Introduction

The topic of work–life balance (WLB) has been researched widely, resulting in complex definitions, theoretical approaches, measures, determinants, and consequences. WLB has been positively associated with many personal and organizational outcomes (Beauregard & Henry, 2009; Brummelhuis & Lippe, 2010; Kelly et al., 2008; Sirgy & Lee, 2018). Since the outbreak of COVID-19, the boundaries between work and personal life have become even more blurred. The results of a multiregional study (Bilge et al., 2020) have shown that the greatest stressor since remote work is work–family life separation (e.g., lack of privacy) and isolation. Considering this, the concept of WLB has become more important than ever, and managers along with organizational settings play a key role in achieving it.

The literature review from Sirgy and Lee (2018) suggests there are two types of predictors of WLB, namely personal (i.e., individual and cultural values) and organizational predictors (i.e., job characteristics and support system). Our study addresses some of the literature gaps pointed out in their literature review. First, most studies use subjective self-assessments of WLB, and second, research is lacking with regard to the interrelationships between antecedents in predicting WLB. In addition, we found that most studies focused on the effect of specific job design characteristics rather than taking a more holistic perspective of enriched job design (JD) as a key element of human resource (HR) management (HRM). This is relevant because such a piecemeal approach captures only a partial view of organizational realities that shape individuals’ WLB through formal job and organizational design but lacks in accounting for their comprehensive assessment. Furthermore, such an approach lacks in considering potentially different effects for individuals who adapt to challenging contexts in different ways with regards to their personal characteristics. The job demand-control (JD-C) model argues that control is an important factor that can alleviate strain (Karasek, 1979), which opens interesting potential avenues of investigation related to whether organizations can contribute to better WLB by increasing control through formalizing organizational arrangements.

Formalization, as an important organizational constraint that shapes individual adaptive responses and work behavior (Crawford & LePine, 2014), rarely has been researched in relation to WLB. It is believed that higher levels of autonomy and new, looser, less-defined, and more-flexible work practices lead to greater job satisfaction and job performance (Saragih, 2015) but often can result in longer overtime hours (Chung, 2017) and therefore worsen WLB. Warren (2021) found that to achieve WLB, the role of management (i.e., allocating workloads, assigning job tasks, and setting work hours) is crucial, especially in low-control/high-demands types of work. Perceptions of control, on the other hand, buffer the impact of job demands on strain and help employees to engage in challenging tasks and adapt to demands (Karasek, 1979). Hence, some studies have argued that formalizing organizational practices can help with these negative consequences and improve WLB (Cegarra-Leiva et al., 2012; Pasamar, 2015), but the extant literature remains silent on how this occurs for different individuals in different job contexts.

Our study enriches the literature on WLB and HRM by exploring both personal (i.e., individual traits of proactivity and resilience) and organizational (i.e., formalization and enriched job design) factors that jointly influence WLB. Figure 1 illustrates our research model with the proposed hypotheses that are founded in the JD-C model (Karasek, 1979). We have identified three potential theoretical contributions to the fields of organizational behavior, work–life interaction, and human resource management. First, we advance existing research on WLB by examining enriched job design in the context of WLB rather than focusing on one specific job design characteristic as most previous studies (e.g., Polat & Özdemir, 2020; Walia, 2014) have done. Second, we also contribute to previous research on WLB that has focused on the Big Five personality traits (i.e., extraversion, openness to experience, conscientiousness, neuroticism, and agreeableness), which are more or less stable (Costa & McCrae, 1992; Kaur, 2013; Kundnani & Mehta, 2014; Leka & de Alwis, 2016; Pandey et al., 2018), by focusing on other individual characteristics (i.e., resilience and proactive personality) that can be trained in organizations and that are more malleable than basic personality traits. Because they are adaptive in nature, they are natural candidates for investigation in a context that responds to the COVID-19 crisis. Last, we extend the theory of WLB with new insights into the moderating effect of increasing control through formalization at the organizational level as an adaptive measure that can serve as a juxtaposition for individual COVID-19 adaptation approaches. As an empirical contribution, to alleviate potential concerns related to common method bias, we include three source assessments (i.e., employees, family members, and supervisors) of key constructs (i.e., WLB, resilience, and proactive personality), which are only self-assessed in most studies, providing additional rigor to tested relationships.

