Aspects of Support and Types of Work–Life Balance Among Employees from Rural Areas in Poland

Abstract

Rural areas offer unique contexts for work–life balance (WLB) development due to distinct working conditions and employment structures. Employees who have access to flexible work arrangements, non-material bonuses, and peaceful workplaces are more productive (lower absenteeism, greater commitment). The aim of the study was to determine the aspects of support and types of WLB among employees from rural areas. Two analyses were conducted: PCA (principal component analysis) for the entire sample, and a fuzzy c-means cluster analysis for wage employees. Based on PCA, three aspects of WLB support were identified: leave, work, and work hygiene (regeneration). The use of emergency and family leave dominated practices supporting WLB among employees in rural areas. The respondents did not attach much importance to social benefits improving work hygiene; moreover, the work hygiene aspect was not applied in parallel with other aspects of WLB support (leave, work). As a result of clustering the respondents’ answers, four employee types were identified based on WLB assessment, demographic characteristics, and work-related factors. Clustering revealed a clear correlation between WLB assessment and employee age and gender. The highest scores in terms of the quality of work–life balance were recorded among middle-aged men (type 4). Younger people, especially women (type 1), rated their WLB as moderately good. Regardless of age and gender, as stress levels increased and personal quality of life declined, thoughts about changing jobs intensified. Employee well-being significantly influences job retention intentions among rural workers. This study uniquely integrates multiple theoretical frameworks and employs principal component analysis and fuzzy c-means clustering to explore work–life balance among rural employees, a group seldom studied. By focusing on rural contexts and offering systemic, multi-domain insights, the findings advance WLB theory and practice and provide recommendations for employers and policymakers.

1. Introduction

Work–life balance (WLB) plays a key role in ensuring employee well-being and organizational effectiveness, while also being an important element of sustainable socio-economic development [1,2]. According to the current regulations of the European Union concerning work–life balance, their implementation brings benefits in three key aspects: social, economic, and environmental. In the face of growing professional demands and dynamic changes in the labor market [3,4], it is particularly important to understand WLB in the context of rural workers.

Unlike urban workers, people employed in rural areas often face unique challenges, including limited access to flexible forms of employment [5,6,7], long commutes [8], or combining professional duties with agricultural activities [9,10]. The results of Gosetti’s study on the quality of working life among farm workers and agricultural enterprises in the province of Verona (Italy) showed that work more often has a negative impact on their personal lives than vice versa, contributing to potential overload [9]. The authors emphasize that farmers’ decision-making often involves a trade-off between adaptability and short-term profitability, which sometimes boils down to strategies aimed solely at “household survival,” ultimately “negatively affecting quality of life” [10].

Against this backdrop, we delineate three core dimensions of work–life balance support that underpin our subsequent analyses: (1) leave, (2) work organization and working time, and (3) occupational health/work hygiene. First, the leave dimension includes care leave, on-demand leave, parental leave, paternity leave, and emergency leave, capturing formal absence solutions that facilitate balance between paid work and private responsibilities. Second, the work organization and working time dimension comprises flexible schedules, remote work, the option to leave work for personal matters, the possibility of bringing a child to work, and adjusting schedules to private responsibilities—practices that directly support everyday role coordination. Third, the occupational health/work hygiene dimension encompasses conditions fostering physical and mental regeneration (quiet workspaces, relaxation zones, additional medical packages, psychological support, subsidized meals, and healthy-lifestyle programs). These categories reflect the socio-economic realities of rural areas in Poland and underpin the variables used in the PCA and clustering analyses.

Studies indicate that different forms of support, such as family and emergency leave, flexible work arrangements, and workplace hygiene solutions, can interact and sometimes act as substitutes [11]. While leave benefits can support regeneration and work–life balance (WLB), their intensive use may reduce engagement with other regenerative practices and may be negatively correlated with working time flexibility. Demographic factors (gender, age) and employee health status further influence individual perceptions of balance, highlighting the need for comprehensive analysis frameworks.

Employer-offered maternity leave (EOML) shapes a mother’s ability and timing to return to work, affecting her WLB, especially when organizational commitment to WLB is low [12,13]. Short leaves limit the time available for self-care and childcare, whereas longer leaves can improve well-being for both mother and child. Flexible work arrangements are widely regarded as best practice [14].

In Poland, the availability and use of WLB benefits vary by sector, company size, gender, and age [15,16,17]. Older employees and women are more likely to report lower WLB, and declining well-being increases turnover intentions [12,18].

Previous studies on WLB in Poland and Europe have primarily focused on urban employees and knowledge-intensive sectors [5,16]. Much less attention has been paid to employees from rural areas, who face distinct challenges such as limited access to public services, long commutes [8], and combining formal employment with agricultural work [9]. The lack of in-depth analyses in this area represents an important research gap, which the present study aims to address.

Finally, forms of support do not always complement each other; frequent use of one (e.g., leave) may reduce engagement with others (e.g., regenerative activities such as meals at work or employee gyms). Conversely, well-organized work encourages employees to manage their well-being more effectively.

To identify the main dimensions of WLB support and employee profiles, the study employs principal component analysis (PCA) combined with fuzzy c-means clustering, allowing for the capture of complex, multidimensional patterns in work–life balance. Detailed methodological procedures and justifications are provided in the Materials and Methods Section.

The aim of the study is to identify the aspects of WLB support and the relationships between them for rural workers and to define types of WLB based on these aspects. Despite extensive research on WLB in urban contexts, rural employees remain insufficiently studied, particularly regarding the links between different WLB support mechanisms. The aspects of WLB were determined based on variables characterizing available WLB support solutions relating to working time and organization, facilities for combining work and care for dependents, adaptation of leave to employees’ private lives, facilities improving occupational health and regeneration, and support guaranteed by the Labor Code (parental leave, etc.).

Although there are no studies directly addressing the impact of any aspects of WLB on attracting workers to rural areas, studies in cities and knowledge sectors may provide some insights [19]. The results of these analyses show that access to flexible working hours, family leave, and regenerative support significantly influences the career and location decisions of employees, including young professionals and women [20,21,22]. It is possible that, for example, the introduction of flexible forms of employment in rural areas could contribute to making these regions more attractive to potential employees and employers. The undertaken research has practical significance and responds not only to the needs of employees and employers, but also to migration policy, gender equality, and social cohesion.

