Managing knowledge exchange
and identiﬁcation in age diverse
Thomas Ellwart and Silke Bu
Institute of Psychology, University of Trier, Trier, Germany, and
Applied Psychology, University of Applied Sciences and Arts
Northwestern Switzerland, Olten, Switzerland
Purpose – This paper aims to examine the impact of individual and group-level variables on knowledge
exchange and identiﬁcation in age diverse teams. From a diversity perspective, inﬂuences of age-related
diversity perceptions and diversity beliefs (level 1) are compared with effects of objective age diversity
(level 2). From a management perspective, the paper goes beyond age diversity and investigates the
incremental effects of team and individual characteristics from a team learning perspective.
Design/methodology/approach – Questionnaire data of 516 team members and their supervisors
in 73 organizational teams were analyzed in a multilevel approach.
Findings – Objective age diversity had a negative effect on knowledge exchange and identiﬁcation.
Beyond that, age-related diversity perceptions and positive diversity beliefs on the individual level
predict the effect of objective diversity. Relativizing the impact of diversity, individual characteristics
(knowing the team experts, clear understanding of goals) and team characteristics (team climate, time
for knowledge exchange) explain the largest proportion of variance in the dependent variables
underlining the importance of team learning variables.
Research limitations/implications – Compared to objective diversity, subjective diversity
perceptions and diversity beliefs are relevant predictors of processes and attitudes in heterogenic
Practical implications – There are multiple leverages for management strategies beyond the
mostly ﬁxed age diversity in teams on the individual and group level.
Originality/value – This paper evaluates the cross-level interplay between objective diversity,
perceived subjective diversity and diversity beliefs and revalues the impact of HR-management in age
Keywords Age groups, Organizational behavior, Managerial psychology
Paper type Research paper
Organizations reﬂect contemporary demographic change characterized by later
retirement and an increase in older workers as well as a shortage and need of younger
The current issue and full text archive of this journal is available at
This research was supported in parts by grants from the Swiss National Science Foundation and
from the Eugen and Elisabeth Schellenberg-Foundation (13DPD3-124662/1; titled “Knowledge
transfer in age diverse cooperations: predictors and interventions”). The authors would like to
thank Kristin Mock, Margarita Neff-Heinrich, Sigrid Jeske-Wo
¨rfel, and Jacqueline Marquardt for
personal assistance in preparing the study and manuscript.
Received June 2011
Revised January 2012
Accepted January 2012
Journal of Managerial Psychology
Vol. 28 No. 7/8, 2014
qEmerald Group Publishing Limited
qualiﬁed employees (Aliaga and Romans, 2006; Baltes et al., 2011). As a result, the
growing heterogeneity in teams (age diversity) leads to manifold challenges for
HR-management (i.e. Roth et al., 2007). This paper focuses on two key variables in age
diverse teams, i.e. knowledge exchange and identiﬁcation. First, HR management
needs to support an intensive intergenerational knowledge exchange between workers
of different ages in order to allow life-long learning within the organization (Argote and
Ingam, 2000; Argote et al., 2003). Moreover, diversity research indicates that the
exchange of knowledge is a central element of the elaboration of task-relevant
information (Homan et al., 2007a; van Knippenberg et al., 2004). Second, facing the “war
for talent” (Michaels et al., 2001), many companies promote a high organizational
identiﬁcation in order to bind younger and older workers to the organizations and
reduce ﬂuctuation and turnover (Ashforth and Mael, 1989; Griffeth et al., 2000; van
Dick et al., 2008; van Knippenberg et al., 2007). In this line, recent research
demonstrates an increased identiﬁcation with diverse teams when diversity beliefs are
high (van Dick et al., 2008; van Knippenberg et al., 2007).
Thus, the overall goal of this paper is to highlight leverages for management on the
individual and team level in order to promote knowledge exchange and identiﬁcation
in age diverse teams. This study compares the effects of input variables originating
from two theoretical perspectives:
(1) age related variables from diversity research (van Knippenberg et al., 2004;
Harrison and Klein, 2007); and
(2) team and individual characteristics from team learning literature (e.g. Argote
and Ingam, 2000; Argote et al., 2003).
Regarding the ﬁrst perspective, we discuss the impact of age diversity on team
performance and provide empirical data, showing that individual perceptions of
diversity (subjective diversity) and beliefs about the beneﬁt of diversity (diversity
beliefs) are heterogeneously observed and evaluated by individual team members. Thus,
beside the often applied group level score of diversity, we promote the importance of
individual level variables in diversity research. Predicting knowledge exchange and
identiﬁcation, we argue that individual perceptions and beliefs are statistically
appropriate and most relevant predictors above objective diversity scores on the group
level. Moreover, we aim to show in cross-level interactions that individual perceptions
and beliefs moderate the impact of objective age diversity on outcome variables. Second,
we compare the impact of age related variables (objective diversity, diversity perception
and beliefs) with variables from team learning literature (e.g. knowledge about expertise
location, team climate) (e.g. Decuyper et al., 2010). This practical focus goes beyond age
diversity, because this study argues that although age diversity is important, many other
inﬂuences have much stronger effects on knowledge exchange and identiﬁcation.
