The effect of ageism on the relationship between the intergenerational environment and the innovation capability

O efeito do ageísmo na relação entre o ambiente intergeracional e a capacidade de inovação

Abstract

Purpose:  The study aimed to examine the relationship between the intergenerational environment and innovation capacity, with ageism mediating and moderating this relationship.

Originality/value:  Using the theoretical lenses of Intergroup Contact Theories and the Value of Diversity, this study evaluates ageism across the full range of age groups in organizations and its impact on innovation capability. That aims to address the gap in research that assesses age prejudices among younger and older individuals independently, without considering their potential value for innovation.

Design/methodology/approach:  This is a survey conducted through a structured questionnaire, with 660 respondents. The data were analyzed using partial least squares structural equation modelling (PLS-SEM). An additional analysis compared people under 50 with those 50 or older.

Findings:  The results indicated a positive relationship between the inter-generational environment and innovation capacity, a negative relationship between the intergenerational environment and ageism, and a negative relationship between ageism and innovation capacity. The mediating effect of ageism was partial, showing that there is a harmful effect of ageism as a mediator. However, the moderating effect was not confirmed. Further analysis showed no differences between the age groups of less than 50 years and at least 50 years (50+).

Keywords:
intergenerational environment; ageism; innovation capability; human capital management

Keywords:
ambiente intergeracional; ageísmo; capacidade de inovação; gestão do capital humano

Resumo

Objetivo:  O estudo almejou examinar a relação entre o ambiente intergeracional e a capacidade de inovação, tendo o ageísmo como efeito mediador e moderador dessa relação.

Originalidade/valor:  Com o auxílio das lentes teóricas das Teorias do Contato Intergrupal e a do Valor da Diversidade, o ageísmo é avaliado por toda a gama de faixas etárias convivendo nas organizações e seu impacto da capacidade de inovação. Isso busca preencher a lacuna de estudos que avaliam os preconceitos etários dos mais jovens ou dos mais velhos de forma independente e sem o entendimento do seu valor para a inovação.

Design/metodologia/abordagem:  Trata-se de survey, por meio de questionário estruturado com 660 respondentes. Os dados foram analisados por meio de equações estruturais por mínimos quadrados parciais (PLS-SEM). Uma análise adicional comparou os grupos de pessoas com menos de 50 anos e com pelo menos 50 anos.

Resultados:  Os resultados indicaram relação positiva entre o ambiente intergeracional e a capacidade de inovação, negativa do primeiro com o ageísmo e negativa deste com a capacidade inovadora. O efeito mediador do ageísmo foi parcial, mas mostrando que há um efeito danoso do ageísmo enquanto mediador. No entanto, o efeito moderador não foi confirmado. Uma análise adicional mostrou que não há diferenças entre os grupos etários de menos de 50 anos e pelo menos 50 anos (50+).

INTRODUCTION

The Brazilian population is aging, as shown by the 5.4 percentage points drop in the number of people under 30 years old between 2012 and 2021 (Instituto Brasileiro de Geografia e Estatístia [IBGE], 2022). During this period, the population aged 60 or older increased from 11.3% to 14.7%, and the trend is for a high concentration in the 45-64 age group until 2047 (IBGE, 2022). This scenario indicates that companies will have to handle diverse age groups, thereby preparing for a collaborative, intergenerational environment that includes senior professionals who are not typically in the formal labor market (Manzi et al., 2019; Santos et al., 2022).

The intergenerational environment, as used in this study, refers to the coexistence of workers of different ages within an organization (Petery & Grosch, 2022). Such an arrangement can leverage maturity and changes that foster an inclusive and innovation-oriented culture (Mothe & Nguyen-Thi, 2021); therefore, it can be positively associated with business performance (Li et al., 2021).

Interaction between generations enables knowledge exchange, given that each generation has unique perspectives, cultural norms, and historical contexts, thereby emphasizing the interdependence of generations and recognizing the value of their perspectives and contributions (Santos et al., 2022). The aspect of cohesion and continuity, which allows the transfer and preservation of practices, can foster understanding, empathy, and respect between them, thereby stimulating innovation, growth, and positive social change (Silva & Helal, 2022). That aligns with the Theory of Diversity Value, which holds that different ages provide better answers to corporate problems, thereby improving efficiency (Cox & Blake, 1991). However, there is a subtle prejudice against mature professionals that occurs in organizations’ daily life, is hard to address, and warrants further research on the intergenerational environment (Silva et al., 2021).

