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Age diversity, age discrimination climate and performance consequences—a cross organizational study

FLORIAN KUNZE*, STEPHAN A. BOEHM AND HEIKE BRUCH

Institute for Leadership and Human Resource Management, University of St. Gallen, St. Gallen, Switzerland

Summary This paper deals with the emergence of perceived age discrimination climate on the company level and its performance consequences. In this new approach to the field of diversity research, we investigated (a) the effect of organizational-level age diversity on collective perceptions of age discrimination climate that (b) in turn should influence the collective affective commit- ment of employees, which is (c) an important trigger for overall company performance. In a large-scale study that included 128 companies, a total of 8,651 employees provided data on their perceptions of age discrimination and affective commitment on the company level. Information on firm-level performance was collected from key informants. We tested the proposed model using structural equation modeling (SEM) procedures and, overall, found support for all hypothesized relationships. The findings demonstrated that age diversity seems to be related to the emergence of an age discrimination climate in companies, which negatively impacts overall firm performance through the mediation of affective commitment. These results make valuable contributions to the diversity and discrimination literature by establish- ing perceived age discrimination on the company level as a decisive mediator in the age diversity/performance link. The results also suggest important practical implications for the effective management of an increasingly age diverse workforce. Copyright # 2010 John Wiley & Sons, Ltd.

Introduction

Vivid terms like the ‘‘demographic time bomb’’ (Tempest, Barnatt, & Coupland, 2002, p. 487) or the

impending ‘‘age quake’’ (Tempest et al., p. 489) describe one of the key challenges for most developed

countries today: Simultaneously shrinking and aging populations resulting from low birth rates and

increased longevity. These factors also impact a country’s workforce as a lack of skilled junior

employees, combined with the potential rise of the legal retirement age, forces companies to retain

older, more experienced personnel, (e.g., Dychtwald, Erickson, & Morison, 2004; Tempest et al.).

Already today, just over half of the United States’ 147 million-member workforce is 40 years old or

older and, until 2016, the number of workers age 25–54 will rise only slightly (2.4 per cent), while the

Journal of Organizational Behavior

J. Organiz. Behav. 32, 264–290 (2011)

Published online 14 December 2010 in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/job.698

* Correspondence to: Florian Kunze, Institute for Leadership and Human Resource Management, University of St. Gallen, Dufourstrasse 40a, CH-9000 St. Gallen, Switzerland. E-mail: [email protected]

Received 30 November 2008 Revised 20 January 2010

Accepted 26 February 2010

workers age 55–64 are expected to climb by 36.5 per cent (U.S. Bureau of Labor Statistics, 2008). The

same is true for Germany where, from 2020 on, the employees aged 55–64 will become the largest age

demographic in the workforce (Destatis, 2006).

As a consequence of these demographics, a growing age diversity has become part of many

organizations. As we know from research on other demographic diversity categories, such as gender or

ethnicity, diversity rarely has an unambiguous effect but is a ‘‘double-edged sword’’ (Horwitz &

Horwitz, 2007, p. 988; for an overview, see Jackson, Joshi, Erhardt, 2003; Van Knippenberg &

Schippers, 2007). In this regard, age diversity is not different: Some studies have reported positive

effects on performance (e.g., Kilduff, Angelmar, & Mehra, 2000), while others have found either no

significant effects (e.g., Bunderson & Sutcliffe, 2002; Simons, Pelled, & Smith, 1999) or negative

effects (e.g., Ely, 2004; Leonard, Levine, & Joshi, 2004; Timmerman, 2000; West, Patterson, Dawson,

& Nickel, 1999; Zenger & Lawrence, 1989).

The reason for these mixed results may be traced back to researchers’ neglect of possible mediators

and moderators in the relationship between age diversity and outcomes in the studies on organizational

demography (e.g., Lawrence, 1997).

The construct of perceived age discrimination climate

The construct of perceived age discrimination climate may play a decisive role in this regard. Butler

(1969) was among the first to define ageism as ‘‘a process of systematic stereotyping and discrimination

against people because they are old’’ (p. 22). Today, the concept of ageism (or age bias) tends to be

conceptualized more broadly, referring to potential prejudices and subsequent discrimination against

any age group, including bias and unfairness toward employees on the grounds of being too young, as

well as too old (Palmore, 1999; Duncan, Loretto, & White, 2000; Snape & Redman, 2003).

Perceived and actual age discrimination are major issues in the corporate world: In fiscal year 2009,

the U.S. Equal Employment Opportunity Commission (2010) received over 22,700 charges of age

discrimination, and many major lawsuits related to age discrimination have been filed and won with

verdicts up to US$11 million (James & Wooten, 2006). Germany, where our sample originates, has had

legislation prohibiting discrimination in the workplace since 2006. This new law costs companies in

Germany Euro 1.75 billion through more costly recruiting procedures, changed structures and

processes, and lawsuits in the first year after it came into effect (Hoffjan & Bramann, 2007).

In order to analyze and structure the different forms of age bias, we employed the meta-framework of

Fiske (2004). Finkelstein and Farrell (2007) built upon Fiske’s ‘‘tripartite view of bias,’’ adapting it for

the special case of age bias. They differentiated among three dimensions of age bias by classifying

‘‘stereotyping’’ as the cognitive component, ‘‘prejudice’’ as the affective component, and

‘‘discrimination’’ as the behavioral component. With our focus on discrimination, we target the

behavioral component of age bias. However, our conceptualization of age discrimination differs from

that in prior work (e.g., Finkelstein and Farrell; Finkelstein, Burke, & Raju, 1995) with regard to two

aspects of the issue: First, we understand age discrimination as unfair, age-related treatment against any

age group, not only against older members of the group. Second, we conceptualize age discrimination

as an organizational-level variable that refers to the aggregate member perceptions about the

organization’ age related treatment of different age groups.

In a first step, such employee perceptions may develop on basis of certain interpersonal processes

and events between employees and their colleagues or supervisors. For example, an employee might

feel treated unfairly by his or her supervisor on the basis of his or her age. Likewise it seems possible

that employees feel certain forms of discrimination with regard to organization wide systems or

processes, e.g., with regard to the firm’s human resource (HR) system. Employees who experience such

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 265

age-related forms of discrimination by their colleagues, their supervisors, or by organizational systems

might form perceptions that age discriminatory behavior is present in their firm. In a second step, such

individual perceptions of age-discriminatory behavior may be amplified through interaction and

exchange with others to form an organizational level phenomenon. As such, age discrimination climate

is an emergent construct that reflects group members’shared perceptions (Kozlowski & Klein, 2000) of

the fairness or unfairness of organizational actions, procedures, and behavior towards different age

groups (e.g., regarding job assignments, promotions, performance evaluations, or leadership behavior).

We are particularly interested in members’ perceptions of the age discrimination climate because

perceived discriminatory practices are as much a problem for organizations as actual discrimination

since ‘‘employees’ beliefs, whether or not they are consistent with reality, affect their behaviors’’

(Ensher, Grant-Vallone, & Donaldson, 2001, p. 53; see also Mor Barak, Cherin, & Berkman, 1998), as

well as their decisions to file equal-opportunity lawsuits and related litigation (Sanchez & Brock,

1996).

The role of perceived age discrimination climate in the diversity/performance link

Antecedents and outcomes of perceived discrimination climate in companies have received only little

attention in the extant literature so far. One exception is the discussion of the relational and

organizational demography framework (e.g., Riordan, Schaffer, & Stewart, 2005; Tsui & Gutek, 1999),

which predicts that higher demographic similarity in the workplace leads to greater perceptions of

support and fairness, while heightened levels of dissimilarity or diversity may lead to perceptions of

discriminatory treatment (e.g., Avery, McKay, & Wilson, 2008), building on theoretical reasoning from

social identity theory (Tajfel & Turner, 1986), self-categorization theory (Turner, 1987), and the

similarity-attraction-paradigm (Byrne, 1971). Going beyond those theories, we integrate other

literature streams from the diversity and ageism literature, such as career timetables violations

(Lawrence, 1984) and prototype matching (Perry, 1994), to explain how organizational-level age-group

composition might impact perceived age discrimination climate. To our knowledge, our study is among

the first to explore such a compositional age-diversity model at the organizational level of analysis.