2. Theoretical background and hypotheses
2.1 Age-diversity as predictor of team outcomes
Age diversity reﬂects the degree to which age related characteristics are objectively
different or subjectively perceived as different between people within the group (van
Knippenberg and Schippers, 2007; Harrison and Klein, 2007; van Dick et al.,2008).With
regard to the impact of diversity on team performance, research on age diversity could
not show a clear positive or negative impact of diversity on team performance or
identiﬁcation (Bantel and Jackson, 1989; Bowers et al., 2000; Horwitz and Horwitz, 2007;
Joshi and Roh, 2009; Webber and Donahue, 2001). In some studies, evidence for negative
effects of age diversity on team performance was provided (Tsui et al., 1995; Williams
and O’Reilly, 1998). Teams with high age diversity were shown to frequently experience
more difﬁcult decision processes (Knight et al., 1999) and communicate less (Zenger and
Lawrence, 1989). At the same time, some authors report a positive effect of age diversity
on performance, for instance operationalized as processing time (Roth et al., 2006) or total
revenues (Shemla et al., 2007). Due to this mixed result pattern of diversity – and more
speciﬁcally age diversity – simple main effect models such as social categorization
theory (Tajfel and Turner, 1986), similarity/attraction theory (Berscheid and Reis, 1998;
Byrne, 1971; Jackson, 1992) or the information/decision-making perspective (Kerschreiter
et al., 2003) do not seem appropriate for explaining the multifaceted effects of diversity.
The categorization-elaboration model (CEM; van Knippenberg et al., 2004) explains
the contradictory result pattern in diversity research by integrating several
moderating and mediating variables. The theory proposes that diversity leads to an
increased elaboration of task relevant information and hence higher team performance
when team members evaluate diversity as positive (see section 2.2). However, when
diversity within a team goes along with intergroup bias evoked by identity threats,
negative effects of diversity on information elaboration and outcomes are expected.
Although objective age diversity may have both positive or negative effects, as
suggested by earlier research (Bantel and Jackson, 1989; Bowers et al., 2000; Horwitz
and Horwitz, 2007; Joshi and Roh, 2009; Roth et al., 2006; Shemla et al., 2007; Webber
and Donahue, 2001), there is an increased likelihood of age diversity being associated
with intergroup bias that is responsible for negative effects of age diversity according
to the CEM (van Knippenberg et al., 2004; Wegge et al., 2008). A number of researchers
ﬁnd that negative stereotypes (i.e. intergroup bias) against older workers are fairly
prevalent in organizations (e.g. Baltes et al., 2011; Fiske et al., 2002; Rosen and Jerdee,
1977; Shore and Goldberg, 2005). Ries et al. (2010) demonstrate a mediating effect of age
salience on the team level (reﬂection about age differences or attributing group
problems to age), explaining the negative effect of age diversity on performance and
identiﬁcation. A recent meta-analysis by Joshi and Roh (2009) suggests a small but
negative weighted correlation of r¼20:06 between objective age diversity and
indicators of team efﬁciency. Thus we expect:
H1. Objective age diversity on the group level has a negative effect on knowledge
exchange and identiﬁcation.
In most cases diversity and diversity beliefs are operationalized as group level
phenomena. In the following we argue that a perceptions and beliefs about
age-diversity may differ between individuals within the same team.
2.2 Perceptions and beliefs about age-diversity: group or individual variable?
In this section we brieﬂy introduce diversity perceptions and beliefs and then discuss
the distinction between subjective and objective measures as well as group versus
individual level issues.
2.2.1 Perceptions of age diversity. Perception in humans describes the process
whereby sensory stimulation is translated into organized, meaningful experience
(Lindsay and Norman, 1977). As recent research points out, subjective variables such as
perceptions of and cognitions about diversity are crucial variables inﬂuencing the effect
of objective diversity within teams on team processes and outcomes (Homan et al., 2007a,
2010; Jehn and Bezrukova, 2010; Meyer et al., 2011; Zellmer-Bruhn et al., 2008; Ries et al.,
2010). Hence, the perception of differences can be considered “as part of the process by
which diversity is translated into thought and action in teams” (Zellmer-Bruhn et al.,
2008, p. 42; see also Harrison et al., 2002). Most importantly, there is no one-to-one
correspondence between actual diversity and perceived diversity: differences may either
go unnoticed (Homan et al., 2010), be augmented or might be perceived on dimensions
other than those assumed to be relevant by researchers (Meyer et al., 2011). For this
reason, researchers should assess not only objective diversity within a team but also the
subjective perceptions of diversity (Meyer et al.,2011).
2.2.2 Diversity beliefs. Diversity beliefs are cognitions about the degree to which
diversity within a team is advantageous for the functioning of teams and not a threat to
it (Homan et al., 2007, 2010). In line with the CEM model from van Knippenberg et al.
(2004), previous research found evidence that in teams consisting of team members
who report high diversity beliefs, objective diversity positively inﬂuences team
processes like information exchange and team performance outcomes (Homan et al.,
2007a; van Dick et al., 2008). Moreover, beliefs in favor of diversity lead to a positive
relationship between subjective diversity and identiﬁcation with the team on the
individual level (van Dick et al., 2008). Consequently, in addition to objective age
diversity and the perception of age diversity, subjective diversity beliefs explain a
unique amount of variance in knowledge exchange and identiﬁcation.
2.2.3 Group versus individual variable. In previous research, evidence for either the
individual or group level conceptualization of diversity beliefs and perceptions can be
found. While some authors model diversity beliefs and perceptions on the individual
level (i.e. low agreement between team members; see Meyer and Schermuly, 2012; van
Knippenberg et al., 2007) others refer to and model them as team level constructs
(i.e. high agreement between team members; see Homan et al., 2007a, 2010; van Dick
et al., 2008).