Fig. 1
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Research model with hypotheses underlying our three-source study

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The findings of our study also have practical implications and are useful for employees, (HR) managers, and policy makers. Policy makers can gain insights into how to develop policies and programs to promote WLB so that employees and managers understand their rights, benefits, and opportunities. Through our study, employees can better understand the importance of formalization, training that helps them become proactive and resilient, and the overall importance of WLB. Finally, our study provides practical guidance on how managers and HR specialists can use organizational frameworks and employee training to help employees achieve better WLB.

Theoretical Background

The existing literature has proposed two types of predictors of WLB: individual characteristics (i.e., job involvement, job importance, family involvement, work-based self-esteem and self-efficacy, conscientiousness, neuroticism, coping style, and time management skills) and cultural values of an individual (i.e., individualism, power distance, masculinity, and uncertainty avoidance) (Sirgy & Lee, 2018). Organizational predictors consist of job characteristics (i.e., job demands, work pressure, autonomy, role ambiguity, and scheduling flexibility) and support systems (i.e., flexible work arrangements, part-time work, childcare support, parenting/breastfeeding resources, health and wellness programs, family leave policies, workplace social support, etc.). Job characteristics are reliable and anticipated predictors of changes in well-being and psychological health, although their importance varies according to context and individual circumstances (Jones et al., 2017; Stansfeld & Candy, 2006; Theorell et al., 2015). Our study examines how personal (i.e., resilience and proactive personality) and organizational (i.e., enriched job design and formalization) predictors interact in predicting WLB.

Work–Life Balance and Enriched Job Design

There are many definitions of WLB, which can be categorized into two groups based on two key dimensions. The first group defines WLB as role engagement in multiple roles and nonwork life, and the second group defines WLB as minimal conflict between work and nonwork roles (Sirgy & Lee, 2018). WLB has many benefits for employees and organizations, and is associated with job and life satisfaction, increased productivity, higher organizational commitment, higher career development, lower absenteeism, and lower turnover intentions (Allen et al., 2000; Baltes et al., 1999; Blazovich et al., 2014; Konrad & Yang, 2012; Kossek & Ozeki, 1998; Whiston & Cinamon, 2015). It has been shown that WLB is influenced greatly by work support, which can come from a variety of sources, such as company policies, supervisors, and colleagues (Allen et al., 2000; Thompson et al., 1999).

Enriched job design is an approach or orientation to designing high-quality jobs that allow employees an element of discretion and flexibility in performing and accomplishing their primary task (Arthur, 1994; Walton, 1985; Wood & de Menezes, 2008). The job characteristics model (JCM) suggests that employees’ job motivation and job satisfaction can be increased by providing greater job enrichment (Hackman & Oldham, 1976). Greater job enrichment can be achieved through higher levels of job characteristics (i.e., skill variety, autonomy, feedback/social support, task identity, task significance, etc.) from the JCM. It has been shown that, if given proper attention in the organization, job enrichment can create a balance between work demands and commitments on the one hand, and family life on the other (Cameron, 1972; Sushil, 2014). A study of 300 managers found that a high level of job design characteristics lead to a higher level of WLB (Jindal et al., 2013); thus, we hypothesize the following:

  • H1: Enriched job design is positively related to higher levels of work–life balance.

Moderating Role of Formalization

Formalization is defined as the extent to which specific rules, policies, and procedures are written and explicitly stated (Pugh et al., 1969). We know about various levels of formalization, from organizational formalization to job role formalization (Griffin et al., 2007; Lin & Germain, 2003; Pugh et al., 1969). Organ and Greene (1981) stated that formalization is needed for goal clarity, and Segars et al. (1998) showed that formalization is also necessary for strategic directions. We are aware of two types of organizational structures that are based on the degree of formalization. A high degree of formalization in an organization is known as a mechanical organizational structure, whereas a low degree of formalization is defined as an organic organizational structure (Alexander & Randolph, 1985; Robbins et al., 2020).

Implementing formal processes that mandate meetings, enable planning, and establish evaluation processes can foster collaboration by defining a space in which the bearers of competing logics can interact (Battilana et al., 2014). It leads to an increase in firm gross profits and investments. Furthermore, it leads to an improvement in employment quality, as measured by a decrease in the use of casual workers (i.e., to an increase in the share of workers with formal labor contracts) (Rand & Torm, 2012). Formal rules can complement informal procedures to facilitate cooperation between adherents of largely incompatible and central logics (Canales, 2014). Tata and Prasad (2004) found that organizational structure enhances the influence of self-management on team effectiveness judgments when the level of organizational formalization is low. Formalization also provides users with a clear understanding of the underlying reasoning behind why certain control mechanisms are in place. Such formalization also codifies best practice experiences, and users receive feedback on their performance (Adler & Borys, 1996).

