Indian IT and ITES sector has been a pioneer in bringing out HR practices to attract and retain women talent. A lot of work life balance initiatives were in this direction. Research reveals in depth study on various aspects of Work life balance like the need for work-life balance practices, types of work-life balance practices, barriers and consequences of availing the practices. The literature on work-life balance ranges from the earlier studies bringing out gender stereotypes, existence of glass ceiling and gender bias in organisations; to the later researchers throwing light on changes in the work-life balance practices provided in company in tune with changing demographics, breaking of glass-ceiling and change in landscape of organization’s gender ratio; and most recently diversity and inclusion practices.  

A 3-step approach is taken up by the researcher to analyse the effectiveness of WLBPs in helping women move up the career ladder. The 3As are - overall view of the practices available in organisations (AVAILABILITY), employee usage of those policies (APPLICATION) and growth of employees (ACCELERATION FOR EMPOWERMENT) in the organisation.

Deloitte’s Women @ Work: A Global Outlook report (2023) report based on the responses of 5,000 women across 10 countries, offers a holistic view of working women and how companies are faring in their efforts to retain and engage women in the workplace. The chart below provides that the top most reason to stay with a company is for Work Life balance.

Figure 1 Deloitte study results

The report strongly suggests that work life balance is a prime factor and there is a strong case for researching on Work life balance practices in the gap identified based on the literature review below.

The literature review presented here brings out the studies related to 3As of Availability of WLBPs, Application of WLBPs and Acceleration for empowerment.

Kodz J, Harper H, Dench S (2002) in their book on Work-Life balance identify that though the employers offer Work-Life Balance practices, there is a take-up gap. The research was purposively conducted within six organisations known to be proactively engaged in implementing work-life balance. The research was based on qualitative interviews and focus groups undertaken within six organisations with well-developed work-life balance and flexible working practices. The researcher found that employees who take up work-life balance options struggle to limit the damage they fear this is doing to their reputations and the way their commitment and performance are assessed. 

Liz Doherty (2004) conducted action research in the UK hospitality industry to explore the effectiveness of work-life balance initiatives in helping women progress to senior management. The findings suggest that senior women rarely have access to flexible hours and that a male model of a career based on commitment in the form of long hours persists.

Sarah Wise (2004) took up a study on Qualified nursing and midwifery staff in Lothian University Hospitals NHS Trust to examine availability, accessibility and implementation of work-life balance policies and practices in NHS nursing and midwifery. The study found that the respondents had Poor awareness on Parental Leave policies though People with career breaks were welcome in the organisation, the ones with longer career breaks were not finding the policies adequate.

Helen De Cieri, et al (2005) Using data from surveys conducted at Australian organizations in 1997, 1998, and 2000, the authors examine extent, usage, and barriers to work-life balance strategies. The findings indicate that common work-life balance strategies in Australian organizations include part-time work, flexible work schedules, job sharing, and telecommuting. Hudson Highland Group, Inc. (2005) reports about practices to close the gap between policies and practices related to work life balance.

Ryan and Kossek (2008) brings our barriers in implementation of work-life balance practices in his research. He identifies implementation attributes including supervisor support for use and universality of practice availability will affect the degree to which work-life practices are seen by employees as fulfilling their work-life needs and signalling support from the organization. Studies by certain researchers point out that Work-life balance practices can enhance the women's role inside the organisation and help them grow to top level (Caroline Straub, 2007, Mercer-NAASCOM, 2008). 

Baral and Bhargava (2009) have attempted to bring out the history and current status of work-life balance practices in India. The paper brings out the legal framework for employees in balancing work and life and traces the organisational interventions. The study highlights the practices offered by various IT companies like Intel India, IBM, TCS, Qualcomm, Agilent Technologies and Shell.