We argue that both the correlation between objective diversity and diversity
perceptions as well as the within group agreement on diversity perceptions and beliefs
(i.e. individual versus team level) are likely to depend on the impact of individual and
environmental (i.e. context) factors. The CEM also mentions these types of inﬂuences
on the perception of diversity (van Knippenberg et al., 2004):
.normative ﬁt; and
Comparative ﬁt resembles a factor rooted in the environment, as it depends on the ratio
of intragroup and intergroup differences in a given comparative context. Normative ﬁt
refers to the extent the categorization makes subjective sense to the individual team
members. Accessibility depends on the activation of the relevant categories within the
individual. In this vein, normative ﬁt and accessibility will be primarily inﬂuenced by
individual experiences, which are current motivational states and personality variables
largely independent from the group (Bargh and Pratto, 1986; Garcia-Prieto et al., 2003;
Secord et al., 1956). With regard to diversity beliefs, the environmental factors (e.g. task
instructions) can suggest that a certain type of diversity is either functional or
dysfunctional for a given task (e.g. Homan et al., 2007a; van Knippenberg et al., 2007).
Individual differences associated with diversity beliefs (van Dick et al., 2008) are stable
personality variables such as openness to experience (Homan et al., 2008, 2010).
Depending on the type of research and the operationalization of diversity, the strength
of environmental and individual variables might differ, leading to varying relationships
between objective diversity and diversity perceptions as well as differences in intra-team
agreement. In an experimental setting, environmental control is very high since the
variables of interest are manipulated and hence held constant within the experimental
conditions (e.g. Homan et al., 2007a). Therefore, individual factors inﬂuencing diversity
perceptions and beliefs lose strength. In this line, a strong correlation between objective
diversity and diversity perceptions as well as a high agreement in the perception of
diversity within the team is more likely when diversity is operationalized in terms of
converging diversity attributes (i.e. faultlines) rather than single diversity features such
as age (see Homan et al., 2010; Meyer et al., 2011). Faultlines enhance comparative ﬁt (van
Knippenberg et al., 2004) and hence make it more likely that objective diversity is
actually perceived. However, in situations with team diversity in terms of single
diversity attribute (such as age in our case), environmental factors loose strength while
individual processing gains strength and thereby reduces within-team agreement and
the correlation between objective diversity and diversity perceptions.
Consequently, depending on the type of study (experimental versus
quasi-experimental) or the constellation of objective diversity, diversity perceptions
may be conceptualized as an individual or team level variable. In our study, only the
impact of age diversity is measured and not manipulated. Thus, we expect a low
convergence of age diversity perceptions and age diversity beliefs within the team,
making them both variables on the individual level.
Harkening back to the previously mentioned association of age diversity with
intergroup bias (Baltes et al., 2011; Fiske et al., 2002; Shore and Goldberg, 2005) and our
reasoning regarding age diversity perceptions as a group or individual level variable,
we predict in line with the CEM (van Knippenberg et al., 2004):
H2a. Perceived age diversity on the individual level will predict less knowledge
exchange and less identiﬁcation.
Based on the reasoning above, it is assumed that diversity beliefs are not homogeneous
within groups and should be modeled as individual-level variables. It is proposed:
H2b. Positive beliefs about age diversity on the individual-level will predict more
knowledge exchange and higher identiﬁcation.
2.2.4 Moderating effects of individual diversity perceptions and beliefs. As discussed
before, objective age diversity resembles a group-level construct, while age diversity
perceptions and age diversity beliefs resemble individual-level constructs in our
research. The role the interaction between individual-level and group-level variables
may play in explaining diversity effects has so far received little research attention
(e.g. Meyer et al., 2011).
Our research adds to these recent ﬁndings and takes a closer look at the cross-level
interaction between objective age diversity, individually perceived age diversity and
age diversity beliefs in predicting knowledge exchange and identiﬁcation. Following
van Knippenberg et al. (2004) and several other empirical studies, diversity beliefs
moderate the effect of objective diversity on outcomes, functioning as a positive
resource that hinders negative inﬂuence of social-categorization processes (e.g. Homan
et al., 2007a; van Dick et al., 2008; van Knippenberg et al., 2007; Wegge et al., 2011). In a
negative way, subjective age-related diversity perceptions may facilitate the negative
effect of objective age diversity when age bias is highly accessible and prevalent in
individual team members (van Knippenberg et al., 2004). In an experimental study,
Abrams et al. (2006) demonstrate that older participants who had experienced few
positive intergenerational contacts revealed more test-related anxiety and hence
performed worse when they were lead to compare themselves to younger participants.
In this study we hypothesize:
H2c. Subjective diversity perception and subjective diversity beliefs will moderate
the effects of objective age-diversity on knowledge exchange and identiﬁcation
(cross-level interaction). Speciﬁcally, team members with positive diversity
beliefs will show high knowledge exchange and identiﬁcation in high age
diverse teams. Team members with high perceptions of age-diversity will show
less knowledge exchange and identiﬁcation.
The research model, variables, and hypotheses are shown in Figure 1.
2.3 Beyond age diversity: management strategies from team learning research
From the perspective of age diversity, HR management may try to change diversity
perceptions or diversity beliefs in order to improve knowledge exchange and
identiﬁcation in age diverse teams. However, this solely age-related focus falls short
because it ignores possible leverages on other predictors, for example team learning
(Argote et al., 2003; Edmondson et al., 2007; Decuyper et al., 2010). In this study we aim
to compare the inﬂuence of diversity related variables with four relevant input factors
from team learning research in order to:
.relativize the impact of age diversity on knowledge exchange and identiﬁcation;
.show speciﬁc management strategies that can explain variance beyond the
effects of objective diversity, diversity perceptions and diversity beliefs.