According to Li et al. (2021), individuals aged 50 or over (or 50+, a term used randomly in this study), that is, those who belong to the baby boomer and (partly) X generations, experience ageism at work. Ageism is a multidimensional concept that includes age stereotypes (positive and negative beliefs), prejudiced attitudes, and age-based discrimination (Azulai, 2014). That stems from age stereotypes of mature people, who are considered more reliable and experienced but less competent, with lower technological capability, greater difficulty with technology, and less flexibility (Harris et al., 2018). On the other hand, the Intergroup Contact Theory states that quality interaction between intergenerational groups minimizes prejudice (Allport et al., 1954).

Studies on age management and intergenerational relations, focusing on Generations X, Y, and millennials, confirm age discrimination in the hiring process in Brazil (Amorim et al., 2019). On the other hand, hubs, labs, platforms, and programs emerge, forming business ecosystems that support professionals aged 50+ who innovate in practice but are outside the walls and formal mechanisms of innovation in organizations (Santos et al., 2022; Fulmer et al., 2020). Innovation capacity, in turn, can continuously translate knowledge and ideas into new products, processes, and systems for the benefit of a company and its stakeholders (Calik et al., 2017). However, these phenomena are not studied in an integrated way.

Hence, the research goal was to examine the relationships between intergenerational environment and innovation capacity in organizations, mediated and moderated by ageism. The proposed relationships were tested through a survey of 660 professionals from different age groups who work with professionals aged 50+ across various organizations. Data were analyzed by using partial least squares structural equations.

There is previous evidence that employers prefer younger professionals, despite the existence of programs within organizations that aim to change judgmental attitudes toward mature professionals (Previtali et al., 2022). This study shows that companies can benefit from age diversity in developing their innovation capacity by recognizing the need to adjust negative mindsets and behaviors toward older colleagues (ageism). This phenomenon was partially addressed by Cepellos and Tonelli (2022) in their study of the role of mature women in organizations. Hence, our theoretical contribution is the originality of proposing a model that recognizes the influence of ageism in an intergenerational environment aimed at achieving innovation capacity.

There are also practical implications for human resources management. By demonstrating how ageism diminishes the direct, positive impact of intergenerational environments on innovation capability, we illuminate the cultural characteristics of Brazil’s labor market and initiate discussions on organizational relationships and strategies to prevent ageism. That aligns with Silva and Helal (2022), who emphasize the collective aspect and the affective, moral, and financial responsibilities between generations.

THEORETICAL BACKGROUND

Intergenerational environment and age discrimination

Two theoretical approaches help understand the intergenerational environment and its implications for organizations, especially age discrimination. Intergroup Contact Theory holds that high-quality contact between groups reduces prejudice (Allport et al., 1954). It also addresses maintaining the symbolic identity of age without mature individuals crossing the line into becoming equal to young people (North & Fiske, 2013), thereby strengthening relationships within an ecosystem more than internal inter-generational collaboration. The Value in Diversity Theory holds that different age groups offer better solutions to corporate challenges, thereby increasing business efficiency and profitability (Cox & Blake, 1991). In different contexts and under varying complexities, prejudice in working relationships remains one of the obstacles to age inclusion (Cheung et al., 2019).

In this study, intergenerational environments are those in which different age groups interact, thereby increasing human and social capital (Li et al., 2021). Intergenerational conflict practices, recurrent at the contemporary workplace, are identified as socio-emotional selectivity – when younger workers believe that their mature colleagues give more importance to emotional goals instead of prioritizing work – and social identity – when younger people notice excessive differences in building groups based on age, reinforcing their group’s view (Patel et al., 2018).

Although some younger workers value the work of those aged 50 + , ageism still prevails, framing them as resistant, slow, and unwilling to progress, suggesting that to have environments suitable for intergenerational welfare, organizations need practices to reduce tension between generations (Patel et al., 2018). Urbancová et al. (2020) stress that age management is a competitive advantage when it is strategically focused on the efficient use of all age groups.

In the United Kingdom, in the third decade of the century, for the first time, four generations are simultaneously active in the workplace. To address that, managers adopt Reverse Mentoring (RM), a practice in which young people mentor older people, aiming to connect generations (Browne, 2021). Evidence shows that the relationship is reciprocal and creates value in mentoring relationships and knowledge exchange in organizations.

In Brazil, aging has its own characteristics, especially the collectivist approach rather than individualism and, therefore, intergenerational emotional, moral, and financial responsibility, which is the main issue (Silva & Helal, 2022). Hence, Silva et al. (2021) recommend expanding practical studies on intergenerational dynamics, as population aging and subtle discrimination against older people occur within organizations, making confrontation challenging.