As mentioned, greater job enrichment can be achieved through higher levels of job characteristics. Many empirical studies have supported this relationship, although it sometimes has been attenuated by personal (e.g., age, income, education, attitudes toward one’s profession, and individual perceptions) and organizational factors (e.g., organizational pressure, culture, and values) (Aldag et al., 1981; Fried & Ferris, 1987; Hackman & Lawler, 1971; Hackman et al., 1975; Loher et al., 1985; Roberts & Glick, 1981; Spector, 1985). Roberts and Foti, (1998) found that there is a need to explore the person–situation interactional perspective as a determinant of work outcomes, with, for example, different levels of self-leadership leading to different levels of job satisfaction with respect to the level of structure in the immediate working environment. This opens opportunities for the study of its boundary conditions.

Hyman and Summers (2004) classified seven major problems that are associated with WLB practices, and one of them was a lack of policy formalization at the organizational level. It already has been shown that the formalization of WLB policies, along with informal support for WLB, plays an important role in improving organizational outcomes such as job satisfaction and employee retention (Cegarra-Leiva et al., 2012). With written rules, organizations show how serious they are about WLB and how strong their commitment to WLB is. When rules are written, employees also have more information about their WLB benefits, so they are more likely to take advantage of them. Formalization also improves the distribution of benefits among employees and the achievement of the full benefits of WLB (Pasamar, 2015). Organizations that are keeping records of their activities and events are more likely to have elaborate benefit plans (Osterman, 1995).

In our study, we suggest that formalization in general (not only WLB practices) helps to achieve better WLB. Enriched job design evokes the motivation to perform and engage (Gallagher & Einhorn, 1976; Wood et al., 2012), and formalized organizational practices, in line with the logic of the JD-C model, help to enable employees to counterbalance their demands with higher levels of control—specifically, to know what to expect and when to expect it, distribute their time and efforts better (Adler & Borys, 1996; Alshwayat et al., 2021), and become able to separate (or balance) their work–life interaction better (Hossen et al., 2018). Therefore, we propose the following:

  • H2: Formalization moderates the positive relationship between enriched job design and work–life balance such that the basic relationship will be more positive when employees work in organizations with more formalization.

The Three-Way Interaction of Personality Traits

Resilience

Resilience means “bouncing back” from difficult experiences, and is defined as employees’ adaptive and resource-utilizing capacity, which reflects the robustness to manage work-related setbacks, challenges, and pressures effectively (Hodliffe et al., 2014). Researchers have claimed that resilience is not a static state but develops over time and comes into play when an individual is faced with unforeseen situations or events and has the capacity to be resilient (Sutcliffe & Vogus, 2003). It is not considered to be a genetic trait, but a set of behaviors, thoughts, and actions that can be learned and developed (American Psychological Association, 2012); thus, it is the process of ongoing development (Kim & Windsor, 2015).

Resilience includes self-regulatory functions that serve to attenuate the negative consequences arising from uncontrollable environmental factors, and it protects individuals from becoming involved in antisocial behaviors (Milczarek et al., 2009). Resilience and investing in human capital in general also are linked with better organizational productivity, innovation capacity, and post-recession competitiveness (Keep, 2016). In a workplace in which organizational support and recognition of effort were low, supervisors rated their employees with better social skills more highly with regard to job performance because they had the skills to make use of the limited resources available to them (Hochwarter et al., 2006). High resilience is positively connected with the ability to maintain WLB (Kim & Windsor, 2015).

High levels of enriched job design (i.e., more autonomy, use of more skills, meaningful and more interdependent tasks, and more social support) help employees achieve their WLB (Jindal et al., 2013). Although formalization can strengthen this relationship (Adler & Borys, 1996; Hossen et al., 2018), a stressful situation or unforeseen event can cause employees to collapse regardless of their enriched job design and formalized organizational practices. Parker et al. (2015) found that more-resilient individuals benefit from high control (e.g., formalization) because it enables adaptive coping. If employees are resilient (Fig. 2, Scenario 1) and therefore more stable, they adapt quickly to a new environment and overcome the stressful situation by still having control over it; their WLB is more likely to still be at the same level as before the stressful situation (Kim & Windsor, 2015). If they are not resilient (Fig. 2, Scenario 4), their WLB usually decreases when a stressful situation occurs (Weerasinghe & Dilhara, 2018), even if their enriched job design is still the same. We propose the following:

  • H3a: There is a three-way interaction effect among enriched job design, formalization, and resilience in predicting work–life balance; The positive relationship between enriched job design and work–life balance in conditions of high formalization is stronger for people who are more resilient compared with those who are less resilient.