We also focus on potential outcomes of a climate of perceived age discrimination. While there is

strong evidence suggesting that gender- and sexuality-based discrimination negatively affects

individuals, groups, and whole organizations (Corning & Krengal, 2002; Gutek, Cohen, & Tsui, 1996;

Mays & Cochran, 2001), the research on the effects of age discrimination is less well developed

(Redman & Snape, 2006). We strive to contribute to this direction of research by building upon work

from scholars (e.g., Hassell & Perrewe, 1993; Redman & Snape, 2006; Snape & Redman, 2003) who

have described the impact of age discrimination on employees’ affective states, including self-esteem,

job satisfaction, job involvement, and organizational citizenship behavior. We analyze how an

increasing age discrimination climate leads to decreased levels of collective affective commitment

which, in turn, negatively affects company performance.

In sum, the present study is an attempt to test an integrative model of the age diversity/performance

link, with a focus on the mediating role of age discrimination climate. In doing so, we make valuable

contributions to two streams of literature. First, our research brings diversity research to a new level by

investigating age diversity as an organizational-level antecedent. In addition, we also introduce

perceived age discrimination climate as a new mediator in the age diversity/performance relationship to

shed more light on the ‘‘black box of organizational demography’’ (Lawrence, 1997). Second, with

regard to the field of ageism, we want to establish perceived age discrimination as an organizational-

level construct and demonstrate the counterintuitive positive link between age diversity and perceived

age discrimination climate.

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266 F. KUNZE ET AL.

Theory and Hypotheses Development

Age diversity and perceived age discrimination climate

While an increase in age diversity has become an organizational reality in most corporations, its

potential effects on age discrimination, commitment, and performance are not yet fully understood.

Several scholars have proposed that an increase in age diversity at the workplace may lead to lower

levels of discrimination, arguing with a familiarization to older workers in an increasingly aging

workforce (Chiu, Chan, Snape, & Redman, 2001; Finkelstein et al., 1995; Hassell & Perrewe, 1995).

Furthermore, for other diversity categories (e.g., gender, ethnicity) some scholars have tended to reason

that increasing diversity should lead to a more positive diversity climate as employees notice a growing

workplace heterogeneity and infer that the organization values diversity (e.g., Kossek & Zonia, 1993;

Kossek, Markel, & McHugh, 2003).

While these arguments may be accurate, we propose a different (i.e., positive) relationship between

increasing levels of age diversity and increasing levels of perceived age discrimination climate. First,

increasing age diversity in companies tends to differ from increasing gender diversity because, in most

cases, age diversity is not actively fostered or managed by the firms (e.g., through affirmative action

programs) but is a direct result of the demographic change in western economies (e.g., Dychtwald et al.,

2004; Tempest et al., 2002), and thus less accompanied by active diversity-management programs.

Second, different theoretical arguments imply a positive relationship between increasing age diversity

and increasing levels of perceived age discrimination climate in the workplace. All of these arguments

assume a negative effect of growing age diversity on members’ social integration, i.e., a weakened

psychological linkage toward striving for common goals (Harrison, Price, & Bell, 1998). In the

following section, we analyze those rationales in more detail, drawing on arguments from four

theoretical perspectives: The similarity-attraction paradigm, the social identity and self-categorization

theory, research on career timetables, and prototype matching.

As a first theoretical argument the similarity-attraction paradigm (Byrne, 1971; Riordan & Shore,

1997) proposes that individuals prefer to affiliate with persons whom they perceive to be similar to

themselves based on demographic characteristics, including age. (Avery et al., 2008; McPherson,

Smith-Lovin, & Cook, 2001; Tsui, Egan, & O’Reilly, 1992). Several authors have argued that such

personal ties and attraction foster cooperation in teams and workgroups (e.g., Chattopadhyay, 1999,

Hobman, Bordia, & Gallois, 2004, Pelled, Xin, & Weiss, 2001). Lawrence (1988, p. 313) referred to

such effects when she explained that employees of similar age ‘‘share comparable experiences and

therefore develop like attitudes and beliefs’’ that, in turn, foster communication and cooperation. These

comparable experiences stem from both historically generated similarities (e.g., graduating and

starting a job during the boom of the ‘‘New Economy’’) and from similar stages in private and

family lives that same-aged colleagues tend to reach simultaneously (e.g., being newly married,

having young children, being near to retirement, etc.) (Lawrence, 1980). We believe that such

processes of homophily are not limited to teams and workgroups but can also take place at the

organizational level. For example, similar aged peers in different teams or departments might prefer to

go to lunch together or pursue common social activities inside and outside the workplace, rather

than with younger or older colleagues from their own units. In sum, the dissociation between younger

and older employees might be stronger than that between employees of different units or departments.

Employees that are either younger or older than such a cohesive age group might infer that the

reason for such behavior (i.e., less intensive contact with colleagues of different ages, not being invited

to joint activities, etc.) is their age, and generate perceptions of age discriminatory behavior at the

workplace.

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 267

As a second theoretical argument, the idea of the development of age-based subgroups within an

organization is also supported by social identity (Tajfel & Turner, 1986) and self-categorization theory

(Turner, 1987) which suggest that individuals tend to classify themselves and others into certain groups

on the basis of dimensions that are personally relevant for them, such as the demographic categories of

gender, race, or age. As a consequence, individuals tend to favor members of their own group (in-

group) at the expense of other groups (out-groups) (Turner, 1987; Tajfel & Turner, 1986), against which

they tend to discriminate. In the organizational context, a large number of such group memberships

may exist simultaneously.

While age has the potential to become a relevant category for classification and formation of

subgroups (e.g., young employees, middle-aged employees, and older employees) (Avery et al., 2008;

Ensher et al., 2001; Finkelstein et al., 1995, Kearney & Gebert, 2009), whether the subgroups actually

develop seems to depend on the organizational context. Growing heterogeneity could play a key role in

this process since an increase in age diversity can heighten the salience (or importance) of age as a

category for classification and identification. As in a group of men, gender is unlikely to be a salient

category, age can only become a relevant criterion for distinction when there is some age diversity in

the organization. In other words, when an organization that had been largely homogenous in terms of

age distribution (e.g., consisting mainly mid-aged and older employees) gradually becomes more age-

heterogeneous (e.g., by hiring more younger graduates for management positions), the importance

attached to membership in one age group of employees or another should increase.

The third theoretical argument related to an increase in age discrimination climate in companies due

to raising levels of age diversity is derived from the concept of career timetables (Lawrence, 1984,

1988) which assumes that clear age norms develop within an organization concerning which

hierarchical level an employee should reach by a given age. While those employees who are ‘‘on

schedule’’ (who are promoted as quickly as their same-aged peers) and those ahead of schedule (who

are promoted more quickly than their peers) face few problems in the way of discrimination, employees

behind schedule often struggle with lower work satisfaction (Lawrence, 1984) and tend to receive lower

performance ratings and development opportunities (Cleveland & Shore, 1992; Lawrence, 1988; Tsui,

Porter, & Egan, 2002).

Demographic changes and growing age diversity within organizations is likely to produce situations

in which such age norms are violated more often and more employees fall behind schedule. For

example, a rising number of older employees who stay in the organization until the legal retirement age

will have to deal with significantly younger supervisors and might perceive that as a violation of the

classic career timetable as more experienced employees have to report to organizational newcomers

(Shore & Goldberg, 2005). Older employees might also feel behind schedule compared to their young

supervisors and perceive certain forms of age discrimination such as lower performance and

promotability ratings (Shore, Cleveland, & Goldberg, 2003; Tsui et al., 2002).