Work–life balance (WLB) is one of the key dimensions of employee well-being, and its support in organizations can be implemented through various mechanisms: flexible work arrangements, leave solutions, or health-promoting activities. In the literature, it is indicated that these forms of support may function both complementarily and substitutively, depending on the organizational context and individual employee needs [1,2,6,8,10,11,12,13,15,16,18,19,20,21,22]. Based on role boundary theory, the JD-R model, and previous empirical studies on work–life balance, five research hypotheses were formulated, addressing the relationships between WLB practices, employee well-being, and sociodemographic factors.

The following hypotheses were put forward:

Hypothesis 1. 

Support for work–life balance (WLB) among employees through selected leave solutions (family and emergency leave without time off) is negatively correlated with occupational health solutions (regeneration).

Justification 1. 

Different forms of WLB support may function as substitutes; using regenerative leave may reduce the use of other practices affecting occupational health [1,12,21].

Hypothesis 2. 

Support for work–life balance (WLB) among employees through work organization and working time (P) is negatively correlated with support for family and emergency leave solutions (U) 0.417.

Justification 2. 

Flexible working hours and autonomy reduce the need to use leave [11,13,16].

Hypothesis 3. 

Support for work–life balance (WLB) among employees through work organization and working time (P) is negatively correlated with support for occupational health (H).

Justification 3. 

High support in work organization may reduce the perceived need for additional occupational health measures [11,18,21].

Hypothesis 4. 

Mature employees (aged 35–55) rate their WLB lower, with mature men maintaining a higher WLB than mature women.

Justification 4. 

Midlife is associated with overlapping professional and family responsibilities, which lowers WLB among women [2,6,15,19].

Hypothesis 5. 

As employee well-being deteriorates, the intentionality of changing jobs increases.

Justification 5. 

Midlife is associated with overlapping professional and family responsibilities, which lowers WLB among women [2,6,15,19].

Hypotheses H1–H3 from PCA apply to all occupational groups, while H4–H5 from clustering do not apply to farmers.

Some hypotheses apply only to non-agricultural employees because the cluster analysis aimed to identify WLB profiles within wage-earning employees, without distortions caused by the specific nature of farmers’ work.

This study contributes three new elements to the WLB literature: (1) it analyzes the issue in a specific and previously neglected context of rural employees in Poland; (2) it uses PCA and clustering methods to identify WLB types based on multiple support dimensions; (3) it shows how the perception and use of WLB benefits differ across demographic groups. Thus, the article combines a theoretical perspective with practical implications for labor market policy and regional development.

2. Materials and Methods

The study covered employees from rural areas (including farmers and people working under employment contracts or a contract of mandate). A proportional random sample was selected based on the place of residence in the NUTS 1 region of people working in rural areas of Poland. The sample was proportionally selected based on the NUTS 1 administrative division, ensuring representativeness and the feasibility of conducting CATI and CAWI interviews. A total of 700 interviews were conducted. The study was conducted using CATI (Computer-Assisted Telephone Interviews) and CAWI (Computer-Assisted Web Interviews) methods. The CATI method was used to conduct 40% of interviews, while CAWI was used for 60%. The maximum estimation error for the entire sample (N = 700) at a confidence level of 95% is ±3.7 percentage points, and for the subsamples, CATI (N = 280): ±5.8 percentage points; CAWI (N = 420): ±4.8 percentage points. The study was conducted between 29 October and 28 November 2024.

Two analyses were conducted: PCA on the entire sample (700 individuals) and a fuzzy c-means cluster analysis on a sample of 571 wage-earning employees (excluding farmers). All respondents, including farmers, were included in the PCA to capture the full variance structure, while only non-agricultural employees were considered in the cluster analysis because the aim was to identify work–life balance profiles among wage-earning employees without distortions caused by the specific nature of farmers’ work.

All variables used in the analyses were coded on a uniform 5-point Likert scale to ensure the comparability of results between respondents. Missing data were imputed using the mean method for the given variable, which allowed us to maintain the full sample size and data consistency.

2.1. Justification for Variable Selection (PCA)

Consistent with the adopted conceptual model, the variables were grouped into three dimensions of WLB support—leave, work organization and working time, and occupational health (work hygiene)—to capture the wide spectrum of WLB practices in our study. This structure guided the selection and coding of variables for the principal component analysis (PCA) and cluster analysis. The selection of variables was based on current trends in WLB studies and the specific nature of socio-economic changes taking place in rural areas in Poland and Europe [23,24]. To conduct a PCA analysis of work–life balance systems for 700 employees from rural areas, variables were selected and assigned to the following aspects: leave (in connection with the introduction of the WLB Directive and the amendment of the Labor Code in Poland, and the introduction of, for example, paternity leave), work organization and working time, and occupational health (physical and mental regeneration). The selection of variables enables a comprehensive understanding of the relationships between factors describing work–life balance issues and helps to identify those variables that most significantly regulate the work–life balance system. These variables represent three key aspects of WLB—leave, work and working time, and occupational health—reflecting sustainable development priorities such as health, social equality, and improving the quality of life in rural areas [25]. A set of variables has been proposed that takes into account data characterizing aspects of work–life balance in terms of leave, work and working time, and health and safety. PCA analysis was performed for employees in all occupations (including farmers).

The first group of variables supporting WLB was related to family and emergency leave and included five variables: (1) care leave, (2) leave on demand, (3) full use of parental leave, (4) paternity leave, (5) leave due to force majeure and urgent family matters. This group highlights the importance of different “leave” needs of employees from rural areas, which are conducive to improving WLB. For example, supporting care services (1) would enable rural residents to remain economically active despite their family responsibilities [26]. (2) Ensuring flexibility in responding to sudden life needs (through leave on demand) is important when access to services is limited in rural areas [23]. (3) Enabling the full use of parental leave is important to support pro-family policies and gender balance in employment in areas with traditional social roles [27]. (4) Paternity leave promotes equality in parental responsibilities and contributes to sustainable social development [28]. (5) Leave due to force majeure and urgent family matters increases the resilience of employees to unforeseen crises, which supports the stability of rural communities [17].

