This document summarizes a research study that examined the impact of work-life balance (WLB) on organizational commitment (OC) among women healthcare workers in India. The study found: 1) There was a significant positive relationship between WLB and OC among the 580 female healthcare workers surveyed across several hospitals. 2) Component-wise analysis showed WLB had a positive relationship with affective and normative commitment, but a negative association with continuance commitment. 3) The study highlighted the importance of organizational policies and support for managing the relationship between work and non-work domains to enhance employees' OC.

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Impact of work–life balance on organizational commitment of women
health-care workers: Structural modeling approach
Article in International Journal of Organizational Analysis · January 2020
DOI: 10.1108/IJOA-07-2019-1820
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2. Impact of work–life balance on
organizational commitment of
women health-care workers
Structural modeling approach
Sana Shabir and Abdul Gani
Department of Management Studies, School of Business Studies,
Central University of Kashmir, Srinagar, India
Abstract
Purpose – This study aims to examine the linkage between work–life balance (WLB) and organizational
commitment (OC) among women employees in the healthcare sector.
Design/methodology/approach – Using a quantitative methodology, this paper drew upon samples of
580 health-care sector employees working in the health-care sector of Jammu and Kashmir in India. A
structured questionnaire was administered to gather the required information. Data were analyzed using
SMART PLS and Statistical Package for the Social Sciences, in which descriptive statistics, t-test, analysis of
variance and structural equation modeling were conducted to achieve the objectives of the study.
Findings – Findings indicated a significant positive relationship between WLB and OC. Further
component-wise analysis revealed a positive relationship between the WLB and affective and normative
commitment. However, WLB demonstrated a negative association with continuance commitment.
Originality/value – The study also brought forth the causes and potential consequences of the work–life
imbalance and stressed upon the role of organizational policies in managing the relationship between work
and non-work domains and subsequently the development of OC among employees.
Keywords Work–life balance, Organisational commitment, Women workers, Health-care, India
Paper type Research paper
Introduction
Work and family issues are becoming increasingly important for organizations to consider
because the nature and composition of the workforce are changing and will continue to
change in the coming years. Women are becoming the emerging entrants in the global work
arena. However, women from developing countries have fewer choices to make. In a country
like India, that is still deep-rooted in traditional patriarchal setup, women are predominantly
assigned with the familial duties and to think of them as breadwinners is still a developing
aspect. Nevertheless, women have dismantled such notions by entering the global workforce
with the gradual change in economic demands and societal norms. As more women pursue
challenging careers and take greater part in fulfilling the family responsibilities, the stress of
managing everything and getting it right endangers the interests of the family and the
The support of Government of India’s Maulana Azad National Fellowship (MANF) for this research
is acknowledged. The funding agency had no role in the study design, the collection, analysis,
interpretation of results, the writing of the report or the decision to submit the article for publication.
Special thanks are extended to all collaborating hospitals and the participants.
We would like to acknowledge that this work is based on our dissertation work. We would also
like to acknowledge help of IBEF organization.
Commitment
of women
health-care
workers
Received 6 July 2019
Revised 14 November 2019
Accepted 14 November 2019
International Journal of
Organizational Analysis
© EmeraldPublishingLimited
1934-8835
DOI 10.1108/IJOA-07-2019-1820
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1934-8835.htm

3. society at large. It embarks upon the significance of work–life interface as a crucial area of
concern for researchers, business establishments and government, and a matter of vital
importance to employers, individuals and their families.
The challenges and complexity surrounding the combination of paid employment and
family responsibilities have taken center stage as an area of interdisciplinary research. As
satisfactorily performance of each of these roles require adequate time, energy and
organizational commitment (OC), any sort of role incompatibility makes it difficult for the
employees to deliver the required performance. Issues pertaining to work–life balance
(WLB) practices in the present day organizations demonstrate a paradigm shift that not only
relate to the challenges of combining paid work and family care, but also recognize the
importance of employees’ other life roles, such as community, elder care, personal values,
leisure and aging. While businesses and human resources (HR) professionals have shifted
their focus, with many aligning and integrating WLB practices with broader and more
strategic business objectives, the decisions regarding the balancing of paid employment
with non-work responsibilities are based mostly on personal choices and present a major
concern in the Indian context.
Like all other work fields, WLB is an important concern in the health-care organizations
also. As public dealing is substantially involved in case of health-care industry, employees
play a crucial role in the smooth functioning of an organization by providing quality
services to the end users. Also, hospitals are intrinsically complex systems and the job of
health-care workers is very challenging and demanding. As such, problems with the
healthcare not only influence the patients; they impact the staff, overall institution and the
society at large. By and large, the health-care organizations lack the implementation of WLB
strategies, which result in their poor OC and performance. This is well-evidenced from the
high number of health-care professionals quitting their organizations because of their
discontentment mainly with scheduling challenges, long duties, illness and patient volume
variability (Nelson and Tarpey, 2010). This study, therefore, attempts to examine the
linkage between the WLB and OC among women employees in the health-care sector.
Review of literature
As a gauge of the quality of life, WLB has gained academic and policy currency throughout
the world (McGinnity and Whelan, 2009). There is a continuous interest among researchers
and practitioners to study WLB as it has been viewed as a means of enhancing OC among
employees (Choo et al., 2016). The issues concerning commitment are central to WLB for
organizational performance as well as commitment to work (Nwagbara and Akanji, 2012).
Particularly, in today’s competitive and fast-paced work environment, demands on personal
and work life are high as people attempt to juggle multiple roles that creates an upsurge in
work–life imbalance in the form of marital and work stress (MacInnes, 2006; Roberts, 2007),
increased absenteeism and turnover (Deery, 2002; Wang and Walumbwa, 2007), recruitment
issues (Doherty, 2004) and psychosomatic symptoms (Burchill et al., 1999; Lewis, 2003). This
situation coupled with social pressures impinges on the commitment and motivation that
women bring to work (Aziz and Cunningham, 2008; Kaufman and Uhlenberg, 2000; Lambert
et al., 2006). Resultantly, difficulty in balancing work and personal life affects job
satisfaction, OC and leads to turnover (Arif and Farooqi, 2014).
Numerous studies have indicated that OC predicts and shapes job satisfaction,
absenteeism, organizational citizenship behavior, performance, turnover and WLB among
other variables (Greenhaus and Beutell, 1985; Lambert et al., 2006). Studies have also been
conducted to understand the role of WLB in enhancing the OC and in turn the efficiency of
an organization (Biwott et al., 2015; Cegarra-Leiva et al., 2012; Choo et al., 2016; Fapohunda,
IJOA

