1. Asstt. Professor, Ubon Ratchathani Rajabhat University, Thailand; wiranya.s@ubru.ac.th
2. Management Development Institute, Gurgaon, India; S_Gupta2@gmail.com

*             Correspondence: S_Gupta2@gmail.com

Keywords: Work-life Balance, employee enthusiasm, Support at Home, and Support at Work

1. INTRODUCTION

Due to the changing employment patterns, public and private sector organizations are adopting new processes, and there is more focus on new aspects of employment, namely flexibility, teamwork, competitiveness, and increased responsibility [1]. The ability to combine job and household responsibilities successfully for an individual, regardless of gender or age, is known as the (WLB) work-life balance. The change in work requirements and work patterns has impacted the work-life balance of men and women similarly [2]. The prevailing work environment is highly stressful for all, especially women, as they hold a key position in managing the household as well. In families where both partners work, achieving and maintaining a healthy work-life balance is a severe issue. “Work” has been defined as a man’s job, and “family and kids” have been categorized as the woman’s domain [3].Employee enthusiasm is the vigorous, dedicated, and absorbed state of mind which provides a fulfilling experience for the individual [4]. The engaged employee is motivated and fully aware of the organizational goals. They also motivate the other employees to achieve the goals of the organization [5]. Recent studies have concluded that human resource management (HRM) practices like achievable work goals and reduced job demands have impacted employee enthusiasm positively [6]. Organizations all over the world are now more employee focused [7] and hence have introduced flexible worktime arrangements for their employees. The immediate results show that productivity is positively affected [8], and the employee displays more energy and dedication [9]. The research targets Indian higher education institutions, as previously limited research has been conducted to analyze employee enthusiasm and how it is affected by Support at Home (SH) and Support at Work (SW) and also with the work-life balance. This sector has undergone immense growth in the last ten years. The public and private universities have a large employee base. Hence, it makes it very important to explore, so that appropriate policies can be developed to reap the benefits from the academicians. The research aims at analyzing the relationship between the WLB and EE, as employee enthusiasm is of prime importance to all organizations. The main aim is to identify the main aspects in which the organizations can help the employee manage Work-Life Balance with supportive policies. There is also a detailed comparison of the effects of SH and SW on WLB. Then the effects of the support at home and support at work are explored in detail. The results will be analyzed so as to better understand the prevalent situation in India.

2. LITERATURE REVIEW

The term work-life balance is a way of dealing with the stress which arises as people try to juggle the different aspects of their lives, such as family, health, friends, work, and spirituality [10]. When an employee is able to maintain a healthy balance between work and personal life, he/she feels peace and contentment [11]. Work-life balance is difficult to attain in dual-career households. However, the flexible time option gives greater control to manage work responsibilities, non-work responsibilities, and care duties, especially for women [12]. The employee and the employer both experience a win-win situation. Employers notice an increase in the productivity and retention of employees and a decrease in absenteeism and cost. The employees experience increased satisfaction [13]. Recent studies explained that when women are unable to balance work and life together, they exit from the mainstream of corporate jobs and move towards an entrepreneurial venture. This gives them greater autonomy over time management [14]. It has been observed that the percentage of women employed in the workforce has steadily increased over time. However, there has not been an increase of equal percentage in the time that fathers have started investing in childcare duties. Hence, women are more stressed about finding the ideal balance in life [15]. The WLB issue is now faced by men and women across the globe [16]. Recent studies in the US show that gender perceptions have already changed in the last 20 years. Women are now adamantly pursuing careers, and men are more engaged with families and kids than in the past and are now experiencing work-life balance issues [17]. Another study has proved that the gendered attitudes of professional men still consider flexibility as much needed option for women as their job is to support their husbands’ careers and provide child care. Moreover, flexibility is offered by an organization for the benefit of both genders; in practice, however, more women utilize the work-life balance policies [18]. The everyday practices about WLB have to be adjusted to the individual and collective norms of society, keeping in view the gender and racial differences prevalent in society. Similar research in India has yielded similar results, as women face a number of challenges, such as family and societal norms and pre-defined gender roles. They concluded that the term work-life balance holds a different meaning for each individual [19]. In France, a study about the reduced work week and flexible time option showed that mainly women opted for these and were not only happy but also were able to achieve more household tasks and better balance in life overall. In Europe, the changing work patterns have put a lot of pressure on individuals and their partners [20]. Men depend more on their spouses, and working women face further pressure regarding childcare as the family units are quite weak. Previous research has also shown that family support is essential for managing the children and household if women are working on International assignments, and only then a positive spillover can be expected [21]. Employee enthusiasm is dependent on a number of factors, like the environment, the line managers’ attitude, and the nature of the employee. An individual can become an engaged employee due to the positive supporting factors, and the same individual may be disengaged in a different environment or with a different manager. In India, some organizations where employees are required to put in more hours have HR personnel to take care of the employee’s family needs and arrange recreational activities; others have introduced flexible work time arrangements.  Another study about the effects of flexible work arrangements or the work-from-home option concluded that women experienced reduced stress and could manage time for leisure activities with family. Household tasks could also be easily managed. Employee enthusiasm can be defined as the state in which the employee exhibits traits like dedication and enthusiasm and loses track of time when engrossed at work. As reported by a study, the employees in an organization are not engaged in the Global economy. At the same time, 18% of the workforce is actively disengaged [22]. As a result, about $7 trillion is being lost in productivity each year. The engagement is optimal if employees spend 60% to 80% of the time working from home or off-site. Whereas 54% of the women who are not working state that children are the main reason. Employee enthusiasm increases the benefits for the organization manifolds. The foremost benefit is increased productivity and lesser turnover, which saves costs. Next is increased satisfaction and employee loyalty, by which the organization reaps benefits [23]. A study regarding the effects of stress induced by the Work-Life conflict and its effects on turnover among marketing executives conducted in India state that companies should focus on effective policy-making regarding work schedules, realistic targets, and periodic respite is utterly necessary for better WLB, and resultantly they experience lesser stress, and the organizations benefit with lesser turnover. When the effects of work-life conflict are studied with reference to job satisfaction, it proves that job satisfaction can only be achieved with minimum work-to-family interference or family-to-work interference in India. However, job autonomy and the support of the organization are also important to achieve job satisfaction [24]. Another study conducted in India about IT professionals discusses the effects of the WLB, job characteristics, and career opportunities on organizational commitment, revealing that work-family-friendly policies and good job prospects helped the IT professional achieve significant organizational commitment. The study also highlighted the difference in the culture between private and public organizations. After reviewing the available literature, a gap has been identified, which is directed toward the analysis of Work-Life Balance and Employee Engagement in Indian higher education institutions. There is limited research on Indian higher education institutions, although a few other sectors have been previously targeted. Based on the above literature review, the following hypotheses have been developed.

