Human resource policies and work–life balance in higher education: Employee engagement as mediator

Abstract

Orientation: This article focuses on workplace policies, employee engagement and work–life balance in higher education.

Research purpose: This study examined the mediation of employee engagement on the relationship between human resource (HR) policies and work–life balance among employees in the College of Distance Education (CODE) in Ghana.

Motivation for the study: Although there is a plethora of research linking HR policies and employees’ work–life balance, the same cannot be said of the variables that mediate this relationship, as there are only few studies in that perspective.

Research approach/design and method: The study adopted a purely quantitative approach, using the descriptive survey design. Data were collected from 232 staff of the CODE in Ghana. The Structural Equation Modelling was used to analyse the data.

Main findings: The results indicated that employee engagement is a complementary partial mediator of the nexus between HR policies and work–life balance. It was also found that HR policies had a statistically significant effect on work–life balance.

Practical/managerial implications: In order to effectively improve their staff’s work–life balance, management of the college must formulate policies that support employee engagement. Specifically, they can use flexible work arrangements, leave policy, and caretaking policies to positively influence their employees’ work engagement.

Contribution/value-add: The results from this study offer a remarkable new knowledge that can be applied in promoting the work–life balance of employees in higher education.

Introduction

Both theoretical and empirical research (Iqbal et al., 2017; Wood et al., 2020) have shown that there is a significant relationship between work–life balance and organisational outcomes. For instance, Wood et al. (2020) conducted a study about the correlation between work–life balance and employee engagement and found that the two constructs or variables had bidirectional relationship. Thevanes and Mangaleswaran (2018) also investigated the effect of work–life balance on job performance of employees in selected private banks of Sri Lanka. The authors found that work–life balance had both positive and significant effect on job performance. Again, Mas-Machuca, Jasmina and Ines (2016) investigated the effect of work–life balance on organisational pride and job satisfaction among the staff of a Spanish pharmaceutical organisation. The authors found that work–life balance was a positive effect on both organisational pride and job satisfaction. Other studies also found similar correlations between work–life balance and variables such as employee loyalty (Kabir & Rahman, 2019) and turnover intention (Taghrid et al., 2016).

The significant effect of work–life balance on several organisational outcomes signals the need for employers to increasingly formulate policies for assisting employees to balance their work and non-work life responsibilities (Iqbal et al., 2017). The development of life-friendly human resource (HR) policies to leverage a well-balanced work and non-work life responsibilities is necessary because work–life balance provides benefits such as better employee morale, increased productivity, enhanced employee retention and lower levels of stress, absenteeism and sickness (Kabir & Rahman, 2019). Several studies (Feeney & Stritch, 2019; Mengistu, 2020; Uddin et al., 2020) have confirmed the significant positive association between HR policies and work–life balance. For instance, Skinner and Chapman (2013) investigated the relationship between family-friendly policies and work–life balance in the public sector of Australia and found that life-friendly HR policies such as paid leave, childcare, shorter working hours and flextime had significant influence on an individuals’ work–life balance. Feeney and Stritch (2019) also conducted a study on how employee take-up of leave policies, access to dependent childcare, alternative work arrangements and family support affected state government employees’ work–life balance. The authors found a significant positive connection between work–life balance and all the workplace policies.

Notwithstanding the plethora of studies that examined the direct association between life-friendly HR policies and employee work–life balance, there is no study in the literature that examined the mechanisms by which HR policies translate into work–life balance. However, previous studies have shown that employee engagement is positively related to both workplace policies (Yuile et al., 2012) and work–life balance (Aveline & Kumar, 2017; Iqbal et al., 2017; Wood et al., 2020). Thus, the current study is conducted to test the mediation of employee engagement for the first time, between HR policies and employee work–life balance in a higher education in Ghana. This study was conducted in the College of Distance Education (CODE), University of Cape Coast, Ghana. The study contributes to both theory and practice by not only documenting the current level of understanding between life-friendly HR policies and work–life balance but also analysing the indirect relationship between the two constructs.

The CODE is one of the five colleges in the University of Cape Coast whose work is very challenging owing to the large number of students they admit each year (John et al., 2020). To deliver active services to their distance students, staff of the college are engaged in their work throughout the week. The staff of the College perform their official duties from Mondays to Thursdays in their offices and travel on Fridays to the study centres during weekends to supervise tutors on a 12-week face-to-face programme, 4-weeks continuous assessment invigilation and a 4-week invigilation at the end of semester examination (Segbenya et al., 2018). The staff also prepare all relevant documents for use during the weekends. Some staff members work overnight while the female staff who nurse their children have to travel with their wards to far away distance centres throughout the day or night, and this makes life very uncomfortable (Akuamoah-Boateng, 2020; Segbenya et al., 2018). Thus, in a study conducted by Akuamoah-Boateng (2020), it was suggested that a further study will be required to examine how staff of the college can balance their work and non-work activities. The current study fills this gap.

Literature review

The review of literature in this study is organised into three sections: (1) theoretical review, (2) conceptual review and (3) empirical review.

The theoretical foundation of the study

The current study is underpinned by work/family border theory, which was proposed by Clark (2000). The work/family border theory was developed to address the issues of crossing borders between spheres of life, particularly the spheres of work and family life (Kossek & Lautsch, 2018). On the basis of the work/family border theory, individuals are viewed as border-crossers as they continuously manage and negotiate work and family spheres or the borders between them in order to create and maintain a desired work–family balance. According to Clark (2000), a desired work–family balance is one that provides satisfaction and good functioning at both work and home with the least degree of role conflict (Karassvidou & Glaveli, 2015). According to Clark (2000), each person’s role in society is within a specific sphere of life, and these spheres may be separated by physical or psychological boundaries. Physical boundaries are artificially occurring barriers between the workplace and the family. Psychological boundaries are procedures established by employees that can determine when to think and when behaviour and emotional patterns are suitable for a particular domain and vice versa. According to Clark (2000), the level of integration, the ease with which transitions are made and the level of conflict that exist between these borders are all affected by the flexibility and permeability of the borders. Clark (2000) maintained that work and family spheres are not separated but are connected with flexible and permeable boundaries.

Clark’s (2000) work/family border theory is based on the assumption that work and non-work domains are two separate spheres of life that affect each other. The relationship between these spheres forms a continuum that ranges from segmentation to integration (Karassvidou & Glaveli, 2015). At the pole end of segmentation, the two spheres are mutually exclusive, whereas at the pole end of integration, they are conceived as identical spheres. According to Karassvidou and Glaveli (2015), the more flexible and permeable the line between the two spheres (weak borderline), the higher the integration between them. Here, the individual hardly sees a distinction between what belongs to work and what belongs to home. This blurring condition usually creates work–family balance conflict. As noted by Karassvidou and Glaveli (2015), ‘if the two domains are closely integrated, it is easier for transmission to occur but this has the tendency of creating work-family conflict’ (p. 86). Alternatively, where the line between the two spheres is impermeable and inflexible (strong borderline), segmentation usually results, deterring physical and behavioural elements from passing from one sphere into another (Kossek & Lautsch, 2018). Flexibility and permeability of work and non-work borders can increase a person’s work–life balance (Kossek & Lautsch, 2018). They may also lead to a conflict when people become confused about the role they should be playing at any given time.