Innovation capacity within an intergenerational environment

Innovation is a path to achieving competitive advantage, and innovation capacity is a construct composed of four pillars (product development capacity, innovation capacity, strategic capacity, and technological competence) that explain better business performance (Vicente et al., 2015). One factor contributing to that is an intergenerational environment, since mature professionals generally have greater emotional competence and more diverse social ties, which help maintain a vast network of internal and external connections (Li et al., 2021).

By arguing that an intergenerational environment affects organizational performance through human and social capital, Li et al. (2021) observe that increasing human and social capital through intergenerational diversity can foster an innovative and creative environment. In addition, they state that inclusive age management conveys an image of a fairer, more reliable, long-term-oriented, and more sustainable organization.

Opposite to the age stereotype, growing companies are mostly made up of professionals with an average age of 45 years old, even in technology startups (Azoulay et al., 2018). Age stereotypes that portray more mature professionals as averse to technology, slower, or less eager to learn are associated with adverse work outcomes and employees’ personal adjustment, thereby hindering productivity (Lõssbroek & Radl, 2019). However, the effect of intergenerationality on technological innovation depends on age distribution: It is positive for companies with heterogeneous age groups and negative for those with concentrated age groups (Mothe & Nguyen-Thi, 2021).

Innovation is a social process in which people interact and participate, and human capital has demographic and cognitive characteristics relevant to performance and innovation, provided interactions are well managed (Mothe & Nguyen-Thi, 2021). In this regard, Singh (2021) warned of the future presence of five generations working together in the workplace.

Age diversity creates pools of complementary knowledge and diverse mental models, thereby increasing collective knowledge, whereas age polarization separates the workforce into distinct homogeneous groups, hindering innovation (Mothe & Nguyen-Thi, 2021). As workers’ average age increases, there is concern that organizations may become less innovative. The inter-generational environment will affect relationships, challenging the stereotype that workers aged 50+ are less creative (Rudolph & Zacher, 2022). There is evidence that companies that hire people with diverse characteristics (ages, genders, races, and backgrounds) and include them in their workforce are more innovative and open to change (Chaudhry et al., 2021).

The relationship between intergenerational environment and innovation capacity is consistent with the principles of innovation (Saunila et al., 2014) and the Theory of Value in Diversity (Cox & Blake, 1991). Therefore, the intergenerational environment provides greater intergroup contact, enhancing the value of age diversity and innovation capacity, which is the first research hypothesis.

• H1: An intergenerational environment has a positive effect on innovation capacity.

Ageism in organizations’ intergenerational relations and search for innovation

Several stereotypes lead to prejudice and discrimination, such as gender, race, ethnicity, sexual orientation, or age—ageism (King & Bryant, 2017). Although the latter receives less attention than the others, it is critical in the workplace and in society at large (King & Bryant, 2017). However, it may affect life satisfaction more than the work environment (McConatha et al., 2022).

Hence, the current situation for workers aged 50+ is paradoxical, as the labor market is becoming increasingly mature and decision-makers tend to prefer younger workers (Rego et al., 2022). And when they choose mature workers, these are not adequately included and engaged, affecting the work environment and quality of life.

In a scenario where employers are unprepared to address aging and combat ageism in the workplace (Egdell et al., 2020), age discrimination may play a more significant role than engagement and reflection on variables related to climate and job satisfaction (McConatha et a., 2022). These are complex biases in the composition of society that include historical, cultural, and economic factors, among others (McConatha et al., 2022).

This is further supported by improvements in the quality of life of older adults, which allow them to age well, remain in good health, and remain economically active (Dalmer et al., 2022). Therefore, programs that combat age stereotypes should encourage respectful treatment of older adults in the workplace (Leedahl et al., 2020). However, even when expressing positive attitudes toward mature adults, many leaders choose younger people, although those aged 50+ are more productive and experienced (Rego et al., 2022).

Opposite to expectations, ageism is more common in Eastern than in Western societies (Ayalon & Cohn-Schwartz, 2022). Brazil needs age-management policies to increase the number of mature professionals active in the labor market, given population aging (Silva et al., 2021).

According to the authors cited, aesthetic interventions are a means of overcoming prejudice against appearance and protecting against ‘lookism’ (Cepellos & Tonelli, 2022). The “aged glass ceiling” hinders employees’ advancement within the organization, and “age sanitation” is justified to preserve its financial health.

The multi-layered approach to ageism, by exploring GATE—gender, age, tenure, experience—also confirmed the impact of the phenomenon across its different features, indicating the intersectionality of ageism (North, 2022). It was also found that mature women must choose between appearing authentic and competent (Cecil et al., 2022) and having their welfare affected by discrimination in the labor market (Sigurðardóttir & Snorradóttir, 2020).

