INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025

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Reimagining Work-Life Synergy and Employee Well-Being through

Digital, Organizational, and Individual Interventions in the Banking

Sector

Shilpa Sharma,

Dr. Neeru Rathore

Research Scholar, Faculty of Management Studies, JRN Rajasthan Vidyapeeth (Deemed-to-be)

University, Udaipur (Rajasthan), India

Assistant Professor, Faculty of Management Studies, JRN Rajasthan Vidyapeeth (Deemed-to-be)

University, Udaipur (Rajasthan), India

DOI: https://doi.org/10.51244/IJRSI.2025.120800248

Received: 22 Sep 2025; Accepted: 28 Sep 2025; Published: 02 October 2025

ABSTRACT

In this age of accelerated digital transformation, the traditional concept of work-life balance (WLB) appears

insufficient to capture the complexities at professional and personal domains of employees. This research

repositions the discourse toward work-life synergy (WLS), a framework that emphasizes integration and

enrichment rather than separation of roles. Anchored in the banking sector of India, the study investigates the

relationship between WLS and employee well-being (EWB), which extends beyond job satisfaction and

psychological health to encompass resilience, engagement, and meaning at work. The moderating roles of

organizational interventions (OI), individual interventions (II), and digital interventions (DI) are introduced,

with DI representing an innovative extension to the literature. Primary data from 2,150 women employees

across public and private sector banks of Rajasthan were collected using a structured questionnaire. Partial

Least Squares Structural Equation Modeling (PLS-SEM) was employed to evaluate measurement and

structural models. Results demonstrated that WLS significantly predicts EWB, while OI and II differentially

moderate this relationship. DI emerged as a powerful moderator, amplifying both positive and negative

outcomes depending on digital readiness. The study contributes theoretically by advancing WLB research

toward synergy and well-being, and practically by offering strategies for integrating organizational, personal,

and digital interventions in dynamic work settings.

Keywords: Work-life Synergy, Employee Well-Being, Organizational Interventions, Digital Interventions,

Individual Resilience, Banking Sector, PLS-SEM

INTRODUCTION

The Indian banking sector has witnessed unprecedented structural and technological transformations over the

past two decades, driven by globalization, liberalization, and the rapid adoption of digital technologies.

Consolidation through large-scale mergers, diversification of services, and the infusion of fintech

collaborations have redefined competitive landscapes (Maani & Rajkumar, 2024). Simultaneously, the

integration of artificial intelligence (AI), blockchain, mobile banking, and advanced analytics has altered

traditional banking operations, shifting toward automation and customer-centric digital platforms (Srivastava

& Dhamija, 2021; Farayola, 2024). While these changes have enhanced efficiency and service delivery, they

have also intensified work demands and imposed continuous pressure on employees to reskill and adapt to

evolving technological ecosystems (Sharma, 2024). The implications of such shifts extend beyond professional

requirements, increasingly spilling over into employees’ personal lives. Digitization blurs temporal and spatial

boundaries between work and home, with expectations of availability beyond office hours, accelerated

decision-making cycles, and heightened accountability (Tarafdar et al., 2019). These dynamics are particularly

pronounced in the case of women employees, who often shoulder dual responsibilities of professional

performance and domestic caregiving. As a result, they face compounded challenges of psychological strain,

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role conflict, and declining overall well-being, especially in high-pressure sectors such as banking (Shaji et al.,

2025; Radha & Aithal, 2024; Roul et al., 2024).

Historically, employee wellness in organizational contexts has largely relied on the construct of work-life

balance (WLB). Rooted in the notion of an equilibrium, WLB emphasizes allocating time and energy

proportionately between professional and personal domains (Alhaider & Alqahtani, 2025; Greenhaus &

Beutell, 1985). While, this metaphor presupposes a zero-sum trade-off, implying that gains at either personal

or professional domain are necessarily offset by losses in the other. In today’s dynamic, digitally mediated

work environment, such a view appears increasingly reductive (Suprayitno, 2024; Kalliath & Brough, 2008).

Few recent studies have emphasized for the shift toward work-life synergy (WLS), which conceptualizes the

relationship between work and nonwork domains as mutually enriching rather than conflicting (Sharma &

Barik, 2024; Dhiman et al., 2025). Unlike, the conventional concept of work-life balance, Work life synergy

emphasizes over the integration of roles, where skills, experiences, and resources gained in one domain to

enhance performance and satisfaction in the other domain. This paradigm resonates strongly with the

contemporary banking workforce, where technology, flexibility, and organizational interventions can either

exacerbate or alleviate strains (Sabat et al., 2024). By repositioning the discourse toward WLS, researchers

seek to capture the dynamic interplay of professional and personal roles in fostering sustainable employee

well-being (Deery & Jago, 2015; Moen et al., 2017). Certain research works performed and broadly or

narrowly discussing the study variables scope are listed below:

Table 1: Studies Incorporating the Study Variables

Author(s)

Significant Contribution and Remarks

Work-Life Synergy (WLS) / Balance

Greenhaus & Beutell (1985)

Established the foundation of WLB as the management of competing demands

between work and family roles.