                H1: Support at Home is positively associated with Work-Life Balance.

                H2: Support at work is positively associated with Work-Life Balance.

                H3:  Work-Life is positively associated with employee enthusiasm.

                H4:  support at home is positively associated with employee enthusiasm.

                H5: support at work is positively associated with employee enthusiasm.

The conceptual framework of the research is shown in Figure 1. It is comprised of 4 latent variables, namely WLB, SH, SW, and EE. The SH and SW are the independent variables, WLB is the mediating variable, and EE is the dependent variable. Initially, the effects of SH and SW will be studied on the WLB. The combined effects of Support at Home and Support at Work will be studied on the dependent variable EE, with the mediating effect of WLB. Lastly, we shall also see how the SH and SW affect EE directly.

 

Figure 01: Conceptual Framework

Table 01 is the list of all the variables along with the indicators used in the study. The abbreviations are also mentioned for the variables and indicators.

 

3. MATERIALS AND METHODS

This is quantitative research. The population of the study is 6 Indian Universities comprising 2 public and 4 private business departments selected on the basis of the information provided [25]. By using a sample calculator [26], the sample size has been calculated from a population of N=304 faculty members. The sample was calculated at a 5% margin of error and 95% confidence interval and which is 250 respondents. Data has been collected from N=250 academicians, from Higher Education Institutes where men and women are working together in India. The data has been collected from 42 employees from each university as per the weighted average of the staff employed. The sample includes full-time and visiting faculty members. The exclusion criteria for the study are the faculty members with lesser than 1 year of total work experience and who are unmarried. A total of 339 questionnaires were given out, and 54 were excluded as the respondents did not meet the inclusion criteria; 35 were not received back, and the remaining 250 were completely meeting the inclusion criteria; hence were analyzed. The data includes responses from 106 men and 144 women. The majority of the respondents were of 40 years and above, whereas the total range of ages is from 20 years old to above 40 years old, with only 4 respondents falling in the 20- 25 years age bracket. 166 respondents are married to a working spouse. And 216 respondents have a child/ children. All the respondents are highly qualified. The questionnaire has been used as the instrument for data collection. The questionnaire has been developed with a seven-point Likert scale of Entirely Disagree, Mostly Disagree, Somewhat Disagree, Undecided, Somewhat Agree, Mostly Agree, and Entirely Agree. The validity and reliability of the research instrument were also tested. The structural equation model is further divided into two models, the measurement model, or the outer model, and the structural model, or the inner model. The relationship between the constructs and their observed variables is specified by the measurement model. For the structural model, the predictive ability of the model for further interpretation can be determined by the path coefficients. The path models are also been tested accordingly. The quality of the measurement model has been checked by assessing the individual items and also the reliability. Furthermore, the convergent and the discriminant validity of the constructs is checked. Initially, the relationship between the latent variables, SH, SW, WLB, and EE, is shown in Figure 2, along with the coefficients and factor loadings. The reliability of the factor loadings on their respective latent constructs is checked to determine the reliability of the factors [27].