The second group of variables was related to work and working hours, including five variables associated with the following: (1) flexible working hours, (2) remote work, (3) possibility of leaving work to attend to personal matters, (4) possibility of coming to work with a child, (5) adjusting the work schedule to private responsibilities. The selection of PCA variables was confirmed in studies by other authors. (1) Flexible working hours enable rural residents to better balance their professional and domestic responsibilities, which is crucial for reducing migration to cities [26]. (2) Remote work increases employment in areas with limited access to the labor market, promoting social inclusion [24]. (3) The possibility of leaving work to attend to personal matters is a significant convenience in rural areas, where access to services (schools, government offices) is often limited, and temporary flexibility supports the sustainable functioning of families [1,29]. (4) Supporting childcare in rural areas with poor preschool infrastructure promotes the professional activation of parents [23]. (5) Individualization of working hours increases employability in rural areas, where family and farming responsibilities are more demanding [30].

The third group (work hygiene) comprised five variables: (1) place to work in peace (2) relaxation area, (3) additional insurance/medical package offer, (4) psychological support, (5) subsidies for employee meals, (6) programs promoting a healthy lifestyle.

(1) Ensuring conditions for quiet work in the professional environment counteracts stress and digital exclusion of employees from sparsely populated areas. Providing an environment free from domestic distractions and conducive to concentration is a key advantage of coworking spaces in rural areas, contributing to a better well-being of users [31]. In a case study of the Kupland chain in southeastern Estonia, it was noted that “many people reported improved well-being as a result of working in close proximity to nature” [32,33]. A study by Akhavan, Mariotti found that employee well-being is “higher in coworking spaces in rural and remote areas” [33]. In the context of French “tiers-lieux” (third places), the authors stress that their goal is to “offer better working conditions and a pleasant (friendly and lively) and appropriate (equipped and functional) environment”. “Closed spaces” were also mentioned as an element of coworking space layout, enabling quiet and focused work [34].

Both sources [33,34] agree that rural areas, supported by the right infrastructure (especially digital and coworking), can offer an environment conducive to calm, focused, and balanced work. Studies by Akhavan and Mariotti [33] provide more detailed evidence as to why coworking spaces in rural areas are perceived as “peaceful” (through reduced disruption and better boundary management), while the Nordregio report [34] places this in a broader strategic context of regional development and the overall attractiveness of rural lifestyles.

(2) Creating relaxation zones counteracts burnout and improves mental well-being [24].

(3) The offer of additional insurance/medical package strengthens access to healthcare, which is often limited in rural areas, directly affecting work–life balance [30].

(4) Psychological Support: Promoting mental health is crucial in environments with greater social isolation, typical of rural areas [35,36].

(5) Subsidies for Employee Meals: There are related studies that point to unequal access to health programs at work in rural areas, and EU initiatives such as the FOOD Programme (FOOD Programme (2009–2012)—an active project in 10 EU countries, including the Czech Republic, France, Italy, Slovakia, Romania, and Spain, aimed at restaurants and businesses, designed to improve the quality of meals through education and standardization of offerings), which promoted awareness and quality of food at work, including in rural areas [37]. Rural workers had limited access to health promotion programs (WHPs). In turn, American studies have shown real limitations in the availability and quality of meals provided by employers [38].

(6) Programs Promoting a Healthy Lifestyle: Health campaigns help to equalize health opportunities between urban and rural residents [39].

Sport improves mental and physical health, which is crucial in areas with limited access to sports infrastructure [40]. The availability of physical activity in the workplace promotes social integration in dispersed areas [35]. Creating relaxation zones counteracts burnout and improves mental well-being [24]. Care support enables rural residents to remain economically active despite their family responsibilities [23,26]. It supports pro-family policies and gender balance in employment in areas with traditional social roles [27]. It promotes equality in parental responsibilities, contributing to sustainable social development [1,29]. It increases the social resilience of workers to unforeseen crises, which supports the stability of rural communities [35].

Despite the confirmation in the literature of the positive significance of variables representing the “health and safety” aspect in the creation of WLB for rural workers, the benefits of their presence are not significant for them (negative correlations of variables with the aspect). Employees from rural areas do not attach much importance to social benefits (non-wage) that improve working conditions.

2.2. Cluster Analysis (Clustering)

However, for the purposes of analyzing average hidden variables, the focus was on those variables that were directly related to the participants’ professional experience, their assessment of working conditions, and the impact of their work on the functioning of the organization. The selected variables allowed us to capture aspects related to job quality, professional effectiveness, and stress at work, as well as access to organizational solutions supporting employment flexibility among respondents.

The analysis primarily included variables such as

• V1: How would you rate your work–life balance?
• V2: Gender.
• V3: I often think about changing jobs.
• V4: The quality of my personal life is declining.
• V5: I work less efficiently.
• V6: I often feel stressed and mentally tense.
• V7: Have you been on sick leave in the last 12 months?
• V8: Possibility to leave during work/interrupt work to attend to personal matters.
• V9: Age.

The classification was carried out on 571 respondents who declared that they were definitely rural residents. The study examined 228 men and 334 women of various ages and educational backgrounds.

Cluster analysis performed using the fuzzy c-means method can be used to cluster respondents according to their responses [41,42,43]. This method was developed by Bezdek in 1973 [44]. It allows for flexible division of records into clusters using the probability of elements belonging to different groups [45]. The fuzzy c-means algorithm allows objects to be assigned to clusters in a fuzzy manner—this means that each record can belong to more than one cluster. This makes it possible to recognize subtle patterns in the data, which is useful for analyzing complex phenomena. Details of the procedure used are presented in numerous scientific publications [44,46,47,48,49]. Therefore, in order to avoid duplication of content, the authors of this article refer to the above-mentioned publications for details of the procedure.

3. Results

3.1. WLB Support Range (PCA)

The Kaiser–Meyer–Olkin (KMO) measure, which was 0.88, confirmed the suitability of the sample for factor analysis (PCA). The Bartlett sphericity test showed a chi-square value of 4863.213 (df = 120), p < 0.001, which meant that the data were also suitable for factor analysis, as the correlations between the variables were significant.

Factor analysis based on the variance table and the scatter plot indicated the existence of three focal points (assuming an eigenvalue above 1); hence, it was possible to distinguish three aspects of WLB supporting work–life balance in the company, which were labeled as follows: (1) leave, (2) work, (3) work hygiene (

Table 1. Factor loads of aspect (1): leave.

Variable Code	Variable Description	Factor Loading
S234.2	Care leave	0.711
S234.4	Leave on demand	0.805
S234.6	Full use of parental leave	0.816
S234.8	Paternity leave	0.758
S234.10	Leave due to force majeure and urgent family matters	0.751

Source: Own study.