Also for younger employees, age norms might be violated more often, if for instance a lack of

middle-aged managers within an organization suddenly improves promotion expectations for young

managers (Lawrence, 1988). After the best of these young managers are promoted to fill the gaps,

promotion chances for younger managers drop again to normal levels. Until employee perceptions re-

adjust, younger workers who were not promoted might feel a certain disillusion and a violation of age

norms because they cannot develop their careers as quickly as their peers, and a negative attitude

toward middle-aged and older managers who are ‘‘in the way’’ might develop.

Finally, a similar, yet distinct, line of argumentation can be derived from the concept of prototype

matching (Perry, 1994; Perry & Finkelstein, 1999), which suggests that an employee’s age is often

compared to the age of a ‘‘prototypical’’ job holder, where certain kinds of jobs are considered jobs for

younger workers (with traits and skills like being energetic and being able to adapt to change quickly),

while other jobs are more suitable for older employees or are age-neutral because of their reliance on

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268 F. KUNZE ET AL.

steadiness and corporate knowledge (Cleveland & Landy, 1987; Perry, 1994; Perry & Bourhis, 1998). As in

the case of career timetables, we suggest that an organization-wide increase in age diversity will lead to an

increase in perceived misfits between job holders and job-age prototypes, where older employees work in

‘‘young-type jobs’’ with apparently high demands regarding pro-activity and stress-handling such as

distribution and customer service, where they might be exposed to different forms of perceived age

discrimination from both younger colleagues and supervisors (‘‘Is he/she really able to keep up with us in

this kind of job?’’). On the other hand, younger employees might have to work in more ‘‘old-type jobs’’ that

call for a lot of experience, such as higher management functions. Just like older employees in young-type

jobs, younger employees in old-type jobs might also face certain forms of perceived age discrimination

from peers, supervisors, and their employees (‘‘Isn’t he/she a bit young for this kind of job?’’).

In sum, we presented theoretical and empirical evidence stemming from different streams of

literature such as the similarity-attraction paradigm, social identity, and self-categorization theory, as

well as theories on career timetables and prototype matching. Taken together, we assert that it is

theoretically plausible to build a model in which higher levels of age diversity on an organizational

level trigger perceptions of organization-wide age discrimination climate.

Thus, we propose:

H1: Higher levels of age diversity will be positively related to respondents’ perceptions of age

discrimination climate within companies.

Perceived age discrimination climate and collective affective commitment

One key attitudinal state of employees is their affective commitment toward the organization, which

was defined by Meyer and Allen (1991) as the ‘‘the employee’s emotional attachment to, identification

with, and involvement in the organization’’ (p. 67). Affective commitment has been shown to be of high

importance for organizations because it increases employees’ acceptance of organizational goals, their

willingness to exert effort on behalf of the organization, and their desire to remain with the organization

(Meyer & Allen, 1997; Mowday, Porter, & Steers, 1982).

Several authors have shown that, in addition to individual commitment, collective forms of

commitment may evolve within an organization (Kirkman & Rosen, 1999; van der Vegt & Bunderson,

2005). Following prior research by González-Romá, Peiró, and Tordera (2002), we also argue with

collective perceptions of affective commitment and aggregate employees’ commitment scores at the

organizational level of analysis.

While a potentially negative impact of perceived age discrimination climate on members’ collective

commitment makes intuitive sense, various theoretical approaches also support such a relationship. First,

social exchange theory suggests that members’ perceptions of a supportive and fair exchange relationship

between the organization and themselves is a necessary precondition for the development and preservation

of high levels of affective commitment (Meyer & Allen, 1997; Shore & Wayne, 1993). Consequently,

‘‘commitment develops as the result of the experiences that satisfy employees’ needs’’ (Meyer & Allen,

1991, p. 70). A perception of age discrimination climate is a clear violation of such an equitable give-and-

take relationship, which is why it should negatively affect employees’ emotional attachment to the

organization, as well as their willingness to contribute. Hassell and Perrewe (1993) showed such a decline

in attachment to the organization for members who perceived age discrimination in the workplace.

Second, employees’ attitudes toward their employers are dependent on their perceptions of whether

their own opportunities and treatment by the organization are equal to those of other groups of

employees (Gutek et al., 1996). Snape and Redman (2003) argued in this regard that individuals who

feel that they have suffered from unfair, age-related treatment are likely to develop a ‘‘sense of being

under-valued by the organization and its members’’ (p. 80). In turn, such members can be expected to

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 269

show decreasing levels of motivation to act on behalf of the organization. Tougas and Veilleux (1989)

referred to such processes as feelings of ‘‘collective relative deprivation’’ where ‘‘individuals feel upset

about the position of their group’’ within the larger organization (p. 122). This may lead to an emotional

withdraw from an organization when employees feel that members of their own group are treated in an

unfair and discriminative manner, as shown for gender subgroups (Gutek et al.). It is likely that such

feelings of collective deprivation are also transferable to the context of perceived age discrimination,

where the group of younger or older employees perceive age discrimination against their own age

group and, consequently, exhibit a collective drop in their level of affective commitment. On the basis

of this evidence, we suggest:

H2a: Higher levels of perceived age discrimination climate will be negatively related to

respondents’ collective affective commitment towards companies.

H2b: The relationship between age diversity and respondents’ collective affective commitment is

mediated through perceived age discrimination climate.

Collective affective commitment and company performance

Decreasing levels of collective commitment might become a serious problem for companies since

research has proposed a direct link between organizational commitment and organizational

performance (Meyer, Paunonen, Gellatly, Goffin, & Jackson, 1989; Meyer, Becker, & Vandenberghe,

2004; Meyer, Becker, & Van Dick, 2006). The theoretical rationale behind this relationship is the

understanding of commitment as being ‘‘a force that binds an individual to a course of action that is of

relevance to a particular target‘‘ (Meyer & Herscovitch, 2001, p. 301). Especially for employees

showing high levels of affective commitment, a distinct willingness to contribute to organizational

goals and, hence, to organizational performance has been assumed (Meyer, Paunonen et al.; Meyer,

Becker, & Vandenberghe; Meyer, Becker, & Van Dick). Compared to individuals showing continuance

or normative commitment (Meyer & Allen, 1991), such employees stay within an organization because

they want to (Meyer, Becker & Van Dick). Giving their services wholeheartedly to the organization and

performing well above the minimum required for retention, employees with high levels of affective

commitment should become a driver of organizational productivity and performance (Ostroff, 1992).

The relationship between affective commitment and performance has also been investigated

empirically. However, most research has focused on the effect of affective commitment on job

performance, rather than on its effect on organizational performance (e.g., Meyer, Stanley,

Herscovitch, & Topolnytsky, 2002).

On the organizational level of analysis, research on the affective commitment/performance link is

scarce. An exception is the study of Ostroff (1992), which found a significant positive correlation

between collective attitudinal commitment and organizational performance in 298 schools in terms of

performance criteria such as academic achievement, student behavior, and administrative performance.

We believe that such an effect is transferable to our research question. Thus, we propose:

H3a: Higher levels of collective affective commitment will be positively related to organizational

performance.

H3b: The relationship between perceived age discrimination and organizational performance is

mediated through collective affective commitment.

Figure 1 gives an overview of all hypothesized relationships.

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270 F. KUNZE ET AL.