4. 2014; Wayne et al., 2013). The studies on the impact of WLB on the commitment of
employees conducted by Akintayo (2010), Atkinson (2011), Kim (2014), Nwagbara and
Akanji (2012) and O’Neill et al. (2009) have found that a significant relationship exists
between WLB and OC among employees. The other cross-section research studies
conducted by Deery and Jago (2015), Kopp (2013), Malone and Issa (2013), Smeaton et al.
(2014) and Tayfun and Çatir (2014) have also revealed that there is a positive relation
between WLB and OC. However, Evangelista et al. (2009), Greenberger et al. (1989), Malan
(2010) and Wallace (2006) found that the correlation between WLB and OC was inconclusive.
Various cross-nation, cross-culture and cross-sector studies, conducted in different health-
care organizations, so far, identify how work–life integration influences numerous
organizational outcomes (Azeem and Akhtar, 2014; Barnett and Gareis, 2002; Ferreira, 2014;
Mafini and Dlodlo, 2014; Poulose, 2017; Pryce et al., 2006; Reumkens, 2011; Russo and
Buonocore, 2012; Sakthivel and Jayakrishnan, 2012; Varma et al., 2016).
Despite the growth of research attention in the area, adequate empirical evidence on the
WLB and OC linkage is still scarce (Ferreira, 2014). The available WLB research has been
criticized as being theoretical (Zedeck, 1992), inadequately conceptualized (Lambert, 1990),
methodologically inappropriate (Casper et al., 2007) and narrowly focused (Carlson et al.,
2010). Most of the existing studies are, by their nature, general, descriptive and devoid of
empirical basis. Besides, a substantial amount of stand-alone research work has been done
in both the fields of WLB and OC, while the exploration of their linkage has remained
overwhelmingly confined to Western societies. The research conducted on the subject to
date is mainly confined to banking, construction, information technology, education, small
and medium-sized enterprises, financial service, defense and manufacturing sectors. Less
attention has been given to explore the impact of WLB on OC in the health-care sector,
specifically in the context of India.
Exploring the WLB and OC linkage in the health-care industry in India is important
because it is emerging as one of the fastest growing sectors – both in terms of revenue and
employment. The sector is expected to grow by compound annual growth rate of 16-17 per
cent to reach US$132.84bn by 2022. Health-care market in India is also expected to reach US
$372bn by 2022, driven by a rising income, greater health awareness, life style diseases and
health insurance (IBEF Report, 2019). The demand for health-care services is expected to be
at 7.4 million in 2022 (KPMG FICCI Report, 2015) and to meet this demand, the health-care
sector calls for a greater number of health-care practitioners and skilled labor. India is set to
increase its public health spending to 2.5 per cent of the country’s GDP by 2025, compared to
1.15 per cent as of now (IBEF Report, 2019). Obviously, the prevalence of employee-related
challenges involving health-care workers demands further research so as to come up with
appropriate solutions. Accordingly, the “reconciliation of work and family” has become a
core concern for policymakers in India and has encouraged a national-level debate and
policy intervention. This gives impetus for further research focus in the area. The present
study is an attempt in this direction.
Objectives
The study aims to:
examine the state of WLB and OC of women working in the health-care sector;
determine the impact of WLB on women workers’ OC; and
ascertain the influence of WLB on the dimensions of affective, continuance and
normative commitment (NC).
Commitment
of women
health-care
workers

5. Methodology
This multi-organizational, descriptive cum exploratory cross-sectional study adopted
quantitative and qualitative research methods. In addition to self-administered
questionnaires, the study made use of the evidence from in-depth interviews and
observations to elicit information from the respondents. The sample for the study
comprised 580 women working in the health-care sector belonging to various
demographic groups and categories, comprising administrators, doctors, nurses,
paramedics and the supporting staff of eight major public and private hospitals of Jammu
and Kashmir, India. The respondents were selected by means of a stratified random
sample process, which included a quota set for each category of employees from selected
public and private hospitals.
Sample profile
As depicted in Table I, out of total 580 respondents, 34.7 per cent were in the age group of
21-30 years and 29.3 per cent in the age group of 30-40 years. Over 72 per cent of them
were married, whereas 22.6 per cent reported being single. Just over half (66.4 per cent) of
the respondents had employed spouse. Around 80 per cent of respondents resided in a
nuclear family and 68.6 per cent had children with 49.1 per cent having two children.
Most of the respondents had children in the age group of below 12 years. About 42.6 per
cent of the respondents did not require child caretakers and half of the respondents
claimed to shoulder dependent care responsibility themselves. Respondents also revealed
to have cohabited mostly with 5-6 family members. In all 30 per cent of the respondents
had a master’s degree, followed by bachelor’s degree (26.2 per cent). Just 5.5 per cent of
the respondents worked as administrators, 37.4 per cent as doctors, 32.4 per cent as
nurses, 18.8 per cent as paramedic staff, whereas, 5.9 per cent worked in non-professional
positions such as house-keepers, sweepers and security guards. A vast majority of 71 per
cent respondents were permanent employees. About 21 per cent of the respondents had
experience of above 15 years while 27.6 per cent had up to 3 years of work experience.
The majority of the respondents were in the income group of Rs 30,000-50,000. Above 30
per cent respondents commuted a distance of 5-10 km daily. More than half of the
respondents worked during night shifts. About 60 per cent of the respondents claimed to
get 6-9 h of sleep on a daily basis, followed by 38 per cent of partakers who reported to get
3-6 h of sleep.
Research instrument
The study used a 78-item structured questionnaire based on a five-point Likert scale,
including some reverse-coded items. Respondents were asked to rate their level of agreement
on each statement from “1” as “strongly disagree” to “5” as “strongly agree.” The
questionnaire’s first section sought to reveal the socio-demographic details of the
respondents while the last two sections comprised statements related to WLB and OC.
The WLB scale developed in the Indian context by Banu and Duraipandian (2014) was
adopted with some minor modifications. The scale comprised 46 statements categorized into
5 constructs including work place support (WPS), work interference with personal life
(WIPL), personal life interference with work (PLIW), satisfaction with WLB (SWLB) and
improved effectiveness at work (IEW). Cronbach’s alpha values for all the five factors had
acceptable reliability estimates and the factor loadings were above 0.6 which further verified
the dimensionality of items. The three-component model developed by Allen and Meyer
(1990), consisting 24 statements was used to assess the 3 components of OC, namely
affective, continuance and normative commitment, each component having 8 statements.
IJOA

6. Demographics Category Frequency (%)
Age 21-30 201 34.7
30-40 170 29.3
40-50 130 22.4
50 79 13.6
Marital status Unmarried 131 22.6
Married 419 72.2
Divorced 14 2.4
Widowed 16 2.8
Spouse employment Employed 385 66.4
Not employed 39 6.7
Not applicable 156 26.9
Family type Nuclear 461 79.5
Extended/joint 119 20.5
Parental status Yes 398 68.6
No 182 31.4
Number of children 1 124 31.1
2 196 49.1
3 71 17.8
4 or more 8 2.0
Age of 1st child #9 142 35.58
10-21 136 34.08
22 121 30.32
Age of 2nd child #12 103 37.59
13-21 86 31.38
22 85 31.02
Age of 3rd child #12 28 35.44
13-20 28 35.44
21 23 29.11
Age of 4th child #12 4 50
13-19 2 25
20 2 25
Child caretaker Spouse 7 1.8
Parents 7 1.8
In-laws 98 24.6
Crèche 22 5.5
Maid/helper 61 15.3
Others 34 8.5
Not needed 170 42.6
Dependents Yes 295 50.9
No 285 49.1
Number of dependents #1 133 45.08
2-2 145 49.15
3 17 5.76
Total family members #4 219 37.75
5-6 239 41.20
7 122 21.03
Academic qualification Up to matriculation 32 5.5
Higher secondary 87 15.0
Diploma 103 17.8
Bachelor’s degree 152 26.2
Master’s degree 174 30.0
Above master’s degree 32 5.5
Current job position Administrator 32 5.5
Doctor 217 37.4
Nurse 188 32.4
(continued)
Table I.
Demographic profile
of the respondents
Commitment
of women
health-care
workers

