Embracing Change Through Team Diversity: The Impact of Age Diversity on Civil Servants’ Perceptions of Organizational Change

Abstract

This study explores the influence of age diversity within teams on civil servants’ perceptions of organizational change. Age diversity is examined through two dimensions: age variety, which refers to the range of different ages within a team, and age polarization, which denotes the extent to which age groups are segregated or clustered within a team. Individual perceptions of change are based on how civil servants evaluated a recent merger. While age polarization shows a significant effect—with less polarized teams exhibiting more positive perceptions of the merger—age variety does not demonstrate a notable impact. These results highlight that while age diversity is important, its impact is nuanced: simply having a range of ages is not sufficient, but reducing age polarization is crucial.

Keywords

public sector adaptability age diversity organizational change diversity aging workforce issues

Introduction

Colorful expressions such as “demographic time bomb” (Hewitt, 2008, p. 3) and “age quake” (Kaplan, 2001) highlight a major challenge faced by many developed nations: the combination of a simultaneously aging and shrinking population (Mahmood & Dhakal, 2023). As populations age, people are now remaining in the workforce longer than ever before. Consequently, this demographic shift has led to an unprecedented age diversity within organizations, a trend experienced not only in private but also in public organizations worldwide (Equal Employment Opportunity Commission, 2017; Thomas, 2020; Truxillo et al., 2015). Much like private firms, public organizations face increasing encouragement to foster diversity within their workforce (Moon & Christensen, 2020; Riccucci, 2021). Meanwhile, there has been a limited examination of its actual outcomes. While some studies have started exploring the link between age diversity and public sector performance, most of this research remains descriptive rather than empirical, leading to a lack of actionable insights for public managers (Sabharwal et al., 2018). Overall, public sector diversity research lags significantly behind private sector research in terms of depth and breadth (Cingolani & Salazar-Morales, 2024; Sabharwal et al., 2018). Additionally, public sector diversity research has focused significantly on dimensions such as race and gender and, to a lesser extent, on age, disability, and sexual orientation (Sabharwal et al., 2018). Despite the emerging interest within public administration on the potential impact of age diversity as a result of the aging workforce, the extent and mechanisms by which it influences performance thus remain unclear. This study, therefore, approaches this issue from a new perspective, investigating whether public organizations can use diversity to better navigate rising turbulence—one of the biggest challenges facing public sector workforces today (Ansell et al., 2020). With series of reform programs being implemented at an accelerating pace to address evolving societal, economic, and political demands (MacCarthaigh, 2014), change has emerged as the sole constant in the public sector (Ansell et al., 2020). Hence, for public organizations, successful performance hinges on their ability to successfully manage change. This requires civil servants to be able to adapt to new processes and expectations, a challenge that is often experienced differently across various age groups (Caldwell et al., 2009). By examining whether working in age-diverse teams helps civil servants better deal with change, we address a crucial gap in our understanding of the relationship between age diversity and handling public sector turbulence.

This study delves into the concept of age diversity within public sector teams by focusing on two distinct aspects: age variety and age polarization. Age variety refers to the presence of a wide range of different ages within a team, encompassing both younger and older employees (De Meulenaere et al., 2016; D. A. Harrison & Klein, 2007). This aspect of age diversity considers how a broad spectrum of ages can bring complementary experiences and viewpoints, potentially enhancing the team’s collective response to organizational change. This aligns with the information-decision-making perspective (Williams & O’Reilly, 1998), predicting age diversity will enhance employee performance by bringing about synergies in competencies and knowledge. Age polarization, in contrast, looks at the degree to which different age groups are segregated within a team or organization (De Meulenaere et al., 2016; D. A. Harrison & Klein, 2007). It assesses to what extent young and older age groups are distinctly separated, forming large homogeneous clusters. Extant literature indicates that when age diversity emanates as age polarization, intergenerational differences in values will be more difficult to bridge, creating tensions and misunderstanding among employees (De Meulenaere et al., 2016; D. A. Harrison & Klein, 2007; Wegge et al., 2012), potentially impacting sense-making processes during change processes. This aligns with the social categorization perspective, suggesting age diversity can also undermine effective decision-making and performance (Byrne, 1971; Carton & Cummings, 2012; Tajfel & Turner, 1979). Hence, distinguishing between these two dimensions of age diversity is crucial in understanding how age group divisions within a team might affect communication, collaboration, and, ultimately, the team’s ability to deal with organizational change.

We examine whether these findings extend to the case of the public sector, analyzing survey data from a Belgian public sector organization undergoing a large-scale merger. This study contributes to public (and general) management literature as it is one of the first to examine whether a large variety of ages leads to better acceptance of change (Yadav & Lenka, 2020) because it combines the strengths of both young and old employees; and two, whether clear age gaps make accepting change more difficult because it creates divides and conflict between different age groups. The resulting insights can help public sector leaders to better manage organizational change, giving a fresh perspective on a longstanding challenge in public administration.

In the following sections, we present our theoretical framework, drawing from the competing information/decision-making and social categorization perspectives to explain how age diversity can both enhance and undermine civil servants’ perceptions of organizational change. The sample and data collection methods are described in the Data section, followed by a description of the results. In the Discussion and Conclusion sections, we discuss our main findings and reflect on their implications for practice.

Theoretical Framework

The evolution of culture and society has elevated diversity in the workplace to a central concern within contemporary public management (Sabharwal et al., 2018). Diversity encompasses the variations among individuals in various attributes that may lead to the perception of differences from one another (Williams & O’Reilly, 1998). It can be identified through obvious social categories, such as age, race, and gender—often referred to as “surface-level” diversity (D. A. Harrison et al., 1998). Additionally, diversity includes deeper distinctions related to knowledge, skills, and experiences, known as “deep-level” or “cognitive” diversity. Since differences in social categories frequently align with variations in background and experiences, recent research (e.g., Carter & Phillips, 2017) suggests that all diverse environments typically exhibit both surface-level and deep-level diversity.