Method Section

Sample

Data for the present investigation were collected between March and June 2008 as part of a larger study

in cooperation with an agency in Germany that specializes in benchmarking small to medium-sized

enterprises. Initially, the agency solicited participation from 164 organizations based on the criteria that

the organizations (a) were located in Germany and (b) did not exceed 5000 employees. Each

organization was promised a detailed technical benchmarking report in return for their participation. Of

the 164 organizations initially contacted, 36 did not participate or failed to provide sufficient data,

resulting in an organizational level response rate of 81 per cent (n ¼ 128). Participating organizations represented companies from a variety of industries, including services (53 per cent), manufacturing

(28 per cent), trade (13 per cent), and finance and insurance (6 per cent), and ranged in size from 10 to

3333 employees (median ¼ 156). Eliminating organizations with 1000 or more employees (n ¼ 9) and those with 20 or fewer employees (n ¼ 2) did not change the pattern of results, so all sample organizations were used in hypotheses testing.

In order to improve equivalence of data collection, standardized procedures were employed across

all organizations. Data were collected in three steps. First, general information on the participating

organizations (organization size, industry affiliation, and so on) was gauged through a key informant

survey completed by the organizations’ HR executives or other members of their top management

teams. Answers to this key informant survey were required in order to confirm organizations’

participation in the study.

Second, employee survey data were collected to obtain information on the focal study variables.

Participating organizations sent a standardized email invitation to all employees through their HR

departments (if applicable) or through a top management team member’s email address. The email

described the study’s purpose and provided a link to a web-based survey hosted by an independent third

Figure 1. Conceptual model

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 271

party. A paper version of the questionnaire was provided to employees who had no web access. Based

on an algorithm programmed in the survey website, respondents were randomly directed to one of four

versions of the survey, thereby implementing a split-sample design (Rousseau, 1985; for similar

approaches, see Dickson, Resick, & Hanges, 2006; Erdogan, Liden, & Kraimer, 2006). To alleviate

concerns about common-source bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), age

discrimination and affective commitment were measured in two different versions of the employee

surveys. All survey versions were translated to German by professional translators following a double-

blind back-translation procedure to ensure semantic equivalence with the original English items

(Schaffer & Riordan, 2003). Respondents were assured full anonymity.

In total, 18 269 employees chose to participate in the survey. The average within-organization

response rate was 65 per cent (standard deviation ¼ 23 per cent). Our study used only the responses from those employees who completed the first and second version of the survey (n ¼ 8651), because these parts included our study variables. The other two versions included items that were used for

other research projects. The algorithm effectively distributed participating employees among the

four versions of the survey, yielding 4,574 respondents for the survey including affective commitment

and 4,077 responses for the survey including age discrimination. Individual respondents were

more heavily represented by males (54 per cent) than females (39 per cent), although 8 per cent

chose not to indicate their gender. Respondents belonged to different age groups with a majority

in the middle age group of 31–51 (60 per cent), followed by 27 per cent in the 16–30 age group and

13 per cent in the over-50 group. Average tenure rate at the companies was eight years. Participants

came from all major divisions and hierarchical levels of their organizations, with 2 per cent in top

management, 11 per cent in middle management, 10 per cent first-line supervisors, and 70 per cent

employees without leadership responsibility. Seven per cent provided no answer about their

employment level.

To account for a potential non-response bias we compared the age and gender demographics of

our sample with data from the working population we sampled, as provided by the HR representatives.

We discovered no substantial difference in the composition. Nevertheless, we additionally reran our

analysis excluding all companies with response rates <30 per cent (n ¼ 9). This did not change our pattern of results, which indicates a very low probability of a nonresponse bias in our study.

In a third step, members of the companies’ executive boards were provided with a separate

questionnaire that mainly targeted company performance information. This procedure was applied

following the idea that executive board members should be the best information source regarding

company performance issues. Within the participating companies, 1–11 members of the companies’

executive boards participated, with an average response rate of 69 per cent from board members.

Box 1 Research context

The companies

Most of the participating companies belonged to the category of small and medium-sized

companies in Germany. In Germany, 99.7 per cent of all companies belong to this class, and they

employ 70.6 per cent of all employees; therefore, they are often described as the ‘‘backbone’’ of the

German economy. Many of these companies, despite being comparatively small, are among the

world market leaders in their fields because of their tight specializations, especially in the field of

engineering. Participating companies came from all parts of Germany, although many were from the

southern regions, where many of the most successful small and medium-sized businesses are

located. All companies had participated in a contest designed to find Germany’s best employers

within the sector of small and medium-sized businesses.

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272 F. KUNZE ET AL.

The environment

During the time of the survey (March through June 2008) and in the preceding five years, the

economy in Germany experienced constant growth rates of around 3 per cent. The implications of the

current international financial and economic crises were not relevant to most of the companies at

that time. Along with the high growth rates, the employment rate in Germany was showing strong

growth, resulting in some shortage of labor for the small and medium-sized companies and a

good deal of competition for the most talented people. The age structure was supposed to change

drastically due to the demographic change. Current projections expected the average age to rise by two

years until 2020 and the age group 50–65 to become the largest subpopulation till that date (Destatis,

2006). Age discrimination was also an increasing issue for the German and European companies. For

example, a recent representative survey by the European Commission (2009) found 58 per cent of the

respondents indicating that age discrimination be widespread in the working world.

Measures

Unless otherwise noted, a seven-point response format (1 ¼ strongly disagree; 7 ¼ strongly agree) was used for all measures.

Age diversity

Our age diversity measure was calculated out of the individual employee responses. In our

operationalization of age diversity, we followed Harrison and Klein (2007) in that the conceptualization

of a specific diversity dimension should determine its operationalization. Following our prior

theoretical argumentation, diversity in our study constitutes separation rather than disparity or variety

(Harrison & Klein, 2007). Thus, we applied the standard deviation to gauge age diversity in companies.

This measure is most often applied if theoretical arguments are proposed concerning social identity,

similarity-attraction, or attraction-selection-attrition theory, as it is the case in our study (Harrison &

Klein).

Perceived age discrimination climate

Perceived age discrimination climate was measured using four items from a scale developed by

Abraham (1993) and recently applied by Robson and Hanson (2007). These items delineated several

occasions that could be source of potential age discrimination in the workplace (e.g., performance

assessment, career opportunities, allocations of tasks, professional and personal development). As a

fifth item, we added perceived leadership behavior as a source of possible age discrimination, since we

have suggested that leadership behavior is a frequent source of discrimination. A referent-shift

composition approach (Chan, 1998) was used to assess the age discrimination climate on the company

level. The exact wording of the questions, together with their descriptive statistics, is provided in

Table 1.

A confirmatory factor analysis (CFA) showed that all items loaded on a single factor and exhibited

sufficient model fit properties (x 2 ¼ 11.638, df ¼ 5; CFI ¼ 0.99, TLI ¼ 0.99, RMSEA ¼ 0.10). We

focused on the Comparative Fit Index (CFI) and the Tucker Lewis Index (TLI) to assess the overall

model fit because of simulation studies that found these indices to perform best, especially in cases of

sample sizes smaller than 200 (e.g., Sharma, Mukherjee, Kumar, & Dillon, 2005). Commonly used

cutoff values for a reasonable fit are >0.95 (e.g., Hu & Bentler, 1998). Additionally we also report the Root Mean Square Error of Approximation (RMSEA) as common practice in many SEM papers.

However, this index should be interpreted with caution, since it tends to over reject models with sample

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 273

size below 200 (Chen, Curra, Bollen, Kirby, & Paxton, 2008; Sharma et al., 2005; Hu & Bentler, 1999)

and also depends on the number of variables in the model (Kenny & McCoach, 2003). Thus, we

decided to set the cutoff value for a still acceptable model at <0.10 (e.g., Browne & Cudeck, 1993). To empirically justify the aggregation and to support the assumptions of the referent shift composition

model (Chan, 1998), we calculated intra-class correlation coefficients (ICC1 and ICC2; Bliese, 2000) and

the average deviation index as an inter-rater agreement ratio (ADM(J); Burke, Finkelstein, & Dusig, 1999).