7. This model was chosen over the other models because it took care of the major components
of OC and has been adopted widely in the Indian context. The model has also confirmed
reliability for each scale (affective commitment scale, 0.87; continuance commitment scale,
0.75; and normative commitment scale, 0.79) and the items loaded highest on their
representative factors.
To validate the results of the field survey, interviews were also conducted while
administering the questionnaires. Questionnaires were delivered to the respondents at
various locations as per their convenience which resulted in a high response rate. A
provision for eliminating the unusable questionnaires was maintained by collecting 2 per
cent more than the required sample size out of the total population. The attrition rate was
fixed as 2 per cent which led to the total sample size of 625. Out of 625 questionnaires,
missing data, unengaged responses, straight-lined responses and non-return ability
rendered 23 questionnaires unusable. Subsequently, such questionnaires were eliminated
yielding an effective sample size of 580.
Tools of data analyses
The results were checked for reliability through Cronbach’s alpha and the validity was
ensured through the measurement of total item correlation during the pilot study. The data
collected through questionnaires were analyzed using a multi-method statistical approach.
Predominantly, SMART PLS 3.2.6 and Statistical Package for the Social Sciences (SPSS)
version 20 were used to analyze the collected data. For missing data analysis and normality
check, SMART PLS was put to use. Normality was measured with the help of skewness and
Demographics Category Frequency (%)
Paramedic 109 18.8
Other 34 5.9
Type of employment Permanent 412 71.0
Contractual 168 29.0
Work experience Up to 3 years 160 27.6
3-7 years 146 25.2
7-11 years 85 14.7
11-15 years 67 11.6
15 years 122 21.0
Monthly income Up to Rs 10,000 139 24.0
Rs 10,000-30,000 151 26.0
Rs 30,000-50,000 164 28.3
Rs 50,000-1,00,000 106 18.3
Rs 1,00,000 20 3.4
Workplace–residence distance Up to 5 km 134 23.1
5-10 km 178 30.7
10-15 km 126 21.7
15-20 km 82 14.1
20 km 60 10.3
Work on night shifts Yes 340 58.6
No 240 41.4
Number of night shifts 1-3 47 13.8
4-6 112 32.9
7-9 118 34.7
Above 9 days 63 18.5
Hours of sleep Up to 3 h 14 2.4
3-6 h 221 38.1
6-9 h 345 59.5
Table I.
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8. kurtosis that revealed the characteristics of a frequency distribution. For un-engaged
responses, zero/lesser standard deviation technique, using Microsoft Excel, was used to
screen them out and outlier detection was done through Z-score calculations in SPSS. Data
set was also assessed for common method bias by examining the construct correlations
among the principal constructs of the study. Descriptive statistics such as frequencies,
percentages, mean, variance and standard deviation were computed to describe the
characteristics of the respondents and construct items.
SMART PLS was used to explore the factors of WLB and OC. As the measurement
model consisted of formative and reflective variables, the formative constructs were
analyzed by evaluating convergent validity, indicator collinearity, statistical significance
and the relevance of the indicator weights and for reflective measured constructs, only
reliability and validity quality criterions were examined. Further, to find the effect of
various factors of WLB on OC, inner models were examined through determination of
collinearity assessment among the constructs, coefficient of determination (R2
), cross-
validated redundancy (Q2
), significance and relevance of structural path coefficients and the
effect size ( f2
).
Analysis of results
Work–life balance
As revealed by Table II, the aggregate mean value across all the components of WLB is
much higher than the average mean score (M = 3.252 and SD = 0.72972) which implies that
the state of existing WLB in the organizations under study is fairly satisfactory and the
respondents are mostly satisfied with the overall WLB situation in their organizations.
Effectiveness at work is the most significant contributor for WLB within health-care
organizations (mean = 4.265 and SD = 0.515) implying that greater the access to WLB
initiatives, higher would be the employee effectiveness at work. Likewise, WIPL (mean =
3.416 and SD = 0.786) acted as the second most contributing factor toward WLB among the
employees, signifying that the lower the interference of work into the family domain,
the better is the WLB. Next, WPS (mean = 3.195 and SD = 0.742) is believed to enhance the
WLB of health-care employees. It entails that the employees perceive that their well-being is
valued by their colleagues, supervisors and the broader organization in which they are set
in.
WPS provides the employees with a greater sense of control and ownership over their
own lives. This brings about the stability in the relationships among the stakeholders and as
such the number of conflicts among coworkers and management get drastically reduced,
which thereby increases organizational productivity. Moreover, SWLB (mean = 2.982 and
SD = 0.884) and PLIW (mean = 2.405 and SD = 0.7194) contribute to WLB among health-
care employees though not to the extent of the above-mentioned factors.
Table II.
Descriptive statistics
for WLB
S. No. Factors Cronbach’s alpha (0.7) Mean SD
1 WPS 0.819 3.195 0.7428
2 WIPL 0.849 3.416 0.7865
3 PLIW 0.756 2.405 0.7194
4 SWLB 0.908 2.982 0.8840
5 IEW 0.767 4.265 0.5159
Total 3.2526 0.72972
Commitment
of women
health-care
workers

9. Organizational commitment
The study results (refer to Table III) indicate that the overall mean score (M = 3.275) is high
for OC which implies that the respondents are largely committed to their organizations. The
highest mean score recorded in respect of continuance commitment (mean = 3.573 and SD =
0.95796) indicates that the majority of employees are committed as they relate to various
costs and risks in the form of accumulated investments or better employment opportunities
that they could lose if they leave their organizations. Affective commitment (AC) scores the
second highest mean value (mean = 3.218 and SD = 0.860) suggesting that the employees
are committed and emotionally attached to their organizations and share a value and goal
congruence with their employers. Employees who are affectively committed to their
organizations demonstrate enhanced positive organizational citizenship behaviors and
reduced negative turnover cognitions. However, the normative commitment has scored
comparatively the lowest mean value (mean = 3.036 and SD = 0.843), implying that the
employees stay back with their employers because they feel morally obligated to do so.
Social norms greatly influence the extent to which employees remain loyal to their
organizations. It is marked by a feeling of indebtedness and need for reciprocity because
employees feel that their organizations invest a lot of resources in their development.
Moreover, relationship with peers, support from supervisors and long working tenure also
guide the commitment levels of employees.
Work–life balance and organizational commitment: measurement model
Herein, the effect of WLB dimensions on overall WLB and its subsequent effect on three
components of OC have been measured. Outer measurement model (Figure 1),
representing a collection of reflective and formative measures, revealed that 98.2 per cent
of the variance in WLB is explained by its WPS, WIPL, PLIW, SLWB and IEW
dimensions. The strongest effect on WLB was that of SWLB, followed by WPS, PLIW,
IEW and WIPL. However, this effect was positive and significant only for SWLB and
WPS. Moreover, this posited that 32.5 per cent of the variance in AC is explained by
WLB, followed by NC (24.8 per cent) and continuance commitment (CC) (9.2 per cent).
WLB significantly affects the AC, CC and NC.
Evaluating the structural model/inner model
This stage is most crucial and involves evaluating the structural model (Figure 2) by
determining whether the proposed structural relationships are significant and meaningful.
To assess the inner model’s quality, collinearity assessment among the constructs,
coefficient of determination (R2
), cross-validated redundancy (Q2
), significance and
relevance of path coefficients and the effect size ( f2
) were examined as recommended by
Hair et al. (2014) and Sarstedt et al. (2014).
The details of each step are given below:
Step 1: First, this study examined each set of predictors in the structural model for
potential collinearity issues prior to path coefficient estimation. Mooi and Sarstedt (2011)
Table III.
Descriptive statistics
for OC
S. No. Factors Cronbach’s alpha (0.7) Mean SD
1 Affective commitment 0.918 3.218 0.86048
2 Continuance commitment 0.908 3.573 0.95796
3 Normative commitment 0.864 3.036 0.84388
Total 3.275 0.88744
IJOA

10. suggest a thorough examination for detection of collinearity issue because path coefficients
estimation is based on the ordinary least squares regressions and the presence of collinearity
may lead to biased outcomes. The results (Table IV) reveal that collinearity among the
predictor constructs in the structural model is not an issue as variance inflation factor (VIF)
values are below the threshold of 5 and/or 10 and tolerance level 0.1 and/or 0.2 (as
suggested by Ali and Park, 2016; Sarstedt et al., 2017; Wong, 2013). As such
multicollinearity does not constitute a problem for the present data.
Step 2: The study assesses estimated path relationships among the latent variables in the
model through a re-sampling bootstrap method with 5,000 sub samples, no sign changes option,
bias-corrected and accelerated (BCa) bootstrap confidence interval and two-tailed sample test at
0.05 significance level to generate statistical significance values. The results highlight a
significant impact of WLB on OC with the path coefficient of 0.643 (mean = 0.665 and SD =
0.021). Moreover, the relationship between variables was found to be significant with t-values
(29.984) more than 1.96 and p-value = 0.000. Thus, WLB has a significant impact on OC.
Step 3: The present model depicts coefficient of determination R2
= 0.414, indicating that
WLB explains 41.4 per cent of the variance in OC. Results suggest that OC, the primary
outcome measure of the model, has a satisfactory R2
value of 0.414.
Step 4: It involves calculation of f2
. Threshold values for f2
are 0.02, 0.15 and 0.35
representing small, medium and large effects, respectively (Chin, 1998). As f2
value is =
0.706, i.e. greater than 0.35, an exogenous construct (WLB) strongly contributes to
explaining an endogenous construct (OC).
Figure 1.
Measurement model
Commitment
of women
health-care
workers