Navjot Kaur and P.Walia (2011) took up a study on Work-life balance programmes offered in IT and ITES companies from the perspective of the employees. Sample consisted of randomly selected 308 employees from fourteen IT and ITES companies (based on revenues) as given in Data Quest, 2008. The analysis brought out various WLBPs like Flexible Work Arrangements, Flexible Location, Child Care arrangements in the organisations but it was also found that many employees were not aware of the availability of existing programs in their organisation. The researchers conclude that except for flexible leave arrangements, employee wellness programmes and employee assistance programmes, not many work like balance enhancement programs or provisions are made available in the IT and ITES industry.

Rupashree Baral, Shivganesh Bhargava, (2011) evaluated the role of family support, co-worker support, supervisor support, work-life balance policies (WLBPs), work-family culture and job characteristics, as the predictors of work-to-family (WFE) and family-to-work (FWE) enrichment. The article suggests that WLBPs have to be offered to women executives and organizations do have to make jobs more enriching in order to increase the level of WFE among women and men, respectively.

Ujvala Rajadhyaksha, (2012) did a study to provide an in-depth country perspective on work-life balance issues in India. A qualitative approach is used that includes case studies of work-life interventions offered by a sample of Indian organizations. The study revealed that Organizational work-life interventions in India are varied and disparate and have focused mainly on the formal sector. There is no overarching government policy addressing work and family issues across different sectors. Implicit gendering of governmental policies and work-life initiatives covertly reifies patriarchal structures that make such interventions necessary in the first place. Based on India's socio-cultural realities additional work-life interventions are suggested in the areas of elder care, employee training and commuting.

Dr. Vijaya Mani (2013) examined the WLB experiences of first-generation women professionals in Tamil Nadu, revealing that factors like role conflict, lack of recognition, workplace discrimination, and social pressure hinder their ability to balance work and personal life. These issues ultimately impact their productivity and career progression. The study calls for gender-sensitive policies and support systems to foster better WLB for working women.

Naz et al. (2017), focusing on female academics in Pakistan, revealed that the absence of institutional support forces women to adopt personal coping strategies, such as rigid scheduling or reliance on family, which often harms personal well-being.

Dassanayake & Fasana (2019)  state a positive relationship is found between work-life balance and upward career mobility. Dimensions such as time balance, satisfaction balance, and involvement balance are positively correlated with career progression.

Kar, Panda, and Pathak (2019) studied WLB among female non-instructional staff at Indian private universities and concluded that compensation, rather than demographic factors or managerial support, plays a critical role in shaping WLB perceptions. The study suggests that fair and adequate pay is key to ensuring employee satisfaction and balance, particularly among lower-tier staff.

Chung, H., & van der Lippe, T. (2020) explore the role of flexible working arrangements in promoting work–life balance and gender equality. It analyzes how flexible work options can support women's career progression by accommodating their dual roles.research indicates that flexible working arrangements can alleviate work–family conflicts, thereby supporting women's participation and advancement in the workforce.

Ying Pan and Gong Sun (2022), in their findings highlight social and family support resources for Chinese professional women from the perspective of Confucianism. This study contributes to current knowledge on work and life issues through the lens of professional women's work-life balance perceptions and experiences in mainland China.

Essandoh E., Kwao I.T., Asandem D.A., Boateng E.A. & Arthur B. (2023) study enriches the theoretical understanding of how work-life balance, if properly managed, would affect female workers in progression in their careers even in a masculine context, as established by the role theory in Sub-Saharan country-context.

The literature surveyed brings out that the organisations offer variety of work-life balance programs and even perceived availability of such programs by the employees cause benefits to the organisation but there is a research gap in wide range of literature studied - it is noted that the measurement of results linking implementation of work-life balance practices and empowerment of women is not assessed. The entire continnum of availability of WLBPs, the application of the practices and outcomes leading to career advancement, i.e., empowerment of women in organisations have not been taken up so far. This study aims to address this gap.

OBJECTIVE OF THE STUDY

The primary objective of the study is to measure the influence of availability and application of Work-Life Balance Practices on Acceleration for Empowerment.

