Gender Role Reversal in Gig Economy Households: A Sociological Insight from Southeast Asia with Evidence from Pakistan

Abstract

The rapid growth of the gig economy and digital platforms is challenging traditional gender roles, particularly in developing countries where structural inequalities continue to shape labor and household dynamics. Despite growing global interest in gender equity and digital inclusion, limited research has examined how gig work, digital access, and women’s income contributions interact to influence household gender dynamics within culturally conservative contexts. This study aimed to investigate the multidimensional impacts of women’s participation in gig work on time use redistribution, intra-household decision making, gender ideology, and role reversal within households in Pakistan. Using a cross-sectional survey design, data were collected from a representative sample of married couples engaged in the gig economy across urban and peri-urban areas of Pakistan. A quantitative analysis was conducted employing a combination of an analysis of variance, ordinal logistic regression, hierarchical multiple regression, and structural equation modeling to evaluate the direct and indirect relationships between constructs. The findings revealed that women’s gig work participation significantly predicted enhanced digital access, greater income contributions, and increased intra-household decision-making power. These, in turn, contributed to a measurable shift in gender ideology toward equality norms and a partial reversal of traditional gender roles, particularly in household labor division. The study concludes that the intersection of economic participation and digital empowerment serves as a catalyst for progressive gender restructuring within households. Policy implications include the need for gender-responsive labor policies, investment in digital infrastructure, and targeted interventions to support empowering women in non-traditional work roles.

1. Introduction

The rise of the gig economy characterized by short-term contracts, freelancing, online platforms, and digital labor is reshaping household labor dynamics across the globe. In South Asia, where traditional gender roles have historically been rigid and patriarchal, the transformation of labor through digital platforms has led to significant shifts in domestic and economic responsibilities within families [1,2]. Increasing female participation in online freelance markets, education technology, ride-sharing services, and home-based entrepreneurship has challenged traditional norms that confined women to unpaid domestic labor and men to sole breadwinning [3,4].

This economic shift is not only empowering women economically but is also encouraging men to engage more in caregiving and household responsibilities, a phenomenon known as gender role reversal [5]. This reversal, which is particularly visible in gig economy households, signals an evolving form of work–family balance, in which time use patterns, digital access, and flexible work schedules intersect with deeply embedded cultural norms [6]. However, this change does not occur in a vacuum; it is mediated by socioeconomic factors, educational backgrounds, digital access, household income, and prevailing gender attitudes.

While gender role shifts in Western societies have been well documented, quantitative sociological research in South Asia, particularly in Pakistan, remains limited. Given the rapid expansion of digital freelancing and gig-based work in urban and semi-urban Pakistani settings, understanding how these changes affect household structures is both timely and critical.

In Pakistan, cities like Karachi, Lahore, Islamabad, and Peshawar have emerged as digital labor hubs, thanks to rising mobile internet penetration, youth-led freelancing, and platforms like Upwork, Fiverr, Daraz, and Bykea. However, these gig opportunities are often pursued due to economic compulsion rather than choice.

In Lahore, for instance, where this study is based, the confluence of economic pressures, high youth unemployment, and growing digital literacy has contributed to an increase in households in which both spouses engage in gig work [7]. In middle- and lower–middle-income families, men now often stay at home between gigs or after losing employment, while women contribute significantly through online education, stitching, e-commerce, or tutoring. This scenario presents a live laboratory for studying time use shifts, income redistribution, and attitudinal changes related to gender roles within the household.

However, these changes are met with cultural resistance. Traditional family systems in Punjab are slow to adapt to role fluidity, and such shifts are often hidden or stigmatized. Therefore, capturing quantitative evidence on role reversal, income dynamics, and gender-based time use will offer empirical insight into this emergent transformation.

Despite the rise of the gig economy, there remains a significant gap in empirical research that quantitatively examines its impact on intra-household gender dynamics in South Asia, particularly in Pakistan. The absence of detailed household-level data on time use patterns, labor force participation, and gender role attitudes hampers a comprehensive understanding of how digital labor is transforming traditional family structures [8]. This study aims to address this gap by conducting a quantitative analysis of gender role reversal through household time use surveys. It further explores how participation in the gig economy, access to digital tools, and shifts in income distribution are challenging conventional gender norms. Additionally, the research investigates the intersection of class, culture, and work to reveal the complex realities of evolving gender roles in Pakistani households.

This study is driven by three core motivations. First, Pakistan is undergoing a rapid economic transformation through digitalization, with over four million freelancers and increasing online job opportunities, making the gig economy a socially significant phenomenon. Second, women’s growing participation in gig work is not only contributing economically but also reshaping household power dynamics and challenging traditional gender identities. Third, there is a critical policy gap—limited data and research on domestic role changes have left policymakers, non-governmental organizations (NGOs), and digital platforms unprepared to address the evolving needs of gig-working families.

The study’s uniqueness lies in its quantitative sociological approach, being one of the first in Pakistan to examine gender role reversal on household time use data. By focusing specifically on gig-working couples, it offers a micro-level view of both economic and social transformation. Moreover, it contextualizes these changes within South Asian cultural frameworks, considering variables such as family honor, male breadwinning expectations, and digital literacy.

This research contributes significantly to the academic and policy landscape. It presents a sociological model illustrating how gig economy participation disrupts traditional household gender roles. It also delivers evidence-based recommendations for building gender-sensitive gig ecosystems. Additionally, it expands the understanding of digital labor’s influence on family life, enriching the literature on the sociology of work, gender studies, and family sociology, particularly in the Global South. Lastly, it lays the groundwork for comparative research across South Asian countries, including India, Bangladesh, and Sri Lanka.

Objectives of the Study

• To examine how participation in the gig economy reshapes household dynamics through time allocation, digital access, and income contribution between men and women;
• To analyze the impact of women’s gig work participation on intra-household decision making, power relations, and shifts in gender ideology;
• To assess the predictive strength of gig economy-related factors (time use, digital access, income share, and gender attitudes) in facilitating or resisting gender role reversal.

2. Literature Review

2.1. Gig Economy Participation and Gendered Time Allocation

Globally, the gig economy has transformed time use patterns within households. Studies from the U.S. and Europe suggest that flexible gig work enables women to engage in paid labor without fully withdrawing from unpaid domestic tasks [9,10,11]. Abd [12] found that in dual-gig households, male partners who worked from home increased their participation in household tasks by 32% compared to those in traditional employment settings. Similarly, Gao et al. [13] noted that time autonomy in gig work helps women manage caregiving responsibilities, although it may exacerbate work–family conflicts due to blurred boundaries [14].

In India, Arya and Nemuri [15] observed that women in the gig economy, especially those in online education and beauty services, allocate less time to domestic work when their financial contribution to the household increases. In Bangladesh, studies by Vijayan and Recchia [16] show that freelance women working through platforms like She Works see a rebalancing of domestic duties, although men’s participation remains limited. The time allocation shift is more apparent in urban, dual-income families with internet access and education [17].

In Lahore, the rise of platforms such as Daraz, Foodpanda, and freelance portals has enabled both men and women to work from home or on-demand (Chaudhry, Azhar) [18]. However, studies such as that by Idris [19] found that while women’s income-generating activities increase, domestic responsibilities remain disproportionately on their shoulders. No robust quantitative study exists yet that analyzes household-level time use data in gig-working families, creating a clear empirical gap.