The work–family border theory applies to a variety of policies such as job sharing, flexible working hours and home-working that are often used in organisations to assist workers to balance competing demands from work and personal life (Itzkovich et al., 2020). These arrangements may improve the employees’ sense of control over the extent of segmentation or integration they desire (Kossek & Lautsch, 2018). The theory has been criticized for being too simplistic as it exclusively focuses on work and family borders without giving much attention to the borders of the other important non-work roles such as leisure, social, religious and recreational works that happen within a person’s life. Electronic gadgets were not also included in the theory as a component that can affect work–life balance. Electronic gadgets can create permeability by acting as a link between different aspects of life. The use of technological gadgets produces an environment that is neither work nor non-work (Kossek & Lee, 2017).

The work–life border theory is relevant for this study because it applies to a variety of policies such as job sharing, flexible working hours and home-working that are often used by management to assist workers to balance competing demands from work and home.

Conceptual literature

Three concepts were relevant for this study: (1) life-friendly HR policies, (2) work–life balance and (3) employee engagement.

Life-friendly human resource policies

Life-friendly HR policies are policies that allow employees to balance their work and non-work life roles (Yuile et al., 2012). Life-friendly HR policies are usually grouped into three: (1) flexible work arrangements, (2) leave policy options and (3) caretaking benefits (Yuile et al., 2012). Flexible work arrangements are human resource policies that provide employees with an option to make choices influencing when, where, and for how long they should engage in a work-related task. Maxwell et al. (2007) defined flexible work arrangements as ‘any policy, practice, formal or informal, which permits people to vary when and where work is carried out’ (p. 138). With flexible work arrangements, employees are better able to work outside the traditional confines of the organisation. There are several forms of flexible work arrangements, such as compressed workweek, telecommuting, part-time work, job sharing and shift work (Austin-Egole et al., 2020).

The second broad life-friendly HR policy that helps the employee to ensure an appropriate balance between work and non-work obligations is the leave policy option. Naithani (2010) defined a leave policy as the sum of rules and guidelines that are designed by management to provide regular break periods for their staff. These rules or guidelines include parental leave, paid annual leave, sick leave, sabbatical leave and maternity leave. Organisations that provide leave to employees are committed perceived as being committed to helping their staff to achieve a good work-life balance (Austin-Egole et al., 2020; Maxwell et al., 2007). Caretaking benefits are the third type of life-friendly HR policy, which can help employees to achieve desirable work and family life balance. Carers’ arrangement, as they are often called, include onsite/offsite childcare, eldercare initiatives, family support programmes, counselling services and health programmes (Austin-Egole et al., 2020).

Conceptualising work–life balance

Work–life balance defies any universally accepted definition. Scholars have defined the concept in diverse ways. For instance, Kirchmeyer (2000) defined the concept of work–life balance as having rewarding experiences both at work and at home. Clark (2000) also defined work–life balance as the enjoyment and satisfaction received from work and home with the least amount of disagreement. Again, Greenhaus and Powell (2003) defined work–life balance as a process by which a person is equally interested in and content with his work–family roles. Greenhaus et al. (2006) further provided a revised definition of work–life balance in 2006. According to the authors, work–life balance is the process by which a person’s efficiency and fulfilment in work–family duties are congruent with their life roles at any given moment. Mengistu (2020) defined work–life balance as the accomplishment of role-related expectations that are negotiated and shared between a person and his or her role-related partners in the office and in the family. According to Lakshmi and Gopinath (2013), although the presence of numerous definitions may provide an adequate understanding of the concept, the plethora of definitions may also be a source of misunderstanding among scholars.

Previous studies (John et al., 2020; Mengistu, 2020; Nathani, 2010; Omar et al., 2015) have acknowledged the main factors that influence employees’ work–life balance. Nathani (2010) grouped the factors into three, namely work-related factors, family and personal related factors and other factors. Nathani defined work-related factors as any activity that affect work either positively or negatively. Examples include supervisor support, good workplace policies and good working conditions. Family and personal related factors are the factors that affect family and personal life either positively or negatively (Omar et al., 2015). Examples include family support, parental support and spousal support.

Mengistu (2020) also grouped the determinants of work–life balance into two – demand predictors and resource predictors. Demand predictors are those factors that affect work–life balance negatively. Examples include work and non-work role overloads. According to Mengistu (2020), work overload arises when the entire job demands on time and energy connected with numerous positions are too great to fulfil efficiently and comfortably. Omar et al. (2015) opined that demand predictors such as work overload and role conflict are the dominant issues affecting work–life balance. The resource predictors, on the other hand, are defined by Mengistu (2020) as the factors influencing work–life balance positively, including social support, family support and flexible policies. Resource predictors are those organizational resources or conditions that support employees in accomplishing their task.

Concept of employee engagement

Schaufeli and Bakker (2010) defined employee engagement as the active and positive work-related state that is characterised by three main factors: vigor, dedication and absorption. Vigor measures the levels of energy and resilience that employees put in their work while dedication refers to employee’s involvement in the work itself. The concentration and enthusiasm manifested by employees at the workplace is known as absorption. Saks (2006) identified two forms of employee engagement: (1) job engagement and (2) organizational engagement. Job engagement consists of the psychological state of fulfillment with one’s task while organizational engagement consists of employee’s physical, cognitive and emotional investment in the work. Fully engaged workers are physically energised, emotionally connected, mentally focused and feel aligned with the purpose of the organisation (Schaufeli & Bakker, 2010). As noted by Motyka (2018), engaged employees have a sense of personal attachment to their work and the organisation; they are motivated and able to give off their best to help the organisation succeed and from that flows a series of tangible benefits for the organisation and employees alike.

There are four major determinants of employee engagement: (1) perceived organisational support, (1) positive organisational politics, (3) job fit and (4) psychological climate (Motyka, 2018; Saks, 2006). Perceived organisational support is described by Eisenberger et al. (1986) as the employees’ ‘beliefs concerning the extent to which the organization values their contribution and cares about their well-being’ (p. 501). A study by Shaw et al. (2013) has shown that employees who demonstrate high level of perceived organisational support do not usually become stress at work. Another determinant of employee engagement is positive organisational politics. Donald et al. (2016) defined organisational politics as the means by which employees influence their colleagues, subordinates and even superiors in order to obtain personal benefits and or satisfy organisational goals. This definition is based on the traditional view of organisational politics whose research concentrated mostly on examining the attempts made by employees to influence the behaviour of others or the organisation as a whole (Donald et al., 2016). Recent studies (Donald et al., 2016; Motyka, 2018), however, focus more on how people perceive political manoeuvres arguing that the notion of politics rests on the perceptions employees hold about the concept rather than the actual influence tactics.

Person-job-fit is another determinant of employee engagement. According to Hoffman and Woehr (2006), person-job-fit relates to the congruence between employee characteristics such as knowledge, skills, abilities, needs and the demands or attributes of the job. In the opinion of the authors, good job-fit helps employees to be involved in a meaningful work, which in turn affects the development of work-related employee attitudes, such as job satisfaction, job involvement and organizational commitment. It also provides the cognitive stimulus that employees require to engage in positive organisational outcomes, such as improved performance, intention to stay and affective commitment. Thus, by implication, employees with good job fit are more likely to perform their jobs with enthusiasm and energy. Alternatively, low job-fit usually results in decreased productivity, decreased job satisfaction and employee engagement and increased levels of turnover (Hoffman & Woehr, 2006).

