HRM Policies’ Impact on Employees’ Employability: The Role of Age Climate and the Offering of Developmental Measures

Abstract

How can (aging) individual employees continue to grow as persons, on the one hand, and contribute to their employing organizations in meaningful ways, on the other hand? In this article, we set out to study how two important instruments of human resource management—age climate and the offering of developmental measures—may help. We use path modeling to investigate the research model based on data of 208 respondents of both rural and urban regions in Austria. The results indicate that a positive organizational age climate has a positive influence on the offering of developmental measures and, subsequently, on employees’ employability. We emphasize the importance of facilitating developmental measures for staff of all ages, as well as the necessity to address the organizational age climate to successfully tackle the challenges of demographic change.

1. Introduction

In the last decades, both human resource (HR) managers and policymakers have been struggling with the question of how (aging) employees can continue to grow as persons and contribute to their employing organizations in meaningful ways. These questions have made employability, the ability to continuously fulfill, acquire, or create work (Van der Heijde and Van der Heijden 2006) an important issue; not only for individuals but also for their respective organizations (Gerken et al. 2018; Van Harten 2016).

Previous research has thoroughly discussed antecedents of employability at the level of individuals. This includes factors such as chronological age (Froehlich et al. 2015a), education and job-related skills (Wittekind et al. 2010), personality traits (Wille et al. 2013), learning activities (Habets 2012; Van der Heijden et al. 2009), or motivational antecedents (Froehlich et al. 2015b; Raemdonck et al. 2015). Also, broader, macro-level influences, such as institutional, cultural, or regional effects have been discussed previously. In fact, preceding definitions of employability made very explicit references to the labor market (Gazier 2001).

However, relatively little is known about the meso-level of antecedents (Van Harten et al. 2017). While there is some knowledge about how a direct supervisor may influence individuals’ employability (Froehlich et al. 2014a; Leisink and Knies 2011; Van Harten et al. 2016), research about broader, organization-wide policies that are related to age (Kooij et al. 2014) and employees’ employability is lacking. Unfortunately, it is information on this level that would be most practical for HR managers—Do HR (development) policies foster employability among (older) employees? What is the role of the organizations’ general stance towards older employees for that relationship?

Therefore, this article focuses on two influences on employees’ employability that address the topics of aging and development, both of which have been identified as important antecedents of employability in more general settings. First, we focus on an organization’s offering of HR developmental activities. To take a rather broad view on this matter, we include on-the-job, off-the-job, and parallel-to-the-job measures. Second, we consider the age climate fostered by the organization as an arguably softer element of organizational influence. After all, Hedge (2008) has mentioned age management and cultural aspects as a major lever of strategic HR management to thrive amidst the demographic shift. Here, age climate means an individual’s impression of the shared perception of existing age stereotypes in the working environment (Noack 2009).

We aim to make two major contributions to the literature about HR management:

• We focus on two levers that HR managers may pull to help employees in becoming more employable: providing a positive age climate and offering specific HR developmental measures. Next to contributing information about the aforementioned gap concerning meso-level influences, we also test the relationship between these two concepts.
• Another contribution that complements previous research is our inclusion of rural regions. Employment—especially employment of older people—is different in rural regions than in urbanized areas (Copus et al. 2006). This represents a link to the somewhat older, demand-based perspective of employability (Froehlich et al. 2018; Gazier 2001). It is important that the empirical studies of the field reflect the heterogeneity of the “real world”; and while this is not the primary focus of this paper, we contribute to closing this gap in the field.

Our guiding research model, which will be discussed in more detail in the following section, is depicted in Figure 1.

2. Theoretical Background

2.1. Employability

Since the 1980s and 1990s, employability research has been focused on the implications of employability for the labor market (Gazier 1998, cited by McQuaid and Lindsay (2005)), the responsibility of each individual to strengthen his or her skills, as well as the interactivity of employability between an individual’s initiative for skill adoption and the labor market demands (Van der Heijde and Van der Heijden 2006). Three perspectives emerged, viewing employability as:

• The sum of knowledge, skills, and other inputs that an individual has (e.g., Fugate et al. 2004; Van der Heijde and Van der Heijden 2006);
• Individuals’ assessments of employability outcomes (e.g., how they perform on the labor market) (e.g., Nelissen 2016; Vanhercke et al. 2014);
• The individuals’ subjective fit with the labor market demands (e.g., De Cuyper et al. 2008).