Fig. 2
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WLB in the context of high levels of formalization by different enriched job design and resilience/proactive personality conditions

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Proactive Personality

Proactive behavior is defined as being motivated, conscious, and goal directed (Parker et al., 2010). It can be influenced by the degree of supervisor interaction and job autonomy (Parker et al., 2006). People with more-proactive personalities usually craft their work environment (Thomas et al., 2010). They work through their behaviors to intentionally and directly affect change in their current situation, essentially adapting the environment to themselves. Because these individuals have a long-term focus, they do not wait to respond to elements in their environment. Rather, they proactively search for information, scan the environment, anticipate future opportunities, and come up with a plan to create new circumstances (Bohlmann & Zacher, 2021; Fay & Frese, 2001; Thomas et al., 2010).

Individuals with higher levels of proactive personality are more aware of the dynamic, shifting nature of the work environment (Crant, 2000). They have a vision of what is possible, are strongly guided by it, and engage in behaviors that work to make it a reality (Gibson et al., 2019). Research has suggested that individuals with a higher expression of this trait engage in additional professional activities beyond the demands of their official role (Bateman & Crant, 1993; Parker, 1998). They usually work more hours per week and are more frequently engaged in task behavior and organizational citizenship behavior (Bergeron et al., 2014). Proactive personality also is associated with socializing and networking with others. Thus, they are establishing a high-quality relationship with one’s supervisor (Li et al., 2010) and with their colleagues (Thomas et al., 2010), meaning that they usually have high social support.

Proactive employees make the most of their circumstances (e.g., they are able to take greater advantage of high-quality relationships) (Li et al., 2010), and with the support that comes from their enriched job design they are able to leverage a high degree of task identity and use a greater variety of skills (Fig. 2, Scenario 1) than those who are not as proactive (Fig. 2, Scenario 4). Formalization helps employees to distribute their time and efforts better (Adler & Borys, 1996), and the adaptability that comes from proactivity allows them to achieve even better control over their working demands and thereby achieve higher WLB. Furthermore, in such a condition, employees’ proactivity drives them to search for information, scan the environment (Fay & Frese, 2001), and act quickly, which makes them more efficient (Walz & Niehoff, 2016) and allows them more time for WLB; thus, we suggest the following:

  • H3b: There is a three-way interaction effect among enriched job design, formalization, and proactive personality in predicting work–life balance. The positive relationship between enriched job design and work–life balance in conditions of high formalization is stronger for people who have a higher level of proactive personality compared with those with a lower level of proactive personality.

Methodology

Sample and Collection of Data

We collected data in Montenegro in March 2021 using the nonprobability convenience purposive sampling strategy to target respondents (working professionals) across a wide variety of industries and workplaces. This was done to ensure variability in various work settings related to the studied organizational context variables (i.e., formalization) and the respondents’ situations related to COVID-19 measures and their implications during the time that our research was executed. To obtain the most comprehensive results and alleviate potential challenges related to common method bias, we applied a three-source research design; we surveyed employees (517 respondents), their family members (456 respondents), and their supervisors or colleagues (464 respondents), resulting in 436 matched responses to constructs in our model.

Our main units of analysis were employees. Their birth years ranged from 1954 to 2001; 59.3% of the respondents were male and 40.7% were female. They worked in a variety of industries, including administration, finance, information technology (IT), construction, and manufacturing.

Measures

We measured all items using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). All the measurement items used in this study are listed in Appendix A along with their references.

Enriched job design was the independent variable. It was a second-order construct consisting of five items, each assessing a specific job characteristic, that were self-reported by employees. Autonomy was measured using three items, interdependence using one item, task identity using one item, skill variety using one item, and social support using two items. Items were measured using questions from Morgeson and Humphrey’s (2006) Work Design Questionnaire (WDQ). The Cronbach’s alpha of the scale was valued at 0.670.

Work–life balance was treated as a dependent variable, it was self-reported by the employee and a family member, and it was measured with four items each, with a composite score from those two respondents used in the analyses. Items were taken from Omar (2016) (α = 0.817).

