know
what is least diverse, but not what is most diverse, and
therefore, the direction of "more" (or less) diversity can
be ambiguous. For example, when a researcher studies
diversity in tenure, personality, or pay, is diversity
maximized when the attribute in question is evenly
distributed among unit members, such that each point
along the continuum, or each category, is represented?
Or, is diversity maximized when unit members are
polarized, half very high and half very low, on the
attribute in question? Could diversity be maximized
when one unit member clearly surpasses all the other
members? Lastly, is the shape of maximum diversity
different depending on the X attribute a researcher is
investigating, his or her theoretical perspective, or the
predicted effects of increasing diversity? One can
confidently order organizational units in terms of
increasing amounts of cohesion, or morale, or size, or
performance. But, units that are ordered in terms of
increasing diversity are difficult to visualize.
Because the diversity literature has devoted little
attention to these kinds of elemental questions, the
construct of diversity is murky. The field needs a
deeper understanding of the meanings(s) of within-unit
differences (Lawrence, 1997). Scholars lack a shared
terminology, or typology, to compare their
conceptualizations of within-unit differences. We
present such a typology of diversity below. We believe
it has the potential to aid scholars in refining their
theoretical propositions, in matching their
operationalizations to their constructs, and in
integrating their research findings.
We start with a brief example to juxtapose and
preview the distinctive nature of each diversity type.
Consider three research teams. Each team is studying
how patients experience medical treatment in hospitals.
Each team consists of eight members. Members of
Team S differ in their attitude toward a particular
research paradigm. Half of the team's members revere
richly descriptive, interpretive inquiry. The other half
disparage it. The members of Team V differ in their
disciplinary backgrounds. One is a psychologist,
another is a human factors engineer, and the others
include a micro-economist, sociologist, anthropologist,
linguist, hospital administrator, and practicing
physician. Finally, the members of Team D vary in
their research eminence or rank. One member of the
team is a highly accomplished professor who is
renowned for having formulated seminal theories of
patient interactions with health care professionals. The
other members of the team are getting their first
behavioral science research experience.
Diversity is obvious within each team. Yet, the
content and likely outcomes of diversity differ across
teams. In Team S, diversity in team members'
endorsement of qualitative research reflects separation:
team members hold opposing positions on a task- or
team-relevant issue. In Team V, team member
diversity in disciplinary background reflects variety:
together, team members bring a multiplicity of
information sources to bear on the research question.
In Team D, diversity is associated with disparity: one
member of the team is superior to the other team
members in research expertise, and presumably in
status as well. The three teams not only differ in the
type of diversity they represent, but also in the attribute
of diversity present in each team (attitude toward
qualitative research, disciplinary background, member
Figure 1 provides a graphic illustration of these
three types of diversity and Table 1 summarizes key
differences among the types. In describing each type
below, we highlight the key assumptions that
investigators implicitly or explicitly make when studying
each one. They deal with (a) the meaning and extent of
diversity within units; (b) between-unit variance in the
amounts of within-unit diversity; and (c) the correlates
and consequences of between-unit variance in within-
unit diversity. We clarify these fairly complex
assumptions for each diversity type. Further, we
describe the nature of minimum, maximum, and
moderate diversity amounts for each type. Finally, we
note examples from the literature of theoretical and
empirical analyses consistent with each diversity type.
In choosing examples, we have temporarily excluded
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those involving organizational demography, precisely
because demographic diversity within a unit may be
conceptualized as separation or variety or inequality.
We argue that this uncertainty contributes to the
difficulty in conceptualizing and testing diversity's
effects. Accordingly, a discussion of alternative
conceptualizations of demographic differences as
separation, variety, or disparity follows our initial
presentation of each diversity type.
Key Assumptions. Many hypotheses or theories
about diversity propose that: (a) within units, members
differ from one another in their position along a single
continuous attribute: a lateral continuum 'S' (e.g.,
positive affect, perceptions of leader charisma,
organizational commitment); (b) units differ in the
extent to which their individual members are co-located
along S: in some units, members are close to one
another, but in other units, members are more widely
spread; and (c) differences among units in the extent to
which their members are dispersed along S lead to a set
of systematic consequences (e.g., higher or lower
cohesion). In our lexicon, theories and studies of this
type rest on an idea of diversity as separation. McGrath,
Arrow, and Berdahl's (1995) description of VBA (value,
belief, attitude) diversity is generally consistent with the
idea of diversity as separation, as is the notion of value
diversity (Williams & O'Reilly, 1998). Typically,
theorists and researchers who examine separation also
assume that this diversity type has symmetric effects.
That is, homogeneity on S is often predicted to be
beneficial whether all members are high on S or all
members are low on S.
Minimum, Maximum, and Moderate
Separation. Row 1 of Figure 1 depicts minimum,
moderate, and maximum separation within a unit.
Minimum separation occurs when all of the members
of a unit occupy the same position at any location along
the S continuum. In some research areas, minimum
separation might be referred to as perfect agreement
within the unit (James et al., 1993). That is, minimum
separation can happen anywhere on the continuum of interest.
On the other hand, number theory shows that maximum
separation occurs only when unit members are equally split and at
opposing endpoints of the S continuum (Harrison & Sin,
2005). That is, the utmost separation occurs when
there are two (and only two) staunchly divided but
balanced blocs within a team, each holding a position
on S as far from the other as possible. Team S, above,
shows maximum separation along the continuum of
"attitude toward interpretive research methods."
Moderate or limited separation occurs when unit
members show some, but only some, spread or
disagreement along the continuum. Perhaps most unit
members occupy the same position and only a few hold
differing positions. Or, perhaps unit members are
uniformly spread across the continuum, as Figure 1
shows (middle cell of first row).
Diversity as Separation in Theory and Research.
Theories of similarity-attraction (Byrne, 1971; Clore &
Byrne, 1974; Newcomb, 1961), social identity and self-
categorization (Hogg & Terry, 2000; Tajfel & Turner,
1979), and attraction-selection-attrition (Schneider,
1987; 1995) are invoked most often for diversity-as-
separation. Investigators adopting these arguments
posit that greater similarity – reduced separation –
yields higher levels of cooperation, trust, and social
integration (e.g., Locke & Horowitz, 1990).
Conversely, diversity scholars often draw on these
theories to posit that units whose members differ
markedly on a continuum will experience low cohesion,
high conflict, high rates of withdrawal, and poor
performance (e.g., Tsui, Ashford, Clair, & Xin, 1995).
For example, Harrison et al. (2002) examined the
consequences of team members' diversity in task
meaningfulness and outcome importance, positing that
"people find it more pleasurable to interact with others
who have similar psychological characteristics, because
that interaction verifies and reinforces their own
beliefs, affect, and expressed behaviors" (p. 1031).
Minimum separation is thus likely to be
psychologically comforting to unit members with
regard to S. No one challenges anyone else's position
on the S continuum because each of their positions is
equivalent. As many have noted, this kind of
attitudinal or psychological conformity on S may cause
individuals to assume that they are not only similar on
S but similar in other ways as well (Janis & Mann, 1977;
Nemetz & Christiansen, 1996). Few diversity theorists
have addressed the shape and likely consequences of
maximum separation. When separation is at its
maximum, unit members are, by definition, polarized
on S. The unit is comprised of two extreme and
opposing factions. If the diversity attribute is of central
importance to team identity and task completion, then
a unit's social network is also likely to bifurcate into
two dense clusters or cliques, with few or no team
members bridging the structural hole between them.
Gibson and Vermeulen's (2003: 203) discussion of
subgroups within teams is consistent with this notion:
"members of a subgroup may … cohere and share
opinions more often within the subgroup than with
others, which may lead to irritation in the team and
disputes between the different factions."
Key Assumptions. In a second approach to within-
unit diversity, theorists and researchers assume that: (a)
within units, members differ from one another
qualitatively – that is, on a categorical attribute 'V' (e.g.,
functional background, source of external information);
(b) units differ in the extent to which their members
are evenly spread across all the categories of V; and, (c)
differences between units in their relative spread or
diversity on V will be associated, usually positively, with
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Meanings and Properties of Within-Unit Diversity Types
Meaning and
Attribute Shape at
Maximum Diversity
(on attribute S)
composition of
differences in (lateral)
position or opinion
among unit members,
primarily of value, belief,
or attitude; disagreement
or opposition
distribution, with
half of unit
members at highest,
lowest endpoints of
S continuum
opinions, beliefs,
values, attitudes,
especially regarding
team goals and
more interpersonal
conflict, distrust,
decreased task
attraction; social
selection, and
attrition (ASA)
(on attribute V)
composition of
differences in kind,
source or category of
relevant knowledge or
experience among unit
members; unique or
distinctive information
distribution, with
even spread of
members across all
possible categories
of V (no
content expertise,
network ties,
industry experience
greater creativity,
innovation, higher
decision quality,
more task conflict,
increased unit
processing; law of
requisite variety;
selection, and
retention (VSR)
(on attribute D)
composition of (vertical)
differences in proportion
of socially valued assets
or resources held among
unit members; inequality
or relative concentration
positively skewed
distribution, with
one member at
highest endpoint of
D continuum,
others at lowest
pay, income,
prestige, status,
authority, social
more within-unit
resentful deviance,
reduced member
input, withdrawal
(in)justice and
(in)equity; status
tournament; social
Generally, but not in all diversity conceptualizations or studies.
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FIGURE 1: Pictorial Representation of Types and Amounts of Three Meanings of Within-Unit Diversity
Amount of Diversity
Type of
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vital unit consequences (e.g., problem-solving or group
decision quality, firm performance). Symmetry or
asymmetry with respect to attribute V is a non-issue; V
has no high or low. Instead, the distribution of V is
assumed to indicate variety: the number and spread of
"batches" of information content, experience, or
unique network ties available across unit members.
Variety broadens the cognitive and behavioral
repertoire of the unit. McGrath et al. (1995) borrow
from human resources management in referring to one
version of variety as KSA (knowledge, skill, and ability)
diversity. Blau (1977) originally termed a form of this
diversity type "heterogeneity," but that term itself has
achieved a variety of meanings in the literature
(Murnighan & Lau, 1998). Others (e.g., Jehn et al,
1999; Williams & O’Reilly, 1998) refer to a similar
concept as information diversity. Miner, Haunschild,
and Schwab (2003: 790) refer to this type as
"categorical variability."
Minimum, Maximum, and Moderate Variety.
As with the other types of within-unit diversity,
minimum variety is straightforward. It occurs when all
members belong to the same category of attribute V
(see row 2 of Figure 1). Using communication theory
terms, such members are redundant in that there is no
information gained by adding more persons to the unit
who occupy the same V category (Shannon, 1948).
Maximum variety is the more interesting case. It is the
richest possible distribution of information, when each
member within a unit comes from a unique category of
V. These upper and lower bounds of variety connect
to how a number of researchers refer to "perfect
homogeneity" or maximum heterogeneity within a unit
(e.g., Priem, 1990). The hospital researchers in Team V
have maximum heterogeneity or variety for scholarly
background; each member represents a distinct
scientific field.