RESEARCH DESIGN

The study is descriptive in nature The researcher has used Purposive Sampling in choosing the top IT/ITES companies for the study and also used Convenience Sampling and Snow-ball Sampling in choosing the employees (respondents) of the identified companies for the study. A structured questionnaire was provided with the variables identified under Availability, Application and Acceleration for empowerment. Around 600 questionnaires were distributed through hard copy, soft copy and online Google Forms for women employees to participate in the study according to the convenience of the researcher. After repeated follow-ups, a total of 504 usable questionnaires were received giving a response rate of 84% and were taken for analysis purpose. SPSS and AMOS were used for analysis.

THEORETICAL MODEL

Based on the literature studied and various theories related to Work life balance practices, the researcher has set out the theoretical model as below.

Availability of Work-Life Balance Practices:

The review of literature under Availability of WLBPs brings out that the various work-life balance practices can be grouped under certain factors and as per the study taken up by the researcher the factor analysis confirmed the formation of the following factors – Employee Time-off, Health and Well-being, Employee and Dependant care, Flexible Work Arrangements and Professional Development.

Application of Work-life balance Practices:

Though organisations offer numerous work-life balance practices, earlier studies have shown that the availability of policies and practices does not guarantee people make use of them (Allen, 2001). Review of related literature gives an overview of difficulties in the implementation of work-life balance practices in organisations. In the study taken up by the researcher the following variables have been identified by way of Factor Analysis - Employee Usage, Barriers Perceived, Awareness Creation and Organisational Culture.

This review of literature shows that Availability of Work-life Balance Practices alone is not sufficient. The women employees must have access to it and be able to use those Practices without being discriminated. Hence, the researcher makes the following hypothesis to be tested using SEM.

Hypothesis: H₀1 - Availability of Work-Life Balance Practices does not influence the application of these practices in organisations.

Acceleration for Empowerment:

It is important to know is whether the work-family practices also support and develop managerial and professional females' career advancement (McGregor, 2002). In the study by the researcher, Factor analysis has brought out 2 factors under Acceleration for Empowerment – Career Advancement and Organisational Contribution.

This brings out that there is a link between work-life balance practices and empowerment of women – that is women can move to higher echelons of management if work-life practices are made available in the organisation and conducive environment is provided to utilise these practices. Here, the following hypothesis is framed by the researcher.

Hypothesis: H₀2  - Application of Work-Life Balance Practices does not influence the Acceleration of women employees to top positions in the organisations.

Based on literature inputs, the researcher had sketched the sequence -

This is developed in to a model using Structural Equation Modeling (SEM) with the help of AMOS.

Hypothesis: H₀3 - The Model showing three A's of Work-Life Balance Practices fits the data.

Figure 2 Theoretical Model showing 3As of WLBPs

Before getting on to SEM, the researcher has used Confirmatory Factor Analysis (CFA) to test the constructs for reliability and validity.

The abbreviations used in the model are listed below for easy reference-

AV                                       Availability

AP                                        Application

AC                                       Acceleration for Empowerment

AV1                                     Employee Time-off

AV2                                     Health and Well-being

AV3                                     Employee and Dependant Care

AV4                                     Flexible Work Arrangements

AV5                                     Professional Development

AP1                                      Employee Usage

AP2                                      Barriers Perceived

AP3                                      Awareness Creation

AP4                                      Organisational Culture

AC1                                     Career Advancement

AC2                                     Organisational Contribution

The Confirmatory Factor Analysis (CFA) process determines whether the hypothesized structure provides a good fit to the data, or in other words, that a relationship between the observed variables and their underlying latent, or unobserved, constructs exist (Child, 1990). The CFA would also verify that all items are properly aligned with the correct facets within the general construct being measured. Therefore, before attempting Structural Equation Modeling, it is necessary to run Confirmatory Factor Analysis.

In the present model, while running CFA for the Measurement Model, it is found that the constructs AV3 (Employee and Dependent Care) and AP2 (Barriers Perceived) are not significant and affected the overall model fit.

The literatures reviewed has also pointed out the non-availability of Employee and Dependent Care practices (like onsite crèche, day care facilities, break for lactating mothers) in various organisations (Comfort et al, 2003; Reimara Valk and Vasanthi Srinivasan, 2011; Susan Halford, Mike Savage and Anne Witz, 1997; The Families and Work Institute, 1998).