Kalliath & Brough (2008)

Critiqued WLB and proposed that “balance” is inadequate; suggested

enrichment and integration as more suitable paradigms.

Wayne et al. (2007)

Introduced the concept of work–family facilitation, aligning with synergy,

where resources from one domain enhance the other.

Deery & Jago (2015)

Found WLB practices improve retention, job satisfaction, and overall well-being

in service industries.

Rahim, Osman, &

Arumugam (2020)

Demonstrated WLB’s positive effects on career satisfaction and psychological

health in Asian contexts.

Employee Well-Being (EWB)

Diener et al. (2010)

Developed the Flourishing Scale, integrating hedonic and eudaimonic

perspectives of well-being.

Martín-Díaz & Fernández-

Abascal (2024)

Proposed the PERMA framework (Positive emotion, Engagement,

Relationships, Meaning, Accomplishment) as a holistic model of well-being.

Hone et al. (2014)

Validated flourishing and well-being scales in organizational contexts, linking

them with performance.

Ilies et al. (2017)

Highlighted that subjective well-being includes happiness, engagement, and

fulfillment, beyond job satisfaction.

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Shahzad (2025)

Found that workplace transformations in India directly influence employees’

psychological health and well-being.

Organizational Interventions (OI)

Kelly et al. (2014)

Demonstrated that supportive HR policies reduce work-family conflict and

enhance employee well-being.

Moen et al. (2017)

Found flexibility/support initiatives reduce turnover intentions and improve

employee health outcomes.

Deery & Jago (2015)

Argued that organizational strategies for WLB improve retention and talent

management effectiveness.

Shahzad (2025)

Showed how HRM practices in Indian banks influence employee resilience and

well-being.

Individual Interventions (II)

Direnzo, Greenhaus, & Weer

(2015)

Found resilience and self-regulation critical for managing career and life roles

effectively.

Connor & Davidson (2003)

Developed the CD-RISC resilience scale, widely applied to measure coping

strategies in stressful work environments.

Ilies et al. (2017)

Identified that employees with better coping skills achieve higher subjective

well-being.

Yadav (2024); Adholiya &

Paliwal (2015)

Showed female employees in Indian banking rely on personal coping strategies

to manage stress and dual role conflicts.

Digital Interventions (DI)

Tarafdar, Cooper, & Stich

(2019)

Identified the “technostress trifecta” techno-eustress, techno-distress, and

design, as critical to understanding digital well-being.

Choudhury, Foroughi, &

Larson (2020)

Found that digital flexibility (e.g., remote work) enhances productivity but

requires digital literacy to avoid burnout.

Sharma (2024)

Examined digital transformation in Indian banks, highlighting opportunities and

challenges for employee experience.

Shahzad (2025)

Observed that digital tools reshape HRM practices in Indian banking,

influencing work demands and employee well-being.

Source: Literature

Prior research has largely linked WLB with job satisfaction, organizational commitment, and psychological

health (Deery & Jago, 2015; Kim, 2014; Rahim et al., 2020). Yet, these outcomes capture only partial

dimensions of human thriving. Building on positive psychology, the construct of employee well-being (EWB)

has emerged, encompassing not only job satisfaction and mental health but also resilience, engagement,

meaning, and accomplishment (Diener et al., 2010; Nur'aini & Mulyana, 2024). Exploring how WLS

influences EF offers a holistic perspective of employee well-being. Moreover, while organizational and

individual interventions such as flexible work policies or personal coping strategies are well-documented

moderators, the role of digital interventions (e.g., AI-enabled workload allocation, digital wellness platforms,

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and remote work tools) remains underexplored. This omission is critical, given that digitization is now a

dominant force shaping employee experience.

Work-Life Balance And The Transition To Work-Life Synergy

Work-life balance (WLB) has traditionally been described as the extent to which individuals can

simultaneously meet professional and personal demands without excessive conflict (Greenhaus & Beutell,

1985). Although widely used, this framework has been criticized for assuming a dichotomous relationship

between the two domains, implying that gains in one area must come at the expense of the other (Kalliath &

Brough, 2008). Such a perspective is increasingly inadequate in modern workplaces characterized by digital

interconnectedness and blurred role boundaries. To address these limitations, scholars have proposed the

concept of work-life synergy (WLS). Unlike balance, WLS emphasizes the integration and enrichment of

roles, where skills, experiences, and energy acquired in one domain positively contribute to the other (Dhiman

et al., 2025; Wayne et al., 2007). This approach reframes work and personal life not as competing entities but

as mutually reinforcing systems, a perspective that resonates strongly in contemporary service sectors such as

banking, where professional and personal spheres are often intertwined.