The fourth determinant of employee engagement is psychological climate. Kataria et al. (2013) defined psychological climate as an individual attribute, measured in terms of employees’ perception and interpretation of their organisational environments or the policies and practices adopted by management of the organisation. Employees feel safe and available when there is a good psychological climate, and this allows them to invest in their work roles without fear of any adverse criticism or career sabotage. When employees perceive favourable psychological climate, they are likely to be motivated, with the results that they will be prepared to invest their personal energies into their respective work roles. Thus, employees with improved psychological climate are more likely to engage in extra in-role discretionary effort, mediated by engagement in work.

There are three levels of employee engagement in the literature – highly engaged, not-engaged and the actively disengaged (Gallup, 2006). Highly engaged employees perform their work with passion. They perform their task with great enthusiasm and pleasure. They are naturally curious about their organisation and would indulge in productive and innovative ways to direct the organisation in the path of development and success. Not-engaged employees just concentrate only on their tasks and its outcomes without heeding to the organisation and its present position in the market. According to Gallup (2006), actively disengaged employees simply walk through their workday, putting time – but not energy or passion – into their work. Employees who are actively disengaged are not only unhappy at the workplace but can also undermine what their engaged co-workers accomplish.

Empirical review and development of hypotheses

This section reviews the association between (1) life-friendly HR policies and work–life balance, (2) life-friendly HR policies and employee engagement as well as (3) employee engagement and work–life balance. After each review, a corresponding hypothesis is provided.

Life-friendly human resource policies and work–life balance

Several studies have examined the positive nexus between HR policies and work–life balance (Feeney & Stritch, 2019; Gudep, 2019; Mengistu, 2020; Uddin et al., 2020). Mengistu (2020) investigated the factors that determine employee work–life balance in selected nongovernmental organisations (NGOs) in Ethiopia and found that among the relevant predictors, work–life balance policies predicted work–life balance more than work overload, family/life role overload and social support. Oludayo and Omonijo (2020) also examined the effect of social support initiatives on work–life balance in Nigeria. The authors found that social support initiatives reduce stress, strengthen workplace/personal relationships and facilitate workplace performance on multiple levels. Much like Oludayo and Omonijo (2020) and Uddin et al. (2020) examined the effect of perceived family and workplace supports and work–life balance policies on work–life balance among female staff in the banking industry in Bangladesh. The authors found that workplace support, superior support and work–life balance policies significantly influence the attainment of better work–life balance among female bankers in Bangladesh. In another study, Uddin et al. (2021) found that emotional support from co-workers and instrumental support from supervisors have significant impact on the work–life balance of employees in developing countries. On the basis of this review, it is hypothesised that:

H₁: Life-friendly HR policies have a significant influence on work–life balance

Life-friendly human resource policies and employee engagement

Previous studies (Eek & Axmon, 2013; Kaewthaworn, 2019; Kangure, 2014; Yuile et al., 2012) have shown that life-friendly HR policies have significant positive association with employee engagement. Eek and Axmon (2013) examined the effect of attitude and flexibility on working parents’ mental wellbeing, stress and employee engagement in the Swedish southern healthcare region. The authors found that workplace flexibility and attitude to parenthood have the strongest effect on working parents’ subjective stress, wellbeing and employee engagement. Kaewthaworn (2019) also investigated the influence of HR policies on employee engagement in the hotel industry. The study was conducted in the Hatyai district, Songkhla Province and Kathu district, Phuket Province. Three family-friendly policies were identified in the study: (1) work flexible policy, (2) leave policy and (3) dependent care policy. It was found that all the three policies have significant positive influence on employee engagement. Out of the three, flexible policy was the strongest. Similarly, Yuile et al. (2012) examined the role of contemporary life-friendly HR management policies on employees’ work–life balance in the Queensland Public Sector Agency. The authors found that flexibility is positively related to greater work–life balance and vice versa. Kangure (2014) also found that work place policy, supervisor support and co-worker support contribute positively to employee engagement. Based on these reviews, it is hypothesised that:

H₂: Life-friendly human resource policies have a significant influence on employee engagement

Employee engagement and work–life balance

Previous studies (Aveline & Kumar, 2017; Iqbal et al., 2017; Jasmina & Miha, 2021; Kangure, 2014) have also shown that employee engagement has a significant positive relationship with work–life balance. Jasmina and Miha (2021) investigated the association between work–life balance and job engagement among lecturers in Austria, Croatia, Czech Republic, Germany, Serbia and Slovenia. The results of their study show that life satisfaction is an important moderator between work–life balance and job engagement. Iqbal et al. (2017) also examined the impact of employee engagement on work–life balance in Faisalabad and found that employee engagement was highly correlated with work–life balance among the banking employees in Faisalabad. The nexus between employee engagement and work–life balance was also investigated by Aveline and Kumar (2017). Data for the study were gathered from respondents in the software industries in Chennai. It was found that engaged employees had better work–life balance than highly disengaged employees. Kangure (2014) also investigated the relationship between employee engagement and work–life balance. In this study, data were gathered from 434 participants in 197 state-owned corporations in Kenya. It was found from this study that work–life balance has a positive significant effect on employee engagement and vice versa. Given these findings, it was hypothesised that:

H₃: Employee engagement has a significant influence on work–life balance

Life-friendly human resource policies, employee engagement and work–life balance

Previous studies (Kaewthaworn, 2019; Kangure, 2014) have shown that life-friendly HR policies have significant positive association with employee engagement. Moreover, other studies (Iqbal et al., 2017; Jasmina & Miha, 2021) have shown that there is a significant positive association between employee engagement and work–life balance. According to the work–life border theory, engaged workers manage work and family responsibilities by controlling the boundaries among the two competing domains. Thus, in this study, employee engagement was expected to play a mediating role in the relationship between life-friendly HR policies and work–life balance. Given this logic, the following hypothesis has been formulated:

H₄: Employee engagement mediates the relationship between life-friendly HR policies and work–life balance

The conceptual framework of this study is presented in Figure 1.

FIGURE 1:

Conceptual model for the study.

[FIGURE OMITTED. SEE PDF]

Methodology

The study organisation, target population and sampling

The current study was carried out in the CODE, University of Cape Coast, Ghana. The population for this study comprised all the 269 employees in the college. The entire population was used for the study because, given its size, it was realistic and feasible to reach out to all the staff in the college (Kim, 2016).

Data collection instruments and procedures

Data were gathered for this study using the survey questionnaire. The survey method was preferred because according to Kim (2016), it is most appropriate for explanatory research. The instrument was divided into three parts. Part one comprised the items used for collecting data about the sex, age, years of work experience, academic qualification and marital status of respondents. The second part comprised the items for measuring work–life balance, while part three comprised items for measuring life-friendly HR policies. The final part comprised items for measuring employee engagement. Each item was measured on a 7-Likert-scale with 1 = strongly disagree and 7 = strongly agree. The questionnaires were self-administered during week days from July 2021 to September 2021. Out of the total 269 respondents, 232 successfully completed and returned. This gave a response rate of 86.25%.