The distinction between minimum and moderate
variety is fairly stark. The latter requires at least one
member to come from a different category on attribute
V than all the other members. That first, unique,
individual with respect to V brings the biggest step up
or the largest marginal increase in information for the
unit (Shannon, 1948). From a variety viewpoint, the
"odd person out" with respect to V is the most crucial.
Note that, because there are only two categories, a dyad
can have only minimum or maximum variety, but not
moderate variety (a dyad can have moderate separation
and moderate disparity). Hence, the study of diversity
as variety is fundamentally altered for units with two
versus more than two members. A more extreme view
(Harrison & Sin, 2004) would be that any type of
diversity is undefined for dyads because there is only a
single difference -- no distribution of differences --
between members.
Diversity as Variety in Theory and Research.
Most formulations of diversity-as-variety are consistent
with the idea that an organizational unit is an
information processing instrument for the organization
(Hinsz, Tindale, & Vollrath, 1997). The unit senses,
evaluates, and responds in ways that are designed to be
adaptive to the environment. Based on well-known
axioms in information processing or cybernetic theory
(law of requisite variety: Ashby, 1956), population
ecology, and even human cognition theory (variation
and selective retention; Campbell, 1960), the
fundamental idea is that teams in organizations can
translate greater information richness within a unit into
better choices, plans, or products. Units whose
members draw from different pools of informational
resources – their knowledge, functional background,
experience, or range of external social ties (e.g., Argote
& Ingram, 2000; Austin, 2003; Finkelstein & Hambrick,
1996) – will make more effective decisions and deliver
more creative products than units whose members
draw the same pool of resources (Jackson et al., 1995).
In fact, Carpenter (2002: 280) describes this type of
diversity as "sociocognitive horsepower." Members
with diverse kinds of human capital or access to
different sources of data (Hambrick & Mason, 1984;
Marsch, 2002; Wiersema & Bantel, 1992) collectively
serve as a team's lens, each filtering unique
environmental cues and interpreting them for the rest
of the unit's members. Similarly, units whose members
have non-redundant (i.e., non-overlapping) external
network ties have access to information that other
units, lacking in such variety, cannot easily obtain
(Austin, 2003; Beckman & Haunschild, 2002). Units
whose members bridge structural holes in an inter-unit
network are thus likely to be more creative and
productive (Burt, 2002; Hansen, 1999; Reagans &
Zuckerman, 2001). As Ferrier (2001: 858) describes it:
"by way of greater awareness in sensing strategic
problems, heterogeneous teams can match complex
competitive challenges and uncertain contexts with a
requisite level of cognitive and experiential variety."
When the within-unit distribution of V is at its
maximum, "everybody is different, and everybody has a
unique viewpoint to offer" (Gibson & Vermeulen,
2003: 208). When maximum variety is present,
members may be quite open and receptive to one
another's views. As each unit member is different from
everyone else, unit members cannot form coalitions
with like others; they have to reach across category
boundaries to coordinate and connect. In contrast,
moderate variety, evidenced by the presence of
substantial overlap in some, but not all, members'
knowledge may lead to problems of "unshared
information" (Stasser, Vaughan, & Stewart, 2000;
Gruenfeld, Mannix, Williams, & Neale, 1996). They
may fail to discuss information not shared by all or the
majority of group members.
Key Assumptions. In the organizational literature,
conceptual and empirical treatments of diversity as
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separation or as variety are relatively common.
Treatments of diversity as disparity are not. They are
more common and long-standing in sociology, where
the term "inequality" reigns (Blau, 1977). We forward
disparity as a different term to clearly distinguish our
within-unit discussion from the dominant focus of
sociological studies on distributions of income across
entire populations. In developing and testing
hypotheses regarding the effects of within-unit diversity
as disparity, theorists and researchers focus on a
socially valued or desired resource 'D' (e.g., pay, power,
prestige, status); more of D is always better. Disparity-
based research assumes that (a) within units, members
can differ in the extent to which they hold, or receive a
share, amount, or proportion of D; (b) units differ in
the extent to which D is distributed among or
possessed by its members – in some units, members
have equal shares of D, but in other units, one or a few
members hold a disproportionate share of D relative to
other unit members; and (c) differences among units in
the extent to which their D is distributed equally
among unit members lead to predictable and important
consequences (e.g., fewer member expressions of
A disparity conceptualization also involves one
more assumption that the direction of difference along
the D continuum matters. Disparity is asymmetric. This
asymmetry assumption is non-intuitive. Because
disparity describes the relative distribution of a valued
asset or resource, disparity is high if 10% of a unit's
members "own" a great deal of D (e.g., prestige or pay),
while 90% of unit members have very little. Disparity
is low, however, if 90% of a unit's members own a
great deal of D, while only 10% have very little. In the
first case, 90% of the unit's members are disadvantaged
relative to the privileged few. In the second case, only
10% of the unit's members are disadvantaged relative
to the overwhelming majority.
Minimum, Maximum, and Moderate
Disparity. Row 3 of Figure 1 depicts minimum,
moderate, and maximum disparity within a unit.
Minimum disparity occurs when all of the members of
a unit occupy the same position. Minimum disparity is
member parity with respect to D. It can occur
anywhere on the D continuum. Maximum disparity,
however, occurs when only one unit member outranks all
others – that is, when one unit member's level of D is at
the top of the continuum and all other members are at
the bottom. Research Team D, studying patients'
treatment experiences in hospitals, exhibits maximum
diversity. There is one veteran researcher accompanied
by seven rookies. Allison (1978: 869) aptly described
maximum disparity as "one individual has everything
and everyone else has nothing." Moderate or limited
disparity occurs when unit members show some, but
only some, difference along the continuum defined by
the valued resource. Some members outrank and
outclass the others, but differences among unit
members are compressed. Moderate disparity is of
focal interest in some areas of sociology (Homans,
1961; Philips & Zuckerman, 2001) and social
psychology (e.g., Berkowitz & McCauley, 1961). Those
in the middle of D are conform more readily to unit
norms than those at high and low ends (although the
latter segments have different reasons for
Diversity as Disparity in Theory and Research.
Disparity (inequality) in asset, resource, or valued good
distribution is a central pattern in sociological theories
of stratification (Grusky, 1994). Although inequality of
valued resources is also typical in organizational units,
theory and research addressing diversity-as-disparity in
organizations is surprisingly rare. The clearest
examples emerge from the literature on pay dispersion
(Bloom, 1999). As Bloom and Michel (2002: 33)
explained: "more dispersed pay structures concentrate
pay among fewer employees at the top . . . thereby
providing relatively rich rewards for these employees
and relatively poor rewards for the rest." Building on
theories of relative deprivation (e.g., Deutsch, 1985)
and tournament compensation (e.g., Lazear, 1995;
Lazear & Rosen, 1981), researchers commonly predict
that status, power, or pay disparity incites competition,
differentiation, and (resentful) deviance among some
unit members (e.g., Bloom, 1999; Gomez-Mejia, 1994;
Homans, 1961; Pfeffer & Langton, 1993). Disparity
might also foster conformity, silence, suppression of
creativity, and withdrawal (Hollander, 1958; Pfeffer,
1988; Pfeffer & Davis-Blake, 1992).
A conceptualization of diversity-as-disparity is also
apparent in theory and research addressing status and
power hierarchies in work units (for some of the
foundational theories, see Philips & Zuckerman, 2001).
When disparity in a group is at its maximum, one
member of the unit outranks all others. He or she
holds the lion's share, if not all, of a valued unit
resource. Eisenhardt and Bourgeois (1988)'s study of
top management team (TMT) strategic decision making
processes provides an example. When a CEO's power
far exceeded that of other TMT members, creating
high power disparity in the team, the CEO was likely to
engage in "tactics for controlling and withholding
information" and the other executives in the team were
to engage in "alliance and insurgency behaviors" (p.
743). Ultimately, marked disparities in team member
power diminished team performance by distracting
team members from key tasks and interrupting the flow
of information. These team-level observations are
corroborated by research documenting likely behaviors
when individuals are located at (dis)advantaged or
disparate status positions in teams (e.g., Berger &
Rosenhotz, 1980; Cohen & Zhou, 1991; Leffler,
Gillespie, & Conaty, 1982). As Keltner, Gruenfeld, and
Anderson (2003: 277) summarized, "high-power
individuals talk more, interrupt more, are more likely to
speak out of turn, and are more directive of others'
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verbal contributions than are lower-power individuals."
Finally, social network analysis provides another
intriguing link to this diversity type. If the valued
resource, D, is social capital, the structure of a network
might illustrate disparity. Social capital is accessed and
conveyed through interpersonal ties (Adler & Kwon,
2002). When a unit's network structure is highly
centralized, network ties are unevenly distributed. Only
one or a few members are highly central, and thus
highly influential. The other unit members are on the
periphery of the network, with few or even no links to
other unit members. Centralization may thus "be
viewed as a measure of how unequal the individual
actor values are" (Wasserman & Faust, 1994: 176).
Suppose knowledge of task process is conceptualized
as a valued resource, D (a form of expert power:
French & Raven, 1959), and it is held by a single
person. Hence, the unit has maximum disparity. Such
a condition might translate into a social network with
maximum centralization, in which unit members build
ties or turn to only that person for advice.
The three diversity types are fundamentally distinct.
Separation describes differences among unit members
in their position on a horizontal continuum. Variety
describes differences among unit members from
different categories, reflecting access to unique sources
of knowledge. Disparity describes differences among
unit members in their portion of a valued resource.
Separation thus reflects position: the distribution of
where members stand on a value, belief, attitude, or
orientation. Variety reflects information: the distribution
of what each unit member knows that is unique from
other members, as a function of the distinct content of
his or her education, training, or experience. Disparity
reflects possession: the distribution of how much of a
socially valued commodity each unit member has. The
three diversity types also differ in their relevance to key
theoretical perspectives, and are commonly (but not
necessarily) associated with different outcomes. These
distinctions are obscured when researchers and
theorists define diversity loosely or not at all.
Conversely, explicit reference to diversity types should
sharpen researchers' predictions, requiring the
adoption, explanation, and justification of a specific
conceptualization. Further, explicit reference to
diversity types allows scholars to contrast and debate
their views; they will have a common language to
capture their convergent or divergent ideas. Finally,
explicit reference to diversity types paves the way for
researcher to address the potentially interactive effects
of separation, variety, and disparity, a point we return
to in the final section of the paper. Accordingly, we
propose the following:
Guideline 1.
Theory building regarding diversity is enhanced by authors’
explicit specification and justification of the diversity type of
interest: separation (S), variety (V), or disparity (D).
Despite distinctions among their meanings, there is
little to distinguish the three types of diversity when
each is minimized (see Figure 1). All members within a
unit hold the same value of a variable, regardless of
what the variable represents. The three diversity types
are strongly differentiated, however, by their distinctive
shape and meaning under maximum diversity.