The relationship between intergenerational environment and ageism is based on the Theory of Value in Diversity (Cox & Blake, 1991), where different age groups provide more solutions to organizational challenges, enhancing business efficiency, and on the Intergroup Contact Theory (Allport et al., 1954), which establishes that high-quality intergroup contacts reduce discrimination. However, given the various implications of ageism, simply building intergenerational teams is not sufficient to combat prejudice in the workplace (França et al., 2017).

The mentioned studies show that an intergenerational environment can affect ageism negatively, that is, reduce it, and that ageism can inhibit organizations’ innovation capacity, which led to the second and third research hypotheses.

• H2: The intergenerational environment negatively affects ageism.

• H3: Ageism negatively affects innovation capacity.

Given hypotheses H2 and H3, ageism can indirectly influence the relationship between an intergenerational environment and innovation capacity. Therefore, we developed the fourth research hypothesis.

• H4: Ageism mediates the relationship between intergenerational environment and innovation capacity.

Among the attributes of diversity, age is the most conducive to innovative attitudes and behaviors, as age differences are among the main pillars of a climate open to innovation (Chaudhry et al., 2021). Therefore, we argue that any prejudice against older people (ageism) is a conditional effect that can weaken the positive relationship between the intergenerational environment and innovation capability. That led us to the fifth hypothesis.

• H5: Ageism moderates (weakens) the relationship between intergenerational environment and innovation capacity.

METHODOLOGY

To achieve the research goal, we used a quantitative approach, with primary data and a cross-sectional sample. The target population included individuals who had professional contact with professionals of different ages and generations at work, or those who had previously worked in the labor market across various sectors, including both private and public companies. The sampling technique adopted was a snowball sampling method. This approach proved appropriate because it enabled reaching individuals with relevant experience in the phenomenon under investigation.

The use of this technique enables building trust between participants, increasing their willingness to collaborate and provide more authentic data (Goodman, 1961), and allowing for the progressive expansion of the sample based on referrals from participants, which is helpful in research with specific audiences, as in this study (Biernacki & Waldorf, 1981). Despite its effectiveness in recruiting respondents, the technique has limitations, such as the potential for selection bias due to the homogeneity of social networks and the inability to statistically generalize the results to the entire population, given the non-random nature of the sample (Heckathorn, 1997; Baltar & Brunet, 2012).

We used scales validated in the literature: Workplace Age Discrimination (WADS) (Marchiondo et al., 2016) reduced (as indicated by the authors) with nine indicators to assess ageism in the workplace; Workplace Intergenerational Climate Scale (WICS) (King & Bryant, 2017), for the intergenerational environment construct, with four indicators representing the Positive Intergenerational Affect (PIA) subscale, that is, positive intergenerational affect; and Measurement of Innovation Capability (Calik et al., 2017), for the innovation capacity construct, with five indicators. The constructs were measured using a 5-point Likert scale (“1 – strongly disagree”; “2 – disagree”; “3 – neutral”; “4 – agree”; “5 – strongly agree”).

To ensure ethical conduct in scientific research involving human subjects, the questionnaire explained the study and obtained respondents’ consent to participate in a direct question. We used it as a control question to ensure reaching the target audience: “Have you ever worked or do you currently work with different age groups (younger and older people)?” In addition, the questionnaire had sociodemographic questions to characterize the sample.

We ran a pre-test with 15 respondents in June 2023, without requiring significant changes. The link to the questionnaire, developed in Google Forms, was sent by WhatsApp, email, and social media to potential respondents. Answers were received between July and August 2023.

The collected results were analyzed using partial least squares structural equation modeling (PLS-SEM—Partial Least Squares Structural Equation Modeling) with SmartPLS v.3.3.3. We reached a sample of 660 respondents, which is similar to the 2022 IBGE census, which revealed an aging population. In this methodology, the algorithm is based on correlation, simple, and multiple regression to estimate the structural equation model presented. We assessed the measurement model’s convergent validity and discriminant validity. After this stage was completed, the structural model partially confirmed the hypotheses. The procedures align with Bido and Silva (2019), Hair et al. (2019), and Sarstedt et al. (2022).