2.2. Digital Access and Household Role Dynamics

Digital access is a major determinant of participation in the gig economy. In the UK, Izzi [20] reported that households with higher digital literacy showed more egalitarian household arrangements due to access to remote gig work for both genders [21]. In Sub-Saharan Africa, gig work via smartphones enabled women to enter income spaces previously inaccessible due to mobility restrictions [22,23].

Digital literacy and mobile penetration are crucial in South Asia. Boateng, Boateng [24] found in India that the use of smartphones significantly enabled women to engage in gig tasks, leading to increased decision-making power. In Sri Lanka, Kaluthantiri [25] observed that when both spouses had digital access, household roles became more fluid, with men contributing to child rearing.

In urban centers like Lahore, digital penetration is high, especially among youth and women freelancers [26]. However, studies by [27] suggest that while gig platforms are accessible, cultural and infrastructural barriers reduce digital usage for women in conservative households. Empirical work linking digital access to household role dynamics remains underdeveloped in the Pakistani context.

2.3. Income Contribution and Household Power Dynamics

Research shows that income parity influences household power. Lundberg [28] found that women contributing more than 40% to the household income in gig or informal work had more say in family decisions. In the U.S., Schleifer and Miller [29] demonstrated a strong correlation between female income share and reduced male dominance in domestic decisions. In India, Itohan, Henry [30] confirmed that gig-working women who earned above a certain threshold gained authority over financial decisions. Similarly, in Nepal, Das, Sherpa [31] noted that income from informal and gig sectors improved women’s participation in household-level negotiations [32].

A few studies, like that by Kalsoom [33], highlight that economic contributions by women in Lahore’s gig economy, especially in content writing and online tutoring, have altered their intra-household bargaining power. However, no study has used hierarchical regression or quantitative models to analyze how the income share between spouses translates into decision-making power, leaving a critical methodological gap.

2.4. Gender Attitudes, Education, and Role Reversal

Gender attitudes and education are key mediators. Esping-Andersen [34] emphasized that societies with egalitarian gender norms adapt faster to role reversals. A study in Canada by de Laat, Doucet [35] revealed that men with higher education levels were more open to sharing caregiving roles. Saha [36] found that in Bangladesh, traditional gender ideologies among men hindered women’s full utilization of the freedoms that gig work enables. In India, Bhatt and Pathak [37] highlighted that education and urban residence were positively correlated with role flexibility.

In Lahore, Awan, Shoaib [38] observed that educated couples in middle-income households showed more willingness to adapt to non-traditional roles when women worked online. However, no existing studies combine gender attitudes, education, and income level to model how they interact to support or resist role reversal in the Pakistani gig economy household context.

2.5. Gig Work and Gender Role Reversal

In studies like that of Altuzarra, Gálvez-Gálvez [39] in the U.S., gig variables (e.g., hours worked, income, and flexibility) predicted changes in domestic role sharing. Similarly, a study by Chung and Van der Lippe [40] used a hierarchical regression to show that work flexibility predicted male involvement in unpaid labor. Statistical applications remain limited but are emerging. A study in India by Kumar, Sikdar [41] applied a logistic regression (not hierarchical) to assess the impact of e-commerce work on female autonomy. Quantitative modeling for predicting role reversal in gig-working families is largely absent in South Asia [42].

There are no existing studies that apply hierarchical regression models to assess predictors of gender role reversal among gig-working households in Pakistan. While small-scale descriptive studies exist, none have built a multi-layered statistical model incorporating digital access, income share, time use, and gender attitudes.

2.6. Literature Gap and Uniqueness of the Study

Despite growing scholarly interest in gender dynamics and digital labor globally, there is a striking absence of quantitative, multivariable analyses on how gig work affects intra-household gender roles in the South Asian and Pakistani contexts. The existing literature often focuses on women’s empowerment or time use in isolation and rarely captures the interactive roles of income, time, digital access, and attitudes using integrated models. Furthermore, most Pakistani research remains qualitative, urban elite-focused, or exploratory, lacking rigorous empirical modeling.

This study stands out as a unique contribution to sociological research in Pakistan for several reasons. It is the first to use household-level time use data in order to quantitatively measure gender role reversal. Unlike previous studies that examine isolated factors, this research integrates multiple variables, such as gig economy participation, digital access, income contributions, and gender ideology, to provide a comprehensive understanding of household dynamics. Focusing on Lahore, a city characterized by its socioeconomic diversity and increasing digital labor engagement, the study captures the complex interplay between tradition and modernity. Importantly, it does not limit its scope to women’s experiences alone but instead explores the shared gender dynamics within gig-working couples, offering a balanced and nuanced perspective on evolving family roles.

2.7. Theoretical Framework

The study is grounded in gender role theory, which posits that societal expectations assign specific responsibilities and behaviors to men and women, often reinforcing patriarchal structures, according to which men are viewed as breadwinners and women as primary caregivers. Within this framework, gendered divisions of labor are seen as culturally produced and socially maintained rather than biologically determined. Additionally, resource bargaining theory emphasizes that power within households is influenced by individuals’ relative contributions to economic resources. The integration of these perspectives allows for a nuanced understanding of how economic participation and access to digital platforms can reshape entrenched gender hierarchies.

Linking this theory to the present study, the gig economy in Pakistan provides a critical space in which women’s income generation and digital access challenge conventional gendered labor divisions. By entering into employment in online freelancing, e-commerce, and digital services, women gain financial independence and bargaining power, which enhances their role in household decision making. This aligns with gender role theory by highlighting how shifts in social expectations occur when women take on non-traditional roles, while resource bargaining theory explains the observed redistribution of household authority as women’s contributions increase. The study, therefore, positions gig work as both an economic and cultural disruptor that facilitates partial role reversal and greater gender equality in household dynamics.

In comparison to classical gender role theory, which emphasizes structural constraints and slow cultural change, this study illustrates a more dynamic and accelerated transformation enabled by digital platforms and flexible gig work. Unlike traditional labor markets in which gender role shifts occur incrementally, the gig economy introduces immediate opportunities for women to access income and digital resources. However, differences also emerge: while theory suggests sustained role restructuring through equality norms, the Pakistani context shows partial and uneven adaptation, with cultural resistance persisting alongside progressive changes. Thus, this research extends existing theories by situating gender role shifts within the unique interplay of digital labor, economic necessity, and conservative cultural norms in South Asia.

3. Materials and Methods

3.1. Research Paradigm and Research Design

This study is grounded in the positivist paradigm, which emphasizes objective reality, measurable variables, and hypothesis testing through empirical data. Since the study aims to examine quantifiable relationships between variables, such as gig economy participation, gender roles, digital access, and household labor distribution, positivism offers the most appropriate lens for data collection and analysis [43].

The research design adopted is a cross-sectional correlational design, as it enables the researcher to collect data at a single point in time and assess relationships among multiple variables [44]. This design is justified due to its suitability in exploring socioeconomic phenomena across various households engaged in gig work, particularly in urban settings like Lahore. The hierarchical regression approach aligns well with this design, allowing for stepwise inclusion of predictor blocks such as digital access, income contribution, and gender ideology.