In this research, we take a learning perspective and are especially interested in how employees build their competencies and thus adopt the competence-based view on employability (Van der Heijde and Van der Heijden 2006).

In times of fast-changing work requirements, a definition of employability based on labor market demands is increasingly inappropriate. Therefore, Van der Heijde and Van der Heijden (2005) conceptualized employability as a set of five competences that are essential for “the continuous fulfilling, acquiring or creating of work” (p. 143). First, occupational expertise such as having the necessary technical know-how is seen as an essential fundament of employability (Froehlich et al. 2018). Furthermore, the adaption of job and career requirements due to potential changes or developments in both proactive and self-initiating ways (i.e., anticipation and optimization) as well as more reactive ways (i.e., personal flexibility) are two further important components of employability. Corporate sense—the willingness to identify with organizational goals and to participate in a workgroup with shared responsibilities—is necessary. The last competence of employability is the appropriate balance between the interests of employers and employees. This set of competences fits naturally to our research model that is focused on themes of learning and development (see below).

2.2. Human Resource Development and Its Role for Individuals’ Employability

2.2.1. Categorizing Human Resource Development Measures

Competency development of individuals is of utmost importance so that individuals can perform effectively and meet performance expectations to enhance organizational effectiveness (Potnuru and Sahoo 2016). Through the demographic shift in industrial countries and the decreasing number of employees in the younger generation, organizations are challenged with maintaining employees’ employability for a longer period of time. HR development plays a crucial role in that (Rimser 2014; Staudinger and Heidemeier 2009)—in conjunction with individual influences (Van Harten et al. 2017). Potnuru and Sahoo (2016) showed that there is a significant positive relationship between HR development interventions, such as trainings, and employee competencies that furthermore impact organizational effectiveness positively. Besides, HR development and learning opportunities can be seen as long-term motivators for older (and younger) employees (Kluge and Krings 2008). Still, studies report that exclusion from training and development activities occurs within organizations and is often based on negative stereotypes about a certain age group (Truxillo et al. 2017).

As a huge amount of different developmental measures exists, we focus on measures that are discussed as especially effective for older employees. We discuss them in three categories: on-the-job measures, off-the-job measures, and parallel-to-the-job measures. In the category of on-the-job developmental measures (Jacobs 2003), which enable learning-by-doing directly at the place of work, we include job enlargement, job enrichment, job rotation, and heterogeneous working groups (McCauley and Brutus 1998). While job enlargement extends the variety of tasks an employee has in the current position, job enrichment enables more responsibility as well as more autonomy of one’s duties. An extension of task variety can also be ensured with rotating tasks within a work group (i.e., job rotation). Heterogeneous working groups enable the exchange of knowledge and know-how between different age groups (Iweins et al. 2013). As learning often occurs informally (Bencsik et al. 2019), these practices have gained importance in organizations (Cross 2007). Learning directly at the workplace can minimize resistance to learning and enables employees to use their expert knowledge and are, therefore, especially suitable for older employees (Froehlich 2017).

While on-the-job training is less structured and does not always happen consciously (Marsick and Watkins 2001; Mulder 2013), several developmental measures like trainings, congresses, self-studies, or online trainings are still highly structured and take place outside the employee’s workplace. These are subsumed as off-the-job developmental measures.

Trainings that support and motivate employees in their daily working life are categorized as parallel-to-the-job trainings. For instance, experienced employees can be deployed as mentors or coaches for recently hired employees. Both experienced and new staff can benefit from that kind of developmental measure. While the former transmits her knowledge and gains recognition, the latter has a more facilitated organizational entry or promotion and builds a productive collegial network more rapidly.

2.2.2. The Impact of Human Resource Development Measures on Employability

Several empirical findings suggest that different kinds of developmental measures can have a significant impact on employees’ employability. For instance, trainings were positively related to both internal (Sanders and De Grip 2004) and external employability (Groot and Van den Brink 2000). Van der Heijden et al. (2009) found positive relationships of job-related trainings with occupational expertise, anticipation, and optimization, as well as corporate sense. In a study by Froehlich et al. (2014b), formal learning was positively related to anticipation and optimization, while informal learning was positively related to occupational expertise, anticipation, and optimization, as well as personal flexibility. Martini and Cavenago (2017) found positive relationships of on-the-job training opportunities like job enlargement, job enrichment, and job rotation on employees’ employability. Also, supervisor’s support through, for instance, mentoring improved employees’ employability.