Employee well-being (EWB) has been increasingly recognized as a multidimensional construct that extends

beyond job satisfaction and psychological health. While earlier studies linked WLB with satisfaction,

organizational commitment, and reduced stress (Ilies et al., 2017; Rahim, 2017), more recent frameworks have

adopted a holistic perspective. Diener et al. (2010) introduced the Flourishing Scale, while Martín-Díaz &

Fernández-Abascal (2024) in PERMA model emphasizing over the Positive emotions, Engagement,

Relationships, Meaning, and Accomplishment to capture both hedonic (pleasure, happiness) and eudaimonic

(purpose, growth, resilience) aspects of well-being. Further, in organizational contexts, higher levels of well-

being are associated with enhanced creativity, stronger resilience, lower turnover intentions, and sustainable

performance outcomes (Hone et al., 2014). Within the banking sector, where women often experience

prolonged working hours, role conflict, and pressures of digital adaptation, employee well-being provides a

comprehensive lens to assess both professional and personal fulfillment.

In continuation, organizational interventions (OIs) include structured policies and cultural practices designed

to support employees in managing work and non-work demands (Pujol-Cols et al., 2025). These may take the

form of flexible scheduling, parental leave policies, career counseling, or supervisory support systems.

Empirical evidence suggests that OIs positively influence the relationship between work demands and

employee well-being, by reducing stress and creating perceptions of organizational fairness and support (Kelly

et al., 2014; Moen et al., 2017). However, the effectiveness of such interventions depends heavily on

institutional culture and employees’ trust in organizational intentions (Deery & Jago, 2015). Individual

interventions (IIs) capture personal strategies and coping mechanisms that employees employ to manage

competing demands. These strategies range from time management, mindfulness, and exercise to broader

resilience-building practices (Connor & Davidson, 2003). Studies indicate that employees with stronger

resilience and self-regulation skills are better positioned to transform work-life challenges into growth

opportunities (Direnzo et al., 2015). For women in the Indian banking sector, such personal strategies are

particularly critical due to dual role expectations and high levels of unpredictability at work (Kim & Yeo,

2024).

A distinctive dimension of this study lies in its focus on digital interventions (DI) as a moderating factor. The

increasing penetration of digital technologies has fundamentally transformed work-life dynamics, offering both

opportunities and challenges (Mirbabaie & Marx, 2024). On one hand, remote work platforms, AI-driven task

allocation, and digital wellness applications have the potential to empower employees by offering flexibility

and efficiency (Choudhury et al., 2020). On the other hand, the same technologies often generate technostress,

digital fatigue, and constant connectivity, which may undermine well-being (Tarafdar et al., 2019). This dual

nature positions DI as a critical yet underexplored moderator in understanding the relationship between WLS

and EWB (Ghai & Sharma, 2025).

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Research Gaps And Research Objectives

While abundant research has connected WLB to job satisfaction and psychological health, few studies

integrate the broader well-being construct. Moreover, digital interventions are conspicuously absent in existing

WLB frameworks. This study addresses these gaps by proposing and empirically testing a model linking WLS

to EWB, moderated by OI, II, and DI. Despite extensive studies on work–life dynamics, several notable gaps

remain:

Table 2: Research Gaps

Identified Research Gap

References

Present Study Addresses

Overemphasis on Work–Life Balance

(WLB): Prior studies have mostly focused on

WLB as a dichotomous construct of trade-

offs, neglecting the positive enrichment

perspective.

Greenhaus &

Beutell (1985);

Kalliath & Brough

(2008); Wayne et

al. (2007)

Reframes discourse from Work-Life

Synergy (WLS), accenting integration,

mutual support, and enrichment rather than

balance and trade-offs.

Narrow Outcome Measures: Research has

mainly linked WLB to job satisfaction,

commitment, and psychological health,

overlooking broader indicators of well-being.

Ilies et al. (2017);

Rahim et al. (2020);

Diener et al. (2010);

Expands outcomes to include Employee

Well-being, about both hedonic

(satisfaction, happiness) and eudaimonic

(resilience, purpose, growth) dimensions.

Neglection of Digital Interventions (DI):

While organizational and individual

interventions have been explored, digital

tools remain under-theorized despite their

growing role.

Kelly et al. (2014);

Direnzo et al.

(2015); Choudhury

et al. (2020);

Tarafdar et al.

(2019)

Introduces Digital Interventions (e.g., AI-

enabled workload allocation, wellness

apps, remote work platforms) as a novel

moderating variable shaping the WLS →

well-being relationship.

Contextual Underrepresentation of Indian

Women in Banking: Most research focuses

on Western contexts, Indian women

employees navigating dual roles in banking

remain underexplored.