With the increasing diversity of bureaucracies, challenges regarding diversity management have surfaced as well. While the diversity literature of recent decades in public administration has predominantly addressed dimensions such as race and gender, with age receiving comparatively less attention, the literature on organizational change has notably explored the influence of age during change processes. In the ensuing sections, we first outline the primary findings of this literature before conducting a more detailed examination of age diversity dimensions (variety and polarization) and their relationship to organizational change. Although not the empirical focus of this paper, we incorporate two competing perspectives (the information/decision-making perspective and the social categorization perspective) to build hypotheses and come to a more nuanced understanding of the potential impacts of age diversity on civil servants’ perceptions of change.

Age Diversity—Resource or Obstacle for Organizational Change?

Extant research reveals that work-group diversity acts as a double-edged sword that can lead to both positive and negative outcomes (Boehm et al., 2015; Carter & Phillips, 2017; Grund & Westergaard-Nielsen, 2008; Kunze et al., 2011, 2013). As a result, research on diversity has been grappling with questions about the processes that underlie these effects. Two competing theoretical perspectives have emerged and dominated most of the research on diversity, including age diversity: the information/decision-making perspective and the social categorization perspective.

From the perspective of information and decision-making, diverse groups are expected to outperform homogeneous ones (Williams & O’Reilly, 1998). This belief arises from the notion that diverse groups have access to a broader range of skills, experiences, and information, which can enhance performance on tasks that benefit from various viewpoints (Carter & Phillips, 2017). While this diversity provides a greater pool of resources, it may also require these groups to engage in more thorough processing and discussion of task-related information in order to determine the best course of action (Page, 2014; Van Knippenberg et al., 2004). Several studies (Phillips et al., 2006) have found that social category differences such as race can improve performance by prompting members to share their unique views on a given task. Race as a surface-level diversity triggers expectations that informational differences may be present in groups and stimulates the expression of unique information.

In the context of age diversity, when individuals of different ages share and combine complementary age-specific knowledge and skills, it can lead to increased creativity, problem-solving capacity, decision-making quality, and, ultimately, organizational performance (Carton & Cummings, 2012; De Meulenaere et al., 2016; Horwitz & Horwitz, 2007). Literature indeed indicates that employees of different ages have differing but potentially complementary knowledge and cognitive abilities. Whereas younger individuals often have stronger technological knowledge and greater cognitive flexibility to adapt to novel circumstances (Gerpott et al., 2017; Skirbekk, 2004), older employees tend to excel in areas that are presumed to accrue with experience and learning, such as domain-specific expertise and problem-solving skills (De Meulenaere et al., 2016; Grund & Westergaard-Nielsen, 2008; Skirbekk, 2004). In the context of organizational change, this may lead younger employees to perceive change initiatives as more feasible and necessary, given their comfort with new technologies and adaptability to new systems or processes (Hershatter & Epstein, 2010). Meanwhile, older employees may feel apprehensive about change due to potential challenges in acquiring new technological skills or adapting to unfamiliar procedures. Additionally, research indicates that younger employees generally tend to be less cynical about change in contrast to their older colleagues, who might be more influenced by previous experience with change (Stueber & Jacobsen, 2018). As such, older employees can benefit from the cognitive flexibility and adaptability of their younger coworkers (Pfrombeck et al., 2024). At the same time, younger employees may obtain support and reassurance from older employees who have already “made it through” several organizational changes during their careers, making it easier for them to put things into perspective (cf. Scheibe et al., 2019). This aligns with research suggesting that older employees may be more able to regulate their emotions during the challenges accompanying change (Scheibe et al., 2016, 2019). Hence, younger employees can also benefit from valuable coping strategies and problem-solving skills older coworkers honed over their careers (Burmeister et al., 2020). Lastly, older employees possess institutional memory, which can serve as both an asset and a liability during organizational change (Terry, 2015). On the one hand, their experience can offer stability and guidance that younger employees can benefit from; on the other hand, their deep understanding of how the organization’s mission and values should guide change may sometimes lead to perceptions of resistance or inflexibility.

Conversely, generational differences in socialization, education, life- and work experiences also lead to substantial differences in values and the way in which each generation thinks about work and life (Parry & Urwin, 2011; Wagner, 2007). Values also tend to evolve with age (Parry & Urwin, 2011): while older adults are said to “live to work,” younger people “work to live” (Sullivan et al., 2009). The competing “social categorization” perspective focuses on these differences in values, arguing that these can impede cohesion, social integration, and cooperation among employees (Byrne, 1971; Carton & Cummings, 2012; Tajfel & Turner, 1979). This theory posits that employees are naturally drawn to colleagues who share similar values, opinions, and attitudes, leading them to identify more closely with coworkers of comparable ages (referred to as the in-group) while categorizing those of differing ages as part of the out-group (De Meulenaere et al., 2016). This identification process often results in favoritism toward the in-group and stereotyping and discrimination against the out-group (Carton & Cummings, 2012; Tajfel & Turner, 1979). These dynamics will introduce tensions and can even contribute to workplace conflicts (Finkelstein et al., 2013; King & Bryant, 2017; North & Fiske, 2015). In recent decades, the social categorization perspective has been utilized to elucidate the dynamics faced by women and people of color as they enter environments previously dominated by men and white individuals. These individuals often encounter the adverse effects of unconscious (implicit) bias across various organizational processes, including hiring, evaluations, and promotions (e.g., Gündemir et al., 2014; Rosette et al., 2008). Implicit bias can also have a detrimental impact on group performance (Carter & Phillips, 2017). For instance, interracial teams that included White members with high levels of implicit bias not only underperformed compared to those with unbiased White members but also fared worse than teams with White members who exhibited high levels of explicit bias (meaning individuals who are aware of their prejudices and might actively endorse or act upon them, Dovidio et al., 2002). The authors suggested that implicit bias likely hindered problem-solving efficiency, as the mixed signals from White members with implicit bias created a disparity between their verbal and nonverbal behaviors. Overall, research on implicit bias indicates that when social categorization processes generate intergroup bias—even unconsciously—the consequences can be harmful to both those who are targeted by the bias and the group’s overall processes and performance (Carter & Phillips, 2017).