The first moderator was the formalization variable. This was assessed by employees’ supervisors or work colleagues, and was measured with five items taken from Schminkeet al. (2000) (α = 0.757). The second set of moderators was resilience and proactive personality. Both were assessed by employees and their family members, and composite scores from the two respondents were used in the analyses. Proactivity was measured with four questions each (Cronbach’s α = 0.697), and resilience with two questions each (Cronbach’s α = 0.685). Items measuring proactivity were taken from Bateman and Crant (1993), whereas items capturing resilience were taken from Smith et al. (2008).

The theoretical background also showed the important factors that potentially could be related to the dependent variable and therefore could influence our findings. For this reason, we also included the following three control variables that were identified as salient: detachment from work during nonwork time, IT experience and training, and work–nonwork interface. The first, detachment from work during nonwork time, is important because it has been shown that people who can detach during nonwork time are less exhausted, have better well-being, and have a lower need for recovery (Barber et al., 2019; Sonnentag & Schiffner, 2019). This construct was self-reported, and was measured with the three questions adapted from Sonnentag and Fritz (2007) assessing techno-invasion. The value of the Cronbach’s alpha was 0.703.

Next, because information and communication technology is blurring the line between work and nonwork life and can influence WLB (Evenstad, 2018; Nam, 2013), it was important to consider it in the study as well. Employees who are more experienced and trained with the use of IT usually report higher satisfaction with WLB (Yordanova & Kirov, 2020). Hence, the IT experience and training construct was self-reported and was measured with five questions from the questionnaire developed by Stapleset al. (1998). The value of the Cronbach’s alpha was 0.881. Last, work-nonwork interface, defined as “an employee's psychological experience of congruence between his/her personal boundary management preference and the boundary management supplies of his/her work environment,” (Bogaertset al., 2018, p. 3) also is important because every person differs with regard to their personal experiences and the fit between their personal boundary management preference and the boundary management supplies provided by their work environment. As Bogaerts et al. (2018) pointed out, this construct is important for employee well-being, job satisfaction, and reduced work–life conflict. The work–nonwork interface construct was assessed by the employee’s supervisor or work colleague and measured using three questions, adapted from Bogaerts et al. (2018), with a Cronbach’s alpha valued at 0.805.

Analytic Procedure

First, we applied principal component analysis (PCA) to verify that the proposed job characteristics represent a second-order construct of enriched job design. Next, to confirm overall model fit, we applied confirmatory factory analysis (CFA) using AMOS software (Arbuckle, 1997). After checking the overall model fit and item choice, we analyzed descriptive statistics. Hypothesis 1 was tested with an independent t-test, and Hypothesis 2 was tested with Model 1 and Hypotheses 3a and 3b were tested with Model 3 in the PROCESS macro version 3 (Hayes, 2018).

In addition to testing the main hypotheses regarding the role of enriched job design on WLB and the moderating joint roles of formalization and personality, we conducted a supplemental analysis to test which of the five enriched job characteristics (i.e., task identity, interdependence, skill variety, social support, and autonomy) had the greatest effect in this three-way interaction model.

Results

Principal Component Analysis and Confirmatory Factor Analysis

The PCA, in which we included all mean scores of job design characteristics (i.e., task interdependence, task identity, skill variety, autonomy, and social support), showed that all five items formed one component (i.e., enriched job design), supporting our proposed model. Enriched job design explained 36.55% of the total variability in the data (KMO = 0.686, approximate. chi-square = 183.523, p = 0.000). The results of CFA showed a good model fit (GFI = 0.905, RMSEA = 0.044, chi-square = 778.342, df = 425, p = 0.000).Footnote 1

Descriptive Statistics

Table 1 provides detailed data on descriptive statistics. The results show that, on average, employees reported very high levels of enriched work design (the means of all five characteristics consisting this construct ranged from 3.47 to 3.963), and on average exhibit high levels of proactive personality (mean = 3.9760), resilience (mean = 3.6955), and work–life balance (mean = 3.998). The correlation between all variables was positive (statistically significant at p < 0.01), but only moderate in size, with Pearson’s bivariate correlation coefficients ranging between 0.09 and 0.36; the only exception of a higher correlation was that between proactive personality and autonomy (0.45).