Data processing and analysis

Data collected for this study were processed using the IBM Statistical Package for Social Sciences (SPSS) Version 25.0 software and analysed using the Partial Least Squares (PLS) structural equation modelling, which allows for simultaneous estimation of co-variation for all variables in a model (Sarstedt et al., 2014). The preparation of the data gathered was in two phasis. Firstly, the raw data were edited, coded and converted into actual variables. Each questionnaire was carefully checked for incompleteness and inconsistencies. The variables were assigned codes to facilitate computer data input. Once entered into the datasheets, the data were carefully screened to minimise entry errors. Frequencies for each variable were checked to detect the out-of-range values. Secondly, the processed data were analysed using SMART PLS 3.2.8. The prepared data file was then converted into ‘comma-delimited’ format ‘CSV’ before the final file was imported into the SMART PLS application for the model configuration (Browne et al., 2019; Kumar et al., 2019; Lew et al., 2019).

The set-up of the PLS tool for the formulation of the model was as follows: Consistent PLS algorithm and consistent bootstrapping were dully marshaled for the analysis with 5000 maximum iterations. Casewise deletion was configured for missing values although there were no missing values in the data (Ringle et al., 2015). A 95% confidence interval with a corresponding 5% level of significance was set for the reflective model. The one-tailed test hypotheses were formulated because of the non-directional nature of the objectives of the study. As a decision rule, indicators that had outer loadings less than 0.7 (not statistically significant) were deleted in order to improve the measurement model. The evaluation of the models began with the measurement model and then the structural model (Fami et al., 2019; Hair et al., 2017; Tabet et al., 2019). Cronbach’s alpha (≥ 0.7) and composite reliability (≥ 0.7) were also computed. Cronbach’s alpha value for all the items exceeded the minimum 0.7 cut-off point (Sarstedt & Ringle, 2019). Cronbach’s alpha and composite reliability are the most common internal consistency measurements (Fami et al., 2019).

The scale’s reliability was measured with the rho_A (≥ 0.7), which is currently the only consistent reliability measure of PLS construct scores (Dijkstra & Henseler, 2015; Henseler, 2017). Convergent validity was measured with the average variance extracted (AVE). Average variance extracted values must be greater than or equal to 0.5 before they can adequately measure convergent validity (Ringle et al., 2015). Discriminant validity was measured with Fornell–Larcker criteria (should follow in descending order). Discriminant validity represents the uniqueness and distinctiveness of each construct relative to other constructs in the model (Afum et al., 2019). As reflective models are susceptible to biases and errors, the authors of the current study thought that it was wise to examine the test of collinearity statistics (Hair et al., 2016). Common method bias was measured with the collinearity statistics (variance inflation factor (VIF) ≤ 5). This was measured with the VIF value, as its usage in this context has been confirmed in reflective models in structural modeling (Kock, 2015). Generally, when collinearity statistics are above 3.3 thresholds, it implies that the model is likely to be affected by common method bias. Alternatively, when the VIF is less than 3.3, the model is deemed to be without common method bias (Afum et al., 2019). Factor loadings for the relevant indicators were measured accordingly, given cognisance to top-values and t-statistics (Ringle et al., 2015).

Path coefficients (unstandardised beta) were used to assess the contributions of both direct and indirect predictors to the variance in the dependent variable (Schberth et al., 2018) while effect size (f ²) was used to quantify their contributions to the changes in the dependent variable (Ahrholdt et al., 2019). Effect size values above 0.35, 0.15 and 0.02 were regarded as strong, moderate and weak (Cohen, 2016). This was assessed by the R-square, regarded as the most common effect size measure in path models (Garson, 2016). To this effect, tentative cut-off points have been recommended (Garson, 2016; Hock & Ringle, 2006). Results above 0.67 are described as being ‘substantial’, those above 0.33 are ‘moderate’ and those above 0.19 are ‘weak’. The findings were presented in tables and figures for easy understanding and reporting. Finally, the Q ² was analysed using the blindfolding procedure. Q-square is predictive relevance that measures whether a model has predictive relevance or otherwise (> 0 is good). A Q ² above 0 shows that the model has predictive relevance.

Measurement of constructs

Three constructs were measured in this study. Work–life balance was measured by using the eight-item scale developed by Valcour (2007). Life-friendly HR policies were measured using the 15-item scale developed by Yuile et al.’s (2012) while employee engagement was measured using the 15-item scale developed by Schaufeli and Bakker (2003).

Ethical considerations

The University of Cape Coast Institutional Review Board (UCCIRB) has granted Provisional Approval for the implementation of your research titled Workplace Policies, Work Engagement and Work-Life Balance among the Staff of College of Distance Education. This approval is valid from 02 February 2022 to 15 February 2023 (UCCIRB/CHLS/2021/72).

Results and discussion

Measurement model evaluation

The measurement model evaluation involved confirmatory factor analysis, indicator loadings, reliability and validity. The model was assessed in order to determine the suitability of the indicators used to measure HR policies, employee engagement and work–life balance. According to Kim (2016), an indicator is suitable if its outer loading is more than 0.7.

Assessing the indicator loadings

The indicator variables were subjected to confirmatory factor analysis. This was done in order to identify the indicators that clearly explained the constructs even though the scales used were developed and modified from existing literature with theoretical support (Sarstedt et al., 2014). To increase the overall model ‘reliability, any indicator that loaded below 0.70 was removed. The indicators in Table 1 met the reliability criterion.

TABLE 1

Indicator loadings.

Variable	WE	WLB	WPP
DCB	-	-	0.895
FWA	-	-	0.879
LPO	-	-	0.865
WE3	0.799	-	-
WE4	0.811	-	-
WE6	0.853	-	-
WE8	0.775	-	-
WLB1	-	0.742	-
WLB5	-	0.901	-
WLB7	-	0.895	-

WE, Work Engagement; DCB, Dependent Care Benefit; FWA, Flexible Work Arrangement; LPO, Leave Policy Options; WPP, Work Place Policies; WLB, Work Life Balance.

Assessing reliability and validity

The reliability and validity of the variables were both assessed. Reliability was assessed using composite reliability. Validity was categorised into convergent and discriminant validity. Convergent validity was measured using the AVE, while discriminant validity was measured using the Fornell–Lacker criteria (see Table 2).

TABLE 2

Validity and reliability.

Variable	Cronbach’s alpha	Rho A	Composite reliability	Average variance extracted (AVE)
Work-life balance	0.825	0.837	0.884	0.655
Work engagement	0.778	0.773	0.873	0.698
Life-friendly HR policies	0.774	0.748	0.854	0.662

AVE, average variance extracted; HR, human resource.

As shown in Table 2 , the composite reliability of all reflectively assessed constructs ranged from 0.854 to 0.884, which is above the minimum threshold of 0.7 (Sarstedt et al., 2014). As a result, all the latent variables were credible. Work–life balance had the highest composite reliability (0.884) followed by employee engagement (0.873) and life-friendly HR policies (0.854). The results indicate that the model has internal consistency and reliability. The AVE was used to assess convergent validity. The AVE in Table 2 for life-friendly HR policies (0.662), employee engagement (0.698) and work–life balance (0.655) met the minimal threshold of 0.50 (Hair et al., 2017). According to Hair et al. (2016), an AVE above 0.50 implies that the construct explains more than half of the variation in its indicators. The Fornell–Lacker criteria were used to assess discriminant validity. The results are provided in Table 3.

TABLE 3

Fornell–Lacker criterion.