These insights suggest that age diversity may undermine not only employee collaboration and performance (Grund & Westergaard-Nielsen, 2008; Kunze et al., 2011; Schneid et al., 2016) but also their attitude toward organizational change. Research has already demonstrated that age-related value differences can have a significant impact on how employees typically view change. Young employees, in general, tend to be more open to change, considering it as an opportunity for learning and personal growth (North & Fiske, 2015). They may also view changes that promote flexibility, autonomy, and work-life balance more positively, as these align with their values and preferences (i.e., “work to live”) (cf. Sullivan et al., 2009). Conversely, older employees, who tend to prioritize stability and tradition, may be more resistant to change that disrupts established norms or work practices (Hershatter & Epstein, 2010). On the one hand, these diverse perspectives can enrich discussions and decision-making processes, and older employees may become less resistant to (radical) change. However, if not managed effectively, these value differences can exacerbate tensions and conflicts within the team, especially in uncertain times of change. When employees struggle to align their values and priorities, communication breaks down, collaboration decreases, and employees’ readiness for change is likely to decrease, even in those who were originally more positive. Ultimately, such value conflicts may undermine the overall success of change efforts (cf. Hillman, 2014).

In summary, the information/decision-making perspective posits that interactions among age-diverse employees encourage the exchange of knowledge and capabilities, which can foster more positive perceptions of change. Conversely, the social categorization perspective suggests that effective knowledge exchange, needed to realize the benefits of age diversity, will be hindered by conflicting values and in/out-group dynamics. Next, we delve deeper into the concept of age diversity to arrive at a more accurate prediction of its effect on civil servants’ perceptions of change.

Age Variety Versus Age Polarization and Their Effect on Change Perceptions

As mentioned, age diversity can act as both a resource or obstacle for organizational and employee performance (e.g., Carton & Cummings, 2012). Extant literature indicates that the precise effect will depend on the specific type of age diversity that is present in the organization, distinguishing between “age variety” and “age polarization” (De Meulenaere et al., 2016). Age variety refers to the range of ages that is present in an organization or work unit (De Meulenaere et al., 2016). In organizations with high age variety, the presence of numerous age groups can help bridge age-related value differences between employees (De Meulenaere et al., 2016; D. A. Harrison & Klein, 2007; Wegge et al., 2012). In the context of organizational change, the presence of age variety can play a crucial role in shaping employees’ perceptions and responses to change initiatives (Caldwell et al., 2009).

Firstly, extant literature suggests that age variety reduces the likelihood of value-based tensions between different age groups since value differences between them are more easily bridged (De Meulenaere et al., 2016; D. A. Harrison & Klein, 2007; Wegge et al., 2012). This decreased salience of value differences allows for an environment of mutual understanding and collaboration, wherein employees are more inclined to work together toward common goals, including embracing and supporting organizational change initiatives (cf. Bell et al., 2011; Carton & Cummings, 2012). Importantly, age variety can also facilitate knowledge exchange and the sharing of diverse perspectives and experiences among employees (De Meulenaere et al., 2016). In the context of organizational change, this diverse knowledge base enables employees to draw upon a wider range of insights, strategies, and solutions to address change-related challenges and adapt to new circumstances. Hence, in organizations with a large age variety, age-based value conflicts may be kept in check, and effective knowledge exchange becomes more likely. This implies it will be easier for young and older employees to benefit from each other’s strong points when it comes to dealing with organizational change. For instance, by cooperating closely, older employees are exposed to the flexibility, tech savviness, and learning attitude of their younger counterparts, which may make them more open to organizational change. On the other hand, a large age variety may make it easier for the experience and accumulated knowledge of older workers to reach their younger counterparts (cf. Wegge et al., 2012), including valuable coping strategies and problem-solving skills (Scheibe et al., 2016, 2019). Accordingly, as age variety increases, we can expect to see more creative problem-solving, enhanced coping, and a higher potential for age-related synergies to be realized (cf. De Meulenaere et al., 2016). Hence, we propose that age variety will enrich and facilitate the change process, helping employees perceive organizational change more positively:

H1: Age variety within teams has a positive effect on civil servant perceptions of organizational change.

Conversely, age polarization refers to the separation of the workforce into distinct homogenous subgroups of a similar age (De Meulenaere et al., 2016, p. 194). The presence of clearly delineated age groups tends to increase the value gap between young and older employees (i.e., value differences will be more difficult to bridge). Moreover, it also reinforces social categorization processes, contributing to in-group/out-group dynamics that hamper cooperation and trigger age-based discrimination and conflict (Carton & Cummings, 2012; De Meulenaere et al., 2016; Kunze et al., 2011; Wegge et al., 2008). This means the potential to realize knowledge-based synergies between the different age groups will be undermined, as in-group members (of a certain age) withhold resources and support from out-group members (of a different age) (Joshi et al., 2006). Extending these insights to the context of organizational change, we can expect age polarization to also undermine civil servants’ perceptions of change. While younger and older employees may still have complementary perspectives on and experiences with organizational change, they will be unlikely to share these with one another. For instance, older employees may decide to keep their expertise to themselves, leaving younger employees to their own devices. At the same time, younger employees may refrain from assisting older employees with technology-related issues. Decreased interaction between the different age groups also implies older employees will not be able to benefit from the flexibility and motivation of younger coworkers, whereas younger employees may miss out on reassurance and advice from their older counterparts. Moreover, if value discrepancies between polarized age groups lead to full-fledged conflicts, the motivation and productivity of the entire organization or work unit might suffer (De Meulenaere et al., 2016). Accordingly, we hypothesize that age polarization will negatively impact employee perceptions of organizational change:

H2: Age polarization within teams has a negative effect on civil servant perceptions of organizational change.