2.3.1 Expertise location. Knowledge about speciﬁc expertise of team mates held by team
members is related to group level constructs of transactive memory systems (Wegner,
1987; Lewis, 2003) or teamwork-related team mental models (DeChurch and
Mesmer-Magnus, 2010). The degree to which team members can identify the location
of expertise within their own team relates to the way they communicate and exchange
Research model, variables,
information (van Ginkel and van Knippenberg, 2009). Members with a good
representation of expertise can easily ask others for help and instruction (Austin,
2003; Ellwart and Konradt, 2007; Faraj and Sproull, 2000; Lewis, 2003). Moreover,
knowledge of expertise roles will increase salience of commonly shared group resources
because shared resources within a team increase the subjective value of a group and
hence identiﬁcation with the team.
2.3.2 Clear objectives for knowledge exchange. There is numerous research and
theory on the effects of speciﬁcally formulated goals on behaviors, such as goal setting
theory (Locke and Latham, 2002), action regulation (Frese and Zapf, 1994; Hacker,
2003) and leadership (Fleishman and Zaccaro, 1992; Weinkauf and Hoegl, 2002). With
regard to knowledge exchange, team members need to know what kind of knowledge
they have to share with whom and what resources they can use. Without this goal
clarity, less knowledge and information will be exchanged because of the missing
guiding facet of action regulation (Ely and Thomas, 2001). Moreover, if management
deﬁnes common goals for knowledge exchange, an important constraint for team
identiﬁcation is given since team goals have been shown to improve team identiﬁcation
(Wegge and Haslam, 2003, 2005):
H3a. On the individual level, expertise location and clear objectives for knowledge
exchange are positively related to knowledge exchange and identiﬁcation and
explain variance beyond the effects of objective and perceived diversity
2.3.3 Team climate. On the group level there are also variables for strategic HR
management that inﬂuence knowledge exchange and identiﬁcation. In line with research
and theory on innovation, a crucial precondition for knowledge exchange is that the group
must be perceived as interpersonally non-threatening and safe (Edmondson, 1999, 2002;
Edmondson et al., 2007; Schoorman et al., 2007; West, 1996, 2000). The teams’
psychological safety climate describes an environmental variable that allows team
members to make mistakes and to exchange ideas unconventionally. According to West
(2000), this safety climate on the group level will lead to an increasing quantity of idea
production and knowledge exchange on the individual level. Improvement of social
relationships in teams with a positive safety climate will also inﬂuence emotional-affective
outcomes, such as identiﬁcation (West, 1996). Consequently it is hypothesized:
H3b. On the group level, team climate (supervisor rating) is positively related to
knowledge exchange and identiﬁcation and explains variance beyond the
effects of diversity-related variables.
2.3.4 Time for knowledge exchange. The antecedent of effective knowledge
management refers to providing sufﬁcient time for knowledge exchange by the
team leader. Offering temporal resources is a basic function of team leadership beyond
the initial planning (Weinkauf and Hoegl, 2002; Zaccaro et al., 2001). Especially from a
practical point-of-view, the exchange of information and the reﬂection of experiences
beyond the speciﬁc team task are desired but most of the time not explicitly scheduled
as work. The explicit deﬁnition of time for knowledge exchange by supervisors will
positively inﬂuence the quantity of knowledge exchange:
H3c. On the group level, time for knowledge exchange (supervisor rating) is
positively related to knowledge exchange and explains variance beyond the
effects of diversity related variables.
Participants in this study were 516 team members from 73 organizational teams in the
ﬁnance and control sector of Swiss companies. Additionally, 73 team supervisors were
surveyed. Team members and supervisors were personally invited to participate in the
online survey based on data of HR-management. From the originally 831 invited
organizational participants of 136 teams, only those participants/teams were included
in the analyses, whose teams had a response rate of 80 percent or more (maximum of
one or two team members missing). The average group size was 9.64 members
(SD ¼5:70). Mean age was 39.9 years (SD ¼11:1). The mean tenure in leading teams
was M ¼10:5 years (SD ¼2:5); mean tenure in leading the teams surveyed was
M¼4:5 years (SD ¼1:9). Individual data of the team members remained conﬁdential
and participation was voluntary.
3.2.1 Objective age diversity (level 2). As indicator of objective diversity, standard
deviation (SD) of age was calculated within each unit (Ries et al., 2010; Roth et al.,
3.2.2 Team climate (level 2, supervisor rating). Team climate was measured with
three items on a seven-point Likert scale from Edmondson (1999) from 1 (totally
disagree) to 7 (totally agree). An example item was “It is safe to take a risk in this
3.2.3 Time for knowledge exchange (level 2, supervisor rating). Following
Weinkauf and Hoegl (2002), administrative time for knowledge exchange was
measured with two items on a seven-point Likert scale from 1 (not at all) to 7 (very
often). An example was “The team has deﬁned periods of time to exchange
3.2.4 Perceived age diversity (level 1, team member rating). Following Harrison et al.
(1998), subjective age diversity was measured using the item “How similar or different
are the members of your syndicate group with respect to their age?”. Ratings were
given on a seven-point Likert-scale from 1 (not diverse) to 7 (very diverse). To validate
measures of age diversity, we aggregated perceived individual age diversity to the
group level. Signiﬁcant correlations between perceived age diversity and:
.supervisor perceived age diversity (r¼0:27, p,0:05); and
.objective age diversity indicators (standard deviation of age within the team:
r¼0:32, p,0:01; Blau-Index of age cohorts in the team: r¼0:24, p,0:05) are
indicators of convergent validity.