,

Table 2. Factor loads of aspect (2): working.

Variable Code	Variable Description	Factor Loading
S23.2	Flexible working hours	0.683
S23.4	Remote work	0.712
S23.6	Possibility to leave work to attend to personal matters	0.769
S230.2	Possibility of coming to work with a child	0.668
S230.4	Adjustment of work schedule to duties arising from private life	0.705

Source: Own study.

and

Table 3. Factor loads of aspect (3): hygiene.

Variable Code	Variable Description	Factor Loading
S232.6	Place for quiet work	−0.611
S232.8	Relaxation zone	−0.606
S233.2	Additional insurance/medical package offer	−0.811
S233.4	Psychological support	−0.646
S233.6	Subsidies for meals for employees	−0.486
S233.7	Programs promoting a healthy lifestyle	−0.684

Source: Own study.

).

The first aspect (leave) covers activities related to the use of various forms of leave, including parental, care, paternity, on-demand, and emergency leave. High factor loadings (0.711–0.816) suggest that the respondents consciously exercise their right to temporary absence from work in order to fulfill family obligations or personal needs (Table 1).

The second aspect (work) focuses on solutions that enable the adaptation of working time to non-professional needs, such as remote work, flexible working hours, or the possibility of bringing a child to work. The variability of loads (0.668–0.769) indicates a diverse but consistent pattern of use of these practices. Their use may be particularly important in rural areas, where access to care infrastructure is limited (Table 2).

Table 3 presents the factor loadings for the “occupational health” aspect, which includes, among others, quiet workspaces, relaxation zones, additional medical packages, psychological support, meal subsidies, and programs promoting a healthy lifestyle. All variables showed negative loadings (ranging from −0.486 to −0.811), which may indicate that despite availability, these activities are relatively rarely used by employees. These results suggest that organizations have the opportunity to increase employee engagement in activities supporting well-being and regeneration.

A review of the correlations between WLB aspects made it possible to identify the nature of the creation of conditions for balance. The correlation matrix between the components indicates a moderate positive relationship between the “leave” and “work” aspects (r = 0.417), which may indicate that people who benefit from flexible working arrangements are also more likely to take leave (

Table 4. Principal component correlation matrix for WLB.

Component	1 Leaves	2 Work	3 Work Hygiene
1_Leaves	1.000	0.417	−0.509
2_Work	0.417	1.000	−0.457
3_Work hygiene	−0.509	−0.457	1.000

Source: Own study.

). On the other hand, negative correlations between the “work hygiene” aspect and the others (leave: r = −0.509; work: r = −0.457) suggest that work hygiene (regeneration) is not used in parallel with other WLB strategies.

A summary of analyses and correlation between the main aspects of WLB (

Table 5. Correlations between PCA aspects for WLB of rural workers.

Aspects	Correlation	What Does this Mean in Practical Terms?
Leave ↔ Work	+0.417	People who have flexible working hours also take more leave, which indicates conscious WLB management.
Leave ↔ Health and safety	−0.509	The more someone takes leave, the less they maintain work hygiene—perhaps they treat it as something external to work.
Work ↔ Health and safety	−0.457	Flexible work does not mean caring about well-being—it can lead to overload (e.g., remote work also means no boundaries).

Source: Own study.

) indicates specific conditions for achieving balance, including the following:

1.

“Separation” of solutions—formal vs. informal. Respondents act selectively, choosing either time or flexibility solutions, and less often resorting to well-being tools.

2.

Hygiene—the weakest link. Despite its availability, this aspect shows the lowest utilization, as indicated by factor loadings ranging from −0.486 to −0.811 (Table 3) and negative correlations with leave (−0.509) and work flexibility (−0.457) (Table 5). This suggests that, although employees have access to facilities such as quiet workspaces, relaxation zones, psychological support, meal subsidies, and medical packages, these resources are underused. Possible explanations include limited perceived availability or cultural taboos (e.g., rest being associated with laziness).

3.

Comprehensive support is needed. Effective WLB requires not only flexibility and leave options but also education and facilitation of regeneration and occupational health practices. All reported correlations are statistically significant (p < 0.01), supporting the practical interpretations. Employees with flexible working hours tend to take more leave, reflecting conscious management of WLB, but frequent leave usage may reduce engagement in hygiene practices. Flexible arrangements alone do not guarantee attention to well-being, as they can sometimes lead to overload or blurred boundaries, particularly in remote work contexts.

The correlations between PCA aspects for WLB of rural workers are shown in Table 5. All reported correlations are statistically significant (p < 0.01), ensuring that the following practical interpretations are supported by the data. These results highlight that employees with flexible working hours tend to take more leave, reflecting conscious management of WLB. Conversely, frequent leave usage may reduce engagement in work hygiene practices. Additionally, flexible work arrangements do not automatically guarantee attention to well-being, as they can sometimes lead to overload or blurred boundaries, particularly in remote work contexts.

3.2. WLB Types (Clustering)

The clustering of respondents’ answers was carried out using JASP version 0.19.1 computer software [50]. The validation of the set of cluster variables (V1–V9) was conducted using McDonald’s reliability coefficient (ω). The obtained result, ω = 0.687 (95% CI: 0.649–0.724), indicates acceptable consistency in exploratory studies. It should be noted that the set of variables was heterogeneous, which reduces internal homogeneity. At the same time, this reflects the complexity of the work–life balance construct.

The number of groups was determined using the elbow method [51]. The homogeneity of the groups was assessed on the basis of the t-SNE (t-Distributed Stochastic Neighbor Embedding) plot obtained in the study [52]. The procedure used took into account the following training parameters: the maximum number of iterations was set at 7, the blur parameter was 2, and the number of clusters was optimized based on the BIC criterion. Figure 1 shows an elbow chart taking into account the following criteria: AIC, BIC, and WSS. The clustering resulted in four groups with a significant degree of diversity. If the number of clusters exceeds the elbow point (i.e., the lowest BIC value), this will not result in a significant reduction in the AIC, BIC, or WSS indices [49]. The first break in the curve occurred at four clusters, suggesting that this solution was optimal for the analyzed data.