Data—Case Study

To examine the effect of team diversity on civil servants’ perceptions of change, we focus on a large Flemish public sector organization comprising 2,867 civil servants distributed across 17 teams, each typically comprising around 150 members. During our survey in 2022, the organization was still undergoing a large-scale merger that was initiated in 2018. The merger aims to streamline operations and improve service delivery by combining resources and expertise from three Flemish agencies. This new organization is designed to foster greater efficiency, integration, and coordination across various service areas within the welfare and healthcare sector. This scenario offers an ideal opportunity to explore whether civil servants’ perceptions of this change process differ significantly in teams with varying degrees of age diversity.

Our analysis is based on survey data from 1,068 respondents across all 17 teams, representing approximately 37.25% of the population. Additionally, we have received age data for all civil servants in these teams, not just those who participated in the survey. This comprehensive age data enabled us to create objective measures of age diversity within the 17 available teams. Using these data in combination with the survey, we will be able to trace back the role of age diversity within these 17 teams on individual perceptions of the merger. The distribution of civil servants across these teams for our sample compared to the population can be found in Table 1.

Table 1.

Distribution Across Teams.

Team	Population		Sample
Team	N	%	N	%
1	115	4.01	38	3.56
2	144	5.02	28	2.62
3	317	11.06	66	6.18
4	341	11.89	68	6.37
5	263	9.17	125	11.7
6	98	3.42	55	5.15
7	168	5.86	84	7.87
8	158	5.51	74	6.93
9	131	4.57	79	7.4
10	180	6.28	62	5.81
11	129	4.5	34	3.18
12	179	6.24	60	5.62
13	118	4.12	43	4.03
14	131	4.57	72	6.74
15	93	3.24	56	5.24
16	141	4.92	64	5.99
17	161	5.62	60	5.62

Table 2 compares the distribution of age, gender, and classification level in the sample with that of the entire population. We observe minor differences across some categories, particularly in the classification level. Notably, B-level civil servants (those with a Bachelor’s degree) are significantly overrepresented in our sample data. A common method to increase representativeness involves calculating and using weights based on specific population characteristics (such as gender, age, and level in our case). However, these characteristics will also be included as explanatory variables in our model, complicating the effectiveness of weighting (see Fitzgerald et al., 1998; Wooldridge, 2002). Consequently, using weights in our case would not resolve the underlying issues. Therefore, applying weights would provide only a false sense of representativeness. Results should thus be interpreted with care.

Table 2.

Representativeness Sample.

Variables	Population (%)	Population freq.	Sample freq.
Age groups
Younger than 25	4.33	46.2444	20
Between 25 and 30	14.44	154.2192	104
Between 30 and 35	15.66	167.2488	152
Between 35 and 40	15.56	166.1808	180
Between 40 and 45	15.03	160.5204	173
Between 45 and 50	9.84	105.0912	132
Between 50 and 55	10.15	108.402	131
Between 55 and 60	8.82	94.1976	126
60 or more	6.17	65.8956	50
Gender
Female	77.08	823.2144	873
Male	22.92	244.7856	195
Level
A	41.03	438.2004	201
B	48.55	518.514	753
C	4.05	43.254	89
D	6.1	65.148	25

Measures

Individual Perception of the Merger

We surveyed respondents across the various teams regarding their perception of the merger. We specifically asked participants to evaluate several statements about the merger and to express their level of agreement using a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The items included in the survey were:

(a) “I believe that the merger will have a positive effect on children, youth, and families,”

(b) “I support the principle that the timing and next steps in the merger process will become clear over time,”

(d) “I feel that we are insufficiently informed about the progress of the merger,”

(e) “I believe that we are sufficiently involved in the practical implementation of the merger.”

This index assessing change perception encompasses various dimensions related to the perception of the merger, including its perceived value for both the target audience and employees, as well as the efficacy of change management practices concerning communication, participation, and implementation process. We posit theoretically that these dimensions collectively contribute to an overarching perception of the merger, thereby encapsulating an overall sentiment toward it. This theoretical proposition finds empirical support through the assessment of the internal consistency among the items. Cronbach’s Alpha analysis demonstrates that the items reliably gage employees’ perception of the merger, with a satisfactory alpha value confirming the reliability (α = .79). We created a composite index by averaging the scores, representing a unified measure of each respondent’s perception of the merger.¹

Measuring Age Variety Within Teams

To calculate age variety within the teams respondents are part of, we used Blau’s index of heterogeneity (D. A. Harrison & Klein, 2007), a well-recognized measure for assessing diversity within a group. Blau’s index is defined as:

Blau' s Index = 1 - Σ_{i = 1}^{K} (π_{i}^{2})

where K represents the number of distinct age groups, and $π_{i}$ denotes the proportion of individuals in each age group i. Specifically, we used the following age groups: individuals younger than 25, those between 25 and 30, between 30 and 35, between 35 and 40, between 40 and 45, between 45 and 50, between 50 and 55, between 55 and 60, and those 60 or older. The age distribution across the 17 teams is visualized in Figure 1. For each age group, we calculated the proportion of employees within that group by dividing the number of employees in the group by the total number of employees within the team. We then squared each of these proportions and summed them. The sum of the squared proportions was subtracted from one to yield Blau’s index. The index ranges from 0 to 1—(1/K), where higher values indicate greater age diversity. A value of 0 represents no diversity, while the maximum value represents the highest possible diversity with an even distribution across all age groups.

Figure 1.

Age distribution across teams.