Moen et al. (2017);

Pandey &

Chaturvedi (2025);

Patel et al. (2025)

Provides empirical evidence from women

employees in Indian banks, reflecting their

unique challenges of digitization,

restructuring, and family responsibilities.

Lack of Comparative Insights Across

Public vs. Private Banks: Limited research

explores sector-specific variations in

interventions and outcomes.

Kaushal, T., &

Predmore (2025);

Chaturvedi (2025)

Conducts a comparative analysis of public

and private sector banks, highlighting

contextual differences in HR, digital

adoption, & employee outcomes.

Drawing from the above gaps, the present study pursues the following objectives:

• To examine the effect of work-life synergy (WLS) on employee well-being among women in the

Indian banking sector.

• To analyze the moderating roles of organizational interventions (OI), individual interventions (II), and

digital interventions (DI) in the relationship between WLS and employee well-being.

• To compare the outcomes of WLS–EWB linkages across public and private sector banks to identify

sector-specific variations.

Research Design

• Conceptual Framework – Drawing upon the theories of role enrichment, positive psychology, and

digital workplace transformation, the proposed conceptual framework positions work-life synergy

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(WLS) as the central predictor of employee well-being (EWB). The framework acknowledges that

employee well-being goes beyond job satisfaction or psychological health to encompass resilience,

engagement, purpose, and overall quality of life.

Figure 1: Research Framework

H2a

Three categories of interventions are theorized to moderate the WLS → EWB relationship:

• Organizational Interventions (OI): HR policies, leadership support, flexible scheduling, and

institutional mechanisms that empower employees to manage work and non-work domains effectively.

• Individual Interventions (II): Personal coping strategies, resilience practices, mindfulness, and time

management techniques adopted by employees themselves.

• Digital Interventions (DI): Technology-driven tools and platforms such as AI-based task allocation,

digital wellness programs, or remote work systems, which can either empower employees or lead to

technostress and digital fatigue.

The model posits that WLS positively influences EWB, and that the strength of this relationship depends on

the moderating role of OI, II, and DI. Additionally, contextual variations between public and private sector

banks are considered, acknowledging differences in culture, work structures, and adoption of digital

technologies.

Hypotheses – Following major hypotheses are under evaluation framed according to the above research

framework.

: Work-life synergy (WLS) positively influences employee well-being (EWB) among women employees in

the banking sector.

: Organizational interventions (OI) positively moderate the relationship between WLS and EWB.

: Individual interventions (II) positively moderate the relationship between WLS and EWB.

: Digital interventions (DI) significantly moderate the relationship between WLS and EWB.

: The strength of the WLS → EWB relationship, and the moderating effects of OI, II, and DI, differ

significantly between public and private sector banks.

• Research Approach and Design: This study adopts a quantitative, cross-sectional design under a

positivist paradigm. The hypotheses are empirically tested using Partial Least Squares Structural

Equation Modeling (PLS-SEM), a technique well-suited for predictive and exploratory models

involving moderating variables.

• Population and Sampling – Study was focused exclusively on women employees working in public

and private sector banks across multiple districts of Rajasthan, selected to capture regional diversity in

terms of urban and semi-urban contexts. A multistage stratified random sampling method was

employed to ensure fair representation across different organizational and locational categories. The

final sample comprised 2,150 respondents, a size considered sufficient to achieve statistical power for

Partial Least Squares Structural Equation Modeling (PLS-SEM). Stratification was carried out on the

basis of bank type (public vs. private), branch size, and district characteristics (urban vs. semi-urban).

This design ensured that the sample accurately reflected the structural and cultural heterogeneity of

Rajasthan’s banking workforce, while also highlighting the unique challenges women employees face

in managing professional and personal responsibilities in the state.

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• Data Collection & Measurement Scale – Data were collected through a structured questionnaire

designed on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree), administered via both

online modes (Google Forms and email) and offline distribution (hard copies at selected bank

branches). To maintain data quality, only women employees with a minimum of two years of work

experience were included, and incomplete responses were excluded from the analysis. The

questionnaire comprised five constructs drawn from validated scales and adapted for the banking

context. Work-Life Synergy (WLS) was measured using 5 items adapted from Wayne et al. (2007) and

Kalliath & Brough (2008), such as “My work positively contributes to my personal life.” Employee

Well-being (EWB) was assessed through 8 items based on Diener et al.’s (2010) Flourishing Scale and

Martín-Díaz & Fernández-Abascal’s (2024) PERMA dimensions. Organizational Interventions (OI)

were captured through 5 items adapted from Moen et al. (2017) and Kelly et al. (2014), while

Individual Interventions (II) were measured using 5 items from resilience and coping scales (Connor &

Davidson, 2003). Finally, Digital Interventions (DI) were assessed with 5 newly developed items

reflecting the role of digital tools, wellness technologies, and techno stress in employees’ work-life

experiences.