As shown in row 1 of Figure 1, maximum
separation occurs when a unit is divided into two
divergent camps. As separation increases from
minimum to maximum, unit members grow
increasingly polarized in their placement along a
continuum. Were a researcher studying other types of
diversity, however, the same distribution would be
indicative of modest variety (only two of a possible
eight categories are present) or of medium disparity
(50% of the unit are "haves" and 50% are "have-nots").
Maximum variety, shown in row 2 of Figure 1, occurs
when a unit is comprised of members who each
represent a different category. As variety increases
from minimum to maximum, unit members grow
increasingly differentiated in their knowledge,
experience, or perspectives; they occupy distinct
information categories within the unit. Were those
eight categories reoriented as points along a continuum,
however, the same distribution would indicate only
modest separation or modest disparity. Distance
among the unit members, which is part of the evidence
for either separation or disparity, is limited. Finally,
maximum disparity is evident when a single (upper
echelon) member towers over all other (lower echelon)
members in a unit, as shown in row 3 of Figure 1 As
disparity increases from minimum to maximum, the
concentration of a unit's resource in a single member
grows disproportionately.
In contemplating these maxima, scholars may gain a
new, deeper appreciation of the meaning of unit
diversity. Attention shifts from a consideration of
isolated differences between the members of dyads
within a unit to a consideration of the pattern of
differences within the unit as a whole. Maximum
separation does not simply mean that unit members are
quite different from one another; it means that they are
polarized into opposing sub-units. Maximum variety
means that each member is one of a kind. Maximum
disparity means that power, status, or other valued
resources are grossly unbalanced.
We see the possibility of new theoretical
propositions and hypotheses as scholars build on these
insights. Perhaps maximum separation leads to strong
subunit identification and weak unit identification
(Gibson & Vermeulen, 2003). In units with maximal
separation on an important team-relevant attribute,
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members are likely to have tight interpersonal bonds
within their subunit and antipathy towards those in the
opposing subunit. Perhaps maximum variety does not
lead to conflict, as sometimes predicted (e.g., Jehn et
al., 1999; Pelled et al., 1999), but to openness to other
ideas, as no one shares the same idea. These conjectures
lead us to suggest:
Guideline 2.
Theory building about diversity is enhanced by a careful
visualization of the shape and consequences of maximum
separation, maximum variety, or maximum disparity. In
contemplating these maxima, scholars shift their focus from
differences within dyads to the pattern of differences within the
unit as a whole.
In some areas of research, the conceptualization of
diversity forms as separation, variety, or disparity is
already quite clear (cf., Bloom, 1999). In others areas,
they are much less so. An important – even dominant
– case under the latter condition is investigation of
demographic diversity. The demographic variables
most frequently included in diversity studies are age,
sex, race/ethnicity, organization and team tenure,
education level, educational content, and functional
background (Jackson et al., 2003; Ragins & Gonzalez,
2003; Williams & O'Reilly, 1998). The last two on this
list: educational content (major or specialty) and
functional background seem to be obvious forms of
diversity as variety; these two attributes capture
qualitative differences in the kinds of information held
by unit members. But, within-unit diversity with
respect to each of the other demographic attributes
may be meaningfully conceptualized as separation or as
variety, or as disparity. Uncertainty regarding the
precise meaning of tenure, age, gender, and
race/ethnicity differences within a unit has, we believe,
compounded the challenge of conceptualizing and
testing diversity effects (Priem, Lyon, & Dess, 1999).
Tenure is a noteworthy and often-studied example
(Pfeffer, 1983). Researchers might argue that tenure
diversity within a unit is best conceptualized as
separation; the less separated that team members are
along the tenure continuum, the more likely they are to
have similar attitudes, beliefs, and values, to be drawn
to one another, and to identify with one another.
O'Reilly, Caldwell, and Barnett (1989: 33) posited that
"similarity in time of entrance into the group may
facilitate both attraction and interaction." Alternatively,
tenure diversity might be conceptualized as variety. In
this view, teams that vary in tenure include members of
differing cohorts who thus differ in their experiences,
their information bases, and their internal and external
network ties (e.g., Ancona & Caldwell, 1992; Hambrick,
Cho, & Chen, 1996). Finally, tenure diversity might be
conceptualized as disparity (e.g., Tsui, Xin, & Egan,
1995). Because individual tenure may be positively
associated with status or authority within a team (e.g.,
Drazin & Rao, 1999), tenure diversity within a team
may result in empowerment (access to valued
resources) of the team's long-entrenched elites and
disempowerment of the team's initiates.
Gender diversity might also be conceptualized as
separation, as variety, or as disparity. It does not fall
along a continuum. But, if gender is instead treated as
a salient marker or a symbol for a status continuum or
for task preferences within a unit (Konrad & Gutek,
1987; Ragins & Sundstrom, 1989; Ridgeway, Johnson,
& Diekema, 1994), each of the other conceptualizations
of diversity appears plausible. Scholars implicitly
conceptualize gender diversity as separation when they
suggest that (a) it reflects a distribution of opposing beliefs
about the appropriateness of critical team processes or
outcomes, (b) it is negatively related to cohesion and
identification within a unit, and (c) these effects are
symmetric such that gender diversity has comparable
effects when a unit is numerically dominated by men or
by women. In suggesting that men and women have
qualitatively different sources of knowledge, such that gender
diversity within a unit may spark creativity and
innovation, scholars instead invoke gender diversity as
variety (e.g., Wood, 1987). Finally, in highlighting power
differences between men and women and the asymmetric
consequences of within-unit gender diversity, scholars
describe gender diversity as disparity (e.g., Cohen &
Zhou, 1991; Linnehan & Konrad, 1999; Ragins &
Sundstrom, 1989; Tsui, Egan & O'Reilly, 1992).
Indeed, Konrad (2003: 11) proposed this
conceptualization, over a conceptualization of gender
diversity as separation or variety: "Scholarship making
arguments that relationship difficulties between identity
groups are caused by misunderstandings due to cultural
differences while downplaying the effects of prejudice
and discrimination denies the everyday inequalities
experienced by disadvantaged identity groups."
Each of these examples still begs a general question.
Absent a deductive theory, if a demographic variable
distributed within a unit can indicate any diversity type,
what meaning should scholars impute for it? In short,
which type is right? We believe that depends on unit
members’ context-dependent interpretations of the variable
in question. Thus, the meaning of within-unit diversity
in demographic attribute X is likely to be be shaped by
(a) the unit’s primary purpose or superordinate goal,
and (b) unit members’ beliefs regarding the association
between demographic attribute X and deeper-level
attributes (see Harrison et al., 1998).
Consider the following contexts for age diversity.
In employee advisory committees for a firm's
retirement benefits, member age might covary with
self-interest in particular policies. Further, members'
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implicit theories of age may include this link, leading
age diversity to operate as separation. In teams of
software engineers assembled to solve problems in a
firm's information systems architecture (e.g., Year 2000
problems), age might covary with a particular "legacy"
versus "object-oriented" programming orientation, and
team members should recognize it. Here, age diversity
might be taken to mark variety. In units such as
orchestras or law enforcement squads or student
project teams, older members (not just longer-tenured
ones) might be seen as possessing higher levels of task-
relevant experience, tacit knowledge, or street smarts.
Age would thus be associated with status, and diversity
in age could be treated as disparity.
Parallel arguments can be made for within-unit
diversity in education level, race or ethnicity as
reflecting each of the three diversity types (e.g.,
Watson, Kumar, & Michaelson, 1995; see Ragins &
Gonzalez, 2003, for a review). Because demographic
variables are the most commonly studied vehicles of
diversity, but perhaps the least well connected to any of
the three diversity types, we offer the following
Guideline 3.
In cases for which diversity of unit members on the same
characteristic (e.g., tenure) may be meaningfully conceptualized in
different ways – that is, as separation, or as variety, or as
disparity – precise specification of diversity type is essential. It
allows theorists to differentiate and compare conceptual models,
facilitating understanding and cross-fertilization, and paving the
way for empirical tests of contrasting conceptions.
The diversity typology also has implications for
research design. Overlooking or blurring the
distinctions among the diversity types can lead to
theoretical confusion. It can also lead to
methodological errors and mistaken research
conclusions. Below, we explain the appropriate
operationalization for each type of diversity (see Table
2). We note the consequences of conceptualizing one
diversity type while operationalizing another, discuss
whether and how to use measures of "overall" diversity
and of "perceived diversity" and, we describe the
sampling schemes that are needed to fully test theories
of every diversity type.
Operationalizing Separation
Standard Deviation (SD). Because of its
symmetric nature, separation on variable S is best
indexed at the unit level by cumulating absolute or
squared distances between pairs of individuals. Within-
unit SD reflects such cumulative distances, and hence
interval scales, of the S continuum. If an organizational
scholar were interested in separation of members'
attitudes about qualitative research in the medical
research team described above, SD is a viable choice.
Note that in this context, SD describes within-unit
diversity as a sample value; it does not estimate a
population parameter. Hence, its denominator
contains n and not n-1.
Standard deviation is maximized under the extreme
bipolar distribution shown in the first row of Figure 1,
where half of the members within a unit are "stacked"
at the lower bound of S (point u) and half are stacked at
the upper bound (point l). The SD equals (u-l)/2 under
such conditions of maximum separation. If, for
example, variable S consisted of a seven-point
continuum that ranged from l = 1 to u = 7, then the
maximum SD would be 3. It occurs when half the
unit's members are located at 1 and half are at 7 (the
same maximum would be obtained if l were -3 and u
were +3; changing the scale by adding or subtracting a
constant does not change the standard deviation).
Unlike some other diversity measures, maximum SD
does not increase with the size of a unit or team; larger
n's do not create larger estimates of within-unit
diversity. However, SD has the same (interval-level)
metric as the original attribute S. One of its
disadvantages, therefore, is that it cannot be readily
compared across different separation variables.
Relative amounts of diversity on, say, team members'
Protestant Work Ethic cannot be put side by side with
their attitudes toward their supervisor, if the Protestant
Work Ethic measure ranges from 1 to 7 and the
attitude toward supervisor measure ranges from 1 - 5.
Euclidean Distance. Within a unit, the Euclidean
distance of one member, i, from all the other members,
j, is the root mean squared distance between each of
those i, j pairs on attribute S: sqrt[Σ(S
/(n)] (Tsui,
Egan, & O'Reilly, 1992). Total Euclidean distance is
the sum of Euclidean distances for all n unit members.
The maximum (team-level) Euclidean distance is
/n]. For our polarized team S, the
maximum is 5.61.
Because this or any other diversity measure summed
over all possible dyads increases with team size, we
recommend use of an average Euclidean distance,
which has a maximum value of (u-l)/ [sqrt(2)] = 4.24.
That formula has a very similar structure to the
maximum SD formula: [(u-l)/2]. Befitting proper
indices of separation, both average Euclidean distance
and SD reach their peak under the same conceptual
conditions of maximum separation -- a bimodal
distribution with the modes occurring on both
endpoints of the S continuum. The Euclidean distance
index shares a weakness with SD in that it also cannot
be compared across variables with different metrics.