3.2.5 Age diversity beliefs (level 1, team member rating). An adapted scale from van
Knippenberg et al. (2007) was used with three items to measure age related diversity
beliefs. The scale was pretested in experimental and ﬁeld studies prior to this
investigation, showing good psychometric properties (Ellwart and Rack, 2009).
Ratings were given on a seven-point Likert scale from 1 (totally disagree) to 7 (totally
agree). An example item was “Teams with members of the same age achieve better
results than age diverse teams” (reversed item). Cronbach’s
3.2.6 Clear objectives for knowledge exchange (level 1, team member rating). This
was measured with an adapted three item scale from Weinkauf and Hoegl (2002). The
general goal perspective of the items was reformulated towards goals of knowledge
exchange. Ratings were given on a seven-point Likert scale from 1 (totally disagree) to
7 (totally agree). An example item was “I know about the goals of the knowledge
exchange in my team”. Cronbach’s
3.2.7 Expertise location (level 1, team member rating). This was measured by a scale
from Ellwart and Konradt (2007) based on scale from Faraj and Sproull (2000). Ratings
were given on a seven-point Likert scale from 1 (totally disagree) to 7 (totally agree). An
example item was “I have a good ‘map’ of other team members’ talents and skills”.
3.2.8 Knowledge exchange (level 1, team member rating). This was measured in a way
following the idea of behavior anchored rating scales (Cocanougher and Ivancevich,
1978). Participants rated the frequency of their exchange interactions with other team
members during the last three months. The ratings were anchored by explaining to
participants what the extreme points of the scale (1 ¼never and 7 ¼very often)
represent in terms of behavior. To validate the measure of knowledge exchange, we
aggregated individual knowledge exchange to the group level. Signiﬁcant correlations
between knowledge exchange and supervisor ratings of team knowledge exchange
(r¼0:25, p,:05) and supervisor rating of team innovation (scale from De Dreu, 2002;,
r¼0:34, p,0:01) indicate convergent validity of this measure.
3.2.9 Identiﬁcation (level 1, team member rating). This was measured using a three
item scale from Doosje et al. (1995). Ratings were given on a seven-point Likert-scale
from 1 (totally disagree) to 7 (totally agree). An example item was “I’m happy to be part
of the team”. Cronbach’s
4.1 Preliminary analyses
To rule out potential distortions in data due to common methods variance, two
techniques/models were applied:
(1) Harman’s one-factor test; and
(2) controlling for the effects of an unmeasured latent methods factor (both
recommended by Podsakoff et al., 2003).
Conﬁrmatory factor analyses (CFA) with maximum likelihood estimation (Mplus
Version 6.1, Muthe
´n and Muthe
´n, 1998-2010) indicate no inﬂuence of common method
bias between predictors and criteria.
According to a consensus model for constructs on different levels of analysis (Chan,
1998), team members’ perception and diversity beliefs within a group can only be
considered as group level variables if team members show a high agreement in their
perceptions and beliefs and hence represent different observations of the same level group
construct. To provide empirical evidence that subjective diversity perceptions and
diversity beliefs are individual variables not homogeneously perceived within the teams,
different coefﬁcients were calculated (see Table I). Results indicate that diversity
perceptions and diversity beliefs are evaluated differently by members within one team.
4.2 Hypotheses testing
For H1,H2 and H3, Table II shows the inter-correlations on the individual level (above
the diagonal) and between level 2 predictors as well as aggregated level 1 variables on
the group level. Because of the nested structure of the data, hypotheses were tested
in an intercept and slope as outcome model using HLM 6.08 (Raudenbush et al., 2009).
M SD Minimum Maximum M SD Minimum Maximum ICC1 Wald Z-test ICC2
Perceived age diversity 0.34 0.32 0.00 0.93 1.29 0.53 0.04 3.00 0.06
Diversity beliefs 0.55 0.30 0.00 1.00 0.96 0.45 0.00 2.67 0.05
Notes: n¼516 individuals in n¼73 teams;
mean deviation, ICC1 and
ICC2 of subjectively
perceived age diversity
and diversity beliefs
Variable Mean (SD) 1 2 345678
1. Level 1: Perceived age diversity (PD) 4.35 – 0.04 –0.06 0.00 20.10 *0.06 – –
2. Level 1: Diversity belief (DB) 5.85 0.18 – 0.13 ** 0.09 *0.16 ** 0.13 ** ––
3. Level 1: Clear objectives 5.25 20.14 0.10 – 0.42 ** 0.50 ** 0.47 ** ––
4. Level 1: Expertise location 5.89 0.05 20.03 0.35 ** – 0.31 ** 0.39 ** ––
5. Level 1: Identiﬁcation 5.42 20.01 0.11 0.45 ** 0.27 *– 0.44 ** ––
6. Level 1: Knowledge exchange 4.60 20.12 0.10 0.54 ** 0.42 ** 0.35 ** –––
7. Level 2: Objective age diversity (OD) 9.58 0.32 ** 0.10 20.09 20.12 20.14 20.20† – –
8. Level 2: Team climate (leader) 6.06 20.07 – 0.09 0.23 *0.05 0.33 ** 0.25 *0.01 –
9. Level 2: Time exchange (leader) 5.05 20.02 0.06 0.06 0.19† 0.12 0.29 ** 20.01 0.20†
Notes: n¼516 for level 1 correlations (above the diagonal); n¼73 for level 2 correlations between objective diversity, supervisor ratings, and
aggregated level 1 variables (below the diagonal); levels of signiﬁcance: †p,0:10; *p,0:05; **
p,0:01 (two tailed)
variables at level 1 and
level 2 (including
aggregated level 1
First, the ICC for knowledge exchange (0.09) and identiﬁcation (0.11) indicate sufﬁcient
between-group variance to justify multilevel analyses. Second, an intercept as outcome
model was run to see whether objective age diversity (standard deviation of age) on the
group level has a negative effect on knowledge exchange and identiﬁcation on the
individual level (H1). As Table III (step 1) reveals, teams with high age diversity team
members marginally exchange less knowledge (B¼20:04, p¼0:08) and report lower
identiﬁcation (B¼20:03, p¼0:14) than teams with low age diversity. However, the
effect for identiﬁcation failed to reach signiﬁcance. Third, it was tested whether
subjective diversity perception (H2a) and diversity beliefs (H2b) explain knowledge