11. Step 5: Blindfolding was put to use to evaluate the predictive validity of the endogenous
construct in the model using partial least squares (PLS). A Q2
value larger than zero for a
particular endogenous construct indicates the path model’s predictive relevance for a
particular construct (Hair et al., 2014). Using the blindfolding procedure with an omission
distance of seven yielded all cross-validated redundancy values for OC considerably above
zero, as shown in Table V, thus providing support for the model’s predictive relevance
regarding the reflective endogenous latent variable. Comparison of Q2
value to zero is just
an indicative of whether an endogenous construct can be predicted. However, it does not
reveal its quality of the prediction (Rigdon, 2014; Sarstedt et al., 2014).
On the basis of construct cross-validated redundancy, Q2
revealed a value greater than
zero indicating the path model’s predictive relevance for a construct (Table VI).
Structural model-II (inner model) evaluation
This study follows Hair et al.’s (2014) five-step approach to measure the structural model; that is,
collinearity assessment among the constructs, structural model path coefficients, coefficient of
determination (R2
value), effect size f2
and predictive relevance Q2
and blindfolding (Figure 3).
The details of each step involved in evaluation of inner model-II are as under:
Step 1: multicollinearity test
First, the inner model is to be tested for potential collinearity issues. Inner model
estimates result from sets of regression and if there are collinearity issues then the
Figure 2.
Structural model – I
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12. Table V.
Q2
-indicator cross-
validated
redundancy
Indicators SSO SSE Q2
(= 1SSE/SSO)
AC1 580.000 447.590 0.228
AC2 580.000 424.378 0.268
AC3 580.000 470.522 0.189
AC4 580.000 504.782 0.130
AC5 580.000 475.095 0.181
AC6 580.000 474.676 0.182
AC7 580.000 459.295 0.208
AC8 580.000 475.910 0.179
CC1 580.000 566.627 0.023
CC2 580.000 567.666 0.021
CC3 580.000 558.180 0.038
CC4 580.000 552.791 0.047
CC5 580.000 527.760 0.090
NC2 580.000 479.481 0.173
NC3 580.000 519.888 0.104
NC4 580.000 520.640 0.102
NC5 580.000 501.420 0.135
NC7 580.000 483.922 0.166
NC8 580.000 478.746 0.175
Notes: SSO = sum of squared observations; SSE = sum of squared prediction errors
Table IV.
Multicollinearity test
Items Tolerance VIF
IEW1 0.553 1.747
IEW2 0.556 1.717
PLIW1 0.533 1.832
PLIW11 0.604 1.601
PLIW12 0.646 1.501
PLIW2 0.558 1.738
PLIW5 0.690 1.385
SWLB1 0.362 2.659
SWLB2 0.322 2.920
SWLB3 0.409 2.326
SWLB4 0.527 1.773
SWLB5 0.346 2.799
SWLB6 0.327 2.961
SWLB7 0.304 3.080
WIPL3 0.480 2.001
WIPL4 0.613 1.587
WIPL5 0.568 1.706
WIPL6 0.630 1.543
WIPL7 0.449 2.157
WIPL8 0.400 2.430
WIPL9 0.385 2.500
WPS1 0.539 1.768
WPS4 0.640 1.490
WPS6 0.506 1.901
WPS7 0.477 1.996
WPS8 0.425 2.236
WPS9 0.356 2.688
Commitment
of women
health-care
workers

13. values and significance of model estimates can be subject to biases (Hair et al., 2014).
During a prior model evaluation process, Fornell–Larcker criterion helps to detect
collinearity issues but this is not the case when formative constructs are involved in the
model because the Fornell–Larcker criterion is based on average variance extracted
(AVE) and AVE is not a meaningful measure for formative constructs (Hair et al., 2014).
The results (Table VII) show no collinearity issues along with the VIF outputs as all the
values are below the threshold of 5 and/or 10 or a tolerance level of 0.1 and/or 0.2 or higher
as suggested by Hair et al. (2011) and Sarstedt et al. (2017).Therefore, collinearity among the
predictor constructs in the structural model is not an issue.
Table VI.
Construct cross-
validated
redundancy
Indicators SSO SSE Q2
(=1SSE/SSO)
OC 11,020.000 9,489.369 0.139
WLB 15,660.000 15,660.000
Figure 3.
Structural model–II
IJOA

14. Step 2: coefficient of determination – R square
The coefficient of determination (R2
) is a measure of the model’s predictive accuracy that
represents the exogenous variable’s combined effect on the endogenous variable(s).
Although the level of acceptance depends on the research context, values of R2
range from 0
to 1, where higher value indicates a greater level of predictive accuracy (Hair et al., 2014;
Sarstedt et al., 2014), i.e. closer the R2
is to 1, the stronger the association. As a rule of thumb,
the R2
values of 0.670 are considered as substantial, values around 0.333 as average and
values of 0.190 and lower as weak (Chin, 1998). Following the rule of thumb, the values of R2
(refer to Table VIII) were found to be as: AC = 0.341, CC = 0.089 and NC = 0.259. Thus, WLB
explains 34.1 per cent of the variance in affective commitment, followed by 25.9 per cent of
the variance in normative commitment and comparably weak i.e. about9 per cent of the
variance in continuance commitment. It implies that WLB has a higher impact on affective
commitment followed by normative commitment.
Table VIII.
R2
matrix
Constructs R2
R2
adjusted
AC 0.341 0.340
CC 0.089 0.088
NC 0.259 0.258
Table VII.
Multicollinearity test
Items Tolerance VIF
IEW1 0.554 1.747
IEW2 0.560 1.717
PLIW1 0.527 1.832
PLIW2 0.558 1.738
PLIW5 0.690 1.385
PLIW11 0.605 1.601
PLIW12 0.645 1.501
SWLB1 0.360 2.659
SWLB2 0.322 2.920
SWLB3 0.406 2.326
SWLB4 0.525 1.773
SWLB5 0.346 2.799
SWLB6 0.326 2.961
SWLB7 0.306 3.080
WIPL3 0.480 2.001
WIPL4 0.613 1.587
WIPL5 0.569 1.706
WIPL6 0.630 1.543
WIPL7 0.450 2.157
WIPL8 0.400 2.430
WIPL9 0.385 2.500
WPS1 0.538 1.768
WPS4 0.642 1.490
WPS6 0.507 1.901
WPS7 0.478 1.996
WPS8 0.425 2.236
WPS9 0.356 2.688
Commitment
of women
health-care
workers