The majority of the studies investigating the relationship between developmental measures and employability focused on the amount of time an employee has participated in a specific measure. Whether an employee is participating in a developmental measure depends on at least two factors: the employer’s offering of developmental measures as well as the employee’s motivation and willingness to participate (Veld et al. 2015). Ybema et al. (2017) found evidence that an employer’s offering of several developmental practices like trainings, job enlargement initiatives, or opportunities for job rotation increase employees’ employability. In line with these results, we expect positive effects of the mentioned developmental measures on individuals’ employability.

Hypothesis 1.

The offering of developmental measures is positively related to employees’ employability competences in terms of (a) occupational expertise, (b) anticipation and optimization, (c) personal flexibility, (d) corporate sense, and (e) balance.

2.3. Age Climate

The relationship between age and employability was often studied in the past, but with mixed results ranging from negative relationships (Raemdonck et al. 2012; Rothwell and Arnold 2007; Van der Heijden 2002; Wittekind et al. 2010) to positive effects (Froehlich et al. 2014b; Patrickson and Ranzijn 2003; Van der Heijden et al. 2009). These mixed effects may stem from other determinants like generational effects (Hall et al. 2007; Meriac et al. 2013) or age stereotypes (Ahmed et al. 2012; Iweins et al. 2013; Malinen and Johnston 2013).

In general, the perception of older employees’ competences in the workplace still seems to be more negative than positive. For instance, older employees are perceived as less productive, less flexible, less willing to learn, and more resistant to change (Posthuma and Campion 2009). The presence of such age stereotypes is negatively related to several outcomes, such as individual performance, health, or motivation to learn (Kite et al. 2005; Ng and Feldman 2012; Posthuma and Campion 2009) and might lead to an age-discriminative behavior. To describe how older employees are seen in a company, different climate conceptions have been developed. While some researchers have focused on either age-supportive or age-discriminative organizational behavior (Boehm et al. 2014; Cadiz 2010; Kunze et al. 2011), Noack (2009) developed the concept of the psychological age climate as an employee´s perception of existing positive age stereotypes in the work environment. He found evidence for agreements in individual age climate ratings within a company as well as for significant differences in ratings between companies (Noack 2009). That said, it is important to consider how employees perceive discriminatory practices, as they are as much a problem for organizations as actual discrimination, since employees’ beliefs, whether or not they are consistent with reality, affect their behaviors (cf. Bieling 2010).

Although empirical research on age climate in the HR management context is scarce, some studies indicate that existing age stereotypes in organizations can lead to discrimination of aging employees (Loretto and White 2006). It has been shown that organizational age climate functions as a predictor of several organizational (Kunze et al. 2011) and individual outcomes (Noack 2009).

Noack (2009) has found a positive relationship between age climate and work-ability, a construct strongly related to employability. Therefore, we expect that the shared positive perception of older employees in an organization will enhance employees’ employability. We argue that in an environment where the experience of older employees is appreciated, the employees will not only be more satisfied with their job or less willing to drop out (Bilinska et al. 2016), but also will find ideal conditions to adapt to fast-moving labor demands.

Hypothesis 2.

A positive age climate is positively related to employees’ employability competences in terms of (a) occupational expertise, (b) anticipation and optimization, (c) personal flexibility, (d) corporate sense, and (e) balance.

2.4. The Interplay between Age Management and Developmental Measures

Organizations have an incentive to discriminate against older employees when it comes to investments in human capital. After all, the benefit of such an investment is temporarily limited, if the trained employee is to be retired soon or might leave due to other reasons like deteriorating health or care for other family members. While such policy may seem economically sound, it stands in conflict with an empowering age climate. Therefore, we suggest that the two major antecedents of employability discussed in this article—the offering of developmental measures and age climate—are interrelated. For instance, Boehm et al. (2014) found that age-equal access to further education and trainings in organizations is positively related to the employees’ perception of the organizational age-diversity climate. While these authors argue that the offering of developmental measures to all age groups has a positive impact on how members of different age groups are treated, we argue in line with Noack (2009) that a more positive age climate—and, therefore, less negative stereotypes against older employees—can also lead to a higher willingness to offer developmental measures also to that age group. This is also in line with the statement from Van Dam et al. (2017) that stereotypes about the lesser ability to learn or the poor performance against older employees lead to fewer career and development opportunities. Because of these decreased opportunities to develop themselves, older employees’ know-how and competences may not be at adequate levels and neither is their employability. However, in an environment where high competences and experience are recognized as a resource, managers may support their employees to develop their skills by offering them different developmental measures (Noack 2009).