Measuring Age Polarization Within Teams

To measure age polarization within the teams respondents are part of, we used the polarization index of Esteban and Ray (1994). This measure is particularly interesting because it accounts for both the distance between age subgroups and the relative size (i.e., balance) of these subgroups, providing a more comprehensive view of polarization within a team. Unlike standard deviation, which is sensitive only to the range of ages, the Esteban and Ray index is sensitive to both the distance between age groups and the relative size (i.e., balance) of the groups. This dual sensitivity allows the Esteban and Ray index to provide a more nuanced measure of age distribution by considering not just how spread out the ages are but also how evenly distributed the individuals are across the different age categories. This index is calculated as:

P = k Σ_{i = 1}^{N} Σ_{j \neq 1}^{N} π_{i}^{1 + α} π_{j} | α_{i} - α_{j} |

and includes both the size balance of age subgroups ( $π_{i}$ and $π_{i}$ ) and the distance between these subgroups (| $α_{i} - α_{j}$ |). A lower value of α places greater emphasis on the distance between subgroups and less emphasis on subgroup balance. Consequently, α is often referred to as a measure of sensitivity to polarization. When α equals 0, the polarization index corresponds to Gini’s coefficient for inequality, which primarily captures distances (Esteban & Ray, 1994). According to Duclos et al. (2006), when calculating the polarization index for continuous variables (e.g., age), α should be set within the range of [0.25, 1] to meet the axioms of polarization. For this analysis, we set α at .8. The parameter k, being a scaling factor for normalization, does not affect the index values; therefore, we selected k = 1 (Esteban & Ray, 1994).

Control Variables

To ensure the robustness of our findings, we included several control variables measured at both the individual and team levels. At the individual level, we controlled for the respondent’s age and the square term of age to account for potential nonlinear effects, tenure to reflect the length of time they have been with the organization, classification level (ranging from level A to D) to indicate their position within the organizational hierarchy, and education level to capture differences in qualifications. We also included gender and employment status (categorized as Civil Servant, Probationary Civil Servant, Contractual Employee, and Probationary Contractual Employee) to control for demographic and contractual variations. Controlling for these individual-level variables is crucial as they can influence both the perception and experience of a merger.

At the team level, we controlled for the size of the team (log transformed). This is important because larger teams might have different dynamics and reactions to a merger compared to smaller teams, which could affect the overall team environment and individual perceptions of the merger.

Descriptive Statistics

In Table 3, the descriptive statistics are presented, while in Table 4, a correlation matrix for all variables is provided. The perception of the merger, our primary dependent variable, shows a modest positive correlation with age variety (.0938) and a slight negative correlation with age polarization (−.096). Age variety, measured using Blau’s index, strongly correlates with team size (.6534*), indicating that larger teams tend to have greater age diversity. Age polarization, assessed using the Esteban and Ray index, shows a strong positive correlation with team size (.2840*), suggesting that larger teams are also more polarized in terms of age. The age of respondents, an individual-level variable, shows significant correlations with tenure (.4072*) and employment status (−.5176*), indicating that older respondents tend to have longer tenure and are more likely to hold permanent positions. Gender, another individual-level variable, shows positive correlations with age polarization (.1280*) and team size (.1924*), suggesting that gender differences are more pronounced in larger and more polarized teams.

Table 3.

Descriptive Statistics.

Variables	Mean	SD
Dependent
Perception of the merger (index)	4.117	1.324
“I believe that the merger will have a positive effect on children, youth, and families”	5.023	1.777
“I support the principle that the timing and next steps in the merger process will become clear over time”	4.500	1.835
“I think that the merger will have a positive impact on my job content”	4.423	1.975
“I feel that we are insufficiently informed about the progress of the merger”	3.402	1.676
“I believe that we are sufficiently involved in the practical implementation of the merger”	3.241	1.706
Team level (N = 17)
Age variety (Blau’s index)	0.856	0.013
Age polarization (index of Esteban and Ray)	0.107	0.019
Team size (log)	5.120	0.373
Individual level (N = 1,086)
Age	5.034	2.088
Gender	1.183	0.387
Statute	1.963	1.037
Education	4.626	1.045
Tenure	3.695	2.505
Classification level	1.943	0.606

Table 4.

Correlation Matrix.

Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Perception of the merger (index)	(1)	1
Age variety (Blau’s index)	(2)	0.0938	1
Age polarization (index of Esteban and Ray)	(3)	−0.096	0.0449	1
Team size (log)	(4)	0.0223	0.6534*	0.2840*	1
Age	(5)	−0.0257	0.034	−0.2303*	−0.1033	1
Gender	(6)	−0.0452	−0.0124	0.1280*	0.1924*	0.0711	1
Statute	(7)	0.1189*	0.1292*	0.0268	0.0389	−0.5176*	−0.1224*	1
Education	(8)	−0.0617	−0.0982	−0.006	−0.0837	0.0666	−0.0026	−0.1076	1
Tenure	(9)	−0.0791	0.0311	−0.1466*	−0.0777	0.4072*	−0.0369	−0.3554*	0.003	1
Classification level	(10)	0.0663	0.1332*	0.2259*	0.1912*	−0.0722	−0.0079	0.091	−0.2934*	0.0271	1

p < .1

Employment status significantly correlates with the perception of the merger (.1189*), age variety (.1292*), age (−.5176*), and tenure (−.3554*), highlighting its role in shaping organizational dynamics. These correlations underscore the importance of controlling for these individual and team-level variables to accurately assess the factors influencing perceptions of the merger.

Methods & Results

To model the fact that our respondents are nested within teams, a linear multilevel analysis with random effects was employed. Random effects are used because they account for the variability both within and between teams, allowing us to better understand the influence of team-level factors on individual perceptions. Corresponding results are presented in Table 5. The table presents the full maximum likelihood estimates. Initially, we estimated an “empty” model to assess the variance between teams (Column (1)). When averaging across respondents and teams, the merger perception indicator is 4.09, aligning closely with the mean merger perception of 4.11 (see Table 3).

Table 5.

Multilevel Results.