15. Step 3: blindfolding (Q2
)
Q2
values are built on blindfolding process and these reflect how well observed values are
reconstructed by the model and its parameter estimates. The smaller the difference between
predicted and original values the greater the Q2
and thus the model’s predictive accuracy
(Sarstedt et al., 2014). As a rule of thumb, Q2
values greater than zero for a particular endogenous
construct indicate that the path model’s predictive accuracy is acceptable for that particular
construct. For the endogenous construct in this study, blindfolding procedure was applied with
an omission distance of seven. Hair et al. (2016) recommend using an omission distance ranging
between 5 and 7. This procedure yielded cross-validated redundancy values for indicators that
are well above zero (refer to Table IX). The cross validated redundancy values for each construct
are above zero in all the cases. Thus, it provides support for the model’s predictive relevance.
As illustrated in Table X, the construct cross-validated redundancy is greater than zero
for all the three endogenous variables, namely, AC, CC and NC, indicating the path model’s
predictive accuracy.
Step 4: path coefficients
Standardized path coefficients represent the hypothesized relationships linking the
constructs and reflect the inner model’s quality. Path coefficient values are standardized on
a range from 1 to þ1, where coefficients closer to þ1 indicate a strong positive
Table X.
Construct cross-
validated
redundancy
Constructs SSO SSE Q2
(=1SSE/SSO)
AC 4,640.000 3,751.644 0.191
CC 2,900.000 2,742.548 0.054
NC 3,480.000 2,987.407 0.142
WLB 15,660.000 15,660.000
Table IX.
Indicator cross-
validated
redundancy
Items SSO SSE Q2
(=1SSE/SSO)
AC1 580.000 450.370 0.223
AC2 580.000 428.765 0.261
AC3 580.000 473.626 0.183
AC4 580.000 506.489 0.127
AC5 580.000 477.194 0.177
AC6 580.000 476.798 0.178
AC7 580.000 461.484 0.204
AC8 580.000 476.920 0.178
CC1 580.000 561.846 0.031
CC2 580.000 561.934 0.031
CC3 580.000 549.048 0.053
CC4 580.000 542.741 0.064
CC5 580.000 526.978 0.091
NC2 580.000 482.012 0.169
NC3 580.000 518.414 0.106
NC4 580.000 521.358 0.101
NC5 580.000 502.271 0.134
NC7 580.000 484.961 0.164
NC8 580.000 478.390 0.175
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16. relationship and coefficients closer to 1 indicate a strong negative relationship (Hair et al.,
2014; Sarstedt et al., 2014). In this study, a bootstrapping process that generates and allows
testing the statistical significance of various PLS-structural equation modeling results such
as path coefficients, Cronbach’s alpha, Heterotrait-Monotrait ratio of correlations and R2
values has been used. While considering the significance and relevance of the inner model
relationships, the results revealed (with sub samples 5,000, no sign changes option, BCa
bootstrap confidence interval and two-tailed sample test at 0.05 significance level) that all
the three structural relationships between variables are significant with t-values more than
1.96 and p-values = 0.000 (refer to Table XI). These results highlight the importance of WLB
in driving affective commitment and normative commitment with path coefficients of 0.584
and 0.509, respectively. WLB has a significant effect on continuance commitment. However,
with a path coefficient of 0.299, this effect is rather weak. Therefore, of the three structural
relationships, WLB has a stronger direct effect on affective commitment than normative and
continuance commitment.
Step 5: F square
The effect size can be determined by calculating Cohen’s f2
. It is calculated as the increase in
R2
of the latent variable to which the path is connected, relative to the latent variable’s
proportion of unexplained variance. Values between 0.020 and 0.150, between 0.150 and
0.350 and exceeding 0.350 indicate whether a predictor latent variable has a small, medium
or large effect on an endogenous latent variable, respectively, while the effect size values of
less than 0.02 indicate that there is no effect (Sarstedt et al., 2017). This study reveals that
WLB has substantial effect on affective commitment ( f2
= 0.517) whereas, WLB has
marginal effect on continuance commitment ( f2
= 0.098) and a medium effect on normative
commitment ( f2
= 0.350).
Discussion
The findings of this study revealed a positive significant relation between WLB and OC
(having path coefficient = 0.643, t-statistic = 29.984 and p-value = 0.000). These findings are
consistent with some previous research on the subject (Choo et al., 2016; Gulbahar et al.,
2014; Malone, 2010; Sethi, 2015; Tayfun and Çatir, 2014; Tewari and Bhasin, 2014; Zuhaida,
2013). A positive significant association between WLB and OC has been attributed to the
varying forms of WLB (e.g. access to compressed work week, part-time work, flextime and
job sharing) available to employees. Today women are entering into the global workforce in
growing numbers and families are increasingly relying on dual-earner setup. But women
workers still have a greater responsibility of household related work, childcare and
eldercare. These obligations of personal life, along with those of the workplace, impact
women workers physiologically and psychologically, leading to their work–life imbalance.
The effects of work–life imbalance on their performance are enormous. For example,
working mothers who are committed more toward the motherhood may have a greater
Table XI.
Path coefficients
matrix
Constructs Path coefficients Sample mean SD t-statistics p-values*
WLB ! AC 0.584 0.602 0.025 23.242 0.000
WLB ! CC 0.299 0.302 0.047 6.318 0.000
WLB ! NC 0.509 0.532 0.031 16.300 0.000
Note: *Significant at 0.05 level
Commitment
of women
health-care
workers

17. tendency to leave their organizations (Kelley et al., 2002). As such, it creates strong desire
and a dire need for having more flexibility options at the workplace because flexible work
policies accommodate varying needs of employees who have such responsibilities to cater to
(Scandura and Lankau, 1997), thereby, making work–life integration easier.
While the health-care workers may not find it feasible to opt for different WLB initiatives
(e.g. telecommuting), the health-care administrators are striving to maintain positive work
culture while remaining responsive to community needs. Employees who feel some form of
flexibility in how they manage to do their work and non-work related tasks respond in a
proactive way in doing things as they “often have a greater sense of responsibility,
ownership, and control of their working life” (Acas Report, 2015). Thus, WLB and OC go
well with each other. Malan (2010), however, argues that there is an absence of any relation
between the WLB and OC because the commitment is not considered to be limited to a
particular role (e.g. a parental role or a spouse role) but guided by intrinsic (e.g. meaningful
and intellectually stimulating work) and extrinsic factors (e.g. performance based pay). Dex
and Smith (2001) also found employees engaged in the private sector and having access to
some family friendly policies reveal improved commitment while it was not observed among
employees working in the public sector. As such, these findings mostly conformed to an
inverse link of WLB with OC.
Taking into account the relationship between WLB and the three components of OC,
results revealed a significant positive relationship between WLB of female employees and
their affective commitment (having path coefficient = 0.584, t-statistic = 23.242 and
p-value = 0.000) and normative commitment (having path coefficient = 0.509, t-statistic =
16.300 and p-value = 0.000) but, WLB was found to be negatively associated with
continuance commitment (having path coefficient = 0.299, t-statistic = 6.318 and p-value =
0.000). The women workers report higher affective commitment than normative
commitment, which suggests that the women workers like to stay in their organization, but
have a low sense of debt toward their workplace. Van Dyk and Coetzee (2012) also opined
that affective commitment has a stronger effect than normative commitment among
workers. These findings are also consistent with Tayfun and Çatir (2014) who found a
positive relationship between WLB and affective and normative commitment on one hand
and a negative relationship with WLB and continuance commitment among nurses on the
other. LaMastro (2000) evidenced a strong positive relation with affective commitment and a
negative relation with continuance commitment. In line with the results of present study,
Ferreira (2014), based on her study in a health-care institute, concluded that affective and
normative commitment were significantly associated with the WLB. Norton (2009) and
Dockel et al. (2006) have also reported WLB to have a significant and direct effect on
affective commitment, while no significant direct effect on continuance as well as normative
commitment was suggested. However, Choo et al. (2016) and Biwott et al. (2015) found a
significant positive link between WLB and affective commitment, normative commitment
and continuance commitment.
The health-care workers, under present study, particularly those working in the public
sector, rendered higher affective commitment, because they enjoy numerous benefits such
as high salaries, greater work autonomy, higher job stability and growth opportunities.
Consequently, these employees feel emotionally attached to their organizations which take
care of their non-work demands. This happens to be more applicable to the workers who
suffer from resource limitations or have dependent/childcare issues. The results have shown
a negative relation between WLB and CC. Continuance commitment is embarked upon by
staying with an organization because of perceived high economic and social costs involved
with leaving a particular job (Phillips and Gully, 2011). Employees tend to remain with the
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18. organization because of the “nontransferable” investments made, such as, retirement
benefits, relationship with coworkers or things that are unique to a particular organization
(Reichers, 1985). Expectedly, employees would reflect a higher commitment to their
organizations when they cannot obtain the same benefits in other organizations (Lee et al.,
2008).
The study also revealed positive effects of WLB on normative commitment. Staying
normatively committed is perceived to be crucial to hospitals because of the critical role that
employees play while delivering health-care services. Health-care workers tend to effectively
respond to the needs of the community at first. Many employees do not relate hospital to a
mere physical facility but as an organization that affects human lives in one way or the
other. As such, they attribute working in a hospital as their moral responsibility where
duties are owed to patients and the community served at large (De George, 1982). Moreover,
because of the familial pressure, and organizational and societal norms, employees feel
obligated to reciprocate to the demands of the organization. For that matter, in any system,
employees largely feel obligated toward their employer for a wide variety of reasons,
especially if employees are exposed to better WLB initiatives, high salary, long working
tenure, specialized training and more opportunities for career growth. Therefore, this stew of
competing pressures ties employees by a psychological contract/reciprocal exchange
between them and their employers and accordingly, employees demonstrate higher levels of
normative commitment.
Conclusion and implications
Women health-care workers under study were found to be satisfied with the level of WLB
but need further HR interventions that would improve their state of WLB. Women workers
highlighted effectiveness at work as the most important factor for achieving a higher degree
of WLB, followed by work–life interference, WPS and SWLB, subsequently. Results further
revealed that women workers are committed to their organizations but this form of
commitment was largely associated to continuance component of OC, signifying the lack of
opportunities in the context of health-care sector. Women employees also reported being
emotionally attached to their employers but they did not feel highly obligated to stay with
their respective organizations as working in any of the health-care institutes provided them
with the equal levels of satisfaction, considering the well-being of others as the primary
work motive. Meanwhile, many of the workers did not feel to be bound to reciprocate to the
needs of a particular organization, especially if other organizations provided better
employment conditions.
The major interesting findings of the study pertain to the confluence between WLB and
AC, CC and NC. The study indicated that WLB is positively and significantly related to AC
and NC. However, WLB–CC tie up, regardless of being significant, exhibited a negative
association. WLB affected OC positively and significantly in totality also, implying that
most of the women workers consider WLB as a necessary factor that makes them stay in
their respective organizations. While the findings have evidenced WLB as a strong predictor
of OC, these also necessitate taking care of the factors that are of paramount importance to
maintaining commitment among the workers. While the health-care industry is concerned
about the issues related to WLB of its employees; the measures taken to improve the
situation are not adequate.
In today’s complex and constantly changing world, flexible work practices are necessary
for health-care organizations to meet women workers’ work–life demands. Not just for
employees but it is essential for employers as well to ensure WLB of employees so that they
can retain the women workers as they are the major care providers in health-care
Commitment
of women
health-care
workers