The sample, whose characteristics are shown in Table 1, was primarily composed of males (66%), aged 34-49 years (49.6%), with postgraduate degrees (58.1%) and formal employment contracts (51.9%). In terms of length of service and income, they are distributed across all age groups. Participation among young people aged 18 to 25 is lower (6.1%). This profile is consistent with the 2022 IBGE census, which shows an increase in the representation of mature workers in the workforce. From 2012 to 2021, the participation of workers aged 40+ in Brazil increased from 38.6% to 45.1%, representing almost 11.5 million workers in this age group. The participation of people aged 18 to 24 fell from 17% to 14% (IBGE, 2022).

Table 1
Demographic data

	Demographic data	%
Gender	Male	66%
	Female	34%
	Not declared	-
Age group	18–25	6.1%
	26–33	11.0%
	34–41	22.8%
	42–49	26.8%
	50+	33.3%
Educatior	High Schoo	5.2%
	Undergraduate	18.8%
	Lato Sensu graduate	58.1%
	Master/PhD	17.9%
Working time	10 or fewer	23.4%
	11–20	26.7%
	21–29	25.3%
	30+	24.6%
Professional Link	Formal contract	51.9%
	Self-employed	22.0%
	Retired	5.9%
	Third Sector	1.1%
	Civil servant	5.3%
	Others	13.8%
Monthly revenue	Less than 1 minimum wage	3.7%
	1–5	27.9%
	5–9	20.0%
	10–14	15.9%
	15–19	8.0%
	20+	15.9%
	Did not answer	8.6%

DATA ANALYSIS

Measurement model

To validate the measurement model, we checked the external loads of the construct variables (Table 2). According to Hair et al.’s (2019) recommendation, the values must be above 0.70. The results achieved validate the proposed model. Following the author’s guidance, variables AGE1 (Professionals aged 50+ are overlooked for a job/task due to their age) and AMB4, reverse (People work better with others of the same age) were removed because their load was below 0.6, according to recommendations by Bido and Silva (2019). For all constructs, there was a trend toward agreement, despite high dispersion in the answers as indicated by the standard deviations.

Table 2
Means, standard deviations, and external loads per variable

		Constructs and Variables	External load	Mean	Std. Deviation
Ageism	AGE2	The contributions of professionals aged 50+ are not as valued due to their age.	0.703	4.689	0.574
	AGE3	Professionals aged 50+ have fewer opportunities to express their ideas due to their age.	0.774	4.694	0.523
	AGE4	Professionals aged 50+ are unfairly evaluated less favorably due to their age.	0.802	4.417	0.730
	AGE5	Professionals aged 50+ receive less social support due to their age.	0.740	2.883	1.240
	AGE6	Professionals aged 50+ are treated as if they were less capable because of their age.	0.845	2.640	1.174
	AGE7	Professionals aged 50+ are treated with less respect due to their age.	0.818	3.068	1.182
	AGE8	Someone delayed or ignored requests from a professional aged 50+ because of their age.	0.801	2.922	1.131
	AGES	Someone blamed professionals aged 50+ for failures or problems due to their age.	0.770	2.944	1.210
Intergenerational environment	AMB1	I feel comfortable when colleagues from other generations try to talk to me.	0.829	2.340	1.060
	AMB2	I enjoy interacting with colleagues from different generations.	0,840	2,325	0.964
	AMB3	My colleagues from other generations are interesting and unique individuals.	0.788	2,421	1.064
Innovation capacity	CIN1	My company recognizes the importance of introducing new ideas and methods to enhance key processes.	0.809	3,984	0.849
	CIN2	My company supports employees in taking the initiative to create new ideas.	0.869	3.984	0.925
	CIN3	My company’s employees skillfully turn information from internal and external sources into valuable knowledge for our company.	0.758	3.729	0.892
	CIN4	My company encourages collaboration and the exchange of ideas between departments to create new approaches.	0.838	3.725	0.988
	CIN5	Our company experiments with new ideas and methods to provide innovative solutions to customer problems.	0.837	3.857	0.898

The internal consistency of the model constructs is shown in Table 3. For Cronbach’s alpha, values should be in the range between 0.70 and 0.90 (Hair et al., 2019), which was achieved. For Composite Reliability (CR) and the Dillon-Goldstein coefficient (rho A), values should be above 0.7, and for Average Variance Extracted (AVE), values above 0.5 (Hair et al., 2019). All values were duly achieved, according to Sarstedt et al. (2022). Therefore, the convergent validity of the model was confirmed. We also note that, in our sample, there is a tendency toward indifference toward the perception of organizational ageism. At the same time, there is a tendency to agree with the perception of an intergenerational environment and innovative capacity.