Index merger perception
Variables	(1)	(2)	(3)	(4)	(5)
Constant	4.093*** (0.0935)	8.329*** (0.999)	1.305 (6.245)	7.824*** (1.494)	3.676 (5.791)
Individual level
Age		−0.159 (0.0995)	−0.158 (0.0994)	−0.170* (0.0997)	−0.169* (0.0997)
Age (sq)		0.0168* (0.00910)	0.0165* (0.00910)	0.0175* (0.00911)	0.0173* (0.00911)
Gender		−0.250** (0.110)	−0.245** (0.111)	−0.238** (0.110)	−0.228** (0.111)
Employment status (civil servants = ref. cat.)		χ²(3) = 8.37**	χ²(3) = 8.09**	χ²(3) = 8.2**	χ²(3) = 7.99**
Probationary civil servant		0.232 (0.246)	0.235 (0.246)	0.239 (0.246)	0.241 (0.246)
Contractual employee		0.182* (0.101)	0.173* (0.101)	0.172* (0.101)	0.162 (0.101)
Probationary contractual employee		0.646*** (0.242)	0.641*** (0.242)	0.644*** (0.242)	0.642*** (0.242)
Education (primary education = ref. cat.)		χ²(6) = 21.75***	χ²(6) = 22.05***	χ²(6) = 21.97***	χ²(6) = 22.06***
Lower secondary education		−3.163*** (0.979)	−3.195*** (0.980)	−3.243*** (0.980)	−3.254*** (0.980)
Upper secondary education		−3.559*** (0.931)	−3.584*** (0.932)	−3.621*** (0.932)	−3.629*** (0.932)
Higher education, short type (2–3 years)		−3.711*** (0.929)	−3.744*** (0.929)	−3.776*** (0.929)	−3.788*** (0.929)
Higher education, long type (4–5 years)		−3.464*** (0.933)	−3.497*** (0.934)	−3.537*** (0.934)	−3.549*** (0.934)
University education		−3.672*** (0.930)	−3.702*** (0.930)	−3.735*** (0.930)	−3.746*** (0.930)
Doctorate		−3.387*** (1.084)	−3.422*** (1.084)	−3.432*** (1.084)	−3.446*** (1.084)
Classification level (A = ref. cat.)		χ²(3) = 8.62**	χ²(3) = 8.51**	χ²(3) = 8.37**	χ²(3) = 8.31**
B		−0.000701 (0.115)	−0.0131 (0.115)	0.00861 (0.116)	0.00437 (0.116)
C		0.431** (0.174)	0.417** (0.174)	0.432** (0.174)	0.427** (0.174)
D		0.0256 (0.297)	0.0120 (0.298)	0.0547 (0.299)	0.0571 (0.299)
Tenure		−0.0359** (0.0177)	−0.0360** (0.0177)	−0.0368** (0.0177)	−0.0371** (0.0177)
Organizational level
Team size (log)			−0.0292 (0.322)	0.298 (0.232)	0.143 (0.305)
Age variety (Blau’s index)			8.448 (8.357)		5.707 (7.689)
Age polarization (index of Esteban and Ray)				−8.483** (4.224)	−7.852* (4.200)
Observations	1,068	1,068	1,068	1,068	1,068
Number of groups	17	17	17	17	17
LR test (conservative)	χ²(1) = 33.42***	χ²(1) = 29.86***	χ²(1) = 22.53***	χ²(1) = 20.45***	χ²(1) = 15.07***
Intra-class correlation	.068
Level-1 R²		.057
Level-2 R²			.129	.292	.348

Note. Standard errors in parentheses.

p < .1. **p < .05. ***p < .01.

Moreover, the Likelihood Ratio (LR) test value equals 32.70 with 1 degree of freedom and a p-value of .000. This p-value must be halved to obtain a less conservative test because the LR test for random effects is a one-tailed test, given that variance components cannot be negative. In this case, halving the p-value does not affect the conclusion. The null hypothesis should be rejected, as there is evidence of cross-team variation in levels of merger perception.

The intra-class correlation coefficient (ICC) measures the proportion of variance attributable to the organization and team levels compared to the total variance. Since the dependent variable is measured at the individual level (civil servant level), this level is expected to exhibit the highest ICC score (Steenbergen & Jones, 2002, p. 231). The ICC is approximately 6.5%, indicating that around 6.5% of the variance in merger perception is due to differences between teams, while 93.5% is explained by individual differences. Although the ICC is relatively low, ignoring it would result in both statistical and empirical inaccuracies.

In the next step (see Column (2)), level-1 covariates were introduced to the model, assuming fixed effects for the time being. However, the intercept was allowed to vary across teams to account for differences in baseline merger perceptions between teams. This column reveals that statute, level, gender, and tenure have a significant impact on merger perceptions. Additionally, evidence of variation in the intercepts remains. Comparing the fit of the random intercept model to a standard regression model produces an LR score of 40.28 with a p-value of .000, allowing us to reject the null hypothesis that all teams share the same intercept, as assumed in the regression model. Furthermore, these level-1 covariates explain approximately 6% of the variance in the outcome.

To account for variation in the intercepts, we add level-2 covariates to columns 3, 4, and 5. Column 3 includes the age variety measure, calculated using the Blau index, which indicates the diversity of ages within the team. An increase in the age variety measure signifies a greater range of ages among team members, reflecting a more diverse age composition. Column 4 incorporates the age polarization measure, based on the Esteban and Ray (1994) index, which captures the extent of age polarization within the team. A higher value for age polarization indicates a more pronounced age divide among team members, suggesting significant age clustering at opposite ends of the age spectrum. In column 5, both measures are included. Across all models, the same level-1 covariates remain statistically significant. In column 3, the age variety measure and team size are not significant. In column 4, age polarization is significant and negative. This implies that teams with a larger age gap among members are more likely to view the merger less favorably.

When both measures are combined in column 5, age polarization remains significant and negative, reinforcing the idea that age disparity is linked to more negative perceptions of the merger, thus providing evidence for hypothesis 2. Meanwhile, age variety, which measures the diversity of ages without considering their distribution, continues to be insignificant, suggesting it does not have a notable impact on perceptions of the merger, thereby failing to provide evidence for hypothesis 1.

Discussion & Conclusion

The primary goal of this study was to explore how team-level age variety and age polarization affect perceptions of organizational change among civil servants. We focused on a case involving a merger within a Belgian agency responsible for policy development and service delivery in the health and welfare sector. This context provided a unique opportunity to examine the complex dynamics of team diversity and its impact on employees’ perceptions during a significant organizational change.