19. institutions and their presence acts as a gauge of the health-care systems’ ability to meet the
ever growing needs of the people (Johnstone, 2007). Moreover, in health-care profession
becoming the employer of choice means that hospital practices employment terms and
conditions that are generous enough to increase satisfaction, motivation and commitment
among their employees. Employers should, therefore, treat their employees as clients by
taking necessary steps toward developing appropriate WLB policies to enhance
commitment among their workers. Unless these issues are well addressed, turnover will
increase, service quality will deteriorate, patient satisfaction will be affected negatively and
consequently, the organizations will face the threat of leavening their services in the long
run. The workers’ needs ought to be cushioned through proper organizational support
system coupled with coordination between organizations and different facilities and support
systems that help employees to fulfill their non-work demands. Convenient working hours,
support for employees who have children and elderly persons at home, adequate vacations
and employee support networks should be made available for employees. Job incentives
including the improved condition of service, promotion opportunities, stable career growth,
higher salary, provision for retirement and other fringe benefits should be provided by the
employers. This will motivate the workers to easily schedule time with family errands,
thereby, ensuring effective management of WLB while increasing commitment levels
among employees with corresponding effects on organizational goal achievement.
It is important that various stake holders in each health-care institute come together to
draft common WLB initiatives to ensure fairness and consistency in the implementation of
WLB policies. Ideally these processes should be supported by the government, trade unions
and other stakeholders. Health-care workers mostly do not have fixed work routine as their
work schedule is dependent on the number and type of cases/emergencies received. For such
employees who are required to stay longer for overtime, the organizations should offer them
lunch, takeout meals and transportation facilities. Such benefits help to ensure safety of
women workers, reduce their work–family conflict levels and consequently pave way for
achieving WLB. While more organizational support needs to be directed to young and fresh
workers who tend to be less satisfied than experienced workers, older workers require more
sense of appreciation and respect. It should be an inclusive process whereby a joint
responsibility is shared between employees, union, government and the employing
organization to carve out well planned, realistic and agreed upon programs that can be
worked together to share the benefits of WLB and achieve positive changes in the
organizations.
Employers should espouse the findings of this study and strive to improve upon WLB
and OC related factors. They are expected to gain better insights regarding interrelation
between WLB and OC, and their impact on productivity and overall institutional
performance. This input should be useful to organizations in formulating and implementing
various WLB initiatives. Meanwhile, the health-care managers and administrators should
take into consideration the consequences of poor WLB and accordingly, create such a
working atmosphere that builds up productiveness in balancing work and non-work
demands through appropriate human resource interventions.
Organizations, along with the government, need to explore how their working practices
make a difference with respect to WLB and commitment among workers, satisfaction level
of patients, and the overall organizational performance. They should converge upon their
efforts not only on provision of basic WLB initiatives, but also concentrate their attempts on
making administrators cognizant of the importance of supportive WLB culture in their
organizations for improving organizational outcomes. Organizations, administrators,
government and all other stakeholders should take notice of changing demographic
IJOA

20. characteristics so as to develop an operational framework for implementing effective family
friendly initiatives for employees.
Limitations and directions for future research
The present study is limited to a sample of predominantly women participants working in
health-care sector. Therefore, the findings cannot be generalized to other occupational,
gender and industry groups. Although multi-method approach was used to arrive at an
optimal sample size, still it may not be large enough to make a sweeping assumption about
the findings of the study and to reach on some explicit conclusions about the relationship
between the variables studied. The sample was chosen from northern India so it needs
exhaustive exploration of whether the findings of this study can be replicated in different
geographical areas for further verification and generalization. Because of the cross-sectional
nature of the data, the findings may not draw appropriate causal inferences among the
constructs investigated. For example, while WLB impacts OC, OC could also act as a
facilitator for balancing chores of family work domains. While the undertaken study was
limited to female workers working in hospitals, the future research may consider mixed
samples across different sectors before one can draw conclusions about the relationship
between employees’ levels of satisfaction with organizations’ WLB factors and their
commitment to their organizations. It would be worthwhile to carry out studies with more
heterogeneous samples beyond a common point of interest made up of an equal number of
males and females to examine potential gender differences. Moreover, future research can
attempt to study the difference on the basis of a generational gap and its impact on WLB
and related factors. The number of variables that were limited to two in this study can be
further increased by introduction of more related dependent (e.g. organizational citizenship
behavior, turnover intentions and employee effectiveness) and independent variables (e.g.
role efficacy, self-efficacy, creativity and decentralization). As the study was based on cross-
sectional data, a follow-up study with a longitudinal design would allow for stronger causal
inferences to be made about the relations among the variables examined in this study. It
would enable researchers to establish the impact of new WLB policies and practices on
employees’ perceptions of WLB.
References
Acas Report (2015), “Flexible working and work-life balance”, available at: www.acas.org.uk/media/
pdf/j/m/Flexible-working-and-work-life-balance.pdf
Akintayo, D.I. (2010), “Work-family role conflict and organizational commitment among industrial
workers in Nigeria”, International Journal of Psychology and Counselling, Vol. 2 No. 1, pp. 1-8.
Allen, N.J. and Meyer, J.P. (1990), “The measurement and antecedents of affective, continuance and
normative commitment to the organization”, Journal of Occupational Psychology, Vol. 63 No. 1,
pp. 1-18.
Ali, M. and Park, K. (2016), “The mediating role of an innovative culture in the relationship between
absorptive capacity and technical and non-technical innovation”, Journal of Business Research,
Vol. 69 No. 5, pp. 1669-1675.
Arif, B. and Farooqi, Y.A. (2014), “Impact of work life balance on job satisfaction and organizational
commitment among university teachers: a case study of university of Gujrat, Pakistan”,
International Journal of Multidisciplinary Sciences and Engineering, Vol. 5 No. 9, pp. 24-29.
Atkinson, C.M. (2011), “Examining differences in work life balance, job satisfaction, organizational
commitment, and learning goal orientation in baby boomers and generation xers”, Doctoral
dissertation, Sciences, San Diego State University.
Commitment
of women
health-care
workers