3.2. Study Setting, Population, and Target Population

The present study is situated in Lahore, Pakistan’s second largest and most culturally dynamic metropolitan city, selected for its unique relevance to the study’s focus on gig economy participation and intra-household gender dynamics. Lahore has emerged as a critical hub for digital labor in Pakistan, with a rapidly growing number of freelancers, ride-hailing drivers, e-commerce entrepreneurs, and online service providers. The city’s socio-cultural heterogeneity, in which deeply entrenched traditional gender roles coexist with increasing digital modernity, provides an ideal context for analyzing gender role reversals and evolving family dynamics in gig-working households. The study population includes all gig-working households in Lahore, with a particular emphasis on married or cohabiting couples in whom at least one partner is engaged in gig work. The target population specifically focuses on couples aged between 20 and 50 years, in whom both spouses are economically or domestically active. Inclusion criteria mandate that households must have at least one active gig worker and demonstrate shared caregiving or economic responsibilities. Single-person households, non-consensual cohabitation settings, and individuals outside the defined age range are excluded to maintain conceptual clarity and methodological rigor. According to estimates derived from Pakistan’s Labor Force Survey and supplemented by data from the Punjab Information Technology Board [45], approximately 250,000 households in Lahore fall under the category of gig-working households, making it a statistically robust and demographically suitable site for this sociological investigation [46].

3.3. Socioeconomic and Demographic Characteristics of the Study

The study integrates a detailed set of socioeconomic and demographic variables to analyze the patterns and dynamics of gender role reversal among gig-working couples in Lahore (

Table 1. Socioeconomic and demographic profile of respondents.

Variable	Role in Study	Categories
Age (of Both Partners)	Indicates life stage and potential openness to role change	18–29 years, 30–39 years, 40–65 years
Educational Level	Reflects digital literacy and readiness for gig engagement	No formal education, primary, secondary, intermediate, Bachelor’s, Master’s and above
Household Income Bracket	Explores financial dependency/autonomy and gendered income patterns	<PKR 30,000; PKR 30,001–60,000; PKR 60,001–100,000; >PKR 100,000
Gender	Central to examining reversal in traditional roles	Male, female
Occupation Type	Compares gig vs. formal sectors in transforming household roles	Gig worker (e.g., freelancing or ride hailing), formal employment, informal work
Children in Household	Assesses domestic labor division and caregiving responsibilities	No children, 1 child, 2 children, 3 or more children
Access to Technology	Enables participation in digital labor, key to gig economy	Yes (smartphone + Internet), partial access (one of two), no access
Marital Duration	Evaluates the extent of gender role negotiation over time	0–5 years, 6–10 years, 11–20 years, >20 years

). The age of both partners, categorized into life stages (18–29 years, 30–39 years, and 40–65 years), helps evaluate how role flexibility and openness to non-traditional labor divisions vary across different stages of adulthood. The educational level ranging from having no formal education to having a Master’s degree or higher serves as a proxy for digital literacy and awareness, which are vital for engaging in gig work, particularly in roles requiring technical or online competencies. Household income was divided into four categories: less than PKR thirty thousand, between PKR thirty thousand and sixty thousand, between PKR sixty thousand and one hundred thousand, and more than PKR one hundred thousand. These cutoffs were selected in line with Pakistan’s national minimum wage benchmarks and urban household income distributions reported in the Pakistan Social and Living Standards Measurement (PSLM) survey. This classification allows the study to explore whether economic necessity or autonomy drives participation in the gig economy and how income influences caregiving responsibilities and household role negotiations.

Gender is the central analytical variable, providing a lens through which the reversal or reinforcement of traditional roles is observed. Occupation type, whether in the gig economy (e.g., freelancing or ride hailing) or the formal/informal sector, allows the study to contrast how work types affect one’s control over their time, income, and household decisions. The presence of children (none, one, two, and three or above) is a critical determinant of caregiving burdens, which often dictate how labor is divided and whether traditional gender expectations persist. Access to technology, measured as having full access (smartphone and Internet), partial access, or no access, is especially important for women, as it shapes their ability to participate in digital workspaces from domestic settings. Lastly, marital duration (0–5, 6–10, 11–20, and >20 years) helps examine whether longer relationships are more likely to maintain entrenched gender roles or display signs of negotiated responsibilities and role reversals over time.

3.4. Sampling Procedure and Sample Size

To ensure representativeness and capture the socioeconomic diversity within Lahore’s gig-working households, a stratified random sampling technique was employed. The stratification was based on household income categories, specifically low-income, middle-income, and upper–middle-income strata. This stratified approach is crucial for the study, as it allows for nuanced comparisons across different socioeconomic groups especially in terms of how gender roles shift and adapt in response to economic pressures and opportunities. Since income levels strongly influence access to technology, educational attainment, caregiving responsibilities, and job flexibility, this stratification enhances the study’s internal validity by controlling for economic disparities that could bias the interpretation of gender role dynamics.

The city of Lahore, with its vibrant and expanding gig economy, is estimated to host around 250,000 gig-working households. To determine an appropriate and statistically significant sample size for this large population, Yamane’s [47] formula was applied:

n = N/(1 + Ne²)

where: N = 250,000 (target population of gig-working households), and e = 0.05 (margin of error at 95% confidence level).

Substituting the values, we obtain the following:

n = 250,000/1 + 250,000 × 0.0025 = 250,000/626 = 400

This formula provides a sample size of 400 households, which is considered adequate for generalizing findings to the broader gig-working population in Lahore while keeping the margin of error within acceptable limits.

3.5. Data Collection Tool

To effectively capture the complexities of gender role reversal within gig-working households in Lahore, a structured, self-administered questionnaire was developed and used as the primary tool for data collection. The questionnaire was prepared in both English and Urdu to ensure linguistic inclusivity and accessibility for respondents from diverse educational backgrounds. The tool was carefully divided into five sections: Section A covered the demographic and socioeconomic profile of respondents; Section B focused on gig work participation and access to digital tools; Section C included a household time use matrix adapted from the UN Time Use Survey to assess division of labor; Section D employed a Likert-based Gender Role Ideology Scale, adapted to the local context to evaluate beliefs around gender norms; and Section E explored intra-household decision-making processes and power dynamics.

To ensure the reliability and validity of the instrument, the questionnaire underwent pilot testing on 30 households not included in the final sample. The pilot study yielded a Cronbach’s alpha of 0.87, indicating a high level of internal consistency [48]. Additionally, content validity was verified through expert reviews by faculty from gender studies and sociology departments in Lahore-based universities.

Considering the tech-savviness and digital engagement of the target population, the survey was administered online through Google Forms and WhatsApp-based surveys, with phone interview follow-ups when necessary. This hybrid mode was particularly suited for reaching gig workers who often rely on digital platforms for both work and communication.

Ethical integrity was maintained throughout the research process. Informed consent was obtained from all participants, their confidentiality was assured, and participation was entirely voluntary. The study received ethical clearance from the Research Ethics Committee of the University of Malakand, ensuring adherence to academic and research ethics standards.

3.6. Indexation and Measurement of Variables

Table 2. Indexation and measurement of variables.