Table 1 Descriptive statistics and correlations
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Hypothesis Testing

Using the split-means approach, we delineated the respondents into a group of low (below the scale mean) and high (above the scale mean) enriched job design. The independent t-test indicated significant differences (α = 0.001%; σ = 0.13366) between the group with high enriched job design (≥ 3; n = 469; WLB score = 4.04, SD = 0.63) and the group with low enriched job design (< 3; n = 45; WLB score = 3.56, SD = 0.88), with t(48.528) = 3.578. This finding also was supported by an alternative approach, with a regression analysis (Table 2), that indicated a positive and significant relationship between enriched job design and WLB (β = 0.24., p < 0.01, R2 = 0.17, F = 22.852, df = 437). Taken together, we can support Hypothesis 1.

Table 2 Results of regression analyses predicting WLB
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To examine the moderation of formalization, Table 3 summarizes the results of PROCESS macro analyses. Model 1 showed that formalization did not significantly (R2 = 0.1648; F = 14.1094; n = 436) moderate the relationship between enriched job design and WLB [interaction term (Int) = –0.0113, SE = 0.0638, t = –0.1776, p = 0.8591]. An alternative approach using enriched job design and formalization split means and control variables predicting WLB with ANCOVA also provided similar results (a non-significant interaction: 0.009, p = 0.869, F = 0.027, df = 436). Based on these results, we could not find support for Hypothesis 2.

Table 3 Results of the analyses with PROCESS macro
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The fact that the two-way interaction between enriched job design and formalization was not significant opens avenues for potential three-way interactions. Model 3a showed that resilience and formalization did not significantly (R2 = 0.1976; F = 10.4631; n = 436) interact in moderating the relationship between enriched job design and WLB (Int = 0.1278, SE = 0.0866, t = 1.4755, p = 0.1408). Based on this result, we could not find support for Hypothesis 3a.

Model 3b (Fig. 3) showed that proactive personality and formalization did interact in significantly (R2 = 0.2279; F = 4.5884; n = 436) moderating the relationship between enriched job design and WLB (Int = 0.25143, SE = 0.1174, t = 2.1421, p = 0.0328). The slope difference tests showed a significant difference between the slopes of Line 1 and Line 3: t-value = 2.882, p < 0.01. Based on this result, we can support Hypothesis 3b.

Fig. 3
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Three-way interaction effect among enriched job design, proactivity, and WLB

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Additionally, by relying on the three-way interaction effect among each separate job design characteristic, WLB, and proactivity/resilience, we performed supplementary analysis to delve deeper into the interactive role of specific enriched job design characteristics. Four analyses (Table 4) showed statistical significance regarding three-way interactions.

Table 4 Results of supplemental analyses
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Model 3c (Fig. 4) showed that proactive personality and formalization did significantly (R2 = 0.2473; F = 13.9655; n = 436) moderate the relationship between social support and WLB (Int = 0.1688, SE = 0.0841, t = 2.0071, p = 0.0454).

Fig. 4
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Three-way interaction effect among social support, formalization, and proactivity

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Model 3d (Fig. 5) showed that proactive personality and formalization did significantly (R2 = 0.2289; F = 12.6840; n = 436) moderate the relationship between task identity and WLB (Int = 0.1592, SE = 0.0691, t = 2.3034, p = 0.0217).

Fig. 5
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Three-way interaction effect among task identity, formalization, and proactivity

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Model 3e (Fig. 6) showed that resilience and formalization did significantly (R2 = 0.1716; F = 8.8028; n = 436) moderate the relationship between skill variety and WLB (Int = 0.1135, SE = 0.0578, t = 1.9621, p = 0.0504).

Fig. 6
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Three-way interaction effect among skill variety, formalization, and resilience

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Model 3f (Fig. 7) showed that resilience and formalization did significantly (R2 = 0.2028; F = 10.8083; n = 436) moderate the relationship between social support and WLB (Int = 0.1618, SE = 0.0641, t = 2.5248, p = 0.0119).

Fig. 7
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Three-way interaction effect among social support, formalization, and resilience

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Discussion and Conclusion

Summary of Findings

The results showed that enriched job design (i.e., high skill variety, autonomy, task identity, task significance, and social support) is positively related to WLB (i.e., individuals who had higher levels of enriched job design also reported higher levels of WLB), both with continuous ordinal assessments (i.e., regression analysis) and with a split means approach (by comparing the levels of WLB in low versus high conditions of enriched job design). Next, we proposed that formalization moderates the positive relationship between enriched job design and WLB, but we could not find support for this hypothesis, both with continuous ordinal assessments (i.e., moderated regression analyses) and with a split means approach (with ANCOVA). We can conclude that formalization alone does not significantly alter the relationship between enriched job design and WLB; however, this finding opened avenues for the potential roles of additional moderators.