Figure 2 presents a t-SNE cluster diagram for this study. The unsupervised machine learning algorithm allowed us to present the probability distribution of the data used to create clusters. It was concluded that the clusters had been correctly identified, confirming the validity of the decision to select four groups.

Table 6. AIC, BIC, and Silhouette indices of the c-means fuzzy clustering solution for the study.

Clusters	N	R2	Γ	AIC	BIC	Silhouette
4	571	0.601	0.437	3379.970	3536.480	0.140

present the AIC, BIC, and Silhouette indices for the adopted fuzzy c-means clustering. It measured the appropriate fit of data within designated groups. The model explained over 60% of the variance (R² = 0.601), confirming its robustness. The Pearson’s γ coefficient (0.437) indicated a moderate but acceptable agreement between the original distances and the cluster assignment, supporting the stability of the solution. The silhouette coefficient (0.140) suggested that the clusters were moderately distinct but partially overlapping, which is consistent with the characteristics of the fuzzy c-means algorithm, allowing for partial membership of cases across groups.

3.3. Comparison of Clusters

The responses of 571 respondents were grouped using the fuzzy c-means clustering method. This grouping also took into account the respondents’ demographic data, resulting in four groups (

Table 7. Characteristics of respondent groups identified using fuzzy c-means clustering (average responses to questions).

	V1	V2	V3	V4	V5	V6	V7	V8	V9
Group 1	3.1	1.9	1.7	2.0	1.7	2.1	0.3	0.2	1.5
Group 2	2.4	1.7	3.3	3.4	3.0	3.5	0.3	0.2	1.8
Group 3	3.0	1.2	2.1	2.7	2.5	2.9	0.2	0.7	2.5
Group 4	3.4	1.3	1.5	1.5	1.4	1.6	0.1	0.5	2.5

) with a significant degree of diversity. The size of the clusters was as follows: group 1, 135 observations; group 2, 242 observations; group 3, 94 observations; group 4, 100 observations.

Before describing the clusters in detail, a multivariate analysis of variance (MANOVA) was conducted to examine whether the identified groups varied on the diagnostic variables (V1–V9). The analysis showed differences at the multivariate level (Wilks’ Λ = 0.095, F(27, 1633) = 74.91, p < 0.001). Comparable results were obtained with other multivariate tests: Pillai’s Trace = 1.418, F(27, 1683) = 55.85, p < 0.001; Hotelling–Lawley Trace = 4.604, F(27, 1673) = 95.08, p < 0.001; and Roy’s Largest Root = 3.305, F(9, 561) = 206.01, p < 0.001. Taken together, these results suggest that the four clusters differed with respect to the analyzed variables, which supports their further interpretation.

3.3.1. Group 1

The first group included participants who rated their work–life balance as rather good. Respondents indicated an average value of approximately 3.1 for variable V1. The first group consisted mainly of women. The average value for the variable “gender” (V2) was close to 1.9 in this group.

The age of the study participants (variable V9) in the first cluster ranged from 18 to 34 years (average: 1.5). Some respondents had been on sick leave during the previous twelve months, but their number was relatively small. The average value for variable V7 was approximately 0.3 in this group.

Most participants in group 1 were unable to leave work or interrupt their duties to attend to personal matters. Respondents indicated an average value of 0.2 for variable V8. In addition, people classified in group 1 rarely reported that they often thought about changing jobs (variable V3, average around: 1.7).

Respondents from the first group did not indicate any significant reduction in their professional effectiveness. The average value for variable V5, “works less effectively,” was approximately 1.7 in this group. Furthermore, individuals assigned to the analyzed cluster described their perceived stress and mental tension as moderate. The average value for variable V6 “I often feel stress and mental tension” was approximately 2.1 in this group. However, the responses provided by respondents regarding the quality of their personal lives indicated a predominance of negative assessments. For variable V4, “The quality of my personal life is declining,” the average response was approximately 2.0.

3.3.2. Group 2

The second group consisted of participants who rated their own work–life balance as rather poor. The average value for variable V1, “How would you rate your work–life balance?”, was approximately 2.4. Women predominated in this group, as evidenced by the average response for variable V2 “gender” at around 1.7.

The participants in the study were mostly between 35 and 55 years old. The average value for variable V9 “age” in this group was approximately 1.8. A certain proportion of respondents had taken sick leave in the last twelve months. The average value for variable V7 was approximately 0.3 in this population.

Most participants in the second group did not have the opportunity to interrupt their work or leave during working hours to attend to personal matters—respondents indicated an average value of approximately 0.2 for variable V8. Respondents classified in cluster 2 reported that they were rather thinking about changing their place of work (variable V3, average approx.: 3.3).

Respondents in the second group reported a decline in their professional effectiveness. The average value for variable V5, “works less effectively,” was approximately 3.0 in this group. The study participants also pointed to high levels of stress and mental tension. The average value for variable V6, “I often feel stress and mental tension,” was close to 3.5 in this group. In addition, responses regarding the personal quality of life of participants in the second cluster confirmed their perception of a decline in this area. The average value for variable V4, “My personal life is getting worse,” was approximately 3.4 in this group.

3.3.3. Group 3

The third group included participants who rated their work–life balance as rather good. The value of variable V1 in this group was approximately 3.0, and the gender structure of the third group was dominated by men. This was confirmed by the average response given by respondents to question V2, which was close to 1.2.

Participants in this group were most often aged between 35 and 55 (variable V9, average approx. 2.5). Only a small proportion of people classified in cluster 3 had taken sick leave in the last twelve months. This was reflected in the value of variable V7 at around 0.2.

Most respondents declared that they had the possibility to leave work or interrupt it in order to attend to personal matters. The V8 variable was approximately 0.7 in this group. Respondents rarely admitted that they often think about changing jobs (variable V3, average around 2.1).

In terms of work efficiency, participants indicated a moderate decrease (variable V5, average around 2.5). The level of perceived stress and mental tension among the study participants in this group was elevated, as shown by the average value of variable V6, which was approximately 2.9. However, respondents’ answers regarding their personal quality of life suggested that some people noticed symptoms of deterioration. Variable V4, “The quality of my personal life is declining,” had a value of approximately 2.7 in this group.

3.3.4. Group 4

The fourth cluster included participants who rated the balance between their professional and private lives as very good. The average for variable V1 was approximately 3.4 in this group. The participants classified into group 4 constituted a diverse group in terms of gender, but men dominated the group—the average value of variable V2 was approximately 1.3 in this group.