Variable	AVE	Work-life balance	Work engagement	Life-friendly HR policies
Work-life balance	0.655	0.810	-	-
Work engagement	0.698	0.316	0.835	-
Workplace policies	0.662	0.492	0.811	0.814

AVE, average variance extracted; HR, human resource.

The values in Table 3 : life-friendly HR policies (0.814), employee engagement (0.835) and work–life balance (0.810) indicate that the square root of each construct’s AVE is larger than the correlations among them (Hair et al., 2016). As all the indicator loadings were higher than the cross-loadings, the model meets the requirements of discriminant validity.

Assessing the structural model

Collinearity among constructs such as the coefficient of determination, predictive relevance, effect size, path coefficient and its significance were evaluated in this study. The direct and indirect models were run together (Nitzl et al., 2016). The collinearity test indicated that the VIF of work–life balance was 3.198 (below the threshold of 10) and the tolerance level 0.619 (below the threshold of 1.00). These results mean that no multicollinearity affected the results negatively. The results are presented in Table 4.

TABLE 4

Collinearitv among constructs.

Variable	Work-life balance (VTF)	Work engagement (VTF)	Work-life balance (Tolerance)	Work engagement (Tolerance)
Workplace policies	3.198	1.07	0.619	0.781
Work engagement	3.198	-	0.384	-

VIF, variance inflation factor.

The absence of common bias was further confirmed by the variance inflator factor result, which was equal to 3.198. Having a VIF value greater than 3.3 means that the results are tainted by common method bias (Hair et al., 2016). As all the VIF values in the collinearity test were 3.3 or below (3.198), this signifies that the model is devoid of collinearity and common method bias. The endogenous variable’s predictive power was also evaluated, and the coefficient of determination (R ²) for the work–life balance (the endogenous latent variable) was 0.835. This means that the two latent constructs accounted for 83.5% variation in staff’s work–life balance as indicated in Table 5 and Figure 2. Workplace policies, on the other hand, explained 22.4% of the variation in employee engagement, as indicated in Figure 2.

FIGURE 2:

Outer and inner model results.

[FIGURE OMITTED. SEE PDF]

TABLE 5

Coefficient of determination.

Variable	Path coefficient	T-R 2 statistics	Adjusted R 2	Q 2	p	F 2
Work-life balance	-	0.835	0.834	0.590	-	-
Work engagement	0.411	8.993	-	-	0.000	0.796
Workplace policies	0.645	14.970	-	-	0.000	1.958

The predictive relevance of the model was also checked, using the Stone-Q² Geisser’s statistic (Stone, 1974). The results are presented in Table 6 and Figure 3.

FIGURE 3:

Structural model with blindfolding.

[FIGURE OMITTED. SEE PDF]

TABLE 6

Predictive relevance of the model.

Variable	R 2	Adjusted R 2	Q 2(=lSSE/SSO)
Work-life balance	0.835	0.834	0.590
Work engagement	0.224	0.221	0.142

After performing the blindfolding procedure, the values of the Q ² in Table 6 and Figure 3 were greater than zero (work–life balance = 0.590, employee engagement 0.142), indicating predictive significance (Hair et al., 2016). Finally, the internal path co-efficient and significance were assessed by running bootstrapping procedure. The results (p = 0.00) indicated that the hypothesised structural paths were significant as presented in Table 7.

TABLE 7

Path coefficients and hypothesis testing.

Hypothesis		Coefficient	T–statistics	P-Value	Supported
H1	Life-friendly human resource policies are significantly related to work-life balance	0.645	14.970	0.000	Yes
H2	Work engagement is directly related to work-life balance	0.411	8.993	0.000	Yes
H3	life-friendly human resource policies are positively related to work engagement	0.474	9.227	0.000	Yes
H4	Work engagement mediates the link between life-friendly HR policies and work-life balance	0.840	NA	NA	Yes

Discussion

The main objective of this study was to examine the mediation of employee engagement between HR policies and employee work–life balance in the CODE in the University of Cape Coast, Ghana. The discussion of results is organised into four sections based on the four hypotheses formulated for the study:

H₁: Life-friendly HR policies have a significant influence on work–life balance

This hypothesis sought to evaluate the influence of life-friendly HR policies on the work–life balance of staff of CODE. The results revealed that life-friendly HR policies significantly influence work–life balance (β = 0.645, p = 0.000, R ² = 0.835) as shown in Table 7. The results imply that a change in life-friendly HR policies will have a positive effect on employees’ work–life balance. As the p-value is less than 0.05, it is considered significant, meaning that life-friendly HR policies have a substantial beneficial influence on work–life balance of staff of CODE. The coefficient of determination (R ²) was = 0.835, showing that life-friendly HR policies account for 83.5% of the variation in work–life balance. Thus, if employees are allowed to utilise life-friendly HR policies such as flexible work hours, flexitime, childcare, etc., their work–life balance will be improved in the college. These findings are consistent with those of Mengistu (2020) that flexible workplace practices influence work–life balance. The findings are also in line with the submissions by Oludayo and Omonijo (2020) that flexible work options help employees achieve a better work–life balance, which leads to higher levels of performance and commitment. The results also supported the recent empirical survey carried out by Gudep (2019) that flexible work systems promote and enhance staff’ work–life balance, which may benefit the organization by improving their productivity. Again, the findings corroborate the current findings of Feeney and Stritch (2019) that flexible scheduling and work–life balance have a significant positive relationship.

Given the positive association between workplace policies and work–life balance as revealed by prior research (Mengistu, 2020; Oludayo & Omonijo, 2020; Uddin et al., 2020) and the findings in the current research, the hypothesis that life-friendly HR policies have a significant influence on work–life balance is supported in this study. This implies that management of the CODE can help improve the work–life balance of their employees by instituting life-friendly HR policies such as flexible work arrangements, leave policies and dependent policies. Management must, however, ensure that the use of the policies is mandatory for every staff in the college:

H₂:Life-friendly HR policies have a significant influence on employee engagement

The second hypothesis sought to examine the influence of life-friendly HR policies on staff work–life balance in CODE. The results in Table 7 and Figure 2 revealed that life-friendly HR policies significantly influenced employee engagement (β = 0.474, p = 0.000). The result of the coefficient of determination (R ²) indicated that 22.4% of the variation in employee engagement was explained by life-friendly HR policies. The results imply that a positive and significant change in the organisations’ life-friendly HR policies may lead to an improvement in the employee engagement of staff even though a substantial variation (77.6%) is explained by other factors (R ² = 22.4%). As the p-value (0.000) is less than 0.05, it is declared significant, meaning that workplace policies have a significant positive effect on staff employee engagement. The results are consistent with the findings of Kangure (2014) that job resources promote and enhance individuals’ employee engagement. The findings also confirm the assertion by Kaewthaworn (2019) that life-friendly HR policies such as flexible working conditions positively relate to employees’ employee engagement. The results again confirm the conclusion by Yuile et al. (2012) that friendly policies and supervisor support positively influence employee engagement. Furthermore, the results corroborate the findings of Oludayo and Omonijo (2020) that work leave arrangement, flexible work arrangement, employee time out, social support and dependent care initiative are predictors of employee behavioural outcomes such as job satisfaction and employee engagement.