Given their conceptual and mathematical similarities,
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there is no particular advantage of either
operationalization over the other, save for researcher
familiarity with SD (see Bedeian & Mossholder, 2000).
Operationalizing Variety
Blau's Index. Continuous distances are not
meaningful under a conceptualization of diversity as
variety; qualitative distinctions are. It is through the
spread of members across qualitatively different or
novel categories that the cognitive and behavioral
repertoire of a unit is presumed to increase. When
variable V is dispersed across members who might be
in one of k = 1, ... K possible categories, such as the
different scientific backgrounds represented by the
research team (V) studying hospital patient experiences
described above, SD is inappropriate. Euclidean
distances are sensible only to the extent that a 0 or 1
"distance" refers to pairs of individuals from the same
or different categories, respectively (see Figure 1;
Harrison & Sin, 2004). Blau's index (1977) builds on
this qualitative difference foundation, and it is the most
commonly employed measure for diversity-as-variety
(e.g., Bunderson & Sutcliffe, 2002).
It is also known as Hirschman's (1964) index, and
Herfindahl's (1950) index, but it was originally
proposed by Simpson (1949) as a measure of species
diversity in an ecosystem. Language from the latter
domain can be helpful in understanding the meaning of
Blau's index scores. Its computational formula is 1-
, where p is the proportion of unit members in kth
category. Values of Blau's index can range from zero
to (K-1)/K. Its maximum occurs when members of a
team, V
, are spread equally: called evenness or relative
abundance of species in the ecological literature (Hill,
1973), over all possible K categories: called richness of
species. Having an even spread of members over the
richest number of information sources corresponds
well to our definition of maximum variety; evenness
and richness contribute to a higher Blau's index. A
more statistical interpretation is that Blau's index
reflects the chance that two randomly selected group
members belong to different categories (tap into
different information sources).
Because its maximum is limited by K (but less so as
K gets larger; if K = +, the limit would be 1.0), Blau's
indices are also not directly comparable when the
number of categories is not the same across diversity
variables. For instance, one cannot equate Blau's index
for gender (maximum Blau value is .5) with Blau's
index for the disciplinary backgrounds in Team V (with
eight persons spread evenly across K = 8 categories, the
maximum Blau value is .875). Some might regard this
difference in maxima as reasonable; if there are K =2
versus K =8 information categories, there is less
potential variety from gender than from disciplinary
background. Further, if two groups differ in size, but
each group shows maximum variety (i.e., in each group,
no two members have the same functional
background), there is a potential for more distinct
categories to be present in the larger group. Therefore,
if one takes Blau's index (or Teachman's, see below) as
a literal or "true" value for variety, maximum possible
variety increases with unit size; there is a "richer" set of
possible information categories to draw from. If this is
an unpalatable assumption, a way to standardize Blau's
index is to divide by its theoretical maximum, yielding
the Index of Quality Variation (IQV: Agresti &
Agresti, 1978; Mueller, Schuessler, & Costner, 1970).
Blau's index and IQV are used in social network
research to operationalize entrée to unique sources of
information or social capital (Marsden, 1990).
Teachman's (Entropy) Index. The computational
formula for Teachman's index (1980; originally
developed by Shannon [1948] in the communications
literature) is: ⊂Σ[p
)], where p is again the
proportion of unit members in the kth category. As
unit members are spread more evenly and across a
richer number of V categories, Teachman's index rises.
Its maximum occurs under the same conditions as
Blau's index, but it takes a slightly different value:
1×ln(1/K). Therefore, Teachman's index is likewise
not directly comparable across different V-type
variables, as long as each variable has a different total
number of categories. It is further limited by the
situation described above for Blau's index, when the
number of unit members n is less than the possible
total number of information categories K. Both indices
have been shown to be part of a general class of
diversity or information richness measures that have
desirable mathematical properties (Hill, 1973), although
Teachman's index always yields a larger number than
Blau's. The only advantage one operationalization of
variety would have over the other is that Blau's index
occupies a somewhat tidier range of 0 to (a value close
to) 1.
Operationalizing Disparity
Coefficient of Variation (CV). The formula for
the coefficient of variation: sqrt[Σ(D
- D
, or more simply, SD(D)/D
, captures the
asymmetry that is fundamental to the conceptualization
of diversity-as-disparity. Disparity reflects both the
distances between unit members and the dominance of
(concentration of the resources in) those who have
higher amounts of attribute D. When a privileged
minority holds most of resource D relative to an
underprivileged majority, there is greater disparity than
when the situation is reversed. Similarly, in the
coefficient of variation, division of the SD by the mean
expresses the idea that diversity on a given attribute
matters less when resource D is typically high versus
typically low (Sorenson, 2002). In the sociology
literature, CV is often used as a measure of income
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Operationalizations of Within-Unit Diversity Types
Minimum to
Assumed Scale of
(on attribute S)
Standard Deviation
- S
0 to [(u-l)/2] Interval
Mean Euclidean Distance
- S
0 to [(u-l)/sqrt(2)] Interval
(on attribute V)
0 to (K–1)/K Categorical
0 to –1·ln(1/K) Categorical
(on attribute D)
Coefficient of Variation
- D
0 to sqrt(n-1) Ratio
0 to 1-(1/n) Ratio
Notes: For separation (S) and disparity (D), the diversity attribute is continuous and can range from a lower bound of l to an upper
bound of u. Theoretically, l can be - for separation attributes and (commonly) 0 for disparity attributes, while u can be + in either
case. Operationally, l and u are limited by the instrument used to measure the attribute in question. For variety (V), the attribute is
nominal or discrete. It can take k = 1, ... K possible categories.
Academy of Management Review - Forthcoming 2007
inequality (Allison, 1978). There, the logic of dividing
the SD by the mean to capture disparity is more
obvious. The same amount of variability (say, SD[D] =
$20K) in unit members' income is of less importance
when unit members' average income is higher (D
$1M) than when it is lower (D
= $40K; Allison,
1978; Sorenson, 2002).
The CV reaches its maximum when n-1 individuals
are at the lower bound, l, of a ratio-level continuum
(Allison, 1978; Kimura, 1994; Bedeian & Mossholder,
2000). If l is zero, then all but one members of the unit
hold none of D, and the absolute value of the something
held by the nth person does not matter. That sole
person has all of D in the unit. For example, if
"number of refereed journal articles" were the status
continuum being studied in Team D, the maximum CV
would not change if the single, highly regarded and
veteran researcher had 1 or 10 or 100 published papers
– as long as the lower-ranking, novice members all had
0. Under these conditions, the coefficient of variation
(CV) is maximized in a way that reflects only within-
unit size: sqrt(n-1), or in this case, sqrt(8-1) = 2.65
(Champernowne, 1974).
Because the maximum CV is sensitive to sample
size, highly dispersed units with fewer members likely
would be regarded as having less disparity than those
with more members. One person who has great power
(D) over dozens of others is assumed to create more
disparity than the individual who has great power over
just a handful of others. When this assumption is
untenable, Martin and Gray (1971) offer a
standardizing adjustment for unit size (see Beckman &
Haunschild, 2002, for an application to external
network ties).
If the lower bound, l, is some number higher than
zero, the upper bound, u, does matter. The farther the
distance on D that one or a few elite persons are above
the rest of the persons within a unit, the higher the CV.
The more lopsided the within-unit distribution of D is,
with more persons at the bottom and a valued resource
concentrated in a few or one person at the top (see row
3 of Figure 1), the larger the resulting CV value. These
properties of CV (and of the Gini coefficient, below)
make disparity measures perhaps the most volatile or
sensitive of the three types of diversity to the status of
a single member. If a situation in which all unit
members have none (or equally small portions) of the
resource in question changes, such that one person
now gains a large share, the unit quickly jumps from
minimum to maximum disparity.
Gini Coefficient. The Gini coefficient or index is
used less often in organizational behavior or human
resources research than in strategy and organization
theory, where it has been borrowed from its
widespread application as a concentration ratio in
finance and economics. Its computational formula is
the sum of all pairwise absolute difference between unit
members on variable D, divided by [2 × D
× n
]; see
Table 1. Its maximum value is 1-(1/n). Similar to CV,
this maximum occurs when all but one unit member
have the lowest possible standing on D. Also as with
CV, the Gini index is only appropriate for D attributes
that have ratio-level properties (Allison, 1978; Kimura,
1994). Researchers might choose the Gini index over
CV if they prefer their diversity measures to follow a
common metric, in this case, ranging from 0 to (nearly)
1. The maximum value of the Gini index depends on n
as well, but it should be less of a limiting factor than for
CV when it is used in larger versus smaller groups (see
Martin & Gray, 1971, for a possible standardizing
correction for n).
On the Folly of Conceptualizing S or V, While
Operationalizing D
When researchers specify one diversity type, but
operationalize another, they can draw erroneous
conclusions from their analyses. Figure 1 helps to
illustrate the point. As noted above, the distribution of
member scores or values that indicates maximum
disparity is not indicative of maximum separation or
maximum variety, but rather of very limited separation
or variety. Williams and O'Reilly (1998) report CV to
be the most widely applied diversity index in the
literature. Yet, using CV (an appropriate
operationalization of disparity) to assess separation or
variety is misleading. Unfortunately, researchers often
do just this (e.g., Harrison et al., 1998; Klein, Conn,
Smith, & Sorra, 2001; Simons, Pelled, & Smith, 1999;
Wiersema & Bird, 1993).
In using CV to operationalize diversity, researchers
frequently cite Allison (1978), noting his arguments that
the use of CV is metric-less and therefore CV provides
a comparable measure of diversity in different
attributes (unlike SD or Teachman's index; Sorenson,
2002). However, Allison recommended CV for
operationalizing our disparity type of diversity. For
example, he noted that CV provides a comparable
measure of income inequality whether the attribute in
question is dollars or yen. In the studies cited above
and in many others, researchers sought to assess either
separation (testing ideas based on similarity--attraction)
or variety (testing ideas based on the leveraging of
unique information sources within a unit), but instead
assessed disparity. Hence, the conclusions of such
research might be incorrect; the index used was asymmetric
but the conceptualization of diversity was symmetric.
Two examples may be instructive. Suppose that
diversity in the variable "commitment to meeting
project deadlines" was conceptualized in terms of
separation. Also suppose the teams had equal
separation; standard deviations of commitment were
the same (SD = 10) in Teams S1 and Team S2. But, in
Team S1 the mean level of commitment was 40 and in
Team S2 it was 20. If an investigator used CV as an
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operationalization of within-unit separation, he or she
would mistakenly conclude that Team S2 (CV =.5) had
twice the separation as Team S1 (CV = .25).
Suppose that diversity in age is conceptualized in
terms of variety: different age cohorts learn and know
qualitatively different things that might contribute to a
unit. If one member of an eight-person team were
relatively old, and all the rest of the members were
young (resulting in a low mean for age), a misapplied
CV to member age would yield the conclusion of
maximum team variety, but only two "batches" of
knowledge would exist. The CV would be nearly
minimized if there were one young member and the
rest were relatively old (resulting in a high mean for
age), but this team would have, as above, just two
“batches” of knowledge.