For the ICC1, values that are based on a significant one-way analysis of variance are generally acceptable.

For the ICC2, values of more than 0.60, are usually considered sufficient (Bliese, 2000; Chen, Mathieu, &

Bliese, 2004; Kenny & la Voie, 1985). The ADM(J) has several advantages over the rwg (James, Demaree, &

Wolf, 1984) inter-rater agreement index. First, no modeling of a random null response distribution is

required; only an a priori specification of a null response range of inter-rater agreement is preconditioned.

Second, estimates in the metric of the original scale are provided, which allows for a more direct

conceptualization and assessment of inter-rater agreement (Burke et al., 1999). As cutoff criteria for the

AD, we followed the c/6 rule (the number of response options for an item divided by 6; in our case, 1.17)

proposed by Burke and Dunlap (2002). The results indicate support for the aggregation of the age

discrimination scale on the company level (ICC2 ¼ 0.09, ICC2 ¼ 0.76 p < 0.001, ADM(J) ¼ 1.05). Internal consistency estimates at the organizational level were a ¼ 0.98.

Affective commitment

We used four items adapted from Allen and Meyer’s affective commitment scale (Allen & Meyer,

1990) following the proceeding by Eisenberger, Armeli, Rexwinkel, Lynch, and Rhoades (2001) and

Table 1. Means and standard deviation for the perceived age discrimination climate and affective commitment measures

Item Item M Item SD

Perceived Age Discrimination Climate 1 Age-discriminatory behavior regarding job

assignments exists in our company 2.09 0.62

2 Age-discriminatory behavior regarding opportunities for individual promotion exists in our company

2.24 0.76

3 Age-discriminatory behavior regarding performance evaluation exists in our company

2.17 0.72

4 Age-discriminatory behavior regarding opportunities for personal and professional development of employees exists in our company

2.24 0.72

5 Age-discriminatory behavior in the daily leadership of the seniors exists in our company

2.02 0.63

Cronbachs a 0.98

Affective Commitment 1 Working at this company has a great deal of

personal meaning to me 5.66 0.45

2 I feel a strong sense of belonging to this company 5.41 0.57 3 I would be happy to work at this company until I retire 4.88 0.59 4 I think that I could easily become as attached to

another organization as I am to this one (R) 4.01 0.59

Cronbachs a 0.94

Note: N ¼ 128.

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274 F. KUNZE ET AL.

added a very similar fifth item from the original scale (Allen & Meyer). A direct-consensus

composition model was applied (Chan, 1998), in which the individual data was aggregated on the

company level based on acceptable inter-rater agreement scores and intra-class coefficients. This

procedure is in line with prior research that has operationalized commitment on a collective level (e.g.,

González-Romá et al. 2002). The wording of the items and their descriptives are depicted in Table 1. A

CFA indicated a non-sufficient overall model fit (x 2¼ 24.9, df ¼ 5; CFI ¼ 0.95, TLI ¼ 0.98,

RMSEA ¼ 0.17), indicated by the high df/x2 ratio and RMSEA value. Therefore, following the procedure by Cheng (2001) we excluded one item (‘‘I am proud to tell others workers that I work at this

company’’), which showed high error correlations with other items and high modification indices from

the model. The following second CFA indicated that all four commitment items loaded on a single

construct and exhibited now very good model fit properties (x 2¼ 1.7, df ¼ 2; CFI ¼ 1.00, TLI ¼ 1.00,

RMSEA ¼ 0.00). Aggregation statistics showed sufficient results (ICC1 ¼ 0.12, ICC2 ¼ 0.83 p < 0.001, ADM(J)

¼ 1.05) and justified aggregation of the commitment scale to the organizational level. Internal consistency estimates at the organizational level were a ¼ 0.92.

Performance

We measured performance following Comb’s and colleagues (2005) recommendation to differentiate

between operational and organizational performance. Thus, we applied three items for each

dimension. For organizational performance we used company growth, financial performance, and

return on assets as indicators. Operational performance was measured through the items employee

retention and fluctuation, employee productivity as well as efficiency of business procedures. In

keeping with prior research (Delaney & Huselid, 1996; Wall et al., 2004), the perceptual measures

were benchmarked, in the sense that we asked key informants to assess firm performance relative

to the performance of their industry rivals. The members of the companies’ boards had to judge

their own firms’ current performances relative to those of their main competitors located within the

same industry and in the same region (1 ¼ far below average; 7 ¼ far above average). Obviously, forward-looking stock market measures would be the best source for the organizational performance

scales. However, given the fact that most of the companies in our sample are privately owned and public

data is the most common source for market measures (Rogers & Wright, 1998), we were not able to

collect such data. Aware of the potential problems regarding the use of subjective performance

measures (Starbuck, 2004), empirical evidence has shown that subjective measures are valid and

may be applied to gain insight into operational and organizational performance (Rowe & Morrow,

1999; Wall et al.).

We assumed that the overall company performance should consist of the two sub-dimensions,

organizational and operational performance. Following this theoretical reasoning we constructed a

second order performance measure that included both dimensions. To justify the structure of this

construct we performed a CFA that showed a sufficient model fit (x 2¼ 9.4, df ¼ 8; CFI ¼ 0.99,

TLI ¼ 0.99, RMSEA ¼ 0.04). Internal consistency estimates were a ¼ 0.87.

Control variables

To account for a potential influence of organization size on our endogenous variables, and because

organization size has shown to be related to various employee attitudes and behaviors (Pierce &

Gardner, 2004; Ragins, Cotton, & Miller, 2000), we included organization size as a control variable in

our analysis to prevent it from biasing our findings. We also controlled for the potential alternative

hypothesis that higher levels of perceived age discrimination climate resulting from increased age

diversity may occur only in smaller companies that feature more direct contact and interaction between

several age groups. As is common practice (e.g., Schminke, Cropanzano, & Rupp, 2002), we measured

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 275

organization size by asking for the number of employees in the organizations (converted to full-time

equivalents).

To account for Finkelstein et al.’s (1995) argument that perceived age discrimination arising from

age diversity may actually decrease if aging workers become a more frequent and integral part of

companies, we also entered the median age of the companies’ employees as a control variable in our

analyses.

We also controlled for organizations’ affiliation with one of the four broad classes of industries (i.e.,

services, manufacturing, trade, and finance and insurance; as reported in the key informant survey), as

has been done in prior research (e.g., Dickson et al., 2006; Sine, Mitsuhashi, & Kirsch, 2006).

Participant organizations were assigned four dummy-coded variables indicating their affiliation with

each of the industry categories.

Analytical procedures

As has been proposed by Anderson and Gerbing (1988) and applied in several publications (e.g.,

Bedeian, 2007; Richardson and Vandenberg, 2005), we conducted our data analysis in two steps. This

was done so as not to confound the meaning of the study variables by the simultaneous estimation of

measurement and structural model (Burt, 1976). In the first step, we executed a simultaneous CFA of all

variables to establish a measurement model. In the second step, we applied structural modeling to

evaluate the relationships among the constructs, as proposed in the conceptual scheme in Figure 1. To

account for the mediation effects, we compared different mediation models to the baseline model

(Judge & Colquitt, 2004) and, following the recommendation by Cheung and Lau (2008) and James,

Mulaik, and Brett (2006), we also carried out bootstrapping procedures to test for the significance of the

indirect effects. The observed data was evaluated using the AMOS 17.0 SEM program.

Results

Descriptive statistics

Means, standard deviations, and bivariate correlations for all study variables are presented in Table 2.

The results indicate that, as hypothesized, (a) age diversity in companies is positively related to

perceived age discrimination climate, (b) perceived age discrimination climate is negatively related to

affective commitment, and (c) affective commitment is positively correlated with performance.