We then examined the joint moderation of formalization and personality traits on the relationship between enriched job design and WLB. We found a significant moderation of formalization and proactive personality (but not resilience) on the relationship between enriched job design and WLB. Interestingly, we found that WLB was highest in conditions with low enriched job design when there was high proactivity and high formalization. Moreover, there was high proactivity in conditions with high enriched job design, whereas formalization did not play as important of a role.

We performed supplementary analyses to determine which job design characteristics play the most important role in predicting WLB, in combination with formalization and proactivity. We found four significant relationships. Proactive personality and formalization moderated the relationship between social support and WLB. Interestingly, we found that the best WLB is achieved when social support is high, combined with high proactivity and low formalization. Furthermore, the results showed that high WLB is achieved when task identity, proactivity, and formalization are high. However, surprisingly, WLB is also high when task identity is low, proactivity is high, and formalization is low. Resilience had no influence on the relationship between enriched job design and WLB, whereas supplemental analysis showed the significant influence on the relationship between some individual job design characteristics and WLB. When analyzing skill variety, we found that the highest levels of WLB are achieved when skill variety is low, employees are resilient, and formalization is low. WLB is highest when support is high and resilience is high, whereas formalization does not matter as much.

Theoretical Contributions

Our study contributes to the fields of organizational behavior, work–life interplay, and HRM in the following ways. First, we contribute to the WLB literature by analyzing the influence of individual job design characteristics on WLB (Polat & Özdemir, 2020; Walia, 2014), advancing this stream of research by taking a holistic approach to capturing enriched job design as a composite second-order construct embodying key job characteristics, and examining its relationship with WLB. We confirmed Jindal et al.’s (2013) findings that enriched job design leads to higher WLB, and then corroborated it by including not only the self-assessments of WLB but also the assessments of their family members.

In doing so, we improved the objectivity of the assessments, which Sirgy and Lee (2018) also highlighted as one of the main gaps in their literature review of WLB research. Our composite multi-informant approach in treating key variables with insights from multiple sources, not just a single perspective, represents an important empirical contribution not only to the study of WLB but of organizational behavior in general, with the aim of improving the empirical assessments of questionnaire-based research. We added to the field by including important control variables, such as detachment from work during nonwork time, IT experience, and training and work–nonwork interface. These did not exhibit a consistent pattern of relationships on WLB; however, in some of our studied models, work–nonwork interface and IT experience and training exhibited a positive relationship, whereas detachment from work, measured by techno-invasion, had a negative relationship with WLB. These findings are in line with those of prior studies (Althammer et al., 2021; Felstead & Henseke, 2017; Nnadozie et al., 2015).

Secondly, we included both types of predictors proposed by Sirgy and Lee (2018): personal (i.e., resilience and proactive personality) and organizational (i.e., enriched job design and formalization). Other studies (Akkani & Oduaran, 2017; Kundnani & Mehta, 2014; Pooja & Kanupriya, 2019) mostly focused on the Big Five personality traits; our study complements this research by focusing on individual traits that can be trained in organizations (Bateman & Crant, 1999; Linz et al., 2019; Strauss & Parker, 2015; Thomas & Albright, 2018) and are more malleable than basic personality traits. This has important implications for the study of organizational behavior and HRM with regard to setting up conditions in organizations that are in line with existing individual, job, or organizational contexts but allow for organizations to develop individual characteristics beneficial for yielding the best results from such contexts in the form of employees’ WLB.

Our findings support findings from previous studies that job characteristics are reliable and anticipated predictors but that their importance varies in the context of individual circumstances (Jones et al., 2017; Stansfeld & Candy, 2006; Theorell et al., 2015). We have shown that individual characteristics are more important than organizational characteristics for the relationship between enriched job design and WLB and that proactive personality is more important than resilience. Employees with proactive personality are better able to take advantage of enriched job design (Li et al., 2010) and prepare for change (Fay & Frese, 2001); therefore, their WLB is not affected severely (Walz & Niehoff, 2016), whereas employees with higher resilience are able to recover quickly after a stressful situation (Kent & Davis, 2010), but their WLB is affected during stressful situations. This provides important insights into the complex interactions of individual, job, and organizational factors that shape organizational realities in predicting employees’ WLB.