Moreover, the literatures provide that barriers tend to exist for employees in the organisations to make use of available work-life balance practices (Thompson, Beauvais and Lyness 1999, Allen 2001, Lewis 2001, Eileen Drew, Eamonn M. Murtagh, 2005, Sarah Wise, 2004)). In view of existence of literature and insignificant regression weights, these two factors have been removed from the model and a revised model is presented below.

Figure 3 Model for CFA

CFA (Confirmatory Factor Analysis) is the Measurement Model which specifies how the observed variables depend on the unobserved or latent variables. The relationships between the constructs are also tested. The results from the CFA run on AMOS 21 for model fit is presented below –

Chi-square = 36.889, Degrees of freedom = 19, Probability level = .008

The chi-square is significant (p=.008) which means that the model does not fit the data. Since Chi-square is strongly influenced by sample size, Relative Chi-square value (CMIN/DF ) is looked into.  However, other indices are also to be checked before proceeding to check the validity of the model. The following table summarises the common fit indices and the acceptable range.

Table 1 Table Showing Fit Indices

Source: Primary Data

The above table shows that the model is fit to be accepted. At this juncture, the        researcher is interested to find out the Construct Reliability, Convergent Validity, Discriminant Validity and Nomological Validity through Confirmatory Factor Analysis before attempting Structural Equation Modeling.

Construct Reliability and Convergent Validity

To assess the Convergent Validity, the unstandardised regression weights should be significant and standardised regression weights should be above 0.5. The following tables bring out both the regression weights.

Table 2 Regression Weights: (Group number 1 - Default model) (Unstandardised)

Source: Primary Data

Table 3 Standardized Regression Weights: (Group number 1 - Default model)

Source: Primary Data

The Unstandardised regression weights show that all the constructs are statistically significant. Looking at the Standardised regression weights, which are the factor loadings for each of the factors,  the weights range from 0.676 to 0.922.  The loadings should be at least 0.5 or higher with 0.7 or higher indicating a better loading. This implies, all loadings are significant as required for Convergent validity.

Convergent validity measures the extent to which indicators of a specific construct “converge” or share a high proportion of variance in common. To assess we examine construct loadings and average variance extracted (AVE).

Since, AVE (Average Variance Extracted) is not computed in AMOS, the following formula is adopted to arrive at the same.

Also, Construct Reliability (CR) is calculated by using the formula below. Reliability between .6 and .7 may be acceptable provided that other indicators of a model’s construct validity are good. A high construct reliability indicates that internal consistency exists. 

Table 4 Table Showing AVE and CR Computation

Source: Primary Data 

AVE should be 0.5 and above for the construct to be acceptable (Fornell & Larcker, 1981). The table shows that values for Availability (AV) = 0.52, Application (AP) = 0.65 and Acceleration for Empowerment (AC) = 0.62. This implies, the convergent validity is present for the study.

CR (Construct Reliability) should be 0.6 and above for the construct to be acceptable (Hair, Black, Babin, Anderson, & Tatham, 2010). From the above table, it is noted that CR for AV = 0.81, AP = 0.85, AC = 0.77. This indicates a good reliability and proves that high internal consistency exists for the items measured.

Nomological Validity and Discriminant Validity:

After confirming that Convergent Validity based on AVE and CR, the researcher tests the Nomological Validity and Discriminant validity.

Nomological validity is the degree to which a construct behaves as it should within a system of related constructs.  It is tested by examining whether the correlations between the constructs in the measurement model are positive and significant.  The construct correlations are used to assess this.

Discriminant Validity brings out the extent to which a construct is truly distinct from other constructs. Discriminant Validity is measured by comparing AVE and SIC. Construct Average Variance Extracted (AVE) estimates should be larger than the corresponding Squared Interconstruct Correlation estimates (SIC).  If they are, this indicates the measured variables have more in common with the construct they are associated with than they do with the other constructs (Fornell & Larcker, 1981).