• Statistical Tools for Analysis – The analysis was carried out in several systematic stages to ensure the

robustness of findings. Reliability of the constructs was first examined using Cronbach’s Alpha and

Composite Reliability (CR), followed by assessment of validity through convergent validity (Average

Variance Extracted, AVE) and discriminant validity (Heterotrait-Monotrait Ratio, HTMT). The

structural model was then tested using Partial Least Squares bootstrapping with 5,000 resamples, which

provided estimates of path coefficients (β), t-values, and significance levels for hypothesis testing. To

capture moderating influences, interaction terms were introduced to evaluate how organizational

interventions (OI), individual interventions (II), and digital interventions (DI) shaped the relationship

between work-life synergy (WLS) and employee well-being (EWB). Additionally, multi-group analysis

(MGA) was employed to compare structural relationships across public and private sector banks,

thereby highlighting contextual differences. Finally, the overall adequacy of the proposed model was

confirmed using the Goodness-of-Fit (GoF) test, which integrates measures of explained variance and

construct validity.

STATISTICAL ANALYSIS RESULTS AND INTERPRETATIONS

A. Frequency Distribution Analysis: To contextualize the study, frequency distribution analysis was

performed on the sample of 2,150 women bank employees. This descriptive overview highlights workforce

diversity in terms of personal attributes, job-related factors, and family responsibilities, which are crucial for

examining work-life synergy and employee well-being.

Table 3: Frequency Distribution of Women Bank Employees

Variable

Category

Frequency (n)

% of Total

Age

30–40 years

326

15.2%

41–50 years

1,105

51.4%

51–60 years

719

33.4%

Marital Status

Single/Widowed

535

24.9%

Married/Cohabiting

1,615

75.1%

No. of Dependents

< 2

1,021

47.5%

2–4

733

34.1%

396

18.4%

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Sector of Bank

Private

872

40.6%

Public

1,278

59.4%

Type of Employment

Contractual

493

22.9%

Permanent

1,657

77.1%

Service Experience

< 5 Years

421

19.6%

5–10 Years

689

32.0%

10–15 Years

547

25.5%

> 15 Years

493

22.9%

Weekly Working Hours

< 42 Hours

364

16.9%

43–48 Hours

812

37.8%

49–54 Hours

623

29.0%

55+ Hours

351

16.3%

Time for Commutation

< 1 Hour

947

44.0%

1–2 Hours

781

36.3%

2+ Hours

422

19.6%

Education Level

Graduate

826

38.4%

Postgraduate

1,034

48.1%

Professional (CA/MBA/CS, etc.)

290

13.5%

Job Role

Clerical/Frontline

972

45.2%

Officer/Managerial

889

41.4%

Senior Management

289

13.4%

Monthly Income

< ₹30,000

428

19.9%

₹30,000–₹50,000

812

37.7%

₹50,001–₹70,000

573

26.6%

> ₹70,000

337

15.8%

Family Structure

Nuclear

1,421

66.1%

Joint

729

33.9%

Source: Primary Data

The demographic distribution of the 2,150 women bank employees provides insights into their socio-

professional characteristics. A majority of respondents fall in the 41–50 years age group (51.4%), followed by

those aged 51–60 years (33.4%), while younger employees between 30–40 years constitute only 15.2%,

indicating that the workforce is relatively mature. Most respondents are married or cohabiting (75.1%), while

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24.9% are single or widowed. With respect to dependents, nearly 47.5% have fewer than two dependents,

34.1% have 2–4, and 18.4% are responsible for more than four, reflecting varied family obligations.

In terms of employment, 59.4% of respondents work in public sector banks, compared to 40.6% in private

banks, and a clear majority hold permanent positions (77.1%), while 22.9% are contractual employees. Service

experience is fairly distributed, with 32.0% reporting 5–10 years, 25.5% between 10–15 years, and 22.9%

exceeding 15 years, while 19.6% have less than 5 years of experience. Weekly working hours reveal that the

largest share (37.8%) work 43–48 hours, followed by 29.0% working 49–54 hours, with only 16.9% working

fewer than 42 hours, highlighting the long-hour culture of banking. Regarding commutation, 44.0% of

employees travel less than one hour, 36.3% commute 1–2 hours, while 19.6% spend more than 2 hours daily,

underscoring time pressures. Educationally, the sample is highly qualified: 48.1% are postgraduates and 13.5%

hold professional degrees (e.g., CA, MBA, and CS). Occupational distribution shows 45.2% in

clerical/frontline roles, 41.4% in managerial positions, and 13.4% in senior management, suggesting gradual

upward mobility. Income levels vary, with the largest segment (37.7%) earning between ₹30,000–₹50,000,

while 15.8% earn above ₹70,000. Family structure is predominantly nuclear (66.1%), though a significant

33.9% live in joint families, reflecting cultural influences on work-life synergy.