People assigned to group 4 were most often in the 35 to 55 age category. This is confirmed by the average response for variable V9, which was approximately 2.5. Only a few respondents from cluster 4 had been on sick leave in the last twelve months. The average response for variable V7 was approximately 0.1 in this group.

Participants classified in cluster 4 reported greater freedom in terms of leaving work or taking breaks to attend to personal matters than respondents from other groups (variable V8, average around 0.5). Furthermore, participants in the study very rarely reported thoughts of changing jobs. The average value for variable V3 was approximately 1.5 in this population.

In terms of professional effectiveness, participants classified in group 4 did not indicate any deterioration. The average response for variable V5 was approximately 1.4 in this group. At the same time, the respondents’ stress and mental tension levels were low. This was reflected in the mean value for variable V6, which was approximately 1.6. The responses of participants in cluster 4 regarding their personal quality of life indicated a predominance of negative assessments. For variable V4, the average response value was approximately 1.5.

4. Discussion

The aim of the study was to determine the aspects of WLB support and the relationships between them for employees in rural areas, and to identify types of WLB, i.e., to indicate the main determinants of work–life balance among rural employees. Previous studies in the field of WLB aspect identification confirm that there is no single universal set of aspects, and their selection depends on the theoretical perspective adopted and the socio-professional context of the groups studied. This diversity stems from the fact that WLB research draws on multiple theoretical frameworks—including social cognitive theory, role accumulation theory, conservation of resources theory, and boundary theory—each emphasizing different mechanisms of how work and non-work roles interact [27]. In addition to the structural approach, which views WLB through the prism of institutional and social conditions—such as work (which builds variables related to professional activity), time (variables related to working time), family (variables related to family condition, including income), health (childcare), and politics (regulations concerning WLB) [53,54]—the literature also develops functional, relational, and subjective approaches. The first focuses on the distribution of an individual’s resources (e.g., time, energy, commitment) between professional and non-professional roles [55,56], the second on the quality of relationships and mutual expectations between role partners [57], and the third on the subjective assessment of balance and the ability to shape its conditions [58]. At the same time, multi-aspect approaches are emerging, which broaden the understanding of “life” beyond the family, pointing, among other things, to the importance of health, social relations, education, and civic participation [59,60]. However, these aspects were not identified in relation to the spatial specificities of residential and work areas (i.e., they do not take into account the distinct economic and social conditions prevailing in urban and rural areas). Working conditions in rural areas are perceived as less satisfying, also due to limited opportunities for development, lower incomes, and poorer infrastructure for recreation [61]. By situating our analysis within this theoretical mosaic, we both identify WLB aspects and also interpret them in relation to resource allocation and boundary management, which is particularly pertinent in rural contexts where socio-economic constraints amplify the tension between roles.

This study uses a systemic approach that also has a multi-domain perspective, pointing to various factors that go beyond those typically considered in the area of work or life outside work. An added value is the focus of the study on rural areas, which, on the one hand, play a key role in the contemporary sustainable approach to economic development and, on the other hand, are marginalized in studies on the condition of human capital in these areas. The focus on rural regions therefore allows us to test how established theories translate into settings characterized by limited institutional support and distinct cultural norms, thereby extending the external validity of WLB research.

The PCA analysis identified three key aspects of WLB support for rural workers: leave (special and family leave), work (flexibility and access to WLB support), and work hygiene (regeneration at work and health). Interpreting these components through the lens of conservation of resources theory suggests that rural employees must prioritize between replenishing their physical and psychological resources and utilizing leave entitlements. When resources are scarce, taking leave may come at the expense of opportunities for on-the-job recovery. These aspects are confirmed by the results obtained so far [53,54], while broadening the perspective with important elements related to regeneration and the availability of emergency and family leave. Variables determining work hygiene indicate that the possibility of regeneration during work and access to comprehensive health care, including psychological support, play a particularly important role in creating conditions for work–life balance in rural areas. This is confirmed by studies on medical workers in rural areas and women farmers. In the case of nurses, the lack of rest and insufficient psychological support lead to overload, reduced job satisfaction, and deterioration of health [5]. In turn, women working in agriculture point to insufficient systemic support, which results in them returning to work too early after giving birth and a deterioration in their physical and mental health [6]. In addition, as confirmed by Istenič [6], access to psychological support in rural areas is often limited due to a lack of anonymity, inadequate infrastructure, and low social acceptance of seeking help outside the family circle, which exacerbates feelings of isolation and hinders the achievement of work–life balance. This aligns with evidence that rural employees often rely on informal social networks for psychological health and may avoid formal health promotion programs because of stigma [20]. Therefore, strengthening workplace-based regeneration strategies and destigmatizing mental health support becomes a practical imperative. Our findings thus bridge micro-level resource theories with macro-level discussions on rural health infrastructure, highlighting the need for integrated policies that combine leave entitlements with accessible psychological and health support.

The PCA results also verify the research hypotheses. In the context of conclusions concerning work hygiene, it is important to confirm both H1 and H3. Hypothesis H1 posited that using leave solutions would be negatively correlated with the use of work hygiene/regeneration measures; our analysis found a clear negative correlation (r = −0.509). People taking special leave and family leave are less likely to take advantage of health promotion activities. From a boundary-theory perspective, this trade-off indicates that employees may perceive formal leave and informal regeneration measures as substitutes rather than complements [57]. Practically, this underscores the need for holistic WLB programs that integrate leave policies with health promotion and regeneration support, rather than offering piecemeal solutions; such integration could help rural employees to maintain their resources without having to choose between rest and professional obligations. This result is consistent with studies that highlight the lack of comprehensive WLB support offered by companies. For example, Sánchez-Hernández et al., showed that even among the most reputable employers, WLB solutions are implemented selectively—76% of companies offered extended leave, but only about 30% provided other facilities such as support for childcare or dependent persons [62]. Similar conclusions can be drawn from a study by Puchalski and Korzeniowska, in which more than half of the companies offered health benefits, but these were sporadic and lacked a well-thought-out strategy [63]. Poor access to regeneration opportunities during work is particularly important in rural areas, where comprehensive support systems are virtually non-existent. Employees are often limited to the limited public healthcare services available or have to resort to expensive private healthcare [64]. The confirmation of H1 also supports the validity of implementing vacation solutions that go beyond the recreational nature of vacation. Our study took into account the leave solutions introduced in Poland (but also throughout the EU) by the so-called Work–life Balance Directive (Directive (EU) 2019/1158) [65]. Its aim was to adapt the WLB support system in Europe to changing socio-economic conditions [16]. Changes such as greater equality in childcare, the introduction of leave to care for other dependents, and the spread of flexible working arrangements make it much easier to balance personal and professional life. These forms of leave largely determined the length of the leave, thus confirming the validity of the reforms, also in the context of people working in rural areas. The positive assessment of the EU Work–Life Balance Directive in our findings suggests that regulatory frameworks can shift organizational practices and empower employees, yet without accompanying investments in workplace health and regeneration these reforms risk creating partial solutions.