Table 5 Covariances: (Group number 1 - Default model)

Source: Primary Data

Table 6 Correlations: (Group number 1 - Default model)

Source: Primary Data

The above tables bring out the correlation among the 3A's of WLBPs, that is, Availability, Application and Acceleration for Empowerment is positive (Table 6) and significant (Table 5). So, the nomological validity is established.

Since the Nomological test is validated, the researcher proceeds to test the Discriminant Validity.

Table 7 Table showing IC and SIC

Source: Primary Data

IC - Interconstruct Correlation

SIC- Squared Interconstruct Correlation

Table 8 Table showing AVE and SIC

Source: Primary Data

AVE - Average Variance Extracted

SIC- Squared Interconstruct Correlation

From Table 7 and 8, it is evident that AVE is larger than the Squared Interconstruct Correlation (SIC) values. So, it is concluded that the measured variables have more in common with the construct they are associated with than they do with the other constructs.

The above tests confirm that the factors formed are valid and can be proceeded further to test the model based on the 3A's of Work-life Balance Practices.

Structural Equation Modeling

The model run on AMOS brings out the following results -

The Chi-Square value is the traditional measure for evaluating overall model fit and, ‘assesses the magnitude of discrepancy between the sample and fitted covariances matrices’ (Hu and Bentler, 1999) The overall fit indices starts with the Chi-square value and p value which is discussed below -

Chi-square = 49.700

Degrees of freedom = 19

Probability level = .000

The Chi-square value is 49.700 and p value is significant which implies that model is not a good fit. But Chi-square is sensitive to sample size and therefore, other fit indices are looked in to for testing the fit of the model. Wheaton et al’s (1977) suggests relative/normed chi-square (χ2/df) is considered the next important statistic. The range for this statistic is as high as 5.0 (Wheaton et al, 1977) to as low as 2.0 (Tabachnick and Fidell, 2007).

Table 9 CMIN

Source: Primary Data

From the CMIN table it is clear that CMIN/DF is less than Wheaton's recommended value of 5. So, the model can be checked further for other indices. Hence, the next important fit statistic, RMSEA (Root Mean Square Error of Approximation), is checked. Below is the revised model based on the Confirmatory Factor Analysis.

Figure 4 Model showing 3As of WLBPs

RMSEA brings out how well the model, with unknown but optimally chosen parameter estimates would fit the populations covariance matrix (Byrne, 1998). RMSEA in the range of 0.05 to 0.10 is considered an indication of fair fit and values above 0.10 indicated poor fit (MacCallum et al, 1996).  RMSEA shows 0.057 in the present model, which indicates it is acceptable fit to proceed further.

Table 10 RMSEA

Source: Primary Data

The Goodness-of-Fit statistic (GFI) was created by Jöreskog and Sorbom as an alternative to the Chi-Square test and calculates the proportion of variance that is accounted for by the estimated population covariance (Tabachnick and Fidell, 2007). This statistic ranges from 0 to 1 but a cut-off of 0.95 is considered to be  more appropriate (Miles and Shevlin, 1998).  GFI is 0.979 in the present model and hence accepted to be fitting with the cut-off specified.

Table 11 RMR, GFI

Source: Primary Data

The RMR (Root Mean Square Residual) and the SRMR (Standardised RMR) are the square root of the difference between the residuals of the sample covariance matrix and the hypothesised covariance model. Values for the SRMR range from 0 to 1.0 with acceptable values to be less than .05 (Byrne,1998); however values as high as 0.08 are deemed acceptable (Hu and Bentler, 1999). In the present model, RMR is .014 and SRMR is .0311 which indicates that the values are acceptable within the fit indices specified.

Table 12 Baseline Comparisons

Source: Primary Data

Under Incremental Fit Indices, Normed-Fit Index (NFI) Values should range between 0 and 1 with Bentler and Bonnet (1980) recommending values greater than 0.90 indicating a good fit. Another important index, which is least affected by sample size is the Comparative Fit Index (CFI). For this, a value of CFI ≥ 0.95 is recognised as indicative of good fit (Hu and Bentler, 1999). NFI of 0.970 and CFI of 0.981 in the proposed model indicate that the model is fitting the data.     