21. Azeem, S.M. and Akhtar, N. (2014), “The influence of WLB and job satisfaction on organizational
commitment of healthcare employees”, International Journal of Human Resource Studies, Vol. 4
No. 2, p. 18.
Aziz, S. and Cunningham, J. (2008), “Workaholism, work stress, work-life imbalance: exploring
gender’s role”, Gender in Management: An International Journal, Vol. 23 No. 8, pp. 553-566.
Barnett, R.C. and Gareis, K.C. (2002), “Full-time and reduced-hours work schedules and marital quality:
a study of female physicians with young children”, Work and Occupations, Vol. 29 No. 3,
pp. 364-379.
Banu, R.A. and Duraipandian, K. (2014), “Development of instrument to measure WLB of IT
professionals in Chennai”, International Journal of Management, Vol. 5 No. 11, pp. 21-33.
Biwott, G.K., Kemboi, A.K. and Goren, P. (2015), “Do employees work life policies and empowerment
strategies drive employee commitment? Evidence from Kenyan referral hospital, synergy
effect”, Global Journal of Human Resource Management, Vol. 3 No. 1, pp. 62-72.
Burchill, B.J., Day, D., Hudson, M., Ladipo, D., Mankelow, R., Nolan, J., Reed, H., Wichert, I. and
Wilkinson, F. (1999), Job Insecurity and Work Intensification: Flexibility and the Changing
Boundaries of Work, Joseph Rowntree Foundation, York.
Carlson, D.S., Grzywacz, J.G. and Michele Kacmar, K. (2010), “The relationship of schedule flexibility
and outcomes via the work-family interface”, Journal of Managerial Psychology, Vol. 25 No. 4,
pp. 330-355.
Casper, W.J., Weltman, D. and Kwesiga, E. (2007), “Beyond family-friendly: the construct and
measurement of singles-friendly work culture”, Journal of Vocational Behavior, Vol. 70 No. 3,
pp. 478-501.
Cegarra-Leiva, D., Sánchez-Vidal, M.E. and Gabriel Cegarra-Navarro, J. (2012), “Understanding the link
between work life balance practices and organisational outcomes in SMEs: the mediating effect
of a supportive culture”, Personnel Review, Vol. 41 No. 3, pp. 359-379.
Chin, W.W. (1998), “The partial least squares approach to structural equation modeling”, Modern
Methods for Business Research, Vol. 295 No. 2, pp. 295-336.
Choo, J.L.M., Desa, N.M. and Asaari, M.H.A.H. (2016), “Flexible working arrangement toward
organizational commitment and Work-Family conflict”, Studies in Asian Social Science, Vol. 3
No. 1, p. 21.
De George, R.T. (1982), “The moral responsibility of the hospital”, Journal of Medicine and Philosophy,
Vol. 7 No. 1, pp. 87-100.
Deery, M. (2002), “Labour turnover in international hospitality and tourism”, Human Resource
Management: International Perspectives in Hospitality and Tourism, pp. 51-63.
Deery, M. and Jago, L. (2015), “Revisiting talent management, work-life balance and retention
strategies”, International Journal of Contemporary Hospitality Management, Vol. 27 No. 3,
pp. 453-472.
Dex, S. and Smith, C. (2001), “Effects of family-friendly policies on employee commitment: an analysis
of the workplace employee relation survey”, The Judge Institute of Management Studies
Working Paper. No. wp20, 2001, pp. 1-36.
Dockel, A., Basson, J.S. and Coetzee, M. (2006), “The effect of retention factors on organisational
commitment: an investigation of high technology employees”, SA Journal of Human Resource
Management, Vol. 4 No. 2, pp. 20-28.
Doherty, L. (2004), “Work-life balance initiatives: implications for women”, Employee Relations, Vol. 26
No. 4, pp. 433-452.
Evangelista, M.J.M., Lim, E.D.N., Rocafor, S.C. and Teh, G.L.Y. (2009), “Worklife balance and
organizational commitment of Generation Y employees”, Thesis Unpublished, Bachelor of
Science in Commerce Business Management Major in Applied Corporate Management De La
Salle University, Manila.
IJOA

22. Fapohunda, T.M. (2014), “An exploration of the effects of WLB on productivity”, Journal of Human
Resources Management and Labor Studies, Vol. 2 No. 2, pp. 71-89.
Ferreira, M.M.F. (2014), “Organizational commitment and supervisor support, perception of procedural
fairness, tenure in the hospital: the mediating effect of work-life balance-study in nurses”,
Clinical Nursing Studies, Vol. 3 No. 2, p. 17.
Greenberger, E., Goldberg, W.A., Hamill, S., O’Neil, R. and Payne, C.K. (1989), “Contributions of a
supportive work environment to parents’ well-being and orientation to work”, American Journal
of Community Psychology, Vol. 17 No. 6, pp. 755-783.
Greenhaus, J.H. and Beutell, N.J. (1985), “Sources of conflict between work and family roles”, The
Academy of Management Review, Vol. 10 No. 1, pp. 76-88.
Gulbahar, C., A.A., Kundi, G.M., Qureshi, Q.A. and Akhtar, R. (2014), “Relationship between work-life
balance and organizational commitment”, Research on Humanities and Social Sciences, Vol. 4
No. 5.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: indeed a silver bullet”, Journal of Marketing
Theory and Practice, Vol. 19 No. 2, pp. 139-152.
Hair, J.F., Jr, Hult, G.T.M., Ringle, C. and Sarstedt, M. (2014), A Primer on Partial Least Squares
Structural Equation Modeling (PLS-SEM), Sage Publications Thousand Oaks, CA.
Hair, Jr, J.F., Sarstedt, M., Hopkins, L. and Kuppelwieser, V., G. (2014), “Partial least squares structural
equation modeling (PLS-SEM) an emerging tool in business research”, European Business
Review, Vol. 26 No. 2, pp. 106-121.
Hair, J.F., Jr, Sarstedt, M., Matthews, L.M. and Ringle, C.M. (2016), “Identifying and treating unobserved
heterogeneity with FIMIX-PLS: part I – method”, European Business Review, Vol. 28 No. 1,
pp. 63-76.
IBEF Report (2019), “Retrieved from India brand equity foundation, healthcare industry in India”,
available at: www.ibef.org/industry/healthcare-india.aspx
Johnstone, M.J. (2007), “Nurse recruitment and retention: imperatives of imagining the future and
taking a proactive stance”, Contemporary Nurse, Vol. 24 No. 2, pp. 3-6.
Kaufman, G. and Uhlenberg, P. (2000), “The influence of parenthood on the work effort of married men
and women”, Social Forces, Vol. 78 No. 3, pp. 931-947.
Kelley, M.L., Hock, E., Jarvis, M.S., Smith, K.M., Gaffney, M.A. and Bonney, J.F. (2002), “Psychological
adjustment of navy mothers experiencing deployment”, Military Psychology, Vol. 14 No. 3,
pp. 199-216.
Kim, H.K. (2014), “Work-life balance and employees’ performance: the mediating role of affective
commitment”, Global Business and Management Research, Vol. 6 No. 1, p. 37.
Kopp, L.R. (2013), “The effects of perceived supervisor work-life support on employee work-life balance,
job satisfaction, organizational commitment, and organizational citizenship behavior”, Doctoral
dissertation, University of Wisconsin–Stout.
KPMG FICCI Report (2015), “Healthcare: the neglected GDP driver need for a paradigm shift”, available
at: www.ficci.com/spdocument/20634/Executive-Summary-FICCI-KPMGKP.pdf
LaMastro, V. (2000), “Commitment and perceived organizational support”, in National Forum of
Applied Educational Research Journal, Vol. 13 No. 3, pp. 1-13.
Lambert, S.J. (1990), “Processes linking work and family: a critical review and research agenda”,
Human Relations, Vol. 43 No. 3, pp. 239-257.
Lambert, E.G., Pasupuleti, S., Cluse-Tolar, T., Jennings, M. and Baker, D. (2006), “The impact of work-
family conflict on social work and human service worker job satisfaction and organizational
commitment: an exploratory study”, Administration in Social Work, Vol. 30 No. 3, pp. 55-74.
Lee, S.H., Lee, T.W. and Lum, C.F. (2008), “The effects of employee services on organizational
commitment and intentions to quit”, Personnel Review, Vol. 37 No. 2, pp. 222-237.
Commitment
of women
health-care
workers