exchange and identiﬁcation beyond the effects of objective group diversity. As
displayed in Table III (step 2), participants with high age diversity perceptions show
increased knowledge exchange (B¼0:09, p,0:05), which is opposite the expected
negative effect. In line with H2b, high (positive) diversity beliefs (B¼0:13, p,0:05)
predict knowledge exchange. Moreover, tests of cross-level interactions (H2c) yield a
moderating effect of diversity beliefs on the relationship between objective age
diversity and knowledge exchange (B¼0:04, p,0:01), but no cross level interaction
for subjective diversity perception. Thus, as Figure 2 displays, in teams with high age
diversity (grey regression lines), team members with positive diversity beliefs show
higher knowledge exchange compared to team members with no positive beliefs about
diversity. In total, including subjective diversity perceptions and diversity beliefs into
the model explains about 4 percent of the variance of knowledge exchange on level 1
and signiﬁcantly improves the model (
For identiﬁcation, in line with H2a, participants with high age diversity perceptions
(B¼20:07, p,0:05) report lower identiﬁcation. Congruent with H2b, participants
with high diversity beliefs (B¼0:13, p,0:01) reveal higher identiﬁcation. Again,
tests of cross-level interactions (H2c) yield a moderating effect of diversity beliefs.
Team members in highly diverse groups show more identiﬁcation when they believe in
the positive effects of age diversity (B¼0:04, p,0:01). No cross level interaction was
found for diversity perception. In total, including subjective diversity perceptions and
diversity beliefs into the model explains 5 percent of the variance in identiﬁcation on
level 1 and signiﬁcantly improves the model (
Fourth, to test the incremental effect of variables from team learning literature on
knowledge exchange and identiﬁcation (H3, see Step 3 in Table III), “clear objectives
for knowledge exchange and expertise location” was included at level 1 (H3a). At
level 2, the supervisor ratings of team climate and also time for knowledge exchange
were added (H3b and H3c). The results support the hypotheses. The exchange of
knowledge was increased when participants knew the location of expertise (B¼0:39,
p,0:001) and had a clear understanding of the objectives (B¼0:44, p,0:001).
Moreover, good team climate (B¼0:19, p,0:01) and time for knowledge exchange
(B¼0:16, p,0:05) could also explain knowledge exchange. In total, including these
variables into the model explains 53 percent of the variance between groups and 24
percent of the variance on level 1 with an improved model ﬁt (
For identiﬁcation, the data yield similar results for H3a with effects for knowledge
about expertise location (B¼0:16, p,0:05) and clear objectives (B¼0:40,
p,0:001). Moreover, in line with H3b, good team climate (B¼0:25, p,0:001)
reported by the supervisor predicts identiﬁcation. Group variables explain 44 percent
of the variance between groups and individual level variables explain 26 percent of the
variance on level 1 with an improved model ﬁt (
Frequency knowledge exchange Identiﬁcation
Variable Variance tau/sigma
BSE Variance tau/sigma
Step 1 0.14/1.74 0.16/1.43
Level 2: Objective age diversity (OD) 20.04† 0.03 20.03 0.02
Step 2 0.14/1.66 0.16/1.36
Level 1: Perceived age diversity (PD) 0.09 *0.04 20.07 *0.03
Cross level interaction PD £OD 0.00 0.01 0.00 0.01
Level 1: Diversity belief (DB) 0.13 *0.05 0.13 ** 0.05
Cross level interaction DB £OD 0.04 ** 0.01 0.04 ** 0.01
Step 3 0.14/1.12 0.14/1.04
Level 2: Team climate (TC, leader) 0.19 ** 0.06 0.25 *** 0.07
Level 2: Time exchange (leader) 0.16 *0.07 0.05 0.06
Level 1: Clear objectives of KE 0.44 *** 0.08 0.40 *** 0.04
Level 1: Expertise location (EL) 0.39 *** 0.09 0.16 *0.06
Notes: n¼516 (level 1); n¼73 (level 2); variance components: tau, level 2 variance; sigma
, level 1 variance; B, effects of predictors; SE, standard error of
coefﬁcient B; levels of signiﬁcance: †p,0:10; *p,0:05; **
p,0:001 (two tailed)
H1,H2 and H3
5.1 Theoretical implications of the age diversity perspective
As shown by the results, the perception of age diversity explains a unique amount of
variance in knowledge exchange and identiﬁcation in addition to the marginal impact
of objective diversity. These ﬁndings support the postulate that perceived diversity is a
useful and substantive construct that is related to but different from objective diversity
and possesses unique explanatory power on top of objective diversity (Harrison and
Klein, 2007; Meyer et al., 2011). Individual age diversity perceptions relate positively to
knowledge exchange but relate negatively to identiﬁcation. With regard to
identiﬁcation, the results are in line with previous research showing that high
diversity perceptions are negative predictors of team identiﬁcation (Haslam, 2001;
Turner et al., 1987). However, the ﬁnding of an increased knowledge exchange when
age diversity is perceived seems contradictive at ﬁrst – considering its expected
negative association with intergroup bias. Yet, it could be explained when considering
the discussed relationship between surface level diversity (like age diversity) and deep
level diversity. The perception of surface-level diversity may function as a cue for deep
level diversity (see section 2.2). In our case, age diversity might activate the perception
of diversity in terms of differences in knowledge (Harrison et al., 2002), which
reinforces knowledge exchange. In this way, the perception of task-related knowledge
diversity could have mediated the effect of age diversity perception on knowledge
exchange. In another case, it might activate the evaluation of age as problem in the
team (Ries et al., 2010), which hinders team performance. In the present study, we did
not assess deep-level evaluations of diversity. Future research should take a closer look
at task and team relevant dimensions of diversity in order to develop a more detailed
understanding of the relationship between the perception of diversity and outcomes.