Table 3
Convergent validity

	Mean	Std. Deviation	Cronbach’s Alpha	Dillon-Goldstein’s Rho_A	CompositeReliability (CR)	Average Variance Extracted (AVE)
AGE	2.693	0.884	0.910	0.920	0.927	0.613
AMB	4.600	0.498	0.755	0.756	0.859	0.671
CIN	3.856	0.751	0.882	0.894	0.913	0.678

• Note. AMB – Intergenerational environment; CIN – Innovation capacity; AGE – Ageism.

Table 4 shows the results of Fornell and Larcker’s (1981) criterion for discriminant validity, which was achieved by all constructs. The square root of the Average Variance Extracted (AVE) for each construct, as shown diagonally, has higher values than those for the other constructs, both vertically and horizontally.

Table 4
HTMT Criterion by Henseler et al. (2015)

	AGE	AMB	CIN
AGE
AMB	0.126
CIN	0.272	0.262

• Note. AMB – Intergenerational environment; CIN – Innovation capacity; AGE – Ageism.

Discriminant validity was confirmed by the Heterotrait-Monotrait Ratio (HTMT) criterion of correlations, by Henseler et al. (2015), which requires values below 0.85 for conceptually distinct constructs. The results are shown in Table 4. According to Sarstedt et al. (2022), this test is sufficient. Nevertheless, discriminant validity was also tested and confirmed by Fornell and Larcker (1981) and Chin (1998) criteria.

Structural model

Based on the proposed model’s hypotheses, we identified the direct effects shown in Table 5.

Table 5
Direct effects of the proposed model

	Hipóteses	β	Desvio Padrão	Estatística t	p-valor	f2	VIF
AMB -> CIN	H1 (+)	0,183	0,039	4,746	0,000	0,040	1,012
AMB -> AGE	H2 (-)	−0,109	0,042	2,571	0,010	0,012	1,000
AGE -> CIN	H3 (-)	−0,242	0,040	5,966	0,000	0,061	1,012

• Note. AMB – Intergenerational environment; CIN – Innovation capacity; AGE – Ageism.

The three hypotheses with direct effects were confirmed, as there is a positive, statistically significant relationship between the intergenerational environment and innovation capacity (β = 0.183). There is also a negative, statistically significant relationship between the intergenerational environment and ageism (β = −0.109), as well as between ageism and innovation capacity (β = −0.242). There were no problems with multicollinearity, as the Variance Inflation Factor (VIF) in all relationships was below 5.000 (Bido & Silva, 2019).

Still, regarding the model’s quality, Table 5 shows that, for hypotheses H1, H2, and H3, f², which indicates the strength of the association between the constructs, is average according to Cohen (1988). The analysis using Pearson’s adjusted coefficient of determination (R²) is 9.8% for Innovation Capacity. The adjusted R² measures the proportion of the endogenous variables’ variance that the structural model explains. According to Hair et al. (2022), values below 0.25 are considered weak. But according to Cohen (1988), this is the median value for studies in the social and behavioral sciences. That reveals that there are still other variables that affect innovation capacity beyond the intergenerational environment and ageism, which should be considered in future research, such as innovation culture, leadership styles, resources and infrastructure, internal processes, and the external environment.

Concerning indirect effects, as shown in Table 6, H4 indicates partial mediation, since the central relationship between intergenerational environment and innovation capacity is significant. However, the direct effect of the intergenerational environment construct on innovation capacity (β = 0.183) is attenuated when ageism is included (β = 0.026). Regarding the expected moderation of ageism in the relationship between intergenerational environment and innovation capacity, there was no confirmation; that is, ageism is not capable of weakening this relationship.

Table 6
Indirect effects of the proposed model

	Hypotheses	β	Std. deviation	t statistics	p-value
MED / AMB → AGE → CIN	H4 (+)	0,0 26	0.011	2,296	0,02°
MOD AGE / AMB → CIN	H5 (−)	0.071	0,045	1.587	0.113

• Note. AMB – Intergenerational environment; CIN – Innovation capacity; AGE – Ageism.

The results of the proposed model are shown in the path diagram of Figure 1.

Figure 1
Path diagram of the proposed structural model

Additional analysis

Table 7 shows the procedures adopted for the Measurement Invariance of Composite Models Assessment (MICOM), with 10,000 permutations, for the age groups of respondents: those under 50 years of age (414 respondents) and those aged 50 years or older (216 respondents). Data indicate that composition invariance was kept, with no need to exclude variables for this predefined group.

Table 7
MICOM Procedures

Predefined data groups	Configuration invariance (Step 1)	Composition invariance (Step 2)	Removed Variables	Final composition invariance (Step 2)
Under 50 years old and at least 50 or older	Yes	Yes	No	Yes

• Note. Dummy for age groups under 50 (1) and 50+ (0).