23. Lewis, S. (2003), “The integration of paid work and the rest of life: is post-industrial work the new
leisure?”, Leisure Studies, Vol. 22 No. 4, pp. 343-345.
McGinnity, F. and Whelan, C.T. (2009), “Comparing work-life conflict in Europe: evidence from
the European social survey”, Social Indicators Research, Vol. 93 No. 3, pp. 433-444.
MacInnes, J. (2006), “Work–life balance in Europe: a response to the baby bust or reward for the baby
boomers?”, European Societies, Vol. 8 No. 2, pp. 223-249.
Mafini, C. and Dlodlo, N. (2014), “The linkage between work-related factors, employee satisfaction and
organisational commitment: insights from public health professionals”, SA Journal of Human
Resource Management, Vol. 12 No. 1, p. 12.
Malan, S. (2010), “Women professionals in South Africa: the interaction between work life balance and
organisational commitment”, Doctoral dissertation, University of Pretoria.
Malone, E. (2010), Work-Life Balance and Organizational Commitment of Women in Construction in the
United States, University of FL, Gainesville, FL.
Malone, E.K. and Issa, R.R. (2013), “Predictive models for work-life balance and organizational
commitment of women in the US construction industry”, Journal of Construction Engineering
and Management, Vol. 140 No. 3, p. 04013064.
Mooi, E. and Sarstedt, M. (2011), A Concise Guide to Market Research: The Process, Data, and Methods
Using IBM SPSS Statistics, Springer Berlin.
Nelson, M.F. and Tarpey, R.J. (2010), “Work scheduling satisfaction and work life balance for nurses: the
perception of organizational justice”, Academy of Health Care Management Journal, Vol. 6 No. 1, p. 25.
Norton, J. (2009), “Employee organisational commitment and work-life balance in Australia”, Carpe
Diem, the Australian Journal of Business and Informatics, Vol. 4 No. 1, pp. 1-7.
Nwagbara, U. and Akanji, B.O. (2012), “The impact of work-life balance on the commitment and
motivation of Nigerian women employees”, International Journal of Academic Research in
Business and Social Sciences, Vol. 2 No. 3, p. 38.
O’Neill, J.W., Harrison, M.M., Cleveland, J., Almeida, D., Stawski, R. and Crouter, A.C. (2009), “Work–
family climate, organizational commitment, and turnover: multilevel contagion effects of
leaders”, Journal of Vocational Behavior, Vol. 74 No. 1, pp. 18-29.
Phillips, J.M. and Gully, S.M. (2011), Organizational Behavior: Tools for Success, Cengage Learning.
Poulose, S. (2017), “Assessing the influence of work-life balance dimensions among nurses in the
healthcare sector”, Journal of Management Development, Vol. 36 No. 3.
Pryce, J., Albertsen, K. and Nielsen, K. (2006), “Evaluation of an open-rota system in a Danish
psychiatric hospital: a mechanism for improving job satisfaction and work–life balance”, Journal
of Nursing Management, Vol. 14 No. 4, pp. 282-288.
Reichers, A.E. (1985), “A review and reconceptualization of organizational commitment”, Academy of
Management Review, Vol. 10 No. 3, pp. 465-476.
Reumkens, F.L.F. (2011), “The impact of work-life balance HR practices on psychological contract
fulfillment and violation in a health care organization”, Master Thesis, Human Resource Studies
presented to Tilburg University.
Rigdon, E.E. (2014), “Rethinking partial least squares path modeling: breaking chains and forging
ahead”, Long Range Planning, Vol. 47 No. 3, pp. 161-167.
Roberts, K. (2007), “Work-life balance – the sources of the contemporary problem and the probable
outcomes: a review and interpretation of the evidence”, Employee Relations, Vol. 29 No. 4,
pp. 334-351.
Russo, M. and Buonocore, F. (2012), “The relationship between work-family enrichment and nurse
turnover”, Journal of Managerial Psychology, Vol. 27 No. 3, pp. 216-236.
Sakthivel, D. and Jayakrishnan, J. (2012), “Work life balance and organizational commitment for
nurses”, Asian Journal of Business and Management Sciences, Vol. 2 No. 5, pp. 1-6.
IJOA

24. Sarstedt, M., Ringle, C.M. and Hair, J.F. (2017), “Partial least squares structural equation modeling”,
Handbook of Market Research, pp. 1-40.
Sarstedt, M., Ringle, C.M., Henseler, J. and Hair, J.F. (2014), “On the emancipation of PLS-SEM: a
commentary on Rigdon (2012)”, Long Range Planning, Vol. 47 No. 3, pp. 154-160.
Sarstedt, M., Ringle, C.M., Smith, D., Reams, R. and Hair, J.F. (2014), “Partial least squares structural
equation modeling (PLS-SEM): a useful tool for family business researchers”, Journal of Family
Business Strategy, Vol. 5 No. 1, pp. 105-115.
Scandura, T.A. and Lankau, M.J. (1997), “Relationships of gender, family responsibility and flexible
work hours to organizational commitment and job satisfaction”, Journal of Organizational
Behavior, Vol. 18 No. 4, pp. 377-391.
Sethi, U.J. (2015), “Influence of work life balance on organisational commitment: a comparative study of
women employees working in public and private sector banks”, International Journal of
Management, IT and Engineering, Vol. 5 No. 1, pp. 243-255.
Smeaton, D. Ray, K. and Knight, G. (2014), “Costs and benefits to business of adopting work life balance
working practices: a literature review”, available at: www.psi.org.uk/images/uploads/bis-14-903-costs-
and-benefits-to-business-of-adopting-work-life-balance-working-practices-a-literature-review.pdf
Tayfun, A. and Çatir, Ö.G.O. (2014), “An empirical study into the relationship between work/life
balance and organizational commitment”, ISGUC the Journal of Industrial Relations and Human
Resources, Vol. 16 No. 1, pp. 20-37.
Tewari, R. and Bhasin, T. (2014), “Work-life balance and commitment among academics in higher
educational institutions”, Viewpoint, Vol. 5 No. 2, pp. 37-41.
Van Dyk, J. and Coetzee, M. (2012), “Retention factors in relation to organisational commitment in
medical and information technology services”, SA Journal of Human Resource Management,
Vol. 10 No. 2, pp. 1-11.
Varma, M.M., Kelling, A.S. and Goswami, S. (2016), “Enhancing healthcare quality by promoting work-
life balance among nursing staff”, Journal of Hospital Administration, Vol. 5 No. 6, p. 58.
Wallace, J.E. (2006), “Work commitment in the legal profession: a study of baby boomers and
generation Xers”, International Journal of the Legal Profession, Vol. 13 No. 2, pp. 137-151.
Wang, P. and Walumbwa, F.O. (2007), “Family-friendly programs, organizational commitment, and
work withdrawal: the moderating role of transformational leadership”, Personnel Psychology,
Vol. 60 No. 2, pp. 397-427.
Wayne, J.H., Casper, W.J., Matthews, R.A. and Allen, T.D. (2013), “Family-supportive organization
perceptions and organizational commitment: the mediating role of work–family conflict and
enrichment and partner attitudes”, Journal of Applied Psychology, Vol. 98 No. 4, p. 606.
Wong, K.K.K. (2013), “Partial least squares structural equation modeling (PLS-SEM) techniques using
SmartPLS”, Marketing Bulletin, Vol. 24 No. 1, pp. 1-32.
Zedeck, S.E. (1992), Work, Families, and Organizations, Jossey-Bass.
Zuhaida, A. (2013), “Worklife balance and organisational commitment of secondary school teachers in
kubang pasu district”, Doctoral dissertation, Universiti Utara Malaysia.
Corresponding author
Sana Shabir can be contacted at: sanashabir007@gmail.com
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