4. RESULTS AND DISCUSSION

4.1 Reliability and Validity

The questionnaire has been adopted and amended according to the requirements [28] to make sure the content validity of the research instrument. The research has been analyzed through multi-item constructs for each factor, which has been further analyzed by the item and the factor analysis. Table 4 depicts the results of the reliability tests. The factor loadings of any single construct to its latent variable determine the reliability of that construct [29]. A higher value of the loading means that there is more shared covariance between the latent variable and the construct; a lower value indicates an error variance. The accepted value of 0.70 or higher is a preferred value [30]. In exploratory research, a value of 0.4 or higher may also be considered acceptable [31]. However, the value of 0.7 may not be found in real-life data. Thus the value has to be interpreted in the light of the theory being tested and not by the generalized cutoff level [32]. The study showed factor loadings from the range of 0.631 to 0.835, as all the values are above 0.7 except 2 values, which are 0.631 and 0.692, and may also be accepted. In Table 4, the factor loadings and the Composite reliability by the method presented forth by Fornell and Larcker [33] and the value of the Average Variance Extracted have been shared. The Fornell and Larcker measure of composite reliability is considered more appropriate than Cronbach’s alpha, as it presents a better-estimated value of the shared variance of the indicators [34]. The value of 0.7 has been suggested to be the qualifying standard value for composite reliability, as has been recommended by Fornell and larcker in 1981. The composite reliability values range from 0.797 to 0.827. The Composite reliability and also the Average Variance Extracted have been shown in Table 2 below:

Table 02: Factor Loadings, Composite Reliability & Average Variance Extracted

4.2. Convergent validity

                The degree of agreement among the measure of the same construct is convergent validity. The convergent validity can be analyzed by calculating the variance for each factor. The accepted standard value should be higher than 0.5. The results for the factor loadings for this study fall under the range of 0.568 to 0.614. Hence it shows that the convergent validity of the variables SH, SW, WLB, and EE are accepted.

4.3 Discriminant validity

                The individual differences between the constructs represent the discriminant validity (Carmines & Zeller, 1979). The pairwise correlation factors are compared with the variance extracted for each possible pair occurring. The test to measure the discriminant validity has been recommended by Fornell and Larcker. The standard acceptable value is 0.5 and higher.

Table 03: Discriminant Validity

 

The discriminate validity is confirmed if the diagonal values are significantly higher than the off-diagonal values. These values are, in fact, the square root of the Average Variance Extracted (AVE) of the SH, SW, WLB, and EE. The values are shown in Table 3 above.

4.4 Structural Model

The structural model, along with the hypotheses, is to be tested by calculating the path coefficient (β). As the normally distributed data is not required in the PLS, so the R2has been calculated for the analysis of the latent dependent variables [35] as well as average Variance [36]. The result showed that SH, SW, and WLB are putting 34% impact on employee enthusiasm, and SH and SW are putting an impact of 30% on WLB. For the R square, the values are 0.347 for the EE and 0.309 forth WLB. The level of predictive acceptance for R square can be explained as 10% being small, 25% being medium, and 36% being larger. Therefore the results for R square fall in the medium to the larger range for the acceptable predictive value [37].

Table 04: R Square

 

Figure 02: Algorithm Result

4.5 Endogenous variable variance

                The coefficient of determination value for Employee Engagement (EE) is 0.347, which is the endogenous latent variable. The latent variables of SW, SH, and WLB impact EE by a predictive acceptance range of moderate to substantial impact. This means that the latent variable explains 34.7% of the variance in EE. Whereas SH and SW explain 30.9% of the variance of the WLB.

4.6 Path Coefficient of the Inner Model

1. The inner model explains that SW has the strongest impact of 0.342on WLB. Next, SH affects the WLB with an impact of 0.315. Then the SW directly affects EE with an impact of 0.305. The WLB impacts EE by 0.273. Followed by the impact of 0.149 from SH to EE directly. Hence the relationship between WLB and EE is statistically significant.
2. The SW also has a strong impact (0.342) on WLB directly. And also, SW has a statistically significant relationship with EE (0.315).
3. SH also has a significant relationship with WLB (0.315). However, the SH has an impact of .0149 on EE directly, which is not statistically significant.
4. Thus, it can be stated that SH and SW are both strong predictors of WLB and then of EE. However, the SW can also be a moderately strong predictor of EE directly.
5. It can also be stated that WLB is also a moderately strong predictor of EE.