B. Reliability Test Analysis Interpretation: Reliability was assessed through Cronbach’s Alpha (α) and

Composite Reliability (CR), where values above 0.70 are generally considered acceptable, and values above

0.80 indicate strong consistency. This step validates that the items within each construct reliably measure the

same underlying dimension.

Table 4: Reliability Statistics (Cronbach’s α and CR)

Construct

No. of Items

Cronbach’s α

Interpretation

Work-Life Synergy (WLS)

0.903

0.942

Good reliability

Employee Well-being (EWB)

0.889

0.931

Good reliability

Organizational Interventions (OI)

0.842

0.915

Good reliability

Individual Interventions (II)

0.816

0.902

Good reliability

Digital Interventions (DI)

0.871

0.921

Good reliability

Source: Primary Data – Cronbach’s Alpha (α) Test Statistics

As shown in Table 4, all constructs achieved high levels of reliability. Work-Life Synergy (WLS) recorded a

Cronbach’s Alpha of 0.903 and CR of 0.942, indicating good consistency. Similarly, Employee Well-being

(EWB) achieved α = 0.889 and CR = 0.931, confirming that the items strongly capture the construct. Among

the moderators, Organizational Interventions (OI) (α = 0.842, CR = 0.915), Individual Interventions (II) (α =

0.816, CR = 0.902), and Digital Interventions (DI) (α = 0.871, CR = 0.921) demonstrated good reliability.

Collectively, these values indicate that all constructs used in the study are reliable and suitable for further

validity testing and structural model estimation.

C. Evaluation of Measurement Model: To confirm that the observed items effectively represent their

respective constructs, convergent validity was assessed. This was examined through factor loadings, Average

Variance Extracted (AVE), and Composite Reliability (CR). Following established criteria (Hair et al., 2012),

item loadings above 0.70 are desirable, AVE values greater than 0.50 indicate adequate shared variance, and

CR values exceeding 0.70 suggest strong internal consistency.

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Table 5: Measurement Model Statistics

Construct

Item Code

AVE

Work-Life Synergy

(WLS)

WLS1

0.844

0.726

0.942

WLS2

0.872

WLS3

0.891

WLS4

0.862

WLS5

0.827

Employee Well-

being (EWB)

EWB1

0.781

0.692

0.931

EWB2

0.803

EWB3

0.876

EWB4

0.854

EWB5

0.799

EWB6

0.869

EWB7

0.812

EWB8

0.845

Organizational

Interventions (OI)

OI1

0.832

0.672

0.915

OI2

0.817

OI3

0.853

OI4

0.801

OI5

0.826

Individual

Interventions (II)

II1

0.794

0.648

0.902

II2

0.825

II3

0.812

II4

0.779

II5

0.833

Digital Interventions

(DI)

DI1

0.868

0.695

0.921

DI2

0.842

DI3

0.871

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DI4

0.794

DI5

0.829

Source: Primary Data (a. AVE - Average variance extracted, b. CR - Composite reliability)

As presented in the table, all constructs achieved acceptable levels of convergent validity. The items for Work-

Life Synergy (WLS) reported loadings ranging from 0.827 to 0.891, with an AVE of 0.726 and CR of 0.942,

confirming strong representation of the construct. Employee Well-being (EWB) demonstrated loadings

between 0.781 and 0.876, with AVE = 0.692 and CR = 0.931, indicating robust validity. For the moderators,

Organizational Interventions (OI) had loadings from 0.801 to 0.853 with AVE = 0.672 and CR = 0.915;

Individual Interventions (II) showed loadings from 0.779 to 0.833, with AVE = 0.648 and CR = 0.902; and

Digital Interventions (DI) recorded loadings between 0.794 and 0.871, with AVE = 0.695 and CR = 0.921.

These results have confirmed that all measurement items load strongly onto their respective constructs,

providing evidence of convergent validity and ensuring the measurement model is suitable for further

structural analysis.

D. Discriminant Analysis: Discriminant validity was evaluated using the Heterotrait-Monotrait (HTMT)

ratio, which assess the distinctiveness of latent constructs in structural equation modeling. Following the

recommendations of Henseler et al. (2015), HTMT values below 0.85 (strict threshold) or 0.90 (lenient

threshold) provide evidence of discriminant validity. Confidence intervals were also examined, and the

absence of values including 1.0 confirms construct distinctiveness.