We also included the median age of the employees, company size and dummy variables for the four

different industries in this analysis to check for an intercorrelation of these potential controls with our

study variables. Results show that company size, median age, and industry dummies of service and

production are significantly related to our focal study variables. The other types of industries are not

found to have any influence on the endogenous variables under observation. Consequently, we decided

to retain only company size, median age, and the two industry variables in further analyses in order to

reduce the number of parameters to be estimated and, thus, to achieve the maximum power for the

following tests (Bedeian, 2007). Additionally, unnecessary control variables may cause biased

parameters estimates (Becker, 2005). Since we observed no remarkable correlation between the study’s

constructs, we assumed sufficient distinctiveness between the different measures.

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276 F. KUNZE ET AL.

T a b le

2 . A g g re g a ti o n st a ti st ic s, m e a n s, st a n d a rd

d e v ia ti o n s, a n d in te rc o rr e la ti o n s o f st u d y v a ri a b le s

V a ri a b le

A g g re g a ti o n st a ti st ic s

M S

A D

M (J

) IC C 1

IC C 2

1 2

3 4

5 6

7 8

9 1 0

1 . A g e d iv e rs it y

0 .2 6

0 .0 5

— 2 . M e d ia n a g e

3 7 .6 3

4 .6

0 .3 2

— 3 . A g e d is c ri m in a ti o n c li m a te

1 .0 5

0 .0 9

0 .7 6

2 .1 3

0 .6 6

0 .4 5

0 .3 9

— 4 . A ff fe c ti v e c o m m it m e n t

1 .0 5

0 .1 2

0 .8 3

5 .1 0

0 .5 1

� 0 .0 9

� 0 .0 8

� 0 .5 7

— 5 . C o m p a n y p e rf o rm

a n c e

5 .7

1 .0 6

� 0 .3 3

� 0 .1 2

� 0 .1 7

0 .2 7

— 6 . C o m p a n y si z e

3 2 0 .4 6

4 9 5 .3 4

0 .0 8

0 .1 6

0 .1 4

� 0 .1 8

� 0 .2 2

— 7 . In d u st ry : se rv ic e

0 .5 0

� 0 .2 6

� 0 .1 7

0 .1 8

� 0 .0 2

� 0 .0 1

� 0 .0 5

— 8 . In d u st ry : tr a d e

0 .0 7

0 .2 6

� 0 .1 9

� 0 .0 2

� 0 .1 1

0 .0 3

0 .1 2

0 .0 1

0 .2 8

— 9 . In d u st ry : m a n u fa c tu ri n g

0 .3 0

0 .4 6

0 .2 7

0 .1 4

0 .2 1

� 0 .0 3

� 0 .0 6

� 0 .0 2

� 0 .6 6

� 0 .1 8

— 1 0 . In d u st ry : fi n a n c e

0 .0 5

0 .2 3

0 .1 4

0 .1 0

0 .0 1

0 .0 2

� 0 .0 2

� 0 .0 0

� 0 .2 4

� 0 .0 6

� 0 .0 8

—

N o te

: C o rr e la ti o n s g re a te r th a n 0 .1 7 a re

si g n ifi c a n t a t th e 0 .0 5 le v e l (t

w o

-t a

il e d ).

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 277

Measurement model

Our measurement model consisted of three latent constructs—age discrimination climate, affective

commitment, and performance—with fifteen indicators and age diversity as a single indicator measure.

In the absence of an independent estimate for the single-item indicator in our model, we had to make

an arbitrary choice for the factor’s reliability (Anderson & Gerbing, 1988). Following the procedure

used by Richardson and Vandenberg (2005) we assumed a conservative reliability of 0.7. The error

terms of the indicator were set to a value of one, minus the reliability of the indicator, multiplied by its

variance. In terms of the overall model fit for the measurement model, we received sufficient results for

all central measures (x 2¼ 124.652, df ¼ 97; CFI ¼ 0.99, TLI ¼ 0.99, RMSEA ¼ 0.05).

In addition to the overall assessment of the model fit, we conducted an item reliability and

convergent validity analysis for each construct, the results of which are shown in Table 3.

Organizational and operational performance are treated here as two separate constructs that together

form the overall performance measure. The first column shows the standardized item loadings for each

construct, all of which were statistically significant ( p < 0.001). None of the items have loadings less than 0.50, a threshold often used in factor analysis (Hulland,

1999). We also inspected the composite reliability (Raykov, 1997) and average variance extracted

(Fornell & Larcker, 1981) for each construct. The composite reliability assesses the unidimensionality

of a construct and should, at best, be above the 0.70 cut-off criteria (Raykov, 2002). All of our

constructs fulfill this requirement. The average variance extracted estimates the proportion of variance

explained in relation to the variance that is due to random error (Bedeian, 2007; Bagozzi & Yi, 1988).

All our measures are above 0.50, indicating good internal consistency and that the amount of variance

captured by each construct is larger than the variances caused by measurement error (Fornell &

Larcker, 1981). All these results point to the sufficient convergent validity and item reliability of our

three latent constructs.

Table 3. Measurement properties for study constructs.

Constructs and indicators Standardized loadings

Composite reliability (CI)

Variance extracted estimate (EVA)

Age Discrimination Climate 0.99 0.95 Item 1 0.954 Item 2 0.975 Item 3 0.979 Item 4 0.941 Item 5 0.943 Affective Commitment 0.96 0.87 Item 1 0.939 Item 2 0.980 Item 3 0.938 Item 4 0.656 Organizational Performance 0.89 0.55 Item 1 0.806 Item 2 0.829 Item 3 0.695 Operational Performance 0.87 0.68 Item 1 0.768 Item 2 0.828 Item 3 0.631

Note: N ¼ 128.

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278 F. KUNZE ET AL.

Structural model

In our second step of the analysis, we examined the structural portion of our specified model, the main

results of which are illustrated in Figure 2. For simplicity, the control variables are not mentioned in this

picture. However, paths from the control variables to each dependent construct were specified in all

structural models, following the proceeding by Richardson and Vandenberg (2005). The overall results

of the main model, as summarized in Table 4, indicate a generally good fit of the model to the data

(x 2¼ 212.347, df ¼ 148; CFI ¼ 0.97, TLI ¼ 0.97, RMSEA ¼ 0.06). Hypothesis 1 predicted that age diversity should be positively related to perceived age discrimination

climate within companies. This hypothesis was supported, since the path between age diversity and age

discrimination climate was significant (b ¼ 0.35, t ¼ 4.15, p < 0.001). Following the advice from Lubinski and Humphreys (1990), we tested for a possible curvilinear relationship by entering the

quadratic predictor variable in our model. Since this term turned out not to be significant, we found no

suspicion of a nonlinear relationship.

Hypothesis 2, which predicted a negative relationship between perceived age discrimination climate and

collective commitment, received support as well (b ¼ �0.68, t ¼ �8.14. p < 0.001). Because both hypotheses 1 and 2 were supported, we further examined the extent to which perceived age discrimination

climate mediated the relationship between age diversity and collective commitment (hypothesis 2b). Our

mediation prediction would be confirmed if the overall model fit would not be improved by the addition of

the direct path from age diversity to collective commitment and the indirect paths would remain significant

in the new model (For application of this method, see Judge & Colquitt, 2004.) As such, we added this

direct path to our hypothesized model, referred to as ‘‘Mediation model 2’’ in Table 4, and re-estimated the

model. Results indicated that the direct path was not significant on a 5 per cent level (b¼ �0.14,

Figure 2. Structural model results

Table 4. Model comparison

Structural model x 2 (N ¼ 128) df x2/df Dx2 Ddf CFI TLI RMSEA

Hypothesized model 1 212.347 �

148 1.44 — — 0.97 0.97 0.06 Mediation model 2 210.658

� 147 1.43 1.69 �1 0.97 0.97 0.06

Mediation model 3 210.567 �

147 1.43 1.78 �1 0.97 0.97 0.06 Model 4 280.933

� 148 1.90 68.59 0 0.94 0.94 0.08

Model 5 272.941 �

148 1.84 60.59 0 0.95 0.95 0.08

Note: CFI ¼ Comparative fit index; TLI ¼ Tucker-lewis index; RMSEA ¼ Root mean square error of approximation. All models are compared to the baseline model 1.