Hypothesis 3.

A positive age climate is positively related to the offering of developmental measures and thus has indirect relationships with employees’ employability competences in terms of (a) occupational expertise, (b) anticipation and optimization, (c) personal flexibility, (d) corporate sense, and (e) balance.

3. Method

3.1. Sample

While former employability research collected data often in urban regions, we included companies located in both rural and urban Austrian regions in the sampling frame. Given that urban and rural areas may differ substantially in terms of employment and employability-related matters (Copus et al. 2006), we believe that this is an important step in aligning empirical research with the challenges of practice.

We contacted companies of all sizes via e-mail; links to the online survey were then distributed via the respective HR departments and via professional networks. In total, 375 individuals followed the link and 208 (55.4%) completed the questionnaire. The respondents were on average 36.5 years old (SD = 10.35 years) and have worked for 10.0 years (SD = 9.27 years) on their current job. A total of 132 (63.5%) respondents were female. The sample included 96 (46.2%) respondents between 15 and 34 years, 88 (42.2%) individuals between 35 and 49 years, and 24 (11.5%) respondents of 50 years or older. More than two-thirds of the sample contains respondents working in a rural region (N = 144, 69.2%). Furthermore, 36 respondents (27.3%) were employed in companies with less than 49 employees, 84 (40.4%) respondents in companies with 50 to 249 employees, and 87 respondents (41.8%) in companies with more than 250 employees. The educational background distribution of the sample was: 3 (1.4%) individuals obtained required schooling, 36 (17.2%) respondents gained vocational education, 75 (36.1%) passed the A-Level degree, and 94 (45.2%) respondents have a university degree.

3.2. Instruments

To gauge competence-based employability, we used the short version by Van der Heijden et al. (2018). This instrument features 22 items measuring the five competences presented above (Sample item: “During the past year, I was in general competent to perform my work accurately and with few mistakes.”). The questions were answered on a five-point Likert scale (1 = “Disagree,” 5 = “Agree”). While the Cronbach alphas associated with the shortened versions of the five respective sub-scales were not satisfactory (α = 0.48 to 0.68), confirmatory factor analysis showed that the data fit the model well (CFI = 0.88, RMSEA = 0.05, SRMR = 0.06).

We measured the offering of HR development measures with 10 items. The questionnaire included items focused on on-the-job, parallel-to-the-job, and off-the-job developmental measures offered to the respondent in the last 12 months (answered on five-point Likert scales from 1 = “Disagree” to 5 = “Agree”). We measured on-the-job-measures with items used from Martini and Cavenago (2017) asking about the degree of job rotation, job enlargement, and job enrichment in their current job and a question about to what degree respondents were working in an age-heterogeneous team (Sample item: “In my job I work in teams with colleagues of different ages”). Parallel-to-the-job was measured with one item (“In my current work I can join programs in which more experienced employees can offer their experience to other (new-hired) employees”). Off-the-job developmental measures focused on the frequency (1 = “Never”, 5 = “Very often”) of external further education measures. We aggregated these items to form one index about the offering of developmental measures. Internal consistency of the scale was acceptable (α = 0.76).

Age climate was measured using Noack’s (2009) twelve-item instrument that was answered on a four-point answer scale (1 = “Disagree,” 4 = “Agree”). Respondents were asked about 12 attributes of how older employees (above 50) are seen in their company (e.g., being cooperative). The internal consistency of the scale was good (α = 0.89).

Questions about age, gender, educational background, the number of years in the current position, and the regional background (rural or urban) were included as covariates.

The online questionnaire was thoroughly pre-tested in two phases. First, five pre-testers commented on the overall clarity and usability of the questionnaire. Second, a think-aloud protocol approach was used to test the refined questionnaire with an expert in survey methodology.

3.3. Analysis

We analyzed the research model using path modeling in lavaan (Rosseel 2012) for R (R Core Team 2014) using maximum likelihood estimation. We assessed model fit using root mean square error of approximation (RMSEA, acceptable if ≤0.08; (Browne and Cudeck 1993), comparative fit index (CFI, acceptable if ≥0.90), standardized root mean squared residuals (SRMR, acceptable if ≤0.08) (Byrne 2010; Hu and Bentler 1999), and the probability-value of the Chi-Square statistics (acceptable if p > 0.05).

In terms of software- and researcher-triangulation (Schoonenboom et al. 2018), the results produced with this analytical approach by the second author were reproduced by the first author using IBM SPSS Statistics (IBM Corp 2017) and the PROCESS macro (Hayes 2013). Given its greater analytical flexibility (e.g., the provision of fit indices), we will report the results of the path model.