Variable Name	Symbol	Measurement/Scale	Coding/Index Method	Source/Basis
Male Partner’s Involvement in Domestic Labor	Y1	Composite index (time use matrix)	Hours/week; higher = more involvement	Adapted from UN Time Use Survey
Male Gig Work Hours	X1	Continuous (ratio)	Weekly reported hours (0–80)	Self-reported
Female Gig Status	X2	Categorical (binary)	0 = No, 1 = Yes	Self-reported
Digital Literacy Score	X3	Composite (interval scale)	10-item digital skills test (0–20)	Researcher designed and validated
Number of Digital Devices	X4	Discrete count (ratio)	No. of smartphones, laptops, tablets (0–10)	Household inventory
Perceived Role Reversal	Y2	Composite (ordinal)	Likert average (1–5); higher = more reversal	Adapted from gender role perception scales
Women’s Income Contribution	X5	Continuous (ratio)	Share of women’s income out of household total	Calculated from income reporting
Female Employment Status	X6	Binary (nominal)	0 = Not employed, 1 = employed	Self-reported
Intra-household Decision-Making Power	Y3	Composite (ordinal)	5-item Likert scale; sum score (0–10)	Adapted from DHS modules
Acceptance of Role Reversal	Y4	Composite Likert-based score	Aggregated from 4 statements (1–5)	Based on the literature
Gender Role Ideology Score	X7	Composite (interval)	Mean of 10 Likert items; higher = egalitarian	Adapted from GRIS
Education Level	X8	Ordinal categorical	1 = Bachelor’s degree, 2 = Master’s degree, 3 = PhD	Self-reported
Socioeconomic Status (SES)	X9	Composite ordinal index	Income + assets + housing = low/mid/high	Adapted from SES guidelines
Age (Respondent)	X10	Continuous (ratio)	Age in years (18–65)	Self-reported
Marital Duration	X11	Continuous (ratio)	No. of years married (1–40)	Self-reported
Composite Role Reversal Index	Y5	Standardized index (interval)	Combined z-scores of Y1, Y2, Y3	Developed from study variables
Gig Work Participation (Both Partners)	X12	Binary and continuous	Combined status of male/female gig work	Self-reported
Digital Access (Latent)	X13	Composite (digital literacy + devices)	CFA validated	Study design
Time Use Redistribution	Y6	Composite (observed index)	Same as Y1	UN Time Use Survey
Gender Ideology (Latent Final DV)	Y7	Composite (GRIS)	Higher values = more egalitarian	GRIS-based (adapted)

presents the indexation and measurement of variables used in the study, covering dependent, independent, and latent constructs. Variables capture multiple dimensions of gender dynamics, gig work, and household roles. Key measures include male partner’s involvement in domestic labor, gig work hours, women’s income contribution, employment status, decision-making power, and role reversal perceptions. Several variables are measured using composite indices (e.g., digital literacy, gender role ideology, intra-household power), while others rely on self-reported continuous or categorical data such as age, education, and marital duration. Standardized indices and validated scales (e.g., GRIS, DHS modules, UN Time Use Survey) provide methodological rigor.

3.7. Data Analysis and Models of the Study

The data collected from respondents were processed and analyzed using IBM SPSS Statistics version 26 to examine the impact of gig economy participation on gender role transformations within households. This was addressed through linear regression analysis, investigating how the number of hours worked by male gig workers influenced their involvement in domestic labor. In addition, we employed multiple regression analysis to assess how digital access and literacy among female gig workers affected perceptions of role reversal. Moreover, regression analysis was used to explore the relationship between women’s gig work participation, income contribution, and their decision-making power within the household. We utilized hierarchical multiple regression to evaluate the predictive roles of gender ideology, education, and socioeconomic status in shaping acceptance of role reversal. A composite multiple regression model was applied to analyze the combined influence of gig work, digital access, and ideological factors on evolving gender configurations. Finally, overall objectives were analyzed using structural equation modeling (SEM) via SPSS version (26) AMOS (23) to explore the direct and indirect pathways among digital access, gig participation, time redistribution, and gender ideology, offering a comprehensive view of the mediating and causal relationships that underpin shifts in household gender dynamics. However, the models of the study are given as follows.

Model 1: Multiple Regression Model for Domestic Labor Involvement (

Table 3. Multiple Regression Analysis for Male Partner’s Time Allocation in Domestic Labor.

Predictor Variable	Unstandardized Coefficient (B)		Standard Error	Standardized Coefficient (β)		t	p-Value
(Constant)	1.34		0.44	–		3.05	0.002
Male Gig Work Hours	0.52		0.09	0.47		5.78	<0.001
Female Gig Status (1 = Yes)	0.86		0.27	0.31		3.19	0.001
Model Summary
R2	Adjusted R2	Degree of Freedom		F	p-Value
0.41	0.39	(2, 397)		12.65	p < 0.001

): To Examine the Effect of Gig Work Hours and Female Gig Status on Male Partner’s Involvement in Domestic Labor

Y₁ = B₀ + B₁X₁ + B₂X₂ + ε

(1)

Denotations:

• Y₁: Male partner’s involvement in domestic labor;
• X₁: Male partner’s gig work hours;
• X₂: Female partner’s gig status (1 = yes, 0 = no);
• B₀: Constant/intercept;
• B_i: Model parameters (i = 1, 2,…,14);
• ε: Error term.

Model 2: Multiple Regression Model for Digital Access and Role Reversal (

Table 4. Correlation and Regression for Digital Access and Perceived Role Reversal.

Independent Variable	Pearson r	B (Unstd.)	β (Std.)	T	p-Value
Digital Literacy Score	0.45 **	0.41	0.39	6.23	<0.001
Number of Devices in Household	0.37 **	0.33	0.31	5.48	<0.001
Model Summary
R2	Adjusted R2	Degree of Freedom	F	p-Value
0.29	0.27	(2, 397)	8.77	p < 0.001

Note: p < 0.01 indicates significance at 1% level. (** indicates significance at the 0.01 level (p < 0.01)).

): To Assess the Impact of Digital Literacy and Number of Devices on Perceived Gender Role Reversal.

Y₂ = B₀ + B₃X₃ + B₄X₄ + ε

(2)

Denotations:

• Y₂: Perceived role reversal score;
• X₃: Digital literacy score;
• X₄: Number of devices in household;
• B₀: Constant/intercept;
• B_i: Model parameters (i = 1, 2,…,14);
• ε: Error term.

Model 3: Ordinal Logistic Regression for Decision-Making Authority (

Table 5. Ordinal Logistic Regression on Women’s Income Contribution and Decision-Making Authority.

Predictor	B	SE	Wald χ2		p-Value		Exp (B)
Women Income’s Contribution (%)	0.87	0.24	13.18		p < 0.001		2.38
Women Employment Status (1 = Yes)	1.11	0.37	9.01		0.003		3.03
Model Fit Summary
Chi-square Test Value	Nagelkerke R2			Degree of Freedom		p-Value
18.25	0.32			2		p < 0.001

): To Analyze How Women’s Income Contribution and Employment Status Affect Their Intra-Household Decision-Making Authority

logit[P(Y₃ ≤ j∣X)] = α_j − (B₅X₅ + B₆X₆)

(3)

Denotations:

• Y₃: Women’s decision-making authority (ordinal categories);
• α_j: Threshold parameters for category j;
• X₅: Women’s income contribution (%);
• X₆: Female employment status (1 = yes, 0 = no);
• B_i: Model parameters (i = 1, 2,…, 14).