The positive verification of hypothesis H3 (support for WLB among employees through work organization and working time is negatively correlated with support for occupational health) indicates that flexible forms of work organization, although considered one of the flagship solutions for supporting WLB, may reduce employees’ tendency to seek additional regenerative or health-related support. This finding points to a potential substitution effect, where greater autonomy in working time and location partly replaces other strategies of maintaining work hygiene. In line with boundary theory, flexible arrangements can blur the demarcation between professional and private spheres; without intentional recovery practices, employees may experience resource depletion and heightened work–family conflict, which our results confirm. This may also suggest that employers offering flexible solutions believe that this is sufficient to create a work–life balance culture, which is confirmed, among others, by the research of Peplińska and Zenfler, [66]. Meanwhile, existing studies suggest that ensuring employee regeneration significantly contributes to improving their professional effectiveness and personal quality of life [24,35]. This is even more important in a flexible work environment, where the blurring of boundaries between work and personal life leads to overload, stress, and ultimately burnout [67,68]. However, in the analyzed group of employees from rural areas, the benefits of regenerative measures (e.g., relaxation areas, psychological support) are clearly marginalized. This may be due to limited availability of these services, cultural barriers to their perception, or lack of awareness of their importance [64]. Hence, practical recommendations should target both organizational awareness—encouraging employers to pair flexibility with structured recovery opportunities—and community-level interventions that destigmatize the use of psychological and health services in rural settings. This requires further detailed qualitative studies.

Hypothesis 2 predicted that flexible work arrangements would substitute for special and family leave. Contrary to this expectation, we found a positive correlation between flexible work arrangements and the use of special and family leave, indicating that employees treat these solutions complementarily rather than substitutively. It should be emphasized that in Poland this type of leave is a right under the Labor Code and does not depend on the employer’s discretion. On the other hand, flexible working arrangements are offered on a more discretionary basis, depending on the employer’s decision and organizational culture, except for parents of children under the age of 8, who will have a statutory right to request such arrangements from 2023.

The lack of confirmation of hypothesis H2 is reflected in the results of other studies, which indicate that work flexibility not only does not limit the use of leave, but actually encourages its more conscious and fuller use, especially by fathers. Studies conducted in Germany by Wanger and Zapf [69], and by Cha and Grady in the USA [70], show that employees with access to flexible forms of employment are more likely to take parental and care leave, which indicates that they are more proactive in maintaining a work–life balance. The implementation of the WLB Directive in Poland has led to an increase in the use of both flexible working time by parents of young children and paternity and parental leave [71]. According to European studies [72], it is expected that the spread of flexible forms of work will further increase the willingness of employees to take care leave and leave on demand in order to reconcile work and private life [71]. The results of the PCA analysis therefore confirm that, in conditions conducive to flexibility, employees do not treat WLB solutions as substitutes, but use them complementarily and proactively, even in rural areas. From a role accumulation and enrichment perspective, this complementarity illustrates how multiple resources and support mechanisms can mutually reinforce each other, enabling individuals to engage more fully in both caregiving and professional roles. This finding signals to policymakers and employers that flexible work should not be seen as a replacement for other forms of support but as part of an ecosystem of measures that collectively facilitate work–life integration.

The second objective of the analysis was to identify factors influencing the assessment of work–life balance and to typologize WLB experiences. Studies conducted in this area point to both endogenous determinants such as gender [73,74,75] and age [76] and exogenous determinants such as industry [77], economic sector, availability of WLB support solutions [56], and country of employment [78,79]. Despite the extensive research literature on the determinants of WLB, this topic has not been sufficiently explored in relation to rural workers. The vast majority of existing studies in this area focus on analyzing gender as the main variable differentiating the level of WLB achieved. A significant part of these analyses concerns exclusively the situation of women working in rural areas [6,64,80,81,82]. These studies justify focusing on the situation of women who are still strongly rooted in the traditional family model, which hinders the achievement of WLB. Women in rural areas experience what is known as a double burden (explained by the second shift theory), associated with paid work and all domestic and care responsibilities [83].

In this part of the study, two hypotheses were put forward: H4: Mature (older) employees and women rate their WLB lower. Mature men maintain higher WLB than mature women. H5: As the well-being of employees (not farmers) deteriorates, the intention to change jobs increases. The analysis of the results partially confirms both hypotheses, although with some reservations resulting from the internal diversity of the groups. Our typology highlights that age interacts with gender to shape WLB, underscoring how life course stages mediate role expectations and resource demands.

The above results partially confirm hypothesis H4, especially with regard to the lower WLB rating among middle-aged women compared to men in the same age group. The observation that mature men maintain a higher WLB than mature women is clearly confirmed. Similar conclusions have been reached by other studies conducted in Europe. Middle-aged women face the demands of work, childcare, and other dependent family members, which leads to increased work–family conflict [6,64]. Studies indicate that in women of this age, work–family conflict particularly reduces life satisfaction, and this effect intensifies with the age of the women surveyed [84]. However, the thesis that women and older people generally assess their WLB worse requires clarification—younger women in group 1 assess WLB positively, which suggests that age, rather than gender alone, may be the decisive factor. It is only when women have children and other loved ones who require care that significant WLB disorders occur [85]. In the literature, this is explained by social role theory, which assumes that women and men are socialized to perform different roles in the family and society [86]. In an aging society, there is also a phenomenon known as the “sandwich generation,” which refers to middle-aged people (mainly women) who are professionally active and still caring for their minor children and elderly parents [87]. These findings support social role theory by showing that WLB is contingent on the number and intensity of roles individuals occupy; for middle-aged rural women, simultaneous responsibilities across paid work, childcare, and eldercare compound role strain, reinforcing the need for targeted policy interventions (e.g., accessible childcare and eldercare services in rural areas) to alleviate this “sandwich generation” burden.