4. Results

4.1. Descriptive Statistics and Correlations

Table 1. Descriptive statistics and correlations.

	Variable	M	SD	1	2	3	4	5	6
1	Age Climate	2.726	0.493
2	HRD measures	3.171	0.681	0.29 **
3	Occ. Expertise	4.157	0.467	0.12	0.19 **
4	Anticip. & Opt.	3.774	0.612	0.22 **	0.28 **	0.42 **
5	Personal Flex.	3.853	0.598	0.17 *	0.33 **	0.57 **	0.62 **
6	Corporate Sense	3.984	0.577	0.22 **	0.34 **	0.41 **	0.37 **	0.51 **
7	Balance	3.300	0.659	0.29 **	0.33 **	0.34 **	0.28 **	0.36 **	0.32 **

** p < 0.01, * p < 0.05.

features descriptive statistics (means and standard deviations) of the focal concepts, as well as the correlations between them. As to be expected, there are positive correlations among the dimensions of competence-based employability as well as between most of these dimensions and age climate and HR development measures.

4.2. Hypothesis Tests

The data fit the model very well: CFI = 0.97, RMSEA = 0.10 (p > 0.05), SRMR = 0.03. In sum, the model explains 9.0% (occupational expertise) to 18.0% (balance) of the employability competences, which is comparable to under studies in the field.

We hypothesized that the offering of developmental programs is strongly related to all dimensions of employability. Indeed, the data show that the offering of developmental measures is related to all dimensions of employability (

Table 2. Path coefficients (B) with bootstrapped 95% confidence intervals (1000 bootstrap samples) in brackets. (Source: Authors).

	Dependent Variable
Independent Variable	Occup. Expertise	Independent Variable	Occup. Expertise	Independent Variable	Occup. Expertise	Independent Variable
Age Climate	0.042 (−0.100 to 0.186)	Age Climate	0.042 (−0.100 to 0.186)	Age Climate	0.042 (−0.100 to 0.186)	Age Climate
HRD measures	0.138 ** (0.040 to 0.236)	HRD measures	0.138 ** (0.040 to 0.236)	HRD measures	0.138 ** (0.040 to 0.236)	HRD measures
Age	0.002 (−0.007 to 0.010)	Age	0.002 (−0.007 to 0.010)	Age	0.002 (−0.007 to 0.010)	Age
Gender	−0.000 (−0.125 to 0.132)	Gender	−0.000 (−0.125 to 0.132)	Gender	−0.000 (−0.125 to 0.132)	Gender
Education	−0.073 * (−0.136 to −0.009)	Education	−0.073 * (−0.136 to −0.009)	Education	−0.073 * (−0.136 to −0.009)	Education
Experience job	0.002 (−0.009 to 0.013)	Experience job	0.002 (−0.009 to 0.013)	Experience job	0.002 (−0.009 to 0.013)	Experience job
Rural	0.162 * (0.005 to 0.307)	Rural	0.162 * (0.005 to 0.307)	Rural	0.162 * (0.005 to 0.307)	Rural

** p < 0.01, * p < 0.05.

): occupational expertise (B = 0.14, p ≤ 0.01), anticipation and optimization (B = 0.22, p ≤ 0.01), personal flexibility (B = 0.26, p ≤ 0.01), corporate sense (B = 0.26, p ≤ 0.01), and balance (B = 0.26, p ≤ 0.01). This confirms Hypotheses 1a–e.

At the same time, age climate only showed direct effects on anticipation and optimization (B = 0.22, p ≤ 0.01) and balance (B = 0.26, p ≤ 0.01). However, and in support of Hypothesis 2, age climate is related to the offering of developmental programs (B = 0.41, p ≤ 0.01). Via this relationship, age climate has statistically significant indirect effects on occupational expertise (B = 0.06, p ≤ 0.05) anticipation and optimization (B = 0.09, p ≤ 0.01), personal flexibility (B = 0.11, p ≤ 0.01), corporate sense (B = 0.11, p ≤ 0.01), and balance (B = 0.11, p ≤ 0.01). This supports Hypotheses 3a–e.

The covariates did not show statistically significant relationships except for a link between educational background (B = −0.07, p ≤ 0.05) and region (B = 0.16, p ≤ 0.05) on occupational expertise. However, these findings need to be interpreted with caution, given the low overall explanation of occupational expertise.