Moreover, the findings validate the findings of Eek and Axmon (2013) that workplace factors that are related to flexibility have a strong positive effect on stress, well-being and employee engagement. Given the findings of previous studies (Eek & Axmon, 2013; Kaewthaworn, 2019), and those of the current study, the hypothesis that life-friendly HR policies have a significant influence on employee engagement is supported in this study.

The results of this study imply that management of the CODE can enhance the employee engagement of their staff by instituting life-friendly HR policies such as flexible work arrangements, leave policies and dependent policies and making sure that these policies are consistently adhered to:

H₃: Employee engagement has a significant influence on work–life balance.

The third hypothesis sought to assess the influence of employee engagement on the work–life balance of employees in the CODE. The aim was to assess whether employees who are truly engaged and dedicated to their work have a better work–life balance as indicated in previous studies (Aveline & Kumar, 2017; Iqbal et al., 2017; Jasmina & Miha, 2021). The result in Table 7 and Figure 2 revealed that employee engagement had a significant influence on the work–life balance in the college (β = 0.411, p = 0.000). Hence, the alternative hypothesis is accepted. The result of the coefficient of determination (R ²) was 0.835, which implies that 83.5% of the variance in work–life balance of staff is predicted by changes in employee engagement. In light of this, an increase in employee engagement will result in an improvement in staff’s work–life balance. In order words, employee engagement has a significant positive influence on staff’s work–life balance. The results corroborate the findings of Aveline and Kumar (2017) that employees who are greatly engaged in their work have better family lives. The findings also confirm the submission by Iqbal et al. (2017) that employee engagement and work–life balance are strongly correlated. Furthermore, the results from this study are consistent with the findings of Jasmina and Miha (2021) who maintained that people who are highly engaged at work are better able to successfully integrate their job and family obligations. The results also corroborate the findings of Aveline and Kumar (2017) that employee engagement is the most proximal predictor of work–family facilitation.

The preceding results imply that management of the CODE can improve the work–life balance of their employees by ensuring that appropriate measures are put in place to help them engage in their work. This can be achieved by providing challenging work, career development programmes and ensuring effective and supportive leadership at all times. Management of the college can also implement employee engagement strategies and interventions that are necessary to meet the needs and aspirations of the staff:

H₄: Employee engagement mediates the relationship between life-friendly HR policies and work–life balance

The fourth hypothesis sought to examine the mediation of employee engagement between life-friendly HR policies and work–life balance. In the partial least square path model, mediator variables absorb some of the links between an exogenous and endogenous component. Mediator reveals the true nexus between the exogenous and endogenous construct. The role of the mediator (employee engagement) is tested on the association between workplace policies (exogenous) and work–life balance (endogenous). The approach devised by Nitzl et al. (2016) to examine the mediation effect was used to test the mediation effect employee engagement between life-friendly HR policies and work–life balance. According to Nitzl et al. (2016), the sole requirement for assessing a mediation effect is a significant indirect effect. The authors proposed that when using Partial Least Squares-Structural Equation Modelling (PLS-SEM), it is not essential to run separate tests for direct and indirect pathways. The sole criterion for demonstrating a mediation effect is that the indirect effect must be substantial. There are two forms of mediation – full mediation and partial mediation (Hair et al., 2017). According to Carrión et al. (2017), when the direct effect is not significant, but the indirect effect is, full mediation occurs, but in partial mediation, the direct and indirect effects are all significant. There is no mediation when the direct effect is significant and the indirect effect is not. There are two forms of partial mediation: complementary and competing partial mediation.

As indicated in Figure 2 , life-friendly HR policies have a stronger direct influence on work–life balance (0.645) as compared to employee engagement (0.411). Nonetheless, when the indirect effect through the mediator (employee engagement), was taken into consideration, the path coefficient was found to be 0.840 as in Table 7. According to De Sivatte et al. (2015), for a mediation to work, the relationship between the exogenous and endogenous variables must be direct. The influence of the exogenous variable on the mediating variable revealed that life-friendly HR policies and work–life balance had a significant association with employee engagement (β = 0.840, p = 0.000). The beta coefficient was in the same direction as hypothesised; hence the hypothesis that employee engagement mediates the relationship between life-friendly HR policies and work–life balance is supported. The results for the total and indirect effect are given by the following equation (Sarstedt et al., 2014):

Total effect = Direct effect + indirect effectTotal effect = 0.645 + 0.474 * 0.411 = 0.8398 = 0.840Indirect effect = 0.474 * 0.411 = 0.195 [Eqn 1]

The outcome of the computation indicates that total effect is significantly greater than the direct effect, highlighting the significance of employee engagement in mediating the association between life-friendly HR policies and work–life balance.

However, to determine whether the mediation was full or partial, Sarstedt et al.’s (2014) variance accounted for approach was adopted. This was computed using the formula:

VAF = indirect effect/direct effect = 0.195/0.840 = 0.232

If VAF > 80 % it is full mediation ;If 20 % ≤ VAF ≤ 80 % partial mediation and ;If VAF < 20 % there is no mediation. [Eqn 2]

The VAF value was 0.232, indicating that, when the rule of thumb is applied, employee engagement partially mediated life-friendly HR policies and work–life balance by 23.2% (Sarstedt et al., 2014), and the type of partial mediation was complementary partial mediation (Baron & Kenny, 1986). This implies that a change in employee engagement can lead to a change in the relationship between life-friendly HR policies and work–life balance. In other words, life-friendly HR policies influence work–life balance properly if staff are engaged in their work. Thus, employee engagement enriches life-friendly HR policies and work–life balance relationships. The results imply that the management of the CODE can effectively improve staff’s work–life balance by putting in place engagement strategies that support life-friendly HR policies. In light of this, the management of CODE can rely on measures such as providing meaningful work, helping staff to manage stress, providing job security and giving appropriate counselling on work-related issues in order to strengthen the direct relationship between life-friendly HR policies and work–life balance.

Theoretical implication

The theoretical contribution of this study rests on its extension of the knowledge and application of work–family border theory as reviewed in the literature. By reviewing the original theory as proposed by Clark (2000), and applying the assumptions to an empirical situation in Ghana, this study is expected to increase student’s understanding of the theory and how it is applied in real-life situations. Secondly, by exploring the association between life-friendly HR policies and work–life balance, based on the work–family border theory, this study has succeeded in contributing to the existing body of knowledge on both variables and how they link to enhance the work of the HR department. Finally, the study contributes to theory by investigating the mediation of employee engagement between life-friendly HR policies and work–life balance.

Practical implication

In addition to its theoretical implication, the current study has some important practical implications. Firstly, the application of work–family border theory to explain the relationships among life-friendly HR policies, employee engagement and work–life balance can guide management of the CODE to incorporate appropriate interventions and policies on how the boundaries between work and family responsibilities are controlled to achieve a balance for their employees. This conclusion is based on an aspect of the theory that holds that work and family spheres are not separated but are connected with flexible and permeable boundaries. Secondly, as the synthesised correlation between employee engagement and work–life balance suggests reciprocal relations (Clark, 2000; Kangure, 2014), managers of the CODE would be required to implement HR practices that improve the two variables from a holistic viewpoint rather than as separate variables. In this way, they will be mindful as to when to change and develop their culture, systems and policies to successfully facilitate higher levels of employee engagement and work–life balance.