Table 6: Discriminant Validity – HTMT Ratios (CI

0.90

)

Constructs

WLS

EWB

WLS

–

0.671

(0.542, 0.772)

0.512

(0.384, 0.653)

0.476

(0.365, 0.589)

0.529

(0.411, 0.672)

EWB

–

0.598

(0.472, 0.725)

0.541

(0.398, 0.651)

0.612

(0.489, 0.734)

–

0.493

(0.372, 0.601)

0.455

(0.338, 0.568)

–

0.438

(0.327, 0.556)

–

Source: HTMT Result Values

Table 6 statistics indicated that all HTMT ratios are well within acceptable thresholds, confirming adequate

discriminant validity. For example, the relationship between Work-Life Synergy (WLS) and Employee Well-

being (EWB) reported the highest HTMT value of 0.671 (0.542, 0.772), still below the recommended cut-off.

Similarly, the HTMT between Organizational Interventions (OI) and Individual Interventions (II) was 0.493

(0.372, 0.601), and between Individual Interventions (II) and Digital Interventions (DI) was 0.438 (0.327,

0.556), both reflecting moderate correlations yet clear distinctiveness. Importantly, none of the confidence

intervals included 1.0, reinforcing the conclusion that each construct measures a unique conceptual dimension.

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Thus, the HTMT analysis validated that WLS, EWB, OI, II, and DI are empirically distinct, and measurement

model is reliable and conceptually robust.

E. Structural Model Determination – The structural model path coefficients, highlights the hypothesized

relationships among work-life synergy (WLS), organizational factors, and employees’ well-being (EWB). The

test also examined moderating effects of organizational innovation (OI), individual initiative (II), and digital

integration (DI) on the link between WLS and EWB.

Fig. 1: Structural Model Path

Table 7(a): Structural Model Path β Coefficient Results and Hypotheses Status

Hypothesis

Relationship

Std. Err.

t-value

Decision

WLS → EWB

0.289

0.047

6.149**

Supported

WLS*OI → EWB

0.137

0.041

3.341**

Supported

WLS*II → EWB

0.095

0.038

2.501**

Supported

WLS*DI → EWB

0.221

0.052

4.250**

Supported

Source: Model Analysis Output (Note: **p < .05, (One-Tailed Test); β = Path Coefficient)

The results indicated that work-life synergy (WLS) has a significant positive effect on employee well-being

(EWB) among women employees in the banking sector (β = 0.289, t = 6.149, p < 0.01), thereby supporting

Hypothesis 1. This finding confirms that fostering a balance between work and personal life directly enhances

employees’ overall well-being. The moderation analysis further reveals that organizational interventions (OI),

individual interventions (II), and digital interventions (DI) significantly strengthen the WLS–EWB

relationship. Specifically, OI positively moderates this relationship (β = 0.137, t = 3.341, p < 0.01), II also

exerts a significant enhancing effect (β = 0.095, t = 2.501, p < 0.01), and DI shows the strongest moderation

impact among the three (β = 0.221, t = 4.250, p < 0.01), supporting Hypotheses 2a, 2b, and 2c. These results

suggests that workplace policies, individual initiatives, and digital support mechanisms play an important role

in amplifying the positive effects of WLS on employee well-being.

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Table 7(b): Multi-Group Analysis (Public vs. Private Banks)

Hypothesis

Test

χ²

p-value

Decision

Public vs. Private

12.87

< 0.05

Significant

Source: Multi-Group Analysis Output

In addition, the multi-group analysis demonstrates a significant difference between public and private sector

banks (χ² = 12.87, p < 0.05), indicating that both the direct effect of WLS on EWB and the moderating

influences of OI, II, and DI vary depending on the organizational context.

F. Goodness of Fit Test - Goodness of Fit (GoF) evaluates overall quality and explanatory power of the

structural model. It assesses how well the proposed model represents the observed data by combining

information on the constructs’ convergent validity and the variance explained in the endogenous variables.

Table 8: Goodness of Fit (GoF) Test

Construct

AVE

R²

WLS

0.726

–

EWB

0.692

0.521

0.672

0.348

0.648

0.316

0.695

0.402

Average

0.687

0.397

GoF

0.522

Source: GoF Test Statistics

Table 8 presents the Goodness of Fit (GoF) statistics for the structural model, providing an overall assessment

of the model’s explanatory and predictive capabilities. The Average Variance Extracted (AVE) values for all

constructs range from 0.648 to 0.726, with an overall average of 0.687, indicating satisfactory convergent

validity and confirming that the latent constructs capture a substantial portion of the variance in their

respective indicators. The coefficient of determination (R²) values range from 0.316 to 0.521, with an average

of 0.397, demonstrating that the model explained a moderate to substantial proportion of the variance in the

endogenous constructs, particularly employee well-being (EWB), which shows the highest R² of 0.521. The

overall GoF value of 0.522 exceeds the recommended threshold for a large effect size, suggesting that the

structural model has a strong overall fit and adequately represents the observed relationships among work-life

synergy (WLS), interventions (OI, II, DI), and employee well-being. Collectively, these results confirm the

robustness and suitability of the model for analyzing the hypothesized relationships.