Drawing from the dual theoretical framework of the information/decision-making (Williams & O’Reilly, 1998) and social categorization perspective (Byrne, 1971; Tajfel & Turner, 1979), we tested two competing hypotheses regarding the effect of age variety (i.e., a wide range of ages within a team) and age polarization (i.e., age groups that are clearly segregated within a team). Age variety was hypothesized to encourage more positive perceptions of organizational change (cf. information/decision-making perspective), whereas age polarization was expected to undermine the same (cf. social categorization perspective).

First, our results show there was significant cross-team variation in merger perceptions, with approximately 6.5% of the variance attributable to differences between teams and 93.5% due to individual differences. Significant predictors of merger perceptions included statute, level, gender, and tenure. Contrary to our theoretical expectations, age variety, reflecting a simple diversity of ages within teams, did not significantly impact merger perceptions. This finding aligns with research suggesting that—while diverse teams can offer a broad range of perspectives and skills—diversity alone does not necessarily lead to positive outcomes.

Moreover, it supports the notion that the realization of diversity-related benefits depends on numerous factors, such as team dynamics, leadership, and organizational culture (Ashikali, 2020; Kunze et al., 2011; Li et al., 2021). Later advancements in the information-decision-making perspective have refined the theory by indicating that key criteria must be met to enhance workplace performance through team diversity. Specifically, individuals from varied demographic backgrounds should provide unique, task-relevant information and be capable of effectively communicating this information to their colleagues (Lazear, 2000).

However, age polarization, indicating a pronounced age divide within teams, was significantly and negatively associated with merger perceptions. This suggests that teams with larger age gaps are more likely to view the merger less favorably. This result is consistent with literature suggesting that significant age gaps can hinder communication, reduce cohesion, and increase conflict within teams (Carton & Cummings, 2012; De Meulenaere et al., 2016; Kunze et al., 2011). Such barriers can negatively affect how team members perceive organizational changes like mergers. Teams with larger age gaps often exhibit more pronounced value differences and, as a result, may struggle more with integrating different perspectives and work styles, leading to more negative experiences during a merger. Finally, when both age diversity and polarization were considered together, age polarization remained significant and negative, while age variety continued to be insignificant.

The findings of this study align with the theoretical propositions presented by Carter and Phillips (2017), who build on the model established by Van Knippenberg et al. (2004) and recent advancements in the diversity literature. They argue that the social categorization perspective and the information-decision perspective should be viewed as complementary and interconnected rather than opposing viewpoints. Carter and Phillips (2017) propose a model that synthesizes these two frameworks, suggesting that both mechanisms operate simultaneously. First, the recognition of diversity activates social categorization processes, prompting individuals to distinguish between in-group and out-group members. In this context, out-group members are perceived as more dissimilar, while in-group members are seen as more alike. This social categorization can lead to two different pathways affecting group performance.

The first pathway indicates that social categorization may hinder group processes when intergroup bias is activated. Previous research has identified several factors that can activate this bias, including competitive environments, threats to group identity, and the presence of strong faultlines (e.g., Chatman et al., 1998; van Knippenberg et al., 2004; van Knippenberg & Schippers, 2007). In situations characterized by intergroup bias, negative outcomes such as interpersonal conflict, avoidance behaviors, and communication issues may arise, ultimately undermining group performance (Carter & Phillips, 2017).

Conversely, when intergroup bias is not activated, social categorization can create a more positive pathway. In such cases, lower interpersonal attraction allows diverse group members to focus their energies on tasks rather than on forming social connections. The recognition of interpersonal differences encourages individuals in diverse groups to anticipate relevant differences related to the task at hand. In environments where such differences are commonplace, group members are more likely to engage in processes described by the information and decision-making framework. This includes considering the perspectives of others and proactively seeking unique information during discussions (e.g., Phillips et al., 2006). Additionally, the expectation of differing viewpoints prompts diverse group members to critically assess their own knowledge (e.g., Loyd et al., 2013; Sommers, 2006). Collectively, these processes promote enhanced problem-solving and creativity.

A critical aspect of Carter & Phillips’ model is whether the social categorization process invokes intergroup bias. The prevalence of intergroup bias was not explicitly examined in this study. One could assume that in cases of age polarization, where there is a clear division between two age groups within teams, intergroup bias might occur, potentially leading to the negative effects previously mentioned and explaining the findings of this study. Conversely, in situations characterized by age variety, intergroup bias based on age might be less prevalent, potentially allowing for deeper differences in expertise, knowledge, and skills to foster collaboration and improve performance. Nonetheless, for this collaborative potential to be fully realized, it must be actively supported by the environment; simply having age variety in teams is not enough to yield meaningful effects (Carter & Phillips, 2017).

This study makes a significant contribution to the diversity literature in public management by offering empirical insights that go beyond mere descriptives. Moreover, a significant portion of public sector diversity research is centered on race and gender, with lesser emphasis on other dimensions like age, disability, and sexual orientation. As such, this study also answers the call for research to explore these underrepresented dimensions of diversity and their impact on organizational outcomes (cf. Sabharwal et al., 2018). Our findings suggest that to mitigate negative perceptions of organizational change at the team level, public sector managers should aim to avoid the polarization of age groups within teams.

Practical Implications

The finding that the polarization of age groups within teams negatively affects perceptions of change carries several practical implications. Firstly, it suggests that when new teams are being formed in the margin of organizational change processes, it is important to consider age polarization as a factor to avoid. By proactively addressing this issue, organizations can prevent potential adverse effects before they arise. However, in many organizational settings, altering the demographic makeup of teams is often limited. In these situations, team managers must remain mindful of possible unconscious biases between age groups in polarized teams and address these issues thoughtfully when providing support during change trajectories.