As a result, Table 8 shows that there are no statistically significant differences (p-value < 0.05) between these groups in the proposed structural relationships.

Table 8
MICOM analysis – differences between groups

	At least 50	Less than 50	Difference between groups	Difference from the permutation average	2.5%	97.5%	p-value of permutation
AMB -> CIN	0.452	0.499	−0.047	0.007	−0.138	0.142	0.520
AMB -> GEN	−0.293	−0.217	−0.075	−0.009	−0.174	0.159	0.379
GEN -> CIN	−0.235	−0.089	−0.146	0.001	−0.164	0.169	0.087

• Notes. AMB – Intergenerational environment; CIN – Innovation capacity; AGE – Ageism. (1) Dummy for age groups under 50 (1) and 50+ (0).

RESULTS AND DISCUSSION

Evidence shows a positive relationship between the intergenerational environment and innovation capacity, in line with Mothe and Nguyen-Thi (2021), who considered the intergenerational environment a lever for maturity and organizational change. By verifying that the intergenerational environment positively affects innovation capacity, but that this is not moderated by ageism, the research aligns with Allport et al.’s (1954) Intergroup Contact Theory, which posits that high-quality contacts between different groups can reduce age discrimination.

Also, by highlighting that contact between different age groups can positively affect innovation capacity, the results confirm the Value in Diversity Theory (Cox & Blake, 1991), which argues that other age groups can provide better solutions to corporate challenges, increasing innovation capacity.

The results are consistent with those of Li et al. (2021), who highlight that the intergenerational environment can enhance performance by increasing human and social capital, which are essential for innovation. Similarly, Chaudhry et al. (2021) demonstrated that companies hiring individuals with diverse characteristics are more innovative and open to change, aligning with the results.

The theory also addresses the preservation of symbolic identity of age without mature individuals crossing the line towards becoming like younger people (North & Fiske, 2013), which can strengthen relationships in ecosystems more than internal intergenerational collaboration. Advances in Value in Diversity Theory indicate that different age groups provide better solutions to corporate challenges, increasing business efficiency and profitability (Cox & Blake, 1991). The results align with these streams of theoretical thinking.

Santos et al. (2022) emphasized that intergenerational interdependence is essential for preserving practices and fostering empathy and respect, thereby stimulating innovation, as shown in this study. Lagacé et al. (2022) also indicated that intergroup contact and knowledge sharing are associated with positive attitudes toward mature workers. Evidence confirming the direct relationship between the intergenerational environment and innovation capacity is consistent with these authors.

Given the presence of older age groups in leadership positions, it is possible that ageism, in the sense of prejudice against older people (50+), may not be relevant in the Brazilian intergenerational environment. It is also possible that the young Brazilian population is learning to be inclusive, or that the difficulty they face in entering the market leads to a higher average age and less age-based judgment (Freire & Saboia, 2021). That is confirmed by the fact that younger people are considered the truth generation, who avoid labels and are naturally more open to diversity (Francis & Hoefel, 2018). We can conclude that measures for intergenerational environmental management are implemented within companies but have not been fully established at the highest levels of the organizational structure, where strategic decision-making requires a wide range of perspectives and diverse views, as previously noted by Rutkowska (2020).

By demonstrating that the intergenerational environment negatively impacts ageism and, consequently, innovative capacity, we can conclude that people are still learning to respect the intergenerational environment, reflecting a society in transition, in line with Silva et al. (2021) and Silva and Helal (2022). The confirmation that ageism is negatively affected by the intergenerational environment, and that this negatively affects innovation capacity, partially aligns with the findings of Patel et al. (2018), who argued that young people have a positive perception of older people, valuing their social skills, work, and characteristics.

On the other hand, these authors mention that there is a perception that older people are resistant to change, less proficient with technologies, and less eager for progress. Their research was qualitative, gathering narratives that deepened respondents’ subjectivity. Additional analyses in our study showed invariance between the groups under 50 years old and those aged at least 50, a finding that contradicts the results of Patel et al. (2018). In this sense, the quantitative evidence of our study enriches the discussion.

The partial mediation of ageism in the relationship between intergene-rational environment and innovation capacity provided a relevant insight. The effect among the three constructs is lower than the effect between inter-generational environment and innovation capacity. This indicated that ageism is present in the sample, though only partially. However, it should not be taken to the point of moderating, in the sense of weakening the direct positive relationship between intergenerational environment and the capacity for innovation. That may be related to characteristics of Brazilian aging, which is more collectivist and involves intergenerational emotional, moral, and financial responsibility (Silva & Helal, 2022). It is also possible that age discrimination was not detected because the data were based on self-perception. In other words, self-perceived ageism can be veiled, ashamed, or not explicit. However, we cannot discard its existence.