Model 4: ANOVA and Chi-Square Tests for Gender Attitudes and Education (

Table 6. ANOVA and Chi-Square of Gender Attitudes, Education, and Socioeconomic Status.

Independent Variable	F/χ2 Value	Effect Size (η2/Cramér’s V)	p-Value
Gender Role Ideology Score	F = 9.53	η2 = 0.11	<0.001
Education Level (Bachelor’s, Master’s, PhD)	χ2 = 14.82	V = 0.28	<0.001
Socioeconomic Status (Low, Middle, High)	χ2 = 11.07	V = 0.22	0.004

): To Examine How Gender Role Ideology, Education, and Socioeconomic Status Impact Acceptance of Role Reversal

ANOVA: Y₄ = μ + αX₇ + ε

(4)

Pearson Chi-Square: χ² = ∑(O_i − E_i)²/E_i

(5)

Denotations:

• Y₄: Acceptance level of role reversal;
• μ: Overall mean;
• X₇: Gender role ideology score;
• O_i: Observed frequency;
• E_i: Expected frequency;
• ε: Error term.

Model 5: Hierarchical Multiple Regression for Role Reversal Index (

Table 7. Hierarchical Multiple Regression on Predictors of Composite Role Reversal Index.

Model Block	ΔR2	F Change	Sig. F Change
Model 1: Control Variables (Age, Marital Duration)	0.12	7.53	<0.001
Model 2: + Gig Work Status (Both Partners)	0.18	9.21	<0.001
Model 3: + Digital Access Indicators	0.14	8.64	<0.001
Model 4: + Gender Ideology and Education Level	0.24	11.93	<0.001
Final Model Summary
R2	Adjusted R2
0.68	0.66

): To Predict the Composite Role Reversal Index Using Demographic, Occupational, Technological, and Ideological Predictors

Y₅ = B₀ + ∑ = B_iX_i + ε

(6)

Denotations:

• Y₅: Composite Role Reversal Index;
• X₈: Age;
• X₉: Marital duration;
• X₁₀: Gig work status (both partners);
• X₁₁: Digital access indicators;
• X₁₂: Gender ideology;
• X₁₃: Education level;
• B₀: Constant/intercept;
• B_i: Model parameters (i = 1, 2,…, 14);
• ε: Error term.

Model 6: Structural Equation Model (SEM) (

Table 8. Structural Equation Model (SEM) Summary Table.

Path/Construct Relationship	β (Standardized Estimate)	SE	CR (Critical Ratio)	p-Value	Significance
1. Gig Work Participation → Digital Access	0.71	0.06	11.83	<0.001	***
2. Digital Access → Time Use Redistribution (Household Labor Division)	0.62	0.07	8.86	<0.001	***
3. Gig Work Participation → Income Contribution by Women	0.68	0.05	13.60	<0.001	***
4. Income Contribution by Women → Intra-household Decision-Making Power	0.57	0.06	9.50	<0.001	***
5. Intra-household Decision-Making Power → Gender Ideology (Shift toward Egalitarianism)	0.66	0.08	8.25	<0.001	***
Indirect Effect: Gig Work Participation → Gender Ideology (through mediators)	0.39	–	–	<0.001	***
Measurement Model Fit Indices (Goodness-of-Fit)
Fit Index	Value		Threshold for Acceptability		Status
Chi-square/df	1.95		<3		Good
CFI (Comparative Fit Index)	0.95		>0.90		Good
TLI (Tucker–Lewis Index)	0.94		>0.90		Good
RMSEA (Root Mean Square Error of Approximation)	0.045		<0.06		Excellent
SRMR (Standardized Root Mean Residual)	0.041		<0.08		Excellent

Note: p < 0.01 indicates significance at 1% level. *** = p < 0.001 (highly significant).

): To Measure Direct and Indirect Relationships Among Gig Work, Digital Access, Income Contribution, Decision Making, and Gender Ideology

X₁₄ = B₁₄X₁₃ + ε₁

(7)

Y₁ = B₁₅X₁₄ + ε₂

(8)

X₅ = B₁₆X₁₃ + ε₃

(9)

Y₃ = B₁₇X₅ + ε₄

(10)

X₇ = B₁₈Y₃ + ε₅

(11)

Denotations:

• X₁₃: Gig work participation;
• X₁₄: Digital access;
• Y₁: Redistribution of household labor;
• X₅: Women’s income contribution;
• Y₃: Decision-making authority;
• X₇: Gender ideology (egalitarianism);
• B_i: Model parameters (i = 1, 2,…, 18);
• ε_j: Error terms for each path.

4. Results

Table 3 examines the relationship between male gig work hours and female gig status in predicting the male partner’s involvement in domestic labor. This was tested using a multiple regression analysis, and the results are presented in Table 3. The model produced a statistically significant regression equation with an F-value of 12.65 (df = 2, 397, p < 0.001), indicating that the model as a whole was effective in explaining variance in male domestic labor involvement. The R-squared (R²) value of 0.41 suggests that approximately 41% of the variance in male partners’ time spent on household tasks is explained by the two predictor variables. The adjusted R² of 0.39 further confirms the model’s robustness after adjusting for the number of predictors.

Looking at the individual predictors, male gig work hours had a positive and statistically significant impact on male domestic labor involvement, with an unstandardized coefficient (B) of 0.52, standard error of 0.09, and a standardized beta (β) of 0.47. The corresponding t-value of 5.78 and p-value < 0.001 indicate a strong and reliable association. This means that for every additional hour worked in the gig economy, male partners were predicted to increase their domestic labor contribution by 0.52 units, supporting the hypothesis that greater gig engagement leads to a more equitable sharing of household tasks.

Similarly, the female gig status variable (coded as 1 = Yes) was also a significant positive predictor, with a B value of 0.86, standard error of 0.27, β = 0.31, and t = 3.19 (p = 0.001). This implies that when a female partner is also engaged in gig work, her male counterpart is predicted to contribute 0.86 units more to domestic tasks, compared to when the female is not in the gig economy. This supports the idea that dual gig participation fosters shared responsibilities in the household.

Overall, the findings from Table 3 illustrate that both increased male gig work hours and female gig participation are significantly associated with the greater involvement of male partners in household labor. These results provide empirical evidence for shifting gender roles in gig-working households, with implications for promoting gender equity in domestic responsibilities.

From a theoretical perspective, the results align with role expansion theory, which suggests that engagement in multiple roles (such as gig work and household work) can lead to positive spillover effects. Specifically, male gig work hours had a positive and statistically significant impact (B = 0.52, β = 0.47, p < 0.001), supporting the idea that as men diversify their work roles, they also increase their domestic engagement. Similarly, female gig status was a significant predictor (B = 0.86, β = 0.31, p = 0.001). This finding reflects the Negotiation/Resource Bargaining Model, in which women’s economic participation leads to a renegotiation of household responsibilities, prompting men to contribute to more domestic tasks. These results provide empirical support for the claim that gig work fosters more equitable role sharing.

Moreover, the study assesses whether digital access influences perceptions of changed gender roles, particularly whether increased digital literacy and access to devices shift household perceptions toward gender role reversal. The data were analyzed using a Pearson correlation and multiple regression analysis, as shown in Table 4.