DISCUSSION AND CONCLUSION

The findings of this study provide important insights into the dynamics of work-life synergy (WLS) and

employee well-being (EWB) among women in the Indian banking sector. The demographic analysis revealed

that the workforce is predominantly composed of mature employees, with over 84% aged above 40 years. This

maturity suggests that women bankers have accumulated significant professional experience, yet they continue

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to face challenges in balancing professional and personal roles. Long working hours, extended commutes, and

multiple dependents further highlight the pressures that shape their daily experiences.

The measurement model results demonstrated high levels of reliability and validity, affirming the robustness of

the constructs. WLS emerged as a significant predictor of EWB (β = 0.289, t = 6.149, p < 0.01), confirming

that positive integration between professional and personal spheres enhances overall psychological and social

functioning. This finding aligns with the enrichment perspective, emphasizing that work and life domains can

complement rather than compete with each other. The moderation analysis highlighted the pivotal roles of

organizational, individual, and digital interventions. Among them, digital interventions (DI) exerted the

strongest effect (β = 0.221, t = 4.250, p < 0.01), underscoring the growing importance of technology-enabled

solutions such as remote working platforms, digital wellness tools, and flexible communication systems in

supporting employees. Organizational interventions (OI) (β = 0.137, t = 3.341, p < 0.01) and individual

interventions (II) (β = 0.095, t = 2.501, p < 0.01) also strengthened the WLS–EWB relationship, highlighting

the complementary roles of HR practices, supportive leadership, and personal coping strategies. The multi-

group analysis revealed significant differences between public and private sector banks (χ² = 12.87, p < 0.05),

indicating that contextual factors such as organizational culture, workload distribution, and digital adoption

levels influence the strength of the relationships. Public sector banks, with more rigid structures, may offer

fewer opportunities for flexible work, whereas private banks appear more open to digital interventions and

adaptive policies.

Lastly, the Goodness-of-Fit (GoF) statistics (overall GoF = 0.522) confirmed that the model demonstrates a

strong fit, with substantial explanatory power (R² for EWB = 0.521). These results collectively validate the

hypothesized framework and extend existing literature by incorporating digital interventions as a novel

moderator.

As the concluding remarks, the present study underscored the critical role of work-life synergy (WLS) in

enhancing employee well-being (EWB) among women employees in the Indian banking sector. Unlike

traditional models of work-life balance that emphasize trade-offs, the synergy approach illustrates how

integration across personal and professional domains generates enrichment and resilience (Wayne et al., 2007;

Kalliath & Brough, 2008). The findings affirm that WLS significantly improves employee flourishing, aligning

with the broader literature on positive psychology and organizational behavior (Martín-Díaz & Fernández-

Abascal, 2024; Diener et al., 2010). The study further demonstrated that organizational interventions (OI),

individual interventions (II), and digital interventions (DI) act as important moderators in the WLS–EWB

relationship. While OI, such as flexible policies and supportive leadership, remain crucial (Moen et al., 2017;

Kelly et al., 2014), the rising significance of DI reflects the increasing digitization of the banking sector

(Tarafdar et al., 2019; Choudhury et al., 2020). The moderation effect of DI was particularly strong, indicating

that digital tools and platforms can serve as powerful enablers of well-being, provided they are managed to

minimize techno-stress. Multi-group analysis further revealed contextual differences between public and

private sector banks, suggesting that structural and cultural variations shape the effectiveness of interventions.

Overall, the research had validated the robustness of the proposed model, as evidenced by strong reliability,

convergent and discriminant validity, and a satisfactory Goodness-of-Fit (GoF). By extending the concept of

work-life balance into the domain of synergy and incorporating digital interventions as a novel moderator, this

study adds theoretical depth to existing scholarship while offering practical relevance for organizational

leaders.

RECOMMENDATIONS FOR FUTURE RESEARCH

Future research should build upon these findings in several directions. First, a longitudinal design could

provide deeper insights into how WLS and EWB evolve over time, especially in response to rapid digital

transformation. Second, while this study focused exclusively on women employees, expanding the scope to

include men would allow for gender-comparative analysis and enrich understanding of diverse work–family

dynamics. Third, applying this framework to other high-pressure sectors such as healthcare, IT, or education

would test the generalizability of the model. Fourth, the role of digital interventions could be expanded to

capture specific dimensions such as digital overload, cyber-fatigue, and AI-enabled personalization in

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workplaces. Fifth, the influence of socio-cultural contexts, including family structures and regional variations,

should be explored to account for India’s cultural diversity. Finally, adopting mixed-method approaches that

combine quantitative SEM results with qualitative insights from employee interviews would offer a richer

understanding of the lived experiences of work-life synergy.

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