The adverse effects of age polarization, such as impeded communication and decreased cohesion stemming from differing values and intergroup biases (Carton & Cummings, 2012; De Meulenaere et al., 2016; Kunze et al., 2011), are assumed to indirectly impact perceptions of change and can be addressed in several ways. Regarding communication, providing training on communication styles and regularly soliciting feedback from team members about communication processes can help accommodate different preferences. Team cohesion can be strengthened by offering diversity workshops and actively promoting intergenerational collaboration (Burmeister, Gerpott, et al., 2021; Burmeister, Hirschi, Zacher, 2021) through for example initiatives like cross-training and mentoring programs. These efforts can mitigate negative impacts on team communication and cohesion, thereby indirectly enhancing the team’s ability to manage change positively and influencing members’ perceptions of that change. Organizations can further address age polarization by fostering a shared understanding of change based on mutual values across different age groups. Recognizing the unique contributions of team members from various age groups and collectively celebrating achievements during the change process fosters mutual respect and support for the change initiative.

Moreover, this study indicates that simply having age diversity within teams does not automatically lead to diversity-related benefits, such as improved perceptions of organizational change. To harness the diverse expertise, skills, and knowledge inherent in teams with a varied age composition, organizations must cultivate a culture that encourages the regular exchange of unique, task-relevant information (Lazear, 2000). This approach allows teams to effectively leverage the individual knowledge and expertise of all members, especially during periods of organizational change.

Limitations and Avenues for Future Research

While this study provides a valuable exploration of age diversity in the context of public sector change, several limitations must be acknowledged.

Notably, the relatively low proportion of variance attributed to team-level factors indicates that individual differences account for the majority of variance in merger perceptions. Moreover, the data utilized for this study is sourced from a single organization. As the impact of team-level factors may vary significantly in different public sector environments, future research could benefit from a comparative approach across diverse public sector contexts to ascertain the extent to which team-level factors contribute to variance in change perceptions.

Another limitation of this study is the overrepresentation of B-level civil servants (i.e., civil servants with a Bachelor’s degree) in our sample. Using weights would not resolve the issue of representativeness in this study, as specific population characteristics were also used as explanatory variables in the model (which would undermine the effect of weighting, see Fitzgerald et al., 1998; Wooldridge, 2002). Therefore, caution is warranted when interpreting results, highlighting the exploratory nature of this study and underscoring the need for further investigation into this topic.

Additionally, the cross-sectional design of the data prohibits causal inferences regarding the relationships between age diversity, age polarization, and merger perceptions. Longitudinal studies could yield more comprehensive insights into how these dynamics evolve over time. Future research might also examine how contextual factors, such as leadership and organizational culture, can mitigate the negative effects of age polarization.

Finally, it is imperative to note that the primary objective of this study was not to offer empirical validation for the theoretical frameworks posited herein, specifically the information/decision-making perspective and the social categorization perspective. Rather, these theories were employed solely to formulate hypotheses concerning the correlation between age diversity and change perception. Given that our empirical analysis predominantly centers on the age composition of teams, prospective qualitative research endeavors could delve deeper into elucidating the socio-psychological mechanisms that underlie the relationship between age diversity within teams and organizational change. Such studies could also enhance our understanding of the role of intergroup bias in this dynamic, as suggested by Carter and Phillips (2017).

In conclusion, while our study highlights the importance of considering team-level age polarization in understanding merger perceptions within public sector organizations, it also underscores the complexity of diversity’s impact on organizational outcomes. Future research should continue to explore these relationships, taking into account the multifaceted nature of diversity and the varying contexts in which public sector organizations operate. This study addresses a crucial gap in understanding how age diversity affects civil servants’ perceptions of organizational change, a particularly relevant issue in the ever-evolving landscape of public sector management.

Footnotes

Appendix

Table A1.

Factor Analysis Merger Perception.

Item	Factor loading
“I believe that the merger will have a positive effect on children, youth, and families,”	0.621
“I support the principle that the timing and next steps in the merger process will become clear over time,”	0.6572
“I think that the merger will have a positive impact on my job content,”	0.6126
“I feel that we are insufficiently informed about the progress of the merger,”	0.696
“I believe that we are sufficiently involved in the practical implementation of the merger.”	0.686
Eigenvalue	2.148

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article came forth from the following research projects: “Slow healing wounds: how public organizations organizations and employees deal with and recover from long-term reform trajectories”, supported by the Flemish Research Council (G085819N); “Do organizational changes paradoxically undermine the adaptability of organizations? A study of the impact of repetitive organizational change on decision-making within organizations”, supported by the University Research Fund of the University of Antwerp (BOF 41466) and “Avoiding repetitive reform injury in the public sector. Can leadership behaviour reduce the damaging effect of repetitive reforms?”, supported by the University Research Fund of the University of Antwerp (BOF 42338). The article benefitted from interactions within the GOVTRUST Centre of Excellence of the University of Antwerp.

ORCID iDs

Jan Wynen

Stéphanie Verlinden

Bjorn Kleizen

Notes

Author Biographies

Jan Wynen is an associate professor in the Department of Management (University of Antwerp), specializing in organizational development and sustainability. His work focuses on fostering employee health, motivation, and productivity, particularly in the face of both internal and external challenges. With a strong emphasis on the public sector, his research explores strategies to build and maintain resilient public organizations that thrive in complex environments.

Stéphanie Verlinden is a postdoctoral researcher at the University of Antwerp’s Department of Management. Her research focuses on organizational change, adaptability, and resilience within public sector organizations, examining topics such as the impact of repeated change, employee proactivity, and the factors that enhance or undermine organizational adaptability. Her work has been published in renowned journals such as Review of Public Personnel Administration (ROPPA), Public Management Review (PMR), and Public Performance & Management Review (PPMR).

Danika Pieters is a doctoral student at the research group Politics & Public Governance, Department of Political Science, Faculty of Social Sciences at the University of Antwerp (Belgium). Her research focuses on public sector change and its impact on civil servants.

Bjorn Kleizen is a postdoctoral researcher at the University of Antwerp. His research focuses on the effects of public sector reform and the psychosocial impact of citizen-state interactions.

Koen Verhoest is full research professor in public governance and coordinator of the GOVTRUST Centre of Excellence as well as of the Politics & Public Governance research group at the University of Antwerp. His research focuses on public governance relations involving autonomy, control, coordination and collaboration and their implications for trust, reputation and innovation.

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