In sum, this research promotes the importance of individual level variables in
diversity research. In particular, it demonstrates the theoretical importance to integrate
objective diversity and subjective processing into one model. Recent approaches like
the CEM (van Knippenberg et al., 2004) discuss individual and contextual inﬂuences on
the processing of diversity (i.e. comparative ﬁt, normative ﬁt and accessibility). It is
between diversity beliefs
and age diversity
suggested that individual factors increasing accessibility or normative ﬁt
(e.g. personality factors such as openness to experience; Homan et al., 2010) as well
as context factors increasing comparative ﬁt (e.g. diversity faultlines; Homan et al.,
2007a, b) inﬂuence how diversity is perceived. Yet, these results are far from
exhaustive. One vein of future research should systematically explore the moderating
variables leading to a stronger inﬂuence of either context factors or individual factors
and hence facilitating either a convergence or a divergence of diversity perceptions and
beliefs within a team. In this way, theoretical knowledge could be expanded regarding
when diversity perceptions and beliefs can be expected to be variables on the
individual or the team level. Another vein of research should address the interplay
between individual processes and group variables (cross level interactions). Besides
the cross-level interactions presented in this study, very few scholars have addressed
this topic so far (Meyer et al., 2011). Although our results underline the importance of
the subjective view, it is not out of question that objective age diversity is a
pre-condition for subjective processes. As the moderating effect of individual diversity
beliefs reveals, group level and individual level effects are interdependent and should
be addressed in future research.
5.2 Implications for the management of age diverse teams
For the management of age diverse teams, the present results point out speciﬁc
leverages. It seems most relevant what team members perceive and what attitudes they
have towards a heterogenic group composition. Diversity beliefs as an individual
variable can indeed be manipulated. For example, diversity beliefs are changeable
through information about group functioning (e.g. Ellwart and Rack, 2009; Homan et al.,
2007a). The way individuals perceive the team’s diversity depends on various factors. If
members in highly age diverse teams understand diversity as a resource rather than a
threat, they will value diversity as a factor increasing group performance and value with
positive effects on knowledge exchange and identiﬁcation (van Knippenberg et al.,2004,
2007). Moreover, supervisors can inﬂuence perceptions and beliefs, for example with the
help of incentives (Homan et al., 2008; Rack et al., 2011), task design (e.g. need for
diversity in highly interdependent groups; Wegge et al., 2008) and by promoting the
value of openly sharing even diverging opinions and reﬂecting critically.
In addition to diversity-related interventions, the present results speak for
supporting team learning related HR strategies, which ours and other studies show
yield the strongest effects in predicting knowledge exchange and identiﬁcation. For
example, knowledge about the expertise location within a team fosters information
exchange (Austin, 2003, Lewis, 2003,). Moreover, the team identiﬁes itself as a group
with different functional specialties, which increases variety perceptions and
identiﬁcation (Ellwart and Konradt, 2007; Faraj and Sproull, 2000). Focusing on
speciﬁc goals will affect knowledge exchange and identiﬁcation in a more direct way
(Locke and Latham, 2002). Management instructions like “Knowledge exchange is
important – do it” will not be effective. Rather, goal setting should take place on a
group level by deﬁning commonly shared goals for knowledge exchange.
As with any research, this study has a number of limitations but also suggests directions
for future research. The ﬁrst potential limitation of this research is that using self-reports
may have led to the inﬂation of our results due to common method variance (Podsakoff
et al., 2003). While common method variance cannot be fully ruled out, research has
shown that common methods bias does not automatically invalidate theoretical
interpretations and substantive conclusions (Doty and Glick, 1998; Spector, 2006).
Furthermore, the differential pattern of relationship between our measures lends support
to the assumption that common method variance is not a major limitation of this study.
Second, a further limitation addresses the measurement of perceived diversity and
knowledge exchange by means of a single item. Although there is some concern
regarding single-item measures (e.g. Loo, 2002), research has clearly demonstrated that
they often show sufﬁcient reliability and validity (Nagy, 2002; Wanous and Hudy,
2001; Wanous et al., 1997). We have chosen a single-item measure of diversity
perceptions for several reasons. It was drawn from previous work by van Dick et al.