� p > 0.5.

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 279

t ¼ �1.65, p < 0.08). The overall model fit remained virtually unchanged (see Table 4). The two paths of hypothesis 1 between age diversity and age discrimination climate (b ¼ 0.35, t ¼ 4.18, p < 0.001), as well as the path of hypothesis 2 between age discrimination climate and affective commitment (b ¼ �0.73, t ¼ �8.27, p < 0.001), remained significant. These results indicate a mediation of perceived age discrimination climate in the relationship between age diversity and collective affective commitment.

In a further step, we applied bootstrapping procedures, as proposed by Cheung and Lau (2008). In

this analysis, the product terms of the direct path from the independent variable to the mediator and the

direct path from the mediator to the dependent variable are examined with bootstrapping methods to

achieve intervals for the mediation effects. As Cheung and Lau recommended, we used 1000 bootstrap

samples to generate the results. Table 5 illustrates the results that further confirmed the proposed

mediation. The indirect effect has the expected negative sign and is significant on a 1 per cent level.

Thus, the effect of age diversity on collective commitment was mediated by perceived age

discrimination climate in the companies.

Hypothesis 3, which stated that collective commitment would be positively related to overall

company performance, was supported (b ¼ 0.21, t ¼ 2.35, p < 0.02). Applying the same proceeding as described above, we also tested for a mediating effect on overall performance of age discrimination

climate through collective commitment. Adding to the model a direct path between age discrimination

climate and performance did not improve the overall model fit, as illustrated by the ‘‘Mediation model

3’’ in Table 4. The x 2 dropped only slightly and all the fit measures remained virtually unchanged (see

Table 4). The direct path between perceived age discrimination climate and performance suggests no 5

per cent significant effect between the two variables (b ¼ 0.13, t ¼ 1.49, p < 0.14), while the two main effects remained significant. A bootstrapping analysis strengthened these outcomes. As Table 5 shows,

the indirect effect was negative and significant (ß ¼ �0.14, p < 0.05). These results predict that collective commitment mediates the relationship between perceived age discrimination climate and

company performance.

We further investigated two alternatives models. The first (model 4) assumed a direct linkage

between age diversity and company performance with all mediation paths set to zero. The second

(model 5) assumes a direct relation between perceived age discrimination climate and performance

excluding the mediation by affective commitment. As the results in Table 4 show both models had a

significantly worse fit to our data compared to the baseline model 1. That further strengthened

indication that we may have discovered a good fitting model to the data.

Discussion

The purpose of this study was to explore the link between age diversity and company performance by

shedding light on several potential mediators of this relationship. In a first step, age diversity on the

Table 5. Mediation analysis via bootstrapping

Indirect effect

Standard error

95% Confidence intervals Significance

Age Diversity ! Age Discrimination Climate ! Affective Commitment

�0.25 0.07 �0.36 – �0.15 0.002

Age Discrimination Climate ! Affective Commitment ! Performance

�0.14 0.09 �0.32 – �0.03 0.048

Note: Standardized estimates are shown. 1000 bootstraps samples were used. Two-tailed significance.

DOI: 10.1002/job

280 F. KUNZE ET AL.

company level was examined for its influence on perceived age discrimination climate and for whether

it indirectly relates to collective affective commitment via this mediator. Second, perceived age

discrimination climate was assumed to have a negative effect on collective affective commitment

within companies, which in turn should be positively linked to company performance. Therefore, we

hypothesized a negative indirect effect of perceived age discrimination climate via collective

commitment on the overall company performance.

In general we found support for all our hypotheses. In line with our hypotheses, age diversity was

related to higher levels of perceived age discrimination climate in companies and indirectly also

negatively influenced collective affective commitment of employees. Furthermore, perceived age

discrimination climate showed the hypothesized negative link to collective affective commitment. Last,

our analysis confirmed the mediated negative relationship of perceived age discrimination climate on

overall company performance.

We believe that these results contribute to the literature by corroborating and extending prior findings

in several ways. Concerning the age diversity literature, we were able to make a contribution regarding

mechanisms influencing the age diversity/performance link by establishing perceived age

discrimination climate on the company level as a mediator for the relationship between increasing

diversity in terms of age and collective commitment of employees, which is positively related to overall

company achievements. By doing so, we added to the stream of research on possible mediators and

moderators that has emerged since Lawrence (1997) originated the definition of the ‘‘black box of

organizational demography.’’ As one of the first studies in the diversity literature to do so, our study also

investigated processes that may occur as a result of increased age diversity on the company level. Since

our results show clear indications for a meaningful effect of age diversity on the company level, rather

than only on the team level, this new level of analysis for diversity research, proposed by Van

Knippenberg and Schippers (2007), appears to be a valuable road to follow for future investigation in

the area. Future studies may also incorporate age diversity in teams and departments through a

multilevel analysis and thereby showing that age diversity on the company level has an impact on the

company performance over and above the lower level influences.

Our findings also contribute to the developing literature on ageism and age discrimination. First, with

perceived age discrimination climate we describe an organizational level variable which we conceptualize

as the aggregate member perceptions about the organizations’ age related treatment of different age

groups. Second, our results are a first attempt to establish a positive link between age diversity on the

company level and perceived age discrimination climate. We relied on processes of similarity-attraction

(Byrne, 1971), social identity (Tajfel & Turner, 1986) and self-categorization (Turner, 1987), as well as on

violation of career timetables (Lawrence, 1984), and prototype matching (Perry, 1994) to theoretically

explain this rather counterintuitive relationship. Overall, increasing age diversity seems to undermine

social integration (Harrison et al., 1998) within companies and thus triggers higher levels of perceived age

discrimination climate. Future studies may expand our results by integrating these mechanisms directly in

empirical models, and thereby test our theoretical reasoning for the first hypothesis. Promising routes to

follow might be to integrate variables like cohesion (Seashore, 1954) or age group identification (Garstka,

Schmitt, Branscombe, & Hummert, 2004) on the organizational level as mediators in future models.

Likewise, the extent of perceived age timetable violations or misfits between job holders and job-age

prototypes should be tried to operationalize in further studies to empirically inspect our complex

theoretical arguments. Integrating those variables might also help to fully understand the emergence of

perceived age discrimination climate and thereby investigate whether this organizational level construct

originates more from individual and leadership interactions or organizational rules and procedures set by

the company’s HR-department or top management.

Moreover, we built on social exchange theory (Shore & Wayne, 1993) and applied earlier findings on

the relationship between certain work experiences and members’ commitment (Meyer & Allen, 1991)

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AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 281

to the context of age discrimination as one relevant violation of social exchange processes. Finally, we

partly replicated Ostroff’s (1992) study and substantiated her findings on the effect of collective-level

affective commitment on organizational performance.

Our control variables allow us to argue against several alternative explications for the relationships

we observed. Since integrating company size in our analysis did not change the pattern of the results,

our findings indicate that the appearance of perceived age discrimination climate on the company level

is not affected by company size and, thus, that perceived discriminatory behavior triggered by the

different processes mentioned above may occur, no matter how big the company is. Second, including

the median age of the employees did not influence the observed relationships substantially, which

indicates that the suggestion by Finkelstein et al. (1995) concerning the lower salience of age in

environments with older personnel is not true for our dataset. Age diversity on the company level seems

to play a decisive role in the appearance of an age discrimination climate, independent of the median

age of the employees.