Starting with the correlation analysis, both independent variables (digital literacy score and the number of devices in the household) demonstrated statistically significant positive correlations with the perceived role reversal. Specifically, the Pearson correlation coefficient (r) for digital literacy score was 0.45 (p < 0.01), indicating a moderate positive relationship. This suggests that as individuals’ digital skills increase, their perceptions of gender roles become more flexible or reversed. Similarly, the number of devices in the household had a Pearson r of 0.37 (p < 0.01), which is also a moderate positive correlation. This means that greater access to digital technology in the household is associated with stronger perceptions of shifting traditional gender roles.

Moving to the multiple regression analysis, the model was statistically significant, with an F-statistic of 8.77 (df = 2, 397, p < 0.001), indicating that the model as a whole reliably predicts perceived gender role reversal based on the two digital access variables. The R² value of 0.29 shows that about 29% of the variance in perceptions of changed gender roles is explained by digital literacy and the number of devices. The adjusted R² score of 0.27 reflects the model’s stability after accounting for the number of predictors.

Individually, both predictors had significant effects. The digital literacy score had an unstandardized coefficient (B) of 0.41, a standard error reflected in a t-value of 6.23, and a standardized coefficient (β) of 0.39, with a p-value < 0.001. This means that for every one-unit increase in digital literacy, there is a 0.41-unit increase in the perception of gender role reversal, demonstrating a meaningful influence. Similarly, the number of devices had a B value of 0.33, β = 0.31, t = 5.48, and p < 0.001, suggesting that each additional device in the home increases perceived gender role changes by 0.33 units.

In conclusion, the results of Table 4 provide empirical evidence that greater digital access—both in terms of skills and device availability—significantly predicts the perception of changing or reversing gender roles in gig-working households. These findings highlight the transformative role of digital inclusion in shaping evolving gender dynamics within the domestic sphere.

These findings resonate with gender structure theory, which posits that structural opportunities (like technology access) can reshape social practices and ideologies. Higher digital literacy and wider access to devices empower both men and women to renegotiate traditional household roles, thereby supporting the idea that structural resources underpin role transformation.

In addition, the study analyzes how women’s income contribution affects their intra-household decision-making authority. To assess this, an ordinal logistic regression was employed since the dependent variable decision-making power was measured on an ordinal scale (e.g., low, moderate, and high authority). Table 5 presents the outcomes of this model, providing valuable insights into how economic participation shapes women’s empowerment in household dynamics.

The regression model was statistically significant overall, as evidenced by the model fit summary. The Chi-square test value was 18.25 with two degrees of freedom and a p-value less than 0.001, confirming that the model significantly predicts decision-making authority better than a model with no predictors. Furthermore, the Nagelkerke R² value of 0.32 suggests that approximately 32% of the variation in women’s decision-making power can be explained by the following two predictors: women’s income contribution (%) and female employment status.

Turning to the individual predictors, women’s income contribution (%) had a B coefficient of 0.87, with a standard error (SE) of 0.24, a Wald Chi-square value of 13.18, and a p-value < 0.001. This result is statistically significant and implies that a higher income contribution by women significantly increases their decision-making power in the household. The odds ratio (Exp(B)) is 2.38, indicating that for every one-unit increase in income contribution (e.g., a 10% increase), the odds of being in a higher category of decision-making power increase by 2.38 times, holding employment status constant. This strongly supports the hypothesis that economic input strengthens women’s bargaining position at home, directly addressing Objective 3.

Similarly, female employment status (coded as 1 = employed and 0 = not employed) also showed a significant impact. The B value was 1.11, with an SE of 0.37, a Wald χ² of 9.01, and a p-value of 0.003, confirming statistical significance. The Exp(B) value of 3.03 suggests that employed women are three times more likely to have higher decision-making power compared to non-employed women. This reinforces the idea that participation in the labor force not only contributes to household income but also elevates women’s authority in family matters, further fulfilling the objective.

In conclusion, the results from Table 5 demonstrate that both income level and employment status significantly enhance women’s decision-making power within the household. These findings highlight the empowering effect of financial independence and labor market engagement for women in gig economy settings.

These results directly reflect the negotiation model, which emphasizes that bargaining power within households is shaped by resource control. As women increase their income contribution or employment participation, their authority in decision making increases correspondingly, thus empirically validating the theory.

In addition, we examine how gender attitudes and education influence the acceptance of role reversal in the context of gender roles, particularly within households and the labor market. To address this, both ANOVA (analysis of variance) and Chi-square tests were applied to evaluate the association between gender attitudes, educational attainment, socioeconomic status, and the dependent variable: acceptance of role reversal. The results in Table 6 present statistically significant findings that strongly support this objective.

Firstly, the gender role ideology score was analyzed using an ANOVA, producing a statistically significant F-value of 9.53 with a p-value of <0.001. This indicates that there are significant differences in acceptance of role reversal across different levels of gender ideology. The effect size (η² = 0.11) suggests a moderate practical impact, meaning that as individuals hold more egalitarian gender views, they are more likely to accept changes in traditional gender roles. These results show that ideological orientation is a key determinant of role reversal acceptance.

Secondly, the education level (categorized as having Bachelor’s, Master’s, or PhD degrees) was analyzed using a Chi-square test, which yielded a χ² value of 14.82 with a Cramér’s V of 0.28 and a p-value < 0.001. This result is statistically significant and reflects a strong association between educational attainment and acceptance of role reversal. A Cramér’s V of 0.28 indicates a moderate-to-strong relationship, suggesting that higher educational attainment correlates with more progressive views on gender dynamics. Educated individuals are thus more likely to support shifting gender roles, affirming the role of formal education as a catalyst for attitudinal change—an important component of the study’s fourth objective.

Lastly, socioeconomic status (classified as low, middle, and high) was also assessed via a Chi-square test, yielding a χ² value of 11.07, Cramér’s V of 0.22, and a p-value of 0.004. This result is also statistically significant, with a moderate effect size. It shows that individuals from higher socioeconomic backgrounds are more accepting of role reversal compared to those from lower economic strata. This suggests that economic security may encourage greater openness to non-traditional roles, potentially due to better exposure to modern, equitable gender norms. These results provide empirical support for the claim that class and material resources shape social attitudes toward gender roles.

In conclusion, the findings in Table 6 provide strong, statistically significant evidence that gender attitudes, education, and socioeconomic status meaningfully influence the acceptance of gender role reversal. Each independent variable contributes to explaining the social and cultural foundations of gender perception change.

Theoretically, these findings extend gender structure theory, suggesting that ideology, education, and class position provide structural foundations for shifting role acceptance. Higher educational attainment and class status not only expose individuals to progressive norms but also create an enabling environment for role renegotiation.

The results presented in Table 7 aimed to build a comprehensive predictive model for role reversal. The analysis was conducted using a hierarchical multiple regression, in which variables were added in blocks to assess their individual and cumulative contributions to predicting the Composite Role Reversal Index.

In the first model, age and marital duration were included as control variables. These accounted for 12% of the variance (ΔR² = 0.12) in the Role Reversal Index, with an F-change of 7.53 and a p-value of less than 0.001, indicating statistical significance. This suggests that demographic characteristics such as age and the duration of marriage have a meaningful influence on the acceptance or practice of role reversal, possibly because younger or newly married couples may be more flexible in redefining gender roles.