Finally, management of the CODE could address the issue of work–life conflict by offering employees, tools, practices and learning opportunities that both reduce work–life conflict and improve employee engagement. Furthermore, management could provide support by developing and implementing HR policies and practices such as employee voice and effective communication to reduce work–life conflict in the college.

Conclusion and suggestions for future research

This study explored the relationship among life-friendly HR policies, employee engagement and work–life balance in a higher institution. The main objective of the study was to examine the mediation of employee engagement between life-friendly HR policies and work–life balance. In this study, it was found that life-friendly HR policies have a positive effect on work–life balance. More importantly, the authors found that employee engagement had a complementary partial mediation between life-friendly HR policies and work–life balance. As the study was restricted to the CODE in the University of Cape Coast, the research framework and hypothesis could be expanded and modified to include other colleges to confirm whether the results of this study can be generalised across other colleges and universities.

Acknowledgements

Competing interests

The author declares that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Authors’ contributions

F.K.O. contributed in the conceptualisation of the study. The methodology was handled by the author, and the original draft was also done by the first author. I.T.K. contributed in the writing, reviewing and editing. A.-P.J. contributed in the investigation and visualisation. The author also contributed in supervision and served as a project administration.

Funding information

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Disclaimer

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

References

Ahrholdt, D. C., Gudergan, S. P., & Ringle, C. M. (2019). Enhancing loyalty: When improving consumer satisfaction and delight matters. Journal of Business Research, 94(C), 18–27. https://doi. org/10. 1016/j. jbusres. 2018. 08. 040

Akuamoah-Boateng, C. (2020). Balancing work, family and personal life: Perspectives of female staff at the College of Distance Education, University of Cape Coast, Ghana. International Journal of Educational Administration and Policy Studies, 12(1), 43–51. https://doi. org/10. 5897/IJEAPS2019. 0643

Austin-Egole, I. S., Iheriohanma, E. B. J., & Nwokorie, C. (2020). Flexible working arrangements and organizational performance: An overview. IOSR Journal of Humanities and Social Science (IOSR-JHSS), 25(5 series 6), 50–59.

Aveline, S., & Kumar, M. (2017). Employee engagement and effects of work life balance in software industries in Chennai. International Journal of Pure and Applied Mathematics, 116(20), 459–466.

Browne, O., O’Reilly, P., Hutchinson, M., & Krdzavac, N. B. (2019). Distributed data and ontologies: An integrated semantic web architecture enabling more efficient data management. Journal of the Association for Information Science and Technology, 70(6), 575–586. https://doi. org/10. 1002/asi. 24144

Carrión, G. C., Nitzl, C., & Roldán, J. L. (2017). Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples. InPartial least squares path modelling(pp. 173–195). Springer, Cham.

Chambel, M. J., Carvalho, V. S., Cesário, F., & Lopes, S. (2017). The work-to-life conflict mediation between job characteristics and well-being at work: Part-time vs full-time employees. Career Development International, 22(2), 142–164. https://doi. org/10. 1108/CDI-06-2016-0096

Clark, S. C. (2000). Work/family border theory: A new theory of work/family balance. Journal of Human Relations, 53(6), 747–770. https://doi. org/10. 1177/0018726700536001

De Sivatte, I., Gordon, J. R., Rojo, P., & Olmos, R. (2015). The impact of work-life culture on organizational productivity. Personnel Review, 44(6), 14–27. https://doi. org/10. 1108/PR-12-2013-0226

Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81(1), 10–23. https://doi. org/10. 1016/j. csda. 2014. 07. 008

Donald, M. F., Bertha, L., & Lucia, M. E. (2016). Perceived organisational politics influences on organisational commitment among supporting staff members at a selected higher education institution. The 2016 WEI International Academic Conference Proceedings, 16(2), 29–37.

Eek, F., & Axmon, A. (2013). Attitude and flexibility are the most important work place factors for working parents’ mental wellbeing, stress, and employee engagement. Scandinavian Journal of Public Health, 41(7), 692–705. https://doi. org/10. 1177/1403494813491167

Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507. https://doi. org/10. 1037/0021-9010. 71. 3. 500

Fami, H. S., Aramyan, L. H., Sijtsema, S. J., & Alambaigi, A. (2019). Determinants of household food waste behavior in Tehran city: A structural model. Resources, Conservation and Recycling, 143, 154–166. https://doi. org/10. 1016/j. resconrec. 2018. 12. 033

Feeney, M. K., & Stritch, J. M. (2019). Family-friendly policies, gender, and work–life balance in the public sector. Review of Public Personnel Administration, 39(3), 422–448. https://doi. org/10. 1177/0734371X17733789

Gallup. (2006). Engaged employees inspire company innovation: National survey finds that passionate workers are most likely to drive organizations forward. The Gallup Management Journal, 8(5), 33–54.

Garson, J. (2016). A critical overview of biological functions. Springer International Publishing.

Greenhaus, J. H., & Powell, G. N. (2003). When work and family collide: Deciding between competing role demands. Organizational Behaviour and Human Resource Journal, 90(2), 291–303. https://doi. org/10. 1016/S0749-5978(02)00519-8

Greenhaus, J. H., Allen, T. D., & Spector, P. E. (2006). Health consequences of work–family conflict: The dark side of the work–family interface. InP. L. Perrewé& D. C. Ganster(Eds. ), Employee health, coping and methodologies(pp. 61–98). Emerald Group Publishing Limited.

Gudep, V. K. (2019). An empirical study of the relationships between the flexible work systems organizational commitment, work life balance and job satisfaction for the teaching staff in the United Arab Emirates (UAE). International Journal of Management, 10(5), 15–25. https://doi. org/10. 34218/IJM. 10. 5. 2019. 002

Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi. org/10. 1504/IJMDA. 2017. 087624

Henseler, J. (2017). Partial least squares path modeling. InAdvanced methods for modeling markets(pp. 361–381). Springer.

Höck, M., & Ringle, C. M. (2006, September). Strategic networks in the software industry: An empirical analysis of the value continuum. InIFSAM VIIIth World Congress.

Hoffman, B. J., & Woehr, D. J. (2006). A quantitative review of the relationship between person–organization fit and behavioural outcomes. Journal of Vocational Behaviour, 68(3), 389–399. https://doi. org/10. 1016/j. jvb. 2005. 08. 003

Iqbal, I., Zia-ud-Din, M., Arif, A., Raza, M., & Ishtiaq, Z. (2017). Impact of employee engagement on work-life balance with the moderating role of employee cynicism. International Journal of Academic Research in Business and Social Sciences, 7(6), 1088–1101. https://doi. org/10. 6007/IJARBSS/v7-i6/3068

Itzkovich, Y., Dolev, N., & Shnapper-Cohen, M. (2020). Does incivility impact the quality of work-life and ethical climate of nurses?. International Journal of Workplace Health Management, 4(7), 22–45.

Jasmina, Z., & Miha, M. (2021). Relationships between work-family balance, job satisfaction, life satisfaction and employee engagement among higher education lecturers. Organizacija, 54(3), 277–237. https://doi. org/10. 2478/orga-2021-0015

John, I., Anthony, N. K., & Bakari, D. Y. (2020). Impact of work life balance on the psychological wellbeing of employees in the University of Cape Coast. Journal of Psychology, 8(1), 8–19. https://doi. org/10. 15640/jpbs. v8n1a2

Kabir, A., & Rahman, M. (2019). Effects of work life balance on employee loyalty in private commercial banks of Bangladesh. Journal of Economics and Sustainable Development, 10(14), 178–191.