Table 13 Table showing Summary of Fit Indices

Source: Primary Data

The above fit indices clearly show that the null hypothesis (Hypothesis: H₀3) can be accepted and conclude that the model fits the data. Next, the researcher proceeds to bring out the significance of the paths used in the model. This is done by checking the Estimates.

Table 14 Regression Weights: (Group number 1 - Default model) (Unstandardised)

Source: Primary Data

The above table shows the unstandardised regression weights and it is found that all the constructs are statistically significant and also it is noted that the paths between Availability and Application (C.R. = 7.462, p value significant at .001 level - two-tailed), and Application to Acceleration (C.R. = 8.451, p value significant at .001 level - two-tailed) are significant.

It means that when Availability of WLBPs goes up by 1, the Application of the practices goes up by 0.367. Also, when the Application of practices goes up by 1, Acceleration for Empowerment goes up by 1.535.

Table 15 Standardized Regression Weights: (Group number 1 - Default model)

Source: Primary Data

From the above table, the standardised estimates values range from 0.629 to 0.971 showing that the factor loadings are above 0.5 and hence at acceptable level. Also, from the table, it can be said that When Availability goes up by 1 standard deviation, Application goes up by 0.651 standard deviations. When Application goes up by 1 standard deviation, Acceleration goes up by 0.971 standard deviations.

Table 16 Squared Multiple Correlations: (Group number 1 - Default model)

Source: Primary Data

The Squared Multiple Correlations brings out that the estimated that the predictors of Application, that is the Availability of WLBPs, explain 42.3 percent of its variance. Also, it is estimated that the predictors of Acceleration, that is Application of WLBPs explain 94.2 percent of its variance.

The above analysis confirms that the null hypotheses (Hypothesis H₀1 and  Hypothesis H₀2) set for the model paths are rejected and hence we establish that Availability of Work-life Balance practices influences Application of these practices and Application of Work-Life Balance Practices influences the Acceleration of women employees to top positions in the organisations.

The above analysis of the various fit indices and estimates brings out clearly that the proposed model is accepted and can be concluded that the Availability of Work-life Balance Practices in an organisations leads to Application of it and when there is application of those policies, it helps women employees to move upward in their career path, that is, Acceleration for Empowerment. This helps the researcher conclude that the Work-Life Balance Practices are effective in empowering women employees in IT and ITES industry. Other findings related to the study are -

1. Employees seem to be moderately aware of the practices related to Work-life balance. It should be made aware with the help of intranet, internet, company website, conducting special events and the like.
2. Women should be responsible in using the work-life balance practices offered to them. Instances where women misuse the practices would give a wrong signal to HR and create an adverse impact for the entire organisation.
3. The companies offer work-life balance practices in different forms. Though the companies cannot offer all the practices, it is suggested that the Bouquet of Benefits (BOB) be provided to the employees based on their demographic profile. By this, women employees would be able to choose what is required for them based on their demographic factor.
4. It can also be suggested that there is wide scope for improvement in practices and the companies can try and bring out policies suitable to the Indian context.

The above findings brings out that the work-life balance practices are available in the IT and ITeS industry. The women employees are aware of the practices available in the organisation and make use of the work-life balance practices. There is a favourable organisational culture at large and also, employees do not face barriers in making use of the WLBPs. The study brings out that, the availability and application of WLBPs helps in the career advancement of women in the organisation. This shows that work-life balance practices are effective in bringing about women empowerment, which fulfills the primary objective of the study.

IT and ITeS industry has changed the way people work. There has been tremendous variation from fixed- timings culture, gender-stereotyped role allocation to hunt for talent. The advent of technology companies created huge job opportunities and soon there was talent for war which necessitated to look beyond boundaries of Gender and other demographic factors. This revolution is also the reason why IT and ITeS companies, developed innovative HR practices – “to attract and retain the best talent available”.

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