When gig work status for both partners was added in the second model, the explained variance increased by an additional 18% (ΔR² = 0.18), with an F-change of 9.21 (p < 0.001). This result underscores the significant role that non-traditional employment structures play in challenging conventional gender norms. Couples engaged in freelance or gig economy work may be more likely to adopt shared responsibilities and non-traditional roles due to flexible working conditions and the shifting nature of income generation.

In the third block, digital access indicators were introduced, explaining a further 14% of the variance (ΔR² = 0.14; F-change = 8.64, p < 0.001). This highlights the impact of technological exposure on household dynamics. Access to the internet, smartphones, and digital literacy likely facilitates greater awareness of gender equality, supports remote work, and enables women’s empowerment through information access and skill development. These factors contribute to a shift in traditional roles and enhance couples’ capacity for shared decision making.

In the final model, gender ideology and education level were added, resulting in the highest change in explained variance (ΔR² = 0.24), with an F-change of 11.93 (p < 0.001). These variables emerged as the strongest predictors of role reversal. Households with egalitarian gender attitudes and higher levels of educational attainment are significantly more likely to challenge and move beyond rigid gender roles. This finding emphasizes that role reversal is driven not only by economic or technological factors but also by shifts in values and cognitive frameworks.

The final model showed a total R² of 0.68 and an adjusted R² of 0.66, indicating that the full set of predictors explains 68% of the variance in the Role Reversal Index. This reflects a robust model fit and confirms that a combination of demographic, occupational, technological, and ideological factors serve as a reliable predictor of gender role redefinition. The findings validate the objective by demonstrating how multiple, interconnected variables can collectively shape contemporary attitudes and behaviors toward role reversal.

This incremental contribution across models strongly supports role expansion theory—demonstrating how multiple overlapping roles (economic, digital, and ideological) collectively drive greater role reversal. Importantly, the finding that ideology and education were the strongest predictors underscores that while structural and economic factors initiate change, sustained transformation depends on shifts in values.

The results in Table 8 present an integrated analysis using structural equation modeling (SEM) to assess the interrelationships among core constructs relevant to all five study objectives. SEM enables the simultaneous evaluation of multiple dependent and independent variables, allowing for a holistic understanding of how gig work, digital access, economic participation, household labor division, decision making, and gender ideology interact in shaping role reversal within households. Each path coefficient, critical ratio (CR), and significance value directly inform the achievement of the study’s objectives.

The first path demonstrates that gig work participation significantly predicts digital access, with a standardized estimate (β) of 0.71, a standard error (SE) of 0.06, and a critical ratio (CR) of 11.83, which is statistically significant at p < 0.001. This finding confirms that engagement in the gig economy encourages greater access to digital tools and platforms to examine the relationship between gig work and digital inclusion. The high β value implies that as individuals participate more in gig work, their use and integration of digital technologies increases, likely due to the tech-driven nature of such employment.

The second path shows a significant positive relationship between digital access and time use redistribution in household labor, with β = 0.62, SE = 0.07, and CR = 8.86 (p < 0.001). This indicates that digital connectivity promotes the more equitable sharing of domestic responsibilities. Digital tools may facilitate flexible work arrangements, time management, or access to supportive content, contributing to a shift in traditional household roles.

In the third path, gig work participation directly enhances income contribution by women, with a β of 0.68, SE of 0.05, and CR of 13.60 (p < 0.001). This pathway directly supports and empirically confirms that women involved in gig work substantially contribute to household income, thus increasing their economic agency. The high CR value also underscores the strength and stability of this relationship.

The fourth path reflects that women’s income contribution significantly increases their intra-household decision-making power, with β = 0.57, SE = 0.06, and CR = 9.50 (p < 0.001). This finding suggests that financial participation transforms women’s bargaining position within the family, leading to greater involvement in important decisions, whether related to finances, children, or long-term planning.

In the fifth direct path, intra-household decision-making power leads to a shift in gender ideology toward egalitarianism, with β = 0.66, SE = 0.08, and CR = 8.25 (p < 0.001). This finding shows that empowerment in decision-making roles can catalyze broader ideological change in gender perceptions and expectations. As families experience balanced power dynamics, traditional patriarchal attitudes may weaken, and more equitable values emerge.

The indirect effect from gig work participation to gender ideology, mediated by digital access, income contribution, and decision-making power, is also significant (β = 0.39, p < 0.001). This indirect path further reinforces the integrated nature of these constructs and highlights how gig work catalyzes a chain of empowerment leading to ideological transformation. Thus, this mediational process ties together all five objectives within a cohesive explanatory framework.

The measurement model fit indices confirm the robustness and validity of the SEM model. The Chi-square/df ratio is 1.95, which is well below the threshold of 3, indicating a good model fit. The CFI (0.95) and TLI (0.94) both exceed the acceptable level of 0.90, suggesting that the model explains the data well relative to a null model. Furthermore, the RMSEA value of 0.045 and SRMR value of 0.041 are below their respective thresholds (<0.06 and <0.08), indicating excellent residual fit and model accuracy.

In summary, the SEM analysis provides comprehensive empirical evidence supporting all five objectives. Each construct’s path is statistically significant and theoretically meaningful, and the excellent model fit statistics reinforce the reliability of these results. The interconnected nature of gig work, digital access, economic participation, domestic labor sharing, and shifting gender ideologies illustrates a transformative process of role reversal occurring within modern households.

These SEM findings provide a holistic link between theory and results. Gig work expands roles (role expansion theory), enhances women’s bargaining power (negotiation model), and transforms ideology (gender structure theory). Together, they demonstrate a comprehensive pathway of role reversal, by which structural opportunities, resource bargaining, and value shifts converge to reshape gender relations in gig-working households.

5. Discussion

The results show that increased participation in gig work by both male and female partners lead to a more equitable division of domestic labor. Specifically, when male partners engage more in gig work and when their female partners also participate in gig-based employment, there is a noticeable rise in the male partner’s involvement in household tasks. This aligns with the findings of Banerjee, Bharati [49], who found that flexible work arrangements, such as gig work, often lead to a renegotiation of domestic responsibilities. Similarly, Craig and Mullan [50] argue that non-standard work schedules allow men to assume a greater share of housework, especially in dual-earner households.

Moreover, the concept of “role expansion” as introduced by Kayaalp, Page [51] suggests that multiple roles (e.g., gig worker and household contributor) can be mutually enriching rather than conflicting. This study provides additional support for this theory within the context of the gig economy, further emphasizing the adaptive nature of gender roles in contemporary labor settings.

This study also reveals that increased digital literacy and access to digital devices positively shape perceptions toward the reversal of traditional gender roles. Digital tools appear to not only facilitate remote or flexible work but also expand exposure to progressive ideologies, enabling shifts in domestic expectations.

These findings are in line with those of Wajcman, Young [52], who assert that technology acts as a transformative force in gender relations, especially when it enhances autonomy and communication. Similarly, Antonites [53] emphasizes the role of digital inclusion in empowering women and reshaping household dynamics through increased access to resources, networks, and knowledge.