Kaewthaworn, A. (2019). The influence of family-friendly policies on employee engagement: A case study of the hotel industry in Hatyai District, Songkhla Province and Kathu District, Phuket Province. Master’s thesis. Prince of Sonkla University.

Kangure, F. M. (2014). Relationship between work life balance and employee engagement in state corporations in Kenya. Doctoral thesis. Jomo Kenyatta University of Agriculture and Technology.

Karassvidou, E., & Glaveli, N. (2015). Work-family balance through border theory lens: The case of a company “driving in the fast lane”. Equality, Diversity and Inclusion: An International Journal, 34(1), 84–97. https://doi. org/10. 1108/EDI-05-2014-0038

Kataria, A., Garg, P., & Rastogi, R. (2013). Organizational effectiveness as a function of employee engagement. South Asian Journal of Management, 20(57), 56–73.

Kim, G. (2016). Partial least squares structural equation modelling: An application in customer satisfaction research. International Journal of u – And e-Service, Science and Technology, 9(4), 61–68. https://doi. org/10. 14257/ijunesst. 2016. 9. 4. 07

Kirchmeyer, C. (2000). Work–life initiatives: Greed or benevolence regarding workers’ time. Organisational Behaviour Journal, 7(4), 79–93.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi. org/10. 4018/ijec. 2015100101

Kossek, E. E., & Lautsch, B. A. (2018). Work–life flexibility for whom? Occupational status and work–life inequality in upper, middle, and lower-level jobs. Academy of Management Annals, 12(1), 5–36. https://doi. org/10. 5465/annals. 2016. 0059

Kumar, P., & Kumar Baradiya, V. (2019). Construction risk assessment through partial least square technique. InProceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (RAITEA).

Kusi, L. Y., Opoku-Danso, A., & Afum, E. (2020). Improving performance of public universities in Ghana through talent management: Does leadership support matter. Global Journal of Management and Business Research. https://doi. org/10. 34257/GJMBRAVOL20IS10PG41

Lakshmi, K. S., & Gopinath, S. S. (2013). Work-life balance of women employees with reference to teaching faculties. Abhinav Journals, 2(3), 53–62.

Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi. org/10. 1016/j. heliyon. 2019. e01788

Mas-Machuca, M., Jasmina, B., & Ines, A. (2016). Work-life balance and its relationship with organizational pride and job satisfaction. Journal of Managerial Psychology, 31(2), 586–602. https://doi. org/10. 1108/JMP-09-2014-0272

Maxwell, G., Rankine, L., Bell, S., & MacVicar, A. (2007). The incidence and impact of flexible working arrangements in smaller businesses. Employee Relations, 29(2), 138–161. https://doi. org/10. 1108/01425450710719987

Mengistu, A. B. (2020). Predictors of work life balance in selected non-government organization in Ethiopia. Journal of Research in Management, 3(1), 33–44. https://doi. org/10. 32424/jorim. v3i1. 101

Motyka, B. (2018). Employee engagement and performance: A systematic literature review. International Journal of Management and Economics, 54(3), 227–244. https://doi. org/10. 2478/ijme-2018-0018

Naithani, D. (2010). Recession and work-life balance initiatives. Romanian Economic Journal, 37, 55–68.

Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modelling: Helping researchers discuss more sophisticated models. Industrial Management and Data Systems, 116(9), 1849–1864.

Oludayo, A. O., & Omonijo, D. O. (2020). Work-life balance: The relevance of social support. Academy of Strategic Management Journal, 19(3), 1–10.

Omar, M. K., Mohd, I. H., & Ariffin, M. S. (2015). Workload, role conflict and work-life balance among employees of an enforcement agency in Malaysia. International Journal of Business, Economics and Law, 8(2), 52–57.

Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87(4), 698–714. https://doi. org/10. 1037/0021-9010. 87. 4. 698

Saks, A. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619. https://doi. org/10. 1108/02683940610690169

Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, Jr., J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115. https://doi. org/10. 1016/j. jfbs. 2014. 01. 002

Schaufeli, W. B., & Bakker, A. B. (2003). Utrecht employee engagement scale: Test manual. Unpublished manuscript, Department of Psychology.

Schaufeli, W. B., & Bakker, A. B. (2010). Defining and measuring employee engagement: Bringing clarity to the concept: Employee engagement. Journal of Research, 12(2), 10–24.

Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Partial least squares path modeling using ordinal categorical indicators. Quality & Quantity, 52(1), 9–35. https://doi. org/10. 1007/s11135-016-0401-7

Segbenya, M., Peniana, F., & Aggrey, E. (2018). Effect of work conflict on employee’s job satisfaction: The case of College of Distance Education, University of Cape Coast. Journal of Business and Enterprise Development, 21(3), 214.

Shaw, W. S., Reme, S. E., Pransky, G., Woiszwillo, M. J., Steenstra, I. A., & Linton, S. J. (2013). The pain recovery inventory of concerns and expectations: A psychosocial screening instrument to identify intervention needs among patients at elevated risk of back disability. Journal of Occupational and Environmental Medicine/American College of Occupational and Environmental Medicine, 55(8), 885–894. https://doi. org/10. 1097/JOM. 0b013e318289ee6c

Skinner, N., & Chapman, J. (2013). Work-life balance and family friendly policies. The Australia and New Zealand School of Government.

Stone, M. (1974). Cross-validation and multinomial prediction. Biometrika, 61(3), 509–515. https://doi. org/10. 1093/biomet/61. 3. 509

Tabet, S. M., Lambie, G. W., Jahani, S., & Rasoolimanesh, S. M. (2020). The factor structure of outcome questionnaire–45. 2 scores using confirmatory tetrad analysis–partial least squares. Journal of Psychoeducational Assessment, 38(3), 350–368. https://doi. org/10. 1177/0734282919842035

Taghrid, S., Ayman, B., & Hannah, D. (2016). The influence of work life balance on turnover intention in private hospitals: The mediating role of work life conflict. European Journal of Business and Management, 8(20), 126–139. https://doi. org/10. 1037/0021-9010. 92. 6. 1512

Thevanes, N., & Mangaleswaran, T. (2018). Relationship between work-life balance and job performance of employees. IOSR Journal of Business and Management (IOSR-JBM), 20(5), 11–16.

Uddin, M., Ali, K., & Khan, M. A. (2020). Impact of perceived family support, workplace support, and work-life balance policies on work-life balance among female bankers in Bangladesh. International Journal of Economics, Management and Accounting, 28(1), 97–122.

Valcour, M. (2007). Work-based resources as moderators of the relationship between work hours and satisfaction with work-family balance. Journal of Applied Psychology, 92(6), 1512–1523. https://doi. org/10. 1037/0021-9010. 92. 6. 1512

Wood, J., Oh, J., Park, J., & Kim, W. (2020). The relationship between employee engagement and work–life balance in organizations: A review of the empirical research. Human Resource Development Review, 19(3), 240–262. https://doi. org/10. 1177/1534484320917560

Yuile, C., Chang, A., Gudmundsson, A., & Sawang, S. (2012). The role of life friendly policies on employees’ work-life balance. Journal of Management & Organization, 18(1), 53–63. https://doi. org/10. 1017/S1833367200001061