These examples lead us to the guideline below. The
basic principle is familiar: conceptualization must be aligned
with operationalization. Theory must guide measurement,
which is nuanced for diversity constructs. CV is not a
universal diversity index. That is:
Guideline 4.
The choice of a diversity index should be driven by the theoretical
specification of diversity type; researchers must answer what the
vital difference is before they choose or design indices to measure it.
If the specified conceptualization and index of diversity are
mismatched, research conclusions may be misleading. Researchers
specifying diversity as separation should use versions of SD or
average Euclidean distance. Researchers specifying diversity as
variety should use versions of Blau's or Teachman's (entropy)
index. Researchers specifying diversity as disparity should use
versions of CV or the Gini coefficient.
Accounting for the Mean When Testing for
Diversity Effects
The only difference between the formulas given
above for CV and SD is that CV is a ratio of SD to the
within-unit mean. This is more than a statistical quirk;
it has significant substantive implications. Thus, it
invites a consideration of the role of the within-unit
mean in analyses of diversity effects.
The mean specifies the typical location of unit
members on an S or D attribute. When separation and
disparity attributes have no upper or lower bounds
(they can range from - to +), the within-unit mean
tends to be uncorrelated with within-unit variability
(Harrison & Sin, 2004). However, most attributes do
have upper and lower bounds in real data. Because the
SD of a within-unit distribution is often lower when
the mean is near the lower or upper bound (members
are clustered near the "floor" or "ceiling"), there may
be an artifactual overlap of means and SDs across units.
In short, mean and SD can be confounded.
Because of that potential confound, what appears to
be the effect of within-unit diversity may be the
(inverse) effect of the mean instead; the substantive
interpretation of the latter effect is entirely different.
Suppose, for example, that a diversity theory predicts
units with members who are part of more
homogeneous (low variability) tenure cohorts tend to
be more socially cohesive and committed to their unit,
and therefore will be less likely to quit. The theory
predicts a positive relationship at the unit level between
tenure diversity and turnover rate. Yet, if lower variability
in tenure was confounded with higher mean tenure
across units, observing the predicted positive
relationship might be attributed to the empirical fact
that those with longer tenure (and greater investment
with a firm) are simply less likely to experience
turnover. Thus, we join others (e.g., Jackson et al.,
2003: 808) in urging investigators to include the mean
in tests of diversity as separation and disparity:
Guideline 5.
In testing the relationship between separation or disparity of an
attribute and other variables, researchers should first statistically
control for the within-group mean of the attribute.
It might be asserted that, because the mean is
already in the denominator of the CV formula, if
researchers use CV as a disparity index they would
already be controlling for the mean effects mentioned
above. However, as Sorenson (2002) showed, the CV
ratio does not statistically control for the mean; it
forces a particular moderating structure on within-unit
data. It compels that the effect of SD(D) be tempered
or weakened at a rate of 1/D
, mandating a SD(D)
× 1/D
interaction. The mean value of D has an
effect on the criterion of interest that opposes the
effect of variability at a very narrow, 1/D
rate. This
mandated form of interaction in disparity measures is
also a testable assumption. Researchers could enter
both the SD(D) and 1/D
in hypothesis testing, as
suggested in Guideline 5 above. That is, in predicting
unit turnover rate, Y, from disparity in tenure the first
regression model would be:
Y = β
+ β
[SD(Tenure)] + β
[1/mean(Tenure)] + ε.
The second regression model that checks the
contribution of the specific functional form of disparity
in the CV formula would be:
Y = β
+ β
[SD(Tenure)] + β
[1/mean(Tenure)] +
[SD(Tenure) × 1/mean(Tenure)] + ε.
The impact of greatest interest to disparity
researchers is reflected in the strength of β
Guideline 6.
The use of common disparity measures carries an implicit
assumption about the shape of a negative interactive effect between
the variability and the mean of a diversity attribute. That
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assumption should be tested by entering variability, mean, and the
variability-by-mean interaction to see if the former is indeed
moderated by latter.
Operationalizing Demographic Diversity:
Separation, Variety, and Disparity.
The two guidelines above augur for a second look at
previous studies of demographic diversity. As we have
shown, diversity of age, sex, race, tenure, and education
can all be, and have been, conceptualized as separation,
variety, or disparity. Researchers have often
conceptualized one form of diversity (most commonly,
separation or variety), but then operationalized another
(disparity). There is no post-hoc statistical adjustment
that can be applied to published findings from such
research. Instead, authors would need to revisit their
previous work and re-analyze their primary data to
match indices to diversity types. In the absence of such
re-analyses, the validity of the conclusions of many
specific studies, as well as narrative and quantitative
reviews of the demographic diversity literature, may be
called into question. Given the importance of the topic
of demographic diversity within organizations, we hope
our observations and guidelines will spur new research,
as well as re-examinations of prior research. But, the
implications of our framework go beyond the sphere of
single demographic differences, and we broaden our
discussion below.
"Overall" Diversity
Diversity indices are sometimes cumulated to
indicate the "total" amount of dissimilarity,
heterogeneity, or difference within a unit – summed
over several attributes. That is, researchers might seek
to assess overall within-unit diversity by adding or
averaging diversity indices for, say, gender, education,
race, and tenure. After doing so, they arrive at a single,
global value that characterizes each unit in the sample
(e.g., Chatman & Flynn, 2001; Randel & Jaussi, 2003).
For several reasons, we believe that this fusing
together of diversity indices for several variables is
unwise. First, as we have noted, a unit or team itself
does not have diversity (Jackson et al., 2003); an
attribute of individuals within units has diversity,
whether the within-unit distribution of that attribute is
thought of as separation, variety, or disparity. Second,
it is clear that more diversity on such attributes has a
unique meaning for each of the three diversity types.
This prohibits summing, say, SD for separation in age
with Blau's index for variety in functional background
and CV for disparity in tenure. The resulting amalgam
has an indefinite interpretation at best. Pitcher and
Smith (2001: 14) noted this, stating "multiple proxies
seem to cancel one another out and almost guarantee
nonfindings." Murnighan and Lau (1998: 327) also
warned that such a combination would be "... like
cross-fertilizing apples and oranges."
That leaves the possibility of adding several
variables that, perhaps together, reflect a single
diversity type. For example, if diversity in gender and
diversity in ethnic background were both
conceptualized as variety, would it be reasonable to
sum their diversity indices and create an overall variety
measure? To use the logic of conventional (reflective)
measurement, one assumes that different
operationalizations are positively correlated,
manifesting the same underlying (variety) construct
But, there is little reason to expect measures of the
variety (or separation or disparity) of two independent
attributes – such as gender and ethnicity – to be
consistently and positively correlated within units.
Most diversity theories do not predict such
relationships, nor do published correlations among
diversity variables reveal convergent validity (e.g., Klein
et al., 2001). Hence, we caution against this approach.
It might be counter-argued that measures of
demographic diversity, such as gender variety and
ethnic background variety, serve not as reflective
indicators but as "formative" indicators (Bollen &
Lennox, 1991; MacCallum & Browne, 1993). As with
the variables in a principle component, each formative
indicator contributes to a linear composite that is
simply the sum of its parts. Diversity variables serving
as formative indicators would not need to share an
underlying construct that positively binds them
together (although then they would have a different
empirical meaning in every study), nor would they have
to be the same diversity type.
However, there are two critical methodological
drawbacks to this approach. First, a measure of overall
diversity treats the causal force of each component
variable as equal (Edwards, 2001), masking effects that
might be due mainly to one variable (e.g., gender
variety) rather than another (e.g., ethnic background
variety). Second, an overall diversity measure masks
substantive differences among units that have the same
overall composite score. Suppose that one of the
medical research teams described above is composed of
six Whites and two African-Americans, or six African-
Americans and two Whites. Its Blau index for race is
the same in either case. Suppose, further, that the team
is composed of six women and two men, or two
women and six men. Again, its Blau index for gender
is the same in either case. As a result, the team's
"overall" diversity would be the same if there were
(a) six White men and two African-American women;
(b) six African-American men and two White women;
(c) six African-American women and two White men;
(d) six White women and two African-American men,
(e) four White men, two White women, and two
African-American men, and so on.
Theory and
evidence suggest, however, that these groups would
differ markedly in their networks, coalitional dynamics,
and information sources (e.g., Ibarra, 1993; Mollica,
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Gray, & Treviño, 2003; Tsui, Egan & O'Reilly, 1992).
To treat all of these groups as the same – and we have
not listed all possible combinations that would yield the
same overall scores – is to introduce a great deal of
theoretical ambiguity into one's operationalization of
diversity. Thus, we suggest:
Guideline 7.
Simple (average or total) operationalizations of overall diversity
should be avoided unless (a) theoretical motivations for their
aggregate (formative) effects are clear, and (b) evidence of their
convergent validity can be shown.
Perceived Diversity
Another approach sometimes used to operationalize
diversity is to ask individuals how they perceive the
diversity variable to be arrayed within their unit
(Dooley, Fryxell, & Judge, 2000; Harrison et al., 1998;
2002; Jehn et al., 1999; Miller, Burke, & Glick, 1998;
Turban & Jones, 1988). Such measures are reasonable
– even required – when authors seek to test theories
specifically addressing perceptions of differences (see
Riordan, 2000, for a review). Indeed, perceived
diversity within a unit may have unique and more
proximal explanatory power than actual diversity, as a
stream of organizational research suggests individuals'
perceptions of their social environment have stronger,
more direct influences on behavior than does the social
environment itself (Eisenberger, Huntington,
Hutchison, & Sowa, 1986; Krackhardt, 1990).
For several reasons, however, measures of perceived
diversity are not likely to be construct-valid measures
of "actual" diversity (McGrath, 1984). First, individuals
may lack the necessary information to accurately assess
the diversity of the rest of their unit members,
especially on the thornier and perhaps more sensitive
psychological characteristics that might have sparked
the researcher's move toward using perceptions in the
first place (see the low correlations in Harrison et al.,
2002, for perceived and actual "deep" diversity, as well
as Miller et al.'s, 1998, correlations between perceived
and actual cognitive diversity). Second, perceived
diversity ratings are likely to be biased, relative to
measures of "actual" diversity. One set of biases
involves attraction and social desirability fostering
overly rosy perceptions of similarity (Clore & Byrne,
1974). On the other hand, theory and research
regarding the out-group homogeneity effect suggest
that under some conditions, in-group members may
overestimate their own unit's diversity. This is
particularly likely when judgments are made relative to
other units (Lee & Ottati, 1993, 1995; Park & Judd,
1990; Park, Ryand, & Judd, 1992; Quattrone & Jones,
1980). Under either form of perceptual error, common
method bias might inflate the observed correlation of
perceived diversity measures and member reports of
group outcomes (e.g., group cognition, conflict,
performance, etc.; Harrison, McLaughlin, & Coalter,
1996). This renders suspect any comparison of the
strength of relationships of perceived diversity relative
to actual diversity with such outcomes. Accordingly,
we recommend:
Guideline 8.