The study clearly confirms H5, which assumes a relationship between deteriorating employee well-being and an increase in the intention to change jobs. This correlation is clearly visible in all four clusters. These results therefore confirm that well-being and the assessment of one’s WLB have a significant impact on job stability, influencing decisions to change jobs. The strong association between well-being and turnover intentions resonates with the conservation of resources theory, wherein sustained resource depletion triggers withdrawal and exit behaviors; conversely, employees who experience resource gains are more likely to remain committed to their organization. For employers, this means additional costs related to recruitment, absenteeism, and training [88,89]. These results correspond to previous research findings, which indicate that the link between well-being and intention to change jobs is particularly strong in the public sector, which also offers jobs in rural areas (health centers, schools, government offices). Cross-sectional studies of medical and social workers from various European countries have found that feelings of burnout (in particular depersonalization), low job satisfaction, and high levels of occupational stress are among the main factors significantly increasing the intention to leave employment [90,91,92]. For organizational and regulatory practice, this means that employee well-being must be considered an integral part of job quality. Therefore, investments in employee well-being should be seen not only as individual-level benefits but as strategic human resource measures that reduce turnover costs and support regional labor markets, particularly in rural areas where recruitment and retention are challenging. Organizations that implement wellness programs, mental health support, and good working conditions report lower staff turnover and higher work efficiency. In the broader perspective, this means lower healthcare costs and a more active labor market policy [93].

The study provided a comprehensive overview of the issue of work–life balance (WLB) in the context of employees living in rural areas in Poland. One direction for future analysis should be an in-depth study of WLB among farmers as a specific occupational group, both in terms of individual assessment of work–life balance and the support system for achieving it. Not all solutions resulting from the WLB Directive apply to persons conducting their own agricultural activity [6]. In addition, it is worth noting structural changes in the labor market, including the growing importance of flexible forms of employment and remote working opportunities. In rural areas, due to their natural assets, more and more coworking spaces are being created, which can foster conditions conducive to work–life balance and retain skilled staff in non-urban areas [94]. Furthermore, drawing on evidence that planning workplace health promotion should consider employees’ place of residence and not only the company location, we recommend that policymakers integrate WLB initiatives with broader rural development strategies, including transportation, digital connectivity, and healthcare access.

5. Conclusions

This study identified aspects of WLB support and types of employees based on their assessment of their WLB in terms of gender, age, sense of professional effectiveness, perceived stress, quality of personal life, and health status. Both research tasks were possible thanks to the use of PCA and the fuzzy c-means method. The following analyses were conducted on a sample (700 people), including farmers (PCA) and excluding farmers (571 people), with cluster analysis using the fuzzy c-means method (clustering).

The PCA results indicate three aspects and a division of WLB strategies among respondents—either temporary solutions (leave) or organizational solutions (flexibility) are used, while work hygiene measures (regenerative) are marginalized. This may result from a lack of knowledge about the available benefits, their low attractiveness, or cultural beliefs that work hygiene is not part of work but a solely private matter.

In the context of human resources policies, it is worth noting the need to integrate WLB solutions that take into account not only time and flexibility, but also the physical and mental well-being of employees. Workers in rural areas may particularly need support in making informed use of regenerative tools, which currently appear to be the least used. It is worth conducting further studies on the reasons for non-use—whether it is lack of availability or cultural taboo (e.g., rest = laziness). There is a clear need for comprehensive support for workers in rural areas in terms of work hygiene (regeneration) and health.

Based on these findings, we recommend that employers operating in rural areas develop holistic work–life balance strategies that combine flexible scheduling, promotion of statutory leave entitlements, and active regeneration measures. Employers should provide clear information and training on the availability of WLB benefits, invest in on-site or remote regeneration facilities (such as rest areas and telehealth psychological support), and actively destigmatize the use of mental health services. Tailoring WLB programs to the needs of women and caregivers—through accessible childcare and eldercare support and recognition of the “sandwich generation” burden—can help to retain experienced staff in rural labor markets.

Policymakers should extend WLB rights and support schemes to self-employed agricultural workers, ensure that mental health and general healthcare services are accessible in rural regions, and introduce incentives for employers to implement comprehensive WLB programs. In addition, WLB considerations should be integrated into broader rural development strategies by improving digital infrastructure, transportation links and the availability of coworking spaces, thereby facilitating flexible work arrangements and access to support services.

Empirical data analysis, conducted using the fuzzy c-means method, showed that clusters with higher work–life balance ratings differed in terms of demographic profile and organizational conditions.

An important conclusion drawn from the analysis of the data is the relationship between age and the assessment of the work–life balance of the respondents. Younger people, especially women classified in the first group, rated the balance between work and private life as moderately good. In contrast, the highest WLB scores were recorded among middle-aged men. This phenomenon may result from their greater professional and family stability associated with a reduction in the intensity of care responsibilities towards adolescent children.

Understanding how women and men perceive their own WLB can facilitate the identification of strategies and policies aimed at retaining women and men in the labor market, and professional organizations (industry associations, trade unions) can develop and implement HRD strategies and policies aimed at creating workplaces that are more supportive of the professional and personal goals of women and men.

This work may inspire further studies on supporting WLB among workers in rural areas (not necessarily farmers). The article provides new insights into patterns of work–life balance in rural contexts, allowing theoretical conclusions to be drawn and specific aspects and types of WLB for rural workers to be constructed. The results highlight the relevance and validity of the issues raised in theoretical, cognitive, methodological, and practical areas.

Overall, this study contributes to WLB scholarship by elucidating how distinct support aspects—leave, work flexibility, and work hygiene—interact in rural settings and by revealing age- and gender-mediated employee typologies through novel PCA and fuzzy c-means analysis. By situating these findings within a multi-theoretical framework and offering targeted recommendations, it advances theoretical understanding and provides actionable guidance for improving the well-being of rural workers.

The limitations of the study may result from the following, among other things: (1) Interpretation of correlations—does leave (other than vacation leave) actually improve work hygiene (regeneration), or is the opposite true? (2) Differences between professions—do leave and work hygiene mean the same thing for all professional groups? (3) The potential influence of hidden variables—are the results affected by factors such as stress, economic status, or work culture?