Practical implications

There are two important implications of our research for company managers. First, they must be aware

that, with an increase in the age diversity of their workforce, higher levels of perceived age discrimination

climate in their companies may occur. This relationship might surprise both line managers and HR

professionals as it partly contradicts popular belief. Second, and perhaps even more important, our results

indicate that perceived age discrimination climate is potentially related to performance. In line with

previous research (Goldman, Gutek, Stein, & Lewis, 2006), we found clear indications that organizations

may experience poor performance when employees perceive discriminatory treatment. Thus, age

discrimination is not only an issue that should be avoided from a normative and ethical point of view, but it

might also have business consequences if it is not adequately addressed.

To address these issues, companies should regularly assess the age composition of their employees in

order to be more aware of the potential occurrence of perceived age discrimination climate. In this respect,

an audit that includes an aging profile analysis and projection should be applied (Jonker & Ziekemeier,

2005). Second, if considerable age diversity is present, assessment tools such as employee opinion surveys,

focus groups, exit interviews, and analysis of patterns of employees’ grievances should be deployed in

order to increase awareness of perceptions of age discrimination (Ensher et al., 2001).

If these analyses indicate serious levels of perceived age discrimination climate on the company level,

several potential measures can be established to lower the perception of age discriminatory behavior in the

company. For example, sensitizing the whole organization to the issues of the aging workforce seems to

be a key requirement for keeping age discrimination on the company level low. As several authors have

proposed (Armstrong-Stassen and Templer, 2005; Elliott, 1995; Rynes & Rosen, 1995) age awareness

trainings for executives should be held to promote a positive view about the potential of different age

groups in the company and to emphasize the relationship of an age-discriminatory corporate culture to

lowered performance levels. Diversity trainings should also educate participants about procedural (e.g.,

process fairness) and interactional justice rules (e.g., fairness in offering information about the decision-

making process and fairness toward affected persons) (Greenberg & Colquitt, 2005) in order to achieve a

lower level of perceived discrimination for all employees (Avery et al.). Ensher et al. (2001) proposed

organization-wide change efforts with a business-driven imperative that should be justifiable in light of our

results, to avoid perceived discrimination. Companies should also show senior leadership’s commitment to

anti-age-discriminatory behavior and make their age-related HR practices transparent to all employees

(Avery et al.). In sum, companies with high levels of age diversity should aim at pro-diverse work climates

(McKay & Avery, 2005).

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282 F. KUNZE ET AL.

Limitations and future research directions

In spite of several methodological strengths (e.g., independent data sources for all focal study

variables), the current study has several limitations that restrict the interpretation and generalization of

our findings. First, the data for this study were collected at only one point in time, and participants

included in the sample participated voluntarily, rather than being randomly assigned to the research.

Therefore, no final conclusion about causality can be drawn. Future studies might overcome these

weaknesses by applying longitudinal and quasi-experimental research designs for the study of

antecedents and outcomes of age discrimination climate in companies (Shadish, Cook, & Campbell,

2002). That is especially the case for the relation between collective commitment and organizational

performance that could also be in the reverse direction. Longitudinal analysis may also enable a test of

the competing hypothesis that individuating information through inter-age contact and the presence of

stereotype-inconsistent information may diminish age discriminatory behavior over time (Cuddy &

Fiske, 2002; Erber, Etheart, & Szuchman, 1992, Hummert, 1999).

Second, although the study used a relatively large sample of German companies, the generalizability

of its findings is limited because the data came from only one cultural environment, Germany. As the

research by Chiu et al. (2001) indicated, there is some evidence for different discriminatory attitudes in

different cultural backgrounds. Hofstede (2001) argued that the German national culture is

characterized by relatively high levels of individualism and masculinity, by relatively low levels of

power distance, and by medium levels of uncertainty avoidance and long-term orientation, any which

characterizations may influence the level and form of age discrimination in companies. Therefore,

future studies could possibly aim at a replication of our results using different cultural backgrounds. In

a similar vein, we caution readers that our sample consisted of small and medium-size organizations

with no more than 5000 employees. Future researchers could obtain study samples that include larger

organizations in order to further generalize the present findings.

Third, the low response rate within organizations may be source of potential bias for our results.

However, our average within-organization response rate of 65 per cent compares favorably to those

reported in prior research (e.g., Griffith, 2006; Lincoln & Kalleberg, 1996). We conducted several post

hoc analyses to investigate the potential influence of such a sampling bias, and those analyses revealed

that the pattern of our results remained unchanged when (a) the response rate per organization was

integrated as a control variable in the model and (b) companies with response rate below 30 per cent

(n ¼ 9) were excluded. The results from this additional analysis indicate a low probability of a biasing effect.

Finally, the firm-level performance outcomes were obtained from a single source, a key

organizational informant, suggesting concerns related to reliability and accuracy, as well as causal

reciprocity. In the literature, the reliability of key informant ratings is much discussed (e.g., Wright

et al., 2001) and should be treated with caution. Future research may thus benefit from replicating the

present study with objective performance outcomes.

Beyond these limitations, our study suggests several directions for future research. Scholars could, for

instance, look in more detail at the antecedent side of the evolution of an age discrimination climate in

companies. Another stream of future research may be to consider organizational factors, such as

structures, cultures, values, and technology (Perry & Finkelstein, 1999), as potential sources of age

discrimination on the organizational level, which might contribute to a more comprehensive

understanding of the evolution of an age discrimination climate in companies. Research that links

perceived age discrimination climate on the organizational level directly to leadership behaviors and HR-

practices in companies may also be worthwhile. On the group level, for example, there is recent evidence

that transformational leadership (TFL) is a potential moderator for the relationship between diversity and

group performance (Kearney & Gebert, 2009). Replicating this effect on the company level with TFL

DOI: 10.1002/job

AGE DIVERSITY, AGE DISCRIMINATION CLIMATE 283

company climate as a boundary condition for decreased age discrimination might be interesting.

Furthermore, although mostly on a descriptive level, current research has emerged about age specific HR-

practices (e.g., age-specific training, incentives, career paths, health management, and recruiting) that

may be a source of competitive advantage in an era of demographic change (e.g., Armstrong-Stassen and

Templer, 2005; Loretto & White, 2006, Streb, Voelpel, & Leibold, 2008). It may be particularly valuable

to investigate whether HR-practices that are adjusted to the specific needs of different age groups reduce

the level of perceived age discrimination climate on the company level. Finally, a diversity climate (e.g.,

Mor Barak et al., 1998, Pugh, Dietz, Brief, & Wiley, 2008) may be a boundary condition that favors or

impedes social integration among different age groups in companies and may thus be incorporated in

future studies.

We hope that this study contributes to a better understanding of the emergence of perceived age

discrimination climate and the performance consequences thereof on the organizational level, and that

it provides a solid foundation for future research on these issues and for practical efforts to address

demographic change in companies.

Author biographies

Florian Kunze is a research associate at the University of St. Gallen, Switzerland. His current research

interests include consequences of the demographic change for companies, within-group processes, and

dynamics in work teams and organizations, discrimination and stereotyping due to demographic

characteristics, and leadership research.

Stephan A. Boehm is a senior lecturer and director of the Center for Disability and Integration at the

University of St. Gallen, Switzerland. His research interests include diversity management with a focus

on the integration of disabled employees, the management of the demographic change, as well as group

and organizational level processes of categorization, stereotyping, and discrimination.

Heike Bruch is a professor of leadership and director of the Institute for Leadership and Human

Resource Management at the University of St. Gallen, Switzerland. Her research interests include

organizational energy, leaders’action, emotions in organizations, as well as team and organization

processes triggered by diversity.

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