An additional analysis showed that, for the model studied, there are no differences in perceptions between the age groups under 50 and 50+. Therefore, the innovation capacity was not assigned to younger generations, confirming Rudolph and Zacher (2022), who denied the stereotype that workers aged 50+ are less innovative and that aging human capital is associated with lower innovation climates. On the other hand, this does not exclude the training of mature professionals to develop the skills required for new and future careers in the digital age (Singh, 2021).

CONCLUSIONS

The results indicate that an intergenerational environment positively affects innovation capacity and negatively affects ageism. In addition, ageism negatively impacts innovation capacity. Therefore, ageism partially mediates the relationship between the intergenerational environment and innovation capacity, but does not moderate (weaken) it. The model studied did not show differences in perceptions between the age groups under 50 and 50+.

Hence, we recommend that organizations adopt an approach to the inclusion of workers aged 50+ to promote age diversity, thereby generating benefits such as increased social and human capital that positively affect innovation capacity. That does not mean ageism does not exist, since evidence shows that the direct effects of the intergenerational environment and innovation capacity constructs are lower when ageism is considered. These results also highlight the need for people management to adopt a more strategic approach to valuing the intergenerational environment and to avoid ageism in developing innovation capacity.

As a theoretical contribution, this study seeks to fill a gap in Brazilian research on ageism across different age groups and generations. It was possible to identify some prejudice against mature individuals, and that the coexistence of age diversity can be positive for organizations. That suggests that awareness and understanding of the topic are growing as the population ages, leading to a greater presence of mature professionals in organizations.

In addition to the results for the company, it is also essential to consider that an intergenerational environment can create a more inclusive and diverse workplace, allowing employees to expand their repertoire as they interact with different generations. Younger employees can benefit from the maturity and experience of older colleagues. In comparison, more mature professionals can benefit from younger employees’ ease with new technologies and multitasking skills, especially among digital natives such as Generation Z.

In practice, the results can help organizations acknowledge the importance of the intergenerational environment and guide managers and Human Resources professionals toward achieving a more inclusive one. As a benefit, we expect that, alongside innovation capacity, organizations will be more sustainable, providing comprehensive solutions that address society’s contemporary challenges.

The study has limitations, including a sample composed mainly of high-income, highly educated male professionals, which may have led to biased results. There are also other populations, such as people with disabilities, LGBTQIA+, black people, native people, immigrants, etc., and professional groups, like athletes, who were not emphasized in the study but may lead to specific findings if studied in light of ageism. Still, the methodology adopted relied on respondents’ self-perceptions, which may have led them to ignore their own prejudices and bias their answers. Therefore, the sample does not allow generalization of results due to its limited representation and socioeconomic profile.

However, limitations offer opportunities for future studies, such as addressing more diverse groups. In addition to those mentioned above, younger professionals and young companies, such as startups, should be addressed. We also suggest comparing across segments (public, private, and business) and firm sizes (micro, small, medium, and large), and further exploring the role of women in organizations regarding ageism. Studies on gender ageism have revealed that women often have to choose between feeling authentic and appearing competent. Mature women can help explain practices such as “age sanitation” and the “aged glass ceiling,” observed across different organizational segments or sizes, which may require more targeted inclusion, awareness, or flexibility efforts. In addition, the application of qualitative methods or multi-method approaches can expand understanding of the ageism phenomenon in a context of generational plurality and pressure to innovate toward sustainable organizations.

Hence, the proposed model provides relevant insights and offers opportunities for replication or expansion across other social and organizational contexts.

• RAM does not have information about open data regarding this manuscript.
• RAM does not have permission from the authors or evaluators to publish this article’s review.

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Edited by

• EDITORIAL BOARD
  Editor-in-chief
  Almir Martins Vieira

• Associated editor
  Renata Silva de Carvalho Chinelato

• Technical support
  Vitória Batista Santos Silva

• EDITORIAL PRODUCTION
  Publishing coordination
  Andreia Ferreira Cominetti

• Editorial intern
  Bruna Silva de Angelis

• Language editor
  Bardo Editorial

• Layout designer
  Emap
  Graphic designer
  Emap

Data availability

RAM does not have information about open data regarding this manuscript.

Publication Dates

• Publication in this collection
  06 Mar 2026
• Date of issue
  2026

History

• Received
  01 Mar 2024
• Accepted
  14 Aug 2025