(2008), following a scale by Harrison et al. (1998). Moreover, the focus was on the mere
perception of differences in age rather than its affective appraisal (Ries et al., 2010),
since the evaluative component was caught by the measurement of diversity beliefs.
However, the measurement of diversity perceptions could have been more ﬁne-grained
in terms of separation, variety and disparity (Harrison and Klein, 2007), which might
have resulted in more differentiated results.
Third, the operationalization of objective diversity may be criticized because many
other operationalizations of age diversity are possible (for a review and suggestions,
see Harrison and Klein, 2007). However, our operationalization as standard deviation of
age indicates diversity as separation (Harrison and Klein, 2007) and is a frequently
applied measure. Moreover, we compared it with the Blau index of age as a measure of
variety. High correlations between the measures indicate that there is statistical
convergence between the operationalization.
Age diversity in teams will be a predominant characteristic of future work groups.
Although objective age diversity may affect team performance and identiﬁcation, this
study underlines the signiﬁcant impact of individuals’ diversity perceptions and
beliefs. Beyond the subjective perception of and cognitions about diversity,
management has both the imperative and the means to control for additional
variables such as team learning. They represent the most inﬂuential variables for
improving knowledge exchange and identiﬁcation.
1. For ease of reading of the paper, the term “diversity perception” is used synonymously with
“subjective diversity”. “Objective diversity” stands for indicators of diversity that are based
on observable data such as age, gender or race. Because these indicators describe the
composition rather than the perception of a group, we label this category “objective
2. Another statistical operationalization of diversity is the Blau index as an indicator of
diversity as variety (Harrison and Klein, 2007), which assumes that variety represents
diversity in terms of a large range in skills, knowledge, and expertise and leads to more
knowledge exchange and better performance. We use this standard deviation as
operationalization of age diversity because: Harrison and Klein (2007) suggest SD as
indicator of diversity as separation and assume negative effects on knowledge exchange and
identiﬁcation (see H1); many studies on age diversity have applied this type of coefﬁcient
(Wegge et al., 2008); preliminary analyses in this sample indicated a high correlation between
age diversity as separation (SD) and variety (Blau index) with r¼0:59 (p,0:001, n¼73).
Post-tests indicate no incremental validity of the Blau index.
3. Please note that the aggregation of the individual-level variables was done to validate the
measures. As analyses of inter-rater agreement and intra-class correlations indicate
aggregation is not appropriate for further analyses because of heterogenic perceptions of age
diversity, diversity beliefs and knowledge exchange within the teams.
4. First, Harman’s single-factor test (Podsakoff and Organ, 1986) was conducted in order to
compare a single factor solution (i.e. including loadings of all items from all constructs) with
a more complex model where all items were allowed to load on their four respective factors of
origin (i.e. motives, objectives, expertise and attitude towards age diversity). Second, as
recommended by Podsakoff et al. (2003), a model was speciﬁed where all items were allowed
to load on their respective latent factors of origin as well as on a common methods variance
factor. In sum, these results suggest that common methods variance was no problem for the
5. Indices of inter-rater agreement (r
; James et al., 1984) reveal insufﬁcient mean agreement
(0.34). The average deviation scores for each team (Burke and Dunlap, 2002) indicate that
team members’ ratings are separated, on average by 0.96 (for subjective diversity) and 1.29
(for diversity beliefs) measurement units or scale options. Following Burke and Dunlap
(2002), the results yield disagreement compared to the heuristic cutoff value of 0.70, which
would indicate homogeneous rating of the team members. ICC1 (McGraw and Wong, 1996;
Shrout and Fleiss, 1979) expresses the proportion of total variance that can be explained by
team membership, and ICC2 (Glick, 1985; Shrout and Fleiss, 1979) examines the reliability of
team members’ average ratings (inter-rater agreement). The presented indices support the
modeling of diversity perception and beliefs as individual level variables. The ICC for
subjective diversity shows that only 6 percent of the variance can be explained on the group
level, and for diversity beliefs, only 5 percent can be explained. Finally, the ICC2 values of
0.32 for subjective diversity perceptions and 0.29 for diversity beliefs are very low according
to standards proposed by Glick (1985) and Klein and Kozlowski (2000), which call for indices
above the threshold of 0.70.
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About the authors
Thomas Ellwart is Full Professor of Business Psychology at the University of Trier, Germany
and works in the ﬁeld of work, organizational and market psychology. Following his PhD at
Dresden University of Technology (2004) he took up positions at the University of Kiel (until
2007) and at the University of Applied Sciences Northwestern Switzerland (until 2010). His
current research interests include diversity in teams, perceived diversity and cognitions, team
mental models and knowledge exchange with a close link between experimental studies, ﬁeld
research and interventions. His work has been published in international academic and
practitioner oriented journals. Thomas Ellwart is the corresponding author and can be contacted
¨ndgens is a PhD student at the University of Trier, Germany. Her ﬁelds of interest are
the antecedents and consequences of diversity perceptions in teams, team mental models and
team negotiations. She graduated in 2010 on “Approach and avoidance orientations in
Oliver Rack is a Lecturer and Researcher at the University of Applied Sciences and Arts
Northwestern Switzerland, School of Applied Psychology. He ﬁnished his PhD 2007 at the
University of Kiel, Germany, and was a Visiting Scholar at the University of Southern California,
Los Angeles, USA. His current research interests include interaction processes in
computer-mediated groups (information and knowledge transfer), team mental models and the
management of distributed collaboration.
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