We also extended the line of inquiry investigating job characteristics and individual characteristics as predictors of WLB (Jindal et al., 2013; Polat & Özdemir, 2020; Saleem & Abbasi, 2015) by including formalization as a moderating organizational factor, on the basis of the JD-C model. Our study showed that organizational formalization has no significant effect on the relationship between enriched job design and WLB or on the relationship between individual job design characteristics and WLB. Our study confirms Li et al.’s (2010) findings that proactive employees are able to derive greater benefits from high-quality relationships. Thus, we point out the importance of setting up a relationally supportive context for proactive individuals that will enable them to capitalize on their proactivity to achieve high levels of beneficial outcomes such as WLB.

Our supplemental analysis confirmed the findings of previous studies (Allen et al., 2000; Bragger et al., 2021; Thompson et al., 1999; Yucel, 2021) by showing the importance of social support on WLB. We developed these studies further by showing that both resilience and proactive personality can positively moderate the relationship between social support and WLB. Furthermore, we confirmed Jindal et al.’s (2013) findings by showing that task identity has a significant impact on WLB, and we advanced their study by showing that this relationship can be positively influenced by proactive personality. Although Jindal et al. did not show a significant effect of skill variety on WLB, our study suggests that the relationship between skill variety and WLB can be positive when individuals are highly resilient.

Practical Implications

Our study also has important practical implications for employees, managers, HR managers and specialists, and policy makers. Policy makers can gain insights into the importance of developing WLB policies and programs so that workers and managers understand their rights, benefits, and opportunities. This can be achieved through policies that promote an enriching job design and by organizing appropriate training and workshops to improve important personality traits such as proactivity and resilience. It has been shown that proactivity can be improved through various trainings on identifying opportunities, planning and pursuing goals, providing training on building self-confidence and self-efficacy, and so on (Bateman & Crant, 1999). Managers can encourage employees to be more proactive also by shaping their work environment in a way that stimulates proactivity (e.g., by providing more social support) (Ghitulescu, 2012). Resilience can be improved when managers enhance social support and self-regulation (Thomas & Albright, 2018).

In addition to training ( Smith & Smith, 2008), workplace design has been shown to be an important element of HR practices intended for better WLB. HR managers are advised to strive for enriched workplace design that promotes this (e.g., more autonomy, more skill variety, better social support). Enriched job design can be achieved when managers redesign jobs so that they are of high quality and allow employees an element of discretion and flexibility in performing and accomplishing their primary task (Arthur, 1994; Walton, 1985; Wood & de Menezes, 2008). Job redesign usually is accompanied by managers giving their employees more discretion, variety, and high levels of responsibility (Wood et al., 2012).

Furthermore, our study showed that individual characteristics are more important than organizational characteristics for the relationship between enriched job design and WLB. Both individual characteristics studied can be trained and developed through various HR practices (Bardoel et al., 2014). These programs help employees deal with pressure and rapid change in ways that are sustainable for their well-being and the organization’s performance (Ollier-Malaterre, 2009). These include flexible job design, wide-ranging training and growth programs, developmental and performance-based appraisals, rigorous selection processes, competitive payment, and wide-ranging benefits (Raza et al., 2018). Employees can better understand the importance of training that helps them become more proactive and resilient, as well as the overall importance of creating WLB.

Limitations and Further Research Directions

A few limitations of our study should be noted. In particular, the data were collected in only one country (i.e., Montenegro) during the period of the pandemic, so the findings cannot be generalized due to possible cultural and exceptional situational influences. In our questionnaire, each job design characteristic was measured using 1–3 items, which means that the job design assessment was quite scarce. Although we measured the job design characteristics with few items, previous studies (O’brien, 1983; Piccolo & Colquitt, 2006) have confirmed the reliability of this measurement scale. Future research could include formalized WLB practices as moderators to explore these, and could use full scales to capture the core constructs.

In future studies, we propose that researchers should extend our study by including cross-cultural participants in the post-pandemic period. They also could advance our study by exploring various additional individual traits that can be trained (e.g., self-efficacy and self-leadership). Our study also could be advanced by exploring other job design characteristics (e.g., job complexity, skill variety, and specialization) that also could have a large impact on WLB. In addition, we also could explore playful work design (PWD), proposed by Bakker et al. (2021), in relation to WLB. It also be would interesting to see if findings differ for various modes of work (i.e., office work, hybrid work, and remote work) (Tremblay & Genin, 2008) that have gained further momentum during the pandemic.