Measures of perceived diversity should not substitute for measures
of actual diversity. Measures of perceived diversity can, however,
provide an operationalization of a useful, substantive construct –
members' perceptions of unit diversity -- that may be related to
but is different than actual unit diversity.
Our efforts to distinguish diversity as separation, as
variety, and as disparity suggest that, if member
perceptions of within-unit differences serve a theoretical
purpose, researchers may need to develop distinct
instruments for perceived separation, perceived variety,
and perceived disparity. Perhaps a researcher
interested in perceived separation in team members'
satisfaction with their supervisor might ask team
members, "How much do team members differ in their
satisfaction with the team supervisor?" An appropriate
response scale might thus be "1" = "Not at all; all team
members feel the same way about the supervisor"; "3"
= "Somewhat; some team members are more satisfied
with the supervisor than others"; and "5" = "A great
deal; half of the team's members are very satisfied with
the supervisor and the other half of the team's
members are very dissatisfied with the supervisor." A
researcher interested in perceived variety of functional
backgrounds might ask respondents, "To what extent
do group members differ in their functional
backgrounds?" An appropriate response scale might be
"1" = "All group members have the same functional
background"; "3" = "Some group members' functional
backgrounds differ from others' "; and "5" = "Each
group member has his/her own unique functional
background." Finally, a researcher interested in
perceived disparity of team member influence in team
decision making might ask, "To what extent do team
members differ in their influence over important team
decisions". Here, an appropriate response scale might
be "1" = "Every member has the same amount of
influence"; "3" = "Some group members have more
influence than others"; and "5" = "One team member
has substantially more influence than all other
The examples above are only suggestions.
However, their verbal descriptors map directly to our
diversity typology, as well as to the minimum,
maximum, and moderate levels of those types.
Regardless of the specific words used in such
perceptual scales, we propose:
Guideline 9.
If measures of perceived diversity are used, questions, response
formats, and anchors should reflect the diversity type under
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consideration, following the distributions shown in Figure 1.
Sampling the Full Range: Between-Unit Variance
of Within-Unit Diversity
The recommendations above are designed to ensure
that a researcher's diversity indices are construct valid,
capturing the specific diversity type specified in the
theory the investigator is testing. But, researchers may
still draw incorrect conclusions from their research if
their sample of units does not show the full breadth of
diversity. To avoid range restriction, researchers
seeking to test diversity theories must gather samples in
which there is sufficient between-unit variability in
diversity to allow effects to be revealed.
The implications of this admonition differ for
researchers studying separation, variety and disparity,
respectively. Investigators testing the unit-level effects
of separation must ensure not only that their sampled
units vary in amount of (maximum to minimum)
separation, but also that sampled units include different
combinations that lead to the same amount of
separation on S. That is, for strong tests of the
assumed symmetric effects of attitude separation on S,
a researcher’s sample must include some units in which
all members have negative attitudes, other units in
which all members have positive attitudes, still other
units in which all members have neutral attitudes, and
of course, some units that are widely split on the focal
attitude, representing maximum separation.
allows a researcher to assess whether the effects of
minimal separation are in fact symmetric. Such a test
would involve entering main effects of the within-unit
SD and mean in an analysis, followed by the SD ×
mean interaction, reiterating our Guideline 6 above. A
significant interaction indicates asymmetric separation
effects (i.e., the impact of separation depends on the
level of S). See Barsade et al. (2000) for an example.
Researchers testing the unit-level effects of variety
require samples that have different amounts of category
richness or unevenness across units. Samples must
include units in which variety is very low and units is
which variety is very high. Without this full sampling,
relationships between variety and its antecedents or
outcomes will be underestimated because of restricted
range. Researchers testing the unit-level effects of
disparity require, of course, samples in which units vary
from a single individual lording resources over all the
others (high disparity) to an even distribution of the
resource across members (low disparity). We thus
Guideline 10.
To allow an adequate test of hypotheses regarding separation,
variety, and disparity, respectively, a researcher's sample must
evidence substantial between-unit variability in within-unit
separation, variety, or disparity. Units should represent the full
range of minimum to maximum separation, variety, or disparity,
respectively. In the specific case of separation, the sample must
allow verification of the symmetry assumption fundamental to
that diversity type.
By justifying their choice of separation, variety, or
disparity, theorists may deepen their understanding of
the likely consequences of attribute-specific diversity
within units and refine their predictions. Further,
precise specification of diversity types facilitates
construct valid operationalizations and appropriate
tests of the effects of each diversity type. The diversity
typology we have presented also suggests new avenues
for diversity research. In explicating the meaning of
disparity, we noted how little research addresses the
unit-level consequences of within-unit inequality in
power, status, and other valued resources. This is an
important and understudied aspect of diversity
(Konrad, 2003). We also see real promise in research
integrating the study of diversity and social networks
(e.g., Reagans & Zuckerman, 2001). Social network
analysis may allow researchers to enrich their
understanding of the relational processes associated
with each diversity type, even allowing a single
demographic variable to be associated with more than
one diversity type, and to have simultaneous, opposing
effects (see Reagans, Zuckerman, & McEvily, 2004).
Further, we encourage more research on the
antecedents of separation, variety, and disparity in
organizations. With some important exceptions (e.g.,
Boone, van Olffen, van Witteloostuijn, & De
Brabander, 2004; Klein et al., 2001; Miner, Haunschild,
& Schwab, 2003; Pfeffer & Davis-Blake, 1990), this
terrain of research remains largely uncharted.
Finally, as we have noted in previous sections, we
see both a real need and a real opportunity for research
and theory-building regarding the relationships among
the three diversity types. Given the complexity of this
broad topic, we focus on it below, addressing three
issues that each touch on and may help to clarify the
independent and interdependent nature of separation,
variety, and disparity in organizations. More
specifically, we consider: (a) strategies for disentangling
assumptions of demographic separation, variety, and
disparity; (b) relationships among and interactive
impacts of diversity types; and (c) multilevel diversity
Disentangling Separation, Variety, and Disparity:
One Step Backward and Several Steps Forward for
Demographic Diversity Research?
We have urged theorists and researchers to specify
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their chosen diversity types. But, we have also shown
that within-unit diversity of several demographic
variables, including tenure, age, gender, race or
ethnicity, and education, can be meaningfully
conceptualized as separation or variety or disparity.
How, then, should scholars interested in demographic
diversity proceed?
Our answer is to suggest research designed to test
the theoretical assumptions specific to each diversity
type. Suppose the demographic attribute in question is
tenure. Within units, is tenure significantly correlated
with key perceptions, beliefs, attitudes, values, or other
vehicles of separation? Or, do tenure cohorts vary in
their knowledge, experience, or perspective, as
suggested by a conceptualization of tenure diversity as
Or, is tenure associated with the unequal
distribution of power and status, consistent with a
conceptualization of tenure diversity as disparity?
These are basic questions of construct validity -- so
basic it is remarkable that, with a few exceptions (e.g.,
Kilduff, Angelmar, & Mehra, 2000), they have received
so little attention.
To answer these questions, a researcher must
examine the individual level relationship of the
variables within units. Suppose a researcher
conceptualizes tenure diversity as separation, assuming
that tenure is associated with organizational
commitment. A unit-level test of that assumption would
cloud interpretation. A significant unit-level
relationship between, say, tenure SD and commitment
SD may occur even in the absence of any connection
between tenure and commitment within units (Ostroff
& Harrison, 1999). Random coefficient modeling (also
referred to as hierarchical linear modeling; Hofmann et
al., 2000) allows a researcher to simultaneously test
within-unit relationships for each unit of his or her
sample. Of course, such a test requires careful
measurement of demographic attributes and their
theoretical sources of diversity (e.g., separation in
member attitudes, variety of knowledge, or inequality in
If this fine-grained, within-unit test reveals that the
data for most or all teams in the sample conform to the
predictions associated with one diversity type, the
researcher has a powerful and empirically grounded
foundation for his or her subsequent investigation of
the unit-level correlates of attribute diversity of that
type. But, the results of such a test might be more
complex, lending support to more than one diversity
type, or revealing predicted relationships of the
demographic attribute within some units but not
others. The tests might even provide no support for
any diversity type. The latter result would suggest that
diversity of the attribute in question is highly unlikely to
have predicted unit-level consequences.
Is such extensive testing really called for, prior to the
investigator's examination of his or her real interest –
the effects of tenure diversity – just because there is
uncertainty in the precise conceptualization of it as one
of the three archetypes? We think so. The "no
support" result is within the realm of possibility given
the inconsistent nature of past diversity findings. In
the past two decades, a wealth of theory and research
on demographic diversity has yielded few cumulative or
conclusive effects. Researchers can, we believe, resolve
some of this uncertainty by stepping backwards, and
down a level of analysis, to test the fundamental and
distinctive assumptions underlying diversity as
separation, as variety, and as disparity. The payoff
from such research will come as researchers then move
several steps forward toward in deciphering the nature
and effects of demographic diversity.
Relationships Among and Interdependent Effects
of Diversity Types
In describing the meaning, shape, mechanism, and
ideal operationalization of each diversity type, we have
described each one in turn. Yet, separation, variety,
and disparity are likely to co-occur within units.
Indeed, separation, variety, and disparity may, in some
cases, be causally related. Further, they may well have
joint consequences for unit outcomes. New theory
development and research are needed to examine these
possibilities. Does within-unit diversity of one type
lead to within-unit diversity of other types? Does
diversity of one type moderate the effects of diversity
of other types? We offer some speculative answers to
these questions below, in the hopes of sparking
researchers' interest in these topics.
Diversity begetting diversity. Although the three
diversity types are qualitatively and distinctly different,
they may nevertheless be linked to one another over
time. Diversity of one type might engender diversity of
a second type. We briefly explore a few possibilities.
Separation can engender variety, particularly
increases from minimum to moderate variety. If unit
members are sharply separated – for example, in their
endorsement of a particular unit goal or strategy – they
might be motivated to get new information to support
their position, including seeking out (external) others
who support their own perspective. In this way, the
members of a sub-unit may develop network contacts
and information not shared by the members of the
opposing sub-unit (Peterson & Thompson, 1997). For
example, the member of the subunit of Team S who
favor interpretive research may seek out experts in
qualitative research in an effort to bolster their own
position. Conversely, the members of the subunit of
Team S who oppose that research paradigm may seek
out experts who are aligned with their own preferences.
In both cases, the knowledge and argument base
associated with each of the factions is likely to increase,
and to be brought forward to the rest of the unit when
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discussions about interpretive methods arise.
Polarization (sharp separation) of attitudes toward
interpretive research may remain, but variety of
research expertise may increase within the unit.
As separation can engender variety, variety can
engender separation. Variety in disciplinary or
scientific training might lead to separation in support
for qualitative research in Team V for example; some
functional backgrounds may be more accepting and
supportive of qualitative research than are others. This
assumption can be seen in research treating variety in
functional background as a source of belief and attitude
differences in top management teams (e.g., Beyer et al.,
1997; Chattopadyay, Glick, Miller, & Huber, 1999). As
separation leads to conflict, separation might then
mediate the relationship between variety and conflict.