 

Figure 03: Boot Strapping Result

From Table 5 given below, it is evident that Beta is 0.149 for the SH over EE. This means that there is only a 14.9 % effect of the SH on EE. The value of “t” is 2.251, which is greater than 1.96 (table value 1.96 at 5 %). This means that there is a significant impact of SH on EE at a 5% confidence interval. However, there is a 31.5 % impact of SH on WLB, as the beta value stands at 0.315. The “t” value for the 5.287 is greater than 2.576 (table value 2.576 at 1 %), which means that SH has a significant impact on WLB with a 1% confidence interval. However, the beta value for the SW on WLB is 0.342, meaning that support at work impact work-life balance by 34.2%. The “t” Value is 5.320, which is greater than 2.576 (table value 2.576 at 1 %), showing the significant impact of SW on WLB. Whereas the beta value is 0.305 for the SW over the EE. This means that support at work affects employee enthusiasm by 30.5%. The “t” value is 4.659, which is greater than 2.576 (table value 2.576 at 1 %), showing the significant impact of SW on EE. The combined effect of SH and SW on WLB further impacts EE by 27.3%, as the beta value is 0.273. The “t” value is 4.364, which is also greater than 2.576 (table value 2.576 at 1 %), showing the significant impact of the WLB on EE.

* Significance at 10% (1.645), ** Significance at 5% (1.96), *** Significance at 1% (2.576)

Table 05: Hypothesis Testing

5. CONCLUSIONS

The research is aimed at establishing the relationship between Support at Home and Work, Work-Life Balance, and employee enthusiasm. The path coefficient beta has been analyzed, and it has been established that there is a significant correlation between SW and WLB and also between SH and WLB. Whereas SW also has a significant impact on EE directly. Also, we have seen that there is a positive correlation between WLB and EE. However, there is an insignificant impact of SH on EE directly. To test the hypotheses, the coefficient of determination (R2) has been applied. After the analysis of the measurement and the structural model, it has been concluded that the relationship between SH and WLB is significant, with a 0.315 path coefficient and t value of 5.287. This very clearly indicates that if there is a 100% change in SH, the same will bring about a change of 31.5% in WLB. The SW also has a significant relationship with WLB with a path coefficient of 0.342 and the t value of 5.320. This indicates that if there is a 100%change in SW, this will bring a change of 34.2%in WLB. The relationship between WLB and EE is also significant, with a path coefficient value of 0.273 and the t value of 4.364. This means that a 100%change in WLB will bring about a change of 27.3% in EE. The relationship between SH and EE is also significant as the path coefficient value is 0.149, and the t value is 2.251. This means that a 100%change in SH will bring about a change of 14.9% in EE only. The relationship between SW and EE is also significant as the path coefficient value is 0.305 and the t value is 4.659; this means that if there is a 100%change in the SW, it will bring a change of 30.5% in EE. After a detailed study, it has been concluded that any changes in the SW and SH will impact the WLB of the individuals and will initially improve the WLB, and then the organizations will benefit in the form of increased EE. However, as the main objective of the universities is to increase EE, hence the foremost step should be taken by the universities in the form of policy-making toward flexible work and time arrangements. There are other supportive arrangements like short/ full day leaves in case of emergencies etc. which the Universities should devise policies about so that they save on the cost incurred by absenteeism, turnover, etc., to increase the engagement of the employees. The SH is also of utter importance to any individual, as close-knit family setups are popularly prevalent in our culture. The WLB of academicians is based on, firstly, SW, which in turn helps them to manage their family affairs easily and on time. When the family receives the due share of time and attention that is when the family also actively helps and supports them. This is how the engagement of academicians increases towards the university. The work-life balance of any individual is dependent upon the support they receive from their respective families and organizations. A healthy work-life balance, if achieved, positively impacts employee enthusiasm of that individual. The organizations will benefit greatly from the increased employee enthusiasm, as it has been previously reported that in the global economy, about 67% of the employees are “Not Engaged,” and resultantly, $7 trillion is lost in productivity each year. Organizations from all economic sectors can greatly enhance EE with a few supportive policies. The research is highly beneficial for India, as it has been conducted here. This research highlights the benefits of the supportive policies which all organizations can implement to save on production costs and missed days at work. A larger number of women are also employed in the active workforce in India; there is about a 52% refined activity rate for women working in India in the years 2020-2022. There has been found to be a lag in the implementation of supportive policies in Universities in India. The study has proved that the factors of employee enthusiasm, like engagement, taking the initiative, and ownership, will be positively affected, and hence the overall productivity will increase and operational cost will decrease.

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