5. Discussion

In this study, we investigated the relationships between age climate, HR development measures, and individual employees’ competence-based employability. The theoretical model based on previous research and the methodological approach of testing is aimed at extending our knowledge in two main directions. First, from a theoretical perspective, we extend the current body of knowledge by testing the effects of two meso-level concepts—age climate and the offering of HR development measures—on employability. Furthermore, we explored the relationship between these two concepts. Second, by sampling predominantly in rural areas and testing for differences between respondents from rural and urban areas, we inform the field (which previously emphasized the latter). Concerning the latter point, we did not find many differences, which (weakly) suggests that many research findings in this field may also be generalizable to a more rural population despite the obvious differences outlined above.

In Hypothesis 1, we proposed that the offering of developmental measures is positively related to employees’ employability. This positive relationship was evident across all five dimensions of employability. This is in line with Veld et al. (2015) and Ybema et al. (2017) and underlines the roles of companies in fostering employee’s employability. By offering developmental measures, employers give the employees the opportunity to develop their competences according to the labor market needs. Nevertheless, it is important to mention that for a successful implementation of developmental measures not only does the employer’s offering matter but also the employee’s willingness and motivation to participate (Vlacsekova and Mura 2017) needs to be taken into account. In line with Ybema et al. (2017), we agree that individuals’ requests for developmental measures need to be factored into the equation.

Furthermore, we investigated the effects of age climate on individuals’ employability. This fills an important gap in the literature, as little research has zeroed in on the individual and organizational outcomes of an age-positive climate. Direct links of age climate were only found for the dimensions of anticipation and optimization and balance. These effects are lower than expected—but may be explained in three different ways: First, other studies show that there is no obvious, direct link between individuals’ chronological age and their employability (Froehlich et al. 2014b). The finding that age climate plays no role in this study might show that this is also true for organization-level, age-related constructs, such as age climate. Second, another explanation lies in the very nature of what a positive age climate entails. As described more fully in the theoretical background, the organizational age climate is largely related to preventing negative age stereotypes and their consequences. However, this also means that a positive organizational age climate is only needed if age discrimination is indeed a problem. National or regional cultures that do convey a positive image of older workers may render a positive age climate at the workplace redundant. Third, it is important to note that age climate does have a statistically significant total effect on individuals’ employability. This is largely due to an indirect effect via the offering of HR developmental measures. The statistical non-significance of the direct effect of age climate on employability can, therefore, also be explained via the complexity of the research model.

The sample used for the analyses has several strengths. First, it is drawn predominantly from rural regions. This population is underrepresented in employability research, despite its important features in terms of employment and employability (Copus et al. 2006). Second, the sample characteristics largely match the population characteristics, which makes the findings very relevant to the target population. While the sample is representative of the population in terms of age distribution, age-related research may benefit from putting an emphasis on older people. The reason for this is that effects of age (or age climate on the organizational level) may not be linear and may be discovered more easily if the sample disproportionally weighs older people.

Any cross-sectional study about the effects of age is left with some uncertainty about the true nature of any effects of age indicated by the analyses. This is because the effects of age may also be attributable to a generation instead (Hall et al. 2007). Longitudinal studies or cross-sectional replication studies may help to provide certainty about these effects. Likewise, such designs may suffer from common method biases (Podsakoff et al. 2003); though, no indication of such problem was found in this particular study based on Harman’s single factor test, which we conducted post-hoc.

Noack (2009) differed two constructs of age climate—psychological and organizational, “shared” age climate. Our study focuses on employees’ perceptions of how older employees are seen in a company. Further research could also focus on the shared view of an age-supportive climate in organizations and their influence on developmental measures to get more knowledge about this potential meso-level antecedent.

6. Conclusions and Practical Implications

We set out to study how on-the-job, off-the-job, and parallel-to-the-job HR development measures and age climate affect (older) employees’ employability in rural and urban samples. This not only has implications for further research, as discussed above but also leads to practical implications. We have found that age climate predicts the offering of HR development measures to employees of all age groups. This gives HR management an important tool to steer the development of the workforce. Age management policies then become relevant also from the point of view of HR development. This is especially true if the organization operates in the context of a regional culture that is negative towards old age. Age climate may be an important lever to raising employability across the ages and across the regions.

We have found that a positive age climate indirectly strengthens employees’ employability. Given this effect, age climate may be presented as a decisive factor to raise the attractiveness of a job. As such, it may be an important criterion in communication towards the labor market.