But, it is also possible to imagine cases in which variety
leads to neither separation nor conflict. Rather, unit
members who differ in their respective areas of
expertise may each fulfill a distinct role in the team,
coordinating efficiently and respectfully with one
another, and paying due attention to one another's
inputs (Stewart & Stasser, 1995). Further, when variety
within a unit grows towards its maximum, the
polarization indicative of maximum separation seems
increasingly unlikely; there are simply too many V types
represented for unit members to fall neatly into two
clearly opposing factions. In sum, we suspect that
variety may sometimes lead to separation and then
conflict, but not inevitably or even generally.
Finally, we consider disparity. Disparity may lead to
both an increase in separation and an increase in
variety. When the members of a unit differ sharply in
their relative power, prestige, or pay, they perceive the
unit's policies and activities from a different vantage
point. Their interests differ as well. Those at the top
of the hierarchy are likely to support the status quo.
Those at the bottom may seek change. As a result,
disparity may cause separation in beliefs, attitudes, or
values related to unit processes and outcomes that
might dislodge the current status hierarchy (Philips &
Zuckerman, 2001). Further, individuals who have less
status, power, influence than an "upper" minority of
their teammates, are likely to respond by forming
coalitions with others who are similarly less powerful,
acquiring valued resources through illicit means or
through contacts outside the unit (e.g., Murnighan,
1978). Further, those at different levels of such
resources are likely to socialize with different
individuals and thus to develop distinct sets of network
ties (e.g., Maman, 2000; McGuire, 2000). In this way,
disparity may engender variety in experiences,
knowledge, and social contacts.
Diversity moderating diversity. The three
diversity types may also interact to influence unit
outcomes. Lau and Murnighan (1998) have already
forwarded a set of rich set of ideas about joint effects
of within-unit demographic differences. In their
theory, for joint occurrences of demographic attributes
to become increasingly potent "faultlines" within a unit,
the diversity attributes in question must (a) be apparent
to unit members, (b) covary or coincide strongly within
the unit, and (c) create a small number of
homogeneous subgroups or factions. Note that the
mere coincidence or correlation of two attributes
within a unit is insufficient to form a faultline. If, for
example, task satisfaction and organizational
commitment were positively correlated within a team,
their joint occurrence would not necessarily divide the
unit into two or more clearly differentiated factions.
Moreover, as the number of factions grows, faultlines
weaken (there is no single, large "chasm" within the
team, but several small "cracks"). This suggests that
strong faultlines are most likely to occur within a unit –
translating Lau and Murnighan's propositions into an
important case in our framework – when two or more
variables have coincidentally maximum separation or
disparity or both, but they are coupled with only
modest variety. Strong or near maximum separation
within a unit creates opposing factions, a building block
of strong faultlines. High disparity within a unit can do
so as well. In contrast, when variety is high, faultlines
weaken (a clear distinction from our framework), as
unit members cannot be divided into two or just a few
sharply divided subsets.
Our differentiation of separation, variety, and
disparity suggests other intriguing interdependent
effects, complementing Lau and Murnighan's (1998)
insights. Consider our recurring example of the
research teams S, V, and D who are studying patient
experiences with medical treatment. What if Team V
(maximum variety with each member from one of eight
different disciplines) and Team D (maximum disparity
with one prestigious researcher and seven novices)
were the same team? Our speculative answer is that
higher levels of disparity would tend to diminish or
even neutralize the benefits expected from the presence
of differing sources of task-relevant knowledge within
the team. Unit members such as the renowned expert
in Team D who hold the highest concentration of the
team's valued resources and have the highest stature are
likely to dictate the team's practices and performance.
Team members with less power or status are likely to
stay mum, deferring to the top members' wishes, and
failing to share critical information (Janis & Mann,
1977; Cruz, Henningsen, & Smith, 1999). Pitcher and
Smith (2001: p. 9) offer a similar interpretation "... a
significant power shift renders the heterogeneity and
the diverse cognitive perspectives that it represents less
and less influential in the strategic decision-making
process." Accordingly, we suspect that disparity within
a team is likely to moderate the expected positive
relationship between variety and positive team
Academy of Management Review - Forthcoming 2007
The interdependent effects of within unit separation
and variety may prove even more complex. What if
Team V (maximum variety) and Team S (maximum
separation in attitude toward qualitative research) were
the same team? Members would find it difficult to
develop and implement an integrative, creative plan of
action. Conflict and division seem more likely than
creativity and performance. If sharing unique
information requires motivated behavior from unit
members, even in the face of risk (others in the unit
cannot validate it), it is likely that motivation would fall
and the risk would rise as members saw themselves
separated from one another on fundamental beliefs
(Wittenbaum, Hollingshead, & Botero, 2004). Thus,
low separation, especially with on team goals and task-
related values, may allow a team to realize the benefits
of team members' variety of expertise and experience
(cf., Edmondson, 1999; Gruenfeld, Mannix, Williams,
& Neale, 1996; Harrison et al., 2003).
And yet, a team that has no separation in member
goals, attitudes, or beliefs, yet is high in variety, may fail
to recognize and make use of members' diverse areas of
knowledge and expertise (Larson, Christensen, Abbott,
& Franz, 1998; Stasser & Titus, 1985, 1987).
Attention-capturing differences – if they are not too
large – could motivate members to "dig deeper and to
learn from contrasting examples" (Beckman &
Haunschild, 2002: 98). Members who see themselves
as identical on those underlying features may also
assume that they share similar experiences and stores of
knowledge, and thus, at least initially, discuss shared
experiences and information (Wittenbaum et al., 2004).
Some minimal separation of attitudes within the unit –
especially early, and about preferences for task
processes (Jehn & Bendersky, 2003) – may trigger
constructive debate and discussion (Simons, Pelled, &
Smith, 1999), stimulating members to reveal to one
another their distinctive knowledge and expertise.
The above ideas deal with potential bivariate effects
of D, S, and V. What about the joint impact of all
three forms of diversity? We consider two possibilities.
Suppose the hospital research team is indeed deeply
divided on attitudes towards research paradigms, and
hence maximized on S. Suppose further that the two
(interpretive, quantitative) factions are perfectly aligned
with D and V: one faction holds frequently published
and renowned members with medical backgrounds,
and the other faction holds behavioral scientists with
no established research records. Although the team is
only moderate on V and D, it exhibits a strong faultline
(Lau & Murnighan, 1998; Li & Hambrick, 2005). The
team would be divided and dysfunctional, but it would
not stalemate because of the power of the first faction.
In contrast, if D and V cross-cut S (Brewer & Miller,
1984; a weak faultline), with prestige and disciplinary
background equally vested in opposing S factions, the
team would have conflict, but would be able to reach
across factional boundaries to leverage its within-unit
variety. Other trivariate configurations are viable and
interesting, and could readily be studied in both field
and laboratory designs.
Multilevel Influences
Diversity is inherently a multilevel construct. It
describes a unit in terms of the collective composition
of its members. Still, most studies of diversity are
single-level studies, focusing on unit-level outcomes of
within-unit diversity. We join others (Jackson et al.,
2003; Tsui & Gutek, 1999) in urging a more in-depth
and comprehensive multilevel analysis of diversity. One
approach would be to move up a level of analysis and
consider diversity across units within organizations along
the S, V, or D attribute in question. The effects of
within-unit diversity may depend, in part, on the
composition – and specifically, the diversity – of the
organizational context (e.g., Alderfer & Smith, 1982).
The effects of minimum separation (perfect agreement)
within a focal unit on S may depend on the extent to
which the unit's perspective is itself close to or
separated from predominant views for S within other
units of the organization.
For example, minimum separation within a
management department regarding the value of
publishing in top-tier journals is likely to be a much
greater source of departmental cohesion if other
departments in the business school do not share the
management department's perspective. If other units
do share the focal unit's perspective, constituting
minimum separation in the organization as a whole,
then minimum separation within a unit is likely to be
less salient to unit members; it doesn't make the unit
distinctive within the organization. Turning back to
Team S, their maximal separation on attitude toward
interpretive research might be taken as a healthy state
of task conflict, if wide, within-unit splits in such
attitudes (maximal separation) is also characteristics of
most other research teams within their sponsoring
organization. On the other hand, if the researchers
who make up the more positivistic faction of Team S
know that virtually all of the other units in their
sponsoring organization are strongly positivistic (and
not split), the presumption of normative correctness of
their own position might lead to deeper, more severe,
and more interpersonally charged battles within the
A complementary multilevel approach would be to
consider the implications of diversity types for the
experiences and reactions of individuals within units. This
is the focus of most studies of relational demography
(e.g., Jackson et al., 1991) and other forms of dyadic
similarity (e.g., Turban & Jones, 1988). For example,
Dose (1999) examined the correlates of an individual
unit member's distance from the other members of the
unit, testing whether individuals whose values are, on
Academy of Management Review - Forthcoming 2007
average, similar to their teammates' are more attached
to the team than are individuals whose values are, on
average, quite different from their teammates'.
The extent of separation, variety, or disparity within
a unit may color and shape an individual's experience
of difference from other unit members. For instance,
if a research team is low in variety of disciplinary
backgrounds, say, with a single psychologist amidst an
array of seven physicians, the lone psychologist is likely
to be seen as outlier on the team. This is documented
in theory and research on minorities and tokenism in
teams (distinctiveness theory: McGuire, 1999; minority
- majority influence: Nemeth, 1986). From a multilevel
perspective, however, the experiences of someone who
is different from all the others in the team will be
radically changed depending on the unit context: Do
all the others on the team also differ from one another,
or are they all the same? If the research team has
maximum variety, like our Team V, the lone
psychologist (whose average difference from everyone
else is still '1') is not an interpersonal outlier on the
team, but in effect, just like everyone else. Like every
other team member, he or she is the only representative
of a particular background.
The challenge of differences, of diversity, is a crucial
one for managers and scholars to meet. However, even
as research has accumulated, the nature and effects of
diversity have remained uncertain. We have tried to
address that challenge by positing that diversity
constructs have three fundamental types: separation,
variety, and disparity. The three types differ in their
substance, shape, maxima, and implications. Crucial
attributes and relevant theoretical perspectives differ
for each type, as do the within-unit processes and
outcomes likely to be associated with each one. We
urge future investigators to specify the diversity types
they are studying, and to align them with specific,
appropriate operationalizations. By systematically
asking and answering "what's the difference?,"
management scholars may reveal a clearer, more
cumulative understanding of diversity in organizations.
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Blau's index was originally developed by Simpson
(1949) for sampling with replacement from an infinite
population. When used to measure diversity within a
group, sampling is done with replacement from a finite
population, and 1-Σp
might be substituted by 1 -
1)/(n(n 1)]. In the latter formula, n
is the
frequency of unit members in the kth category, and n is
unit size.
This example recalls Lau and Murnighan‘s (1998)
insightful analysis of group faultlines. The gender and
race faultline is stronger in the first four groups
specified than in the fifth. The construct of faultline
strength, to which we return later in this article, bears
some similarity to the notion of “overall diversity,”
although the construct of faultline strength is far more
precise and focused. Even so, it has proven
challenging to operationalize faultline strength outside
of environments, such as the laboratory., where
individuals can be assigned to groups to create weak vs.
strong faultlines Recent developments in the
measurement of faultline strength in naturally occurring
groups are encouraging (Shaw, 2004), but remain little
Racial diversity – conceptualized as separation – may
provide a more intuitively obvious example. If a
researcher wants to argue that the effects of racial
separation are symmetric, he or she needs to show that
the effects of homogeneity (minimal separation) are the
same regardless of whether the group is all Black or all
White. Further, he or she needs to show that the
effects of separation are the same regardless of whether
the group is numerically dominated by Whites or by
Blacks (by definition, at the point of maximum Black-
White separation, a team has equal numbers of Whites
and Blacks).
When conceptualizing tenure as variety, researchers
may be tempted to simply divide tenure into
convenient subgroupings: 0-5 years, 6-10 years, etc.
But, in fact, this choice warrants careful consideration
as it is not always clear what the cohorts -- representing
different "batches" of knowledge or experience --
should be. Cohort definitions should ideally be
supported by a conceptualization that specifies why, for
example, 1-4 years of tenure is markedly different from
5-8, or 9-15 (or whatever cohort boundaries are
chosen). Clear events in a firm or unit, such as before
and after a reorganization or acquisition, might support
demarcations of tenure cohorts as sources of variety.
These arguments are relevant beyond the bounds of
demographic research. For example, if political
ideology is conceptualized not as a continuous variable
from conservative to liberal (engendering potential
separation within a unit), but as a source of variety of
perspectives within a unit, a researcher faces a
comparable puzzle: Where should he or she draw the
lines distinguishing those who are very conservative
from those who are conservative, those who are
moderate from those who are liberal, and so on?
Academy of Management Review - Forthcoming 2007
Disparity does not always trump variety. If the source
of disparity is status based on task experience or
expertise, the unit's "ear" is likely to be tuned toward
those who have the best and most relevant information
to share (Wittenbaum, 1998). However, if the source
of disparity is based on another attribute unrelated to the
unit's task (physical attractiveness, wealth), disparity may
mitigate the advantages of variety (Hollingshead, 1996).
... Age diversity refers to the, "distribution of differences among members with respect to age" (Li et al., 2020, p1; also see Harrison & Klein, 2007) and can be conceptualized as separation, variety, or disparity (Harrison & Klein, 2007). In line with previous research (e.g. ...
... Age diversity refers to the, "distribution of differences among members with respect to age" (Li et al., 2020, p1; also see Harrison & Klein, 2007) and can be conceptualized as separation, variety, or disparity (Harrison & Klein, 2007). In line with previous research (e.g. ...
... In line with previous research (e.g. Rabl & Triana, 2014;Li et al., 2020), we conceptualize age diversity as variety, that is "composition of differences in kind, source, or category of relevant knowledge or experience among unit members" (Harrison & Klein, 2007, p. 1203. Although the findings of research linking age diversity to team outcomes are often mixed (Williams & O'Reilly, 1998;Wegge & Meyer, 2020), the variety perspective argues that age diversity will have a positive effect on team outcomes when tasks are cognitive, complex and interdependent, such as problem-solving, innovation, and decision making tasks (van Knippenberg et al., 2004;Wegge et al., 2008;Wegge et al., 2012;Ries et al., 2013;Homan et al., 2020). ...
Full-text available
While prior research has shown a relationship between age diversity and outcomes in teams, little knowledge exists regarding the theoretical mechanisms driving these outcomes. Furthermore, mixed findings from prior research indicate that these relationships differ depending upon contextual factors. Our field study tests two potential mediators and one possible moderator of the relationship between age diversity and team performance. Multi-source data collected from 71 Chinese township government leadership groups showed that team age diversity is positively associated with survey measures of two emergent states, specifically, perceived team effectiveness and collective team identification, which in turn positively predict an objective measure of team performance. The fully mediated model was moderated by leader moral identity, which strengthened the relationships between age diversity and both team emergent states. Results also showed a curvilinear relationship between age diversity and the two team emergent states which was not significantly moderated by leader moral identity. These findings contribute to extant knowledge from an integrative approach within a non-Western cultural context. Theoretical and practical implications are discussed.
... Since then, few universities have reported at least one academic staff member belonging to that category, but we nevertheless keep it when computing tionships and/or involve work away from the supervision of the normal work provider (see support/definitions/staff). 8 As Harrison and Klein (2007) note, diversity can be captured with either Shannon's index or Blau's index. The properties of the indexes are qualitatively similar, so researchers sometimes use one as a robustness check for the other (see Campbell and Mínguez-Vera, 2008). ...
Full-text available
Modern workplaces are becoming increasingly demographically diverse. However, the influence of workforce diversity on organizational outcomes is not fully understood. In this work, we study how and why workforce gender and racial diversity impacts collective turnover at the organizational level, and whether participation in and experience with diversity charters moderate this link. We particularly argue that greater workforce gender and racial diversity leads to greater collective turnover because it prompts social categorization and negative contagion in organizations. To mitigate these processes, organizations may participate in diversity charters, which are expected to provide support with managing workforce diversity and employee retention. We further argue that the influence of diversity charters follows a trajectory of maturity, so their benefits are magnified as an organization's experience with them increases. Drawing on a panel of UK universities, we find strong evidence that greater workforce racial diversity is associated with higher levels of collective turnover, but only weaker evidence for the positive link between workforce gender diversity and collective turnover. We further find that diversity charters may attenuate this link, but simply participating in them is not sufficient: instead, organizations must develop experience with charters over time.
This study aims to examine an integrated research model of employee empowerment from the perspectives of managers (the empowering) and employees (the empowered) with a newly proposed construct, empowerment disparity that captures a difference in employees' perception of empowerment within a team. A multilevel analysis was conducted using Hierarchical Linear Modeling (HLM) to test the hypotheses using a matched sample of 286 restaurant employees along with 51 managers and 2129 customers from five tourist cities in Thailand. The findings show that psychological empowerment and the psychological contract have a positive effect on customers' assessment of interaction quality, and empowerment disparity moderates the links between a manager's empowering behavior and psychological empowerment, and psychological empowerment and the psychological contract. The theoretical and managerial implications suggested by the findings are provided for researchers and practitioners.
This study examines how differentiation in leader–member exchange relationships (LMXD) provokes different individual responses depending on personal dispositions—internal locus of control and self-efficacy. Further, we identify three types of individual behavioral responses to LMXD: in-role and extra-role performance as self-focused responses, impression management toward the leader as a leader-focused response, and social undermining of coworkers as a coworker-focused response. Analysis of data from a survey conducted in South Korea demonstrates cross-level interaction effects between LMXD and the two dispositional variables on these behavioral responses of individuals. Specifically, LMXD is more positively related to in-role and extra-role performance and impression management toward the leader for individuals high than low in internal locus of control and for individuals high than low in self-efficacy. However, the cross-level interaction effects are not supported regarding social undermining of coworkers. Theoretical and practical implications of the findings are discussed.
Although the practitioner and the academic literatures both note the importance of ethno-racial diversity in the nonprofit sector, we pursue a better understanding of this sector’s ethno-racial diversity dynamics by exploring the drivers of ethno-racial diversity at multiple organizational hierarchy levels—boards of directors, executive staff teams, and full-time staff. Using data from nonprofit organizations participating in Candid’s Diversity, Equity, and Inclusion initiative, we find evidence that upward mobility for ethno-racial minority group members remains limited. Our findings also indicate a positive relationship between ethno-racial diversity in a nonprofit’s local community and ethno-racial diversity at each organizational hierarchy level. This relationship is partially mediated by the political liberalness of the local community, with the strongest mediating effects at the board of directors and executive staff organizational levels. This partial mediation suggests that the local environment’s tolerance of societal hierarchies and unequal outcomes may be related to nonprofit organizational diversity, equity, and inclusion efforts.
Purpose Though affective commitment has been thought to be critical in team selection, it is yet to be integrated into current models on team selection primarily due to lack of understanding of its contribution to teamwork effectiveness and performance. This study tests the contribution of complementary person team-fit (CF), supplementary person team-fit (SF) and affective commitment (AC) to teamwork effectiveness in construction project teams. By this examination, a theoretical case is made for the inclusion of affective commitment in team selection and deployment in construction project delivery. Design/methodology/approach By using a deductive questionnaire survey on project team participants and experienced human resource managers in recruitment and team selection in project organizations, structural equation model (SEM) was used to test the contribution of CF, SF and AC parameters to teamwork effectiveness from a total of 105 responses gathered from project team participants in project delivery. Findings The SEM revealed that, CF, SF and AC together account for about 81% predictive effect to teamwork effectiveness which is higher than joint effect of CF and SF in existing models in team selection and deployment. By this, it can be affirmed that, in considering CF, SF and AC in an integrated manner in team selection and deployment, higher team effectiveness can be achieved that can stimulate high performing teams and effectiveness in project delivery. Originality/value The findings give theoretical assertiveness to extending consideration of competence and values in team selection to embrace affections and commitment to engender effectiveness in teamwork, cohesion, collaboration and performance. This present novel attempts to include affective commitment in teamwork toward higher team effectiveness.
Information fusion is one of the critical aspects in diverse fields of applications; while the collected data may provide certain perspectives, a fusion of such data can be a useful way of exploring, expanding, enhancing, and extracting meaningful information for a better organization of the targeted domain. A nature-inspired evolutionary approach, namely, genetic algorithm (GA) is adopted for a variety of applications including stock market prediction. The complex, highly fluctuating financial market-related problems require optimized models for reliable forecasting. Also, it can be observed that stock market etiquettes are generally non-linear in nature and therefore, a broader understanding and analysis of such market behaviors necessitate the collection and fusion of relevant information based on different associated factors. In this article, we propose an information fusion-based GA approach with inter-intra crossover and adaptive mutation (ICAN) for stock price and trend prediction. Inspired by the genetic diversity and survival capability of various organisms, our proposed approach aims to optimize parameters of a long short-term memory prediction model, and selects a set of features; to address these problems of interest, we integrate inter-chromosome as well as conditional intra-chromosome crossover operations along with adaptive mutation to diversify the potential chromosome solutions. We illustrate the step-by-step procedure followed by GA with ICAN and evaluate its performance for one-day-ahead stock price and trend prediction. GA with ICAN-based optimization results in an average reduction of 43%, 27%, and 26% using mean squared error, mean absolute error, and mean absolute percentage error, respectively, as compared to the existing GA-based optimization approaches; further, an average improvement of 61% is encountered using R2 score. We also compare our work with Ant Lion Optimization approach and demonstrate the significance of GA with ICAN-based optimization. We analyze statistical significance, as well as convergence functions, for GA with ICAN and discuss remarkable performance enhancement; we provide necessary concluding remarks with potential future research directions.