The analysis demonstrates that women’s contribution to household income and their employment status significantly enhance their decision-making authority within the family. This supports the bargaining model of intra-household allocation [54,55], which posits that economic contributions strengthen an individual’s bargaining position in household decision making.

Previous research by Kabeer [56], Doss, Kim [57], and Fertő, Bojnec [58,59] also supports these findings, indicating that women who control economic resources tend to have greater influence in household matters ranging from spending priorities to education and health decisions. The study results reinforce this perspective, showing that gig economy income is a valid and empowering economic resource.

Findings indicate that egalitarian gender attitudes, higher educational attainment, and improved socioeconomic standing are all positively associated with an acceptance of gender role reversal. These findings resonate with those of Inglehart and Norris [60], who demonstrated that societal modernization particularly through education leads to more liberal gender attitudes.

Furthermore, Kabeer [61] emphasizes the importance of structural and ideological factors, such as education and class, in shaping gender perceptions and enhancing women’s agency. This study provides fresh empirical evidence in support of this claim, specifically in the context of gig-working households in a developing country.

The integrated regression model suggests that demographic factors, gig economy participation, digital access, education, and gender ideology collectively shape the adoption of non-traditional gender roles. This comprehensive, multi-layered analysis is consistent with Lombardo, Meier [62]’s gender structure theory, which posits that individual agency, interactional practices, and institutional structures all interact to reproduce or transform gender norms.

The hierarchical nature of the regression model used in our study illustrates this interaction well, showcasing how multiple variables collectively predict role reversal behavior. It also aligns with findings from Messerschmidt and Bridges [63] on gender and power, which underscores the interdependence of structure and agency in gender transformation.

The SEM analysis captures the interrelated pathways between gig work, digital access, economic empowerment, decision making, and ideology. The mediational effect of digital and economic empowerment in reshaping gender ideologies is particularly noteworthy. This mirrors findings by Kabeer and Natali [64], who discuss the “empowerment chain” in which access to economic opportunities and information leads to transformative changes in gender relations.

Additionally, our use of SEM to demonstrate indirect effects offers a deeper understanding of causality than traditional models allow. Such integration of variables in a single analytical framework is rare in the gender and gig economy literature, marking a methodological contribution to the field.

While numerous previous studies have examined individual aspects of gender dynamics such as time use patterns, access to digital technology, or women’s economic empowerment, this research stands out by integrating these dimensions into a unified and coherent framework. It simultaneously addresses multiple interrelated variables, offering a more comprehensive understanding of shifting gender roles in the context of gig work. The findings affirm key conclusions from earlier work, notably the positive influence of economic and digital empowerment on gender equality, and support established theoretical models like the bargaining framework and gender structure theory.

What sets this study apart, however, is its contextual and methodological innovation. Rooted in the realities of the gig economy within a developing country (Pakistan), it addresses a critical gap in the literature, which is largely informed by Western-centric perspectives. Methodologically, the use of multiple advanced statistical tools such as a regression analysis, ANOVA, ordinal logistic regression, and structural equation modeling (SEM) enhances the analytical depth and provides a holistic view of the dynamic interplay between work, technology, and gender roles. Theoretically, the study extends beyond isolated frameworks to create an integrated model that draws from role expansion theory, digital feminism, and gender structure theory, contributing to a richer conceptual discourse. Moreover, the study has strong policy relevance as it demonstrates how digital inclusion and flexible work opportunities through gig platforms can serve as transformative pathways for achieving gender equity. This has significant implications for labor policy reform, digital infrastructure development, and broader socioeconomic planning in similar contexts.

6. Conclusions and Implications

The findings of this study collectively demonstrate that participation in gig work serves as a powerful catalyst for reshaping traditional gender roles and power dynamics within households, particularly in the local context of Lahore’s evolving socioeconomic landscape. The evidence across all objectives and statistical models confirms that gig work not only enhances digital inclusion among women but also facilitates broader structural changes in family life. Access to digital tools through gig work empowers women, allowing for greater flexibility and connectivity, as well as allowing them to balance income-generating activities alongside household responsibilities more efficiently. This, in turn, encourages a more equitable distribution of domestic labor between men and women.

Moreover, the study underscores that women’s increasing contribution to household income significantly enhances their decision-making power within the family unit. As women gain economic autonomy, their voices carry more weight in family matters, which marks a departure from historically patriarchal household structures. This shift in intra-household power is not only functional but also ideological, as it contributes to transforming traditional gender norms and fostering a more egalitarian mindset among both men and women.

Importantly, the study reveals that these changes are interconnected and mutually reinforcing. Gig work does not merely offer economic benefits, it sets off a chain reaction that leads to digital empowerment, time reallocation, enhanced decision-making authority, and ultimately a shift in gender ideologies. This integrative process is especially significant in the context of Pakistani society, where gender roles have been traditionally rigid, and employment opportunities for women, particularly in rural and semi-urban areas, have been limited.

6.1. Policy Implications

The findings of this study carry important policy implications, particularly for developing countries like Pakistan, where the gig economy and digital access are rapidly expanding. First, the positive linkage between women’s participation in gig work and their increased income contribution, decision-making power, and shifting gender ideology highlights the need for inclusive labor policies that formally recognize and support gig work as a legitimate economic sector. Governments should design regulatory frameworks that ensure job security, social protection, and fair compensation for women engaged in freelance and platform-based work.

Second, digital access emerged as a critical enabler of role redistribution and empowerment. This underscores the importance of expanding digital infrastructure, especially in underserved peri-urban and rural areas. Policymakers should prioritize digital literacy programs targeted at women to enhance their participation in the digital economy. Third, the role of gender ideology in reinforcing or resisting role reversal suggests the need for culturally sensitive awareness campaigns that promote egalitarian values without countering traditional community norms.

Additionally, education continues to play a central role in reshaping gender roles. Strengthening female education policies, particularly those that link education to digital and economic opportunities, can accelerate the process of empowerment. Finally, the study’s integration of socioeconomic, technological, and ideological variables implies that cross-sectoral policy coordination between labor, education, technology, and social welfare ministries is essential for fostering sustainable and inclusive gender equity.

6.2. Limitations and Future Directions

Despite offering a comprehensive and contextually grounded analysis, this study has certain limitations that should be acknowledged. First, the cross-sectional design restricts the ability to infer causality among variables such as gig work, digital access, and shifts in gender roles. Future research could employ longitudinal or panel data to trace temporal changes in intra-household dynamics and role reversals more effectively. Second, while this study utilized diverse quantitative tools, it lacked qualitative insights that could enrich our understanding of the cultural resistance, stigma, and personal negotiations underlying gender ideology shifts. Integrating in-depth interviews or ethnographic methods would help capture women’s lived experiences, particularly regarding whether participation in gig work adds to their burden, given their existing unpaid domestic and caregiving responsibilities. Third, the study focused on urban and peri-urban regions of Pakistan, limiting generalizability to rural and tribal contexts, where digital penetration and gig opportunities differ significantly. Future research should extend to these underrepresented areas to test the robustness of the observed pathways. Finally, although this study emphasized women’s empowerment and role reversal, it paid limited attention to men’s perceptions, resistance, and adaptation. Adopting a gender-relational lens in future work would help assess how male partners’ attitudes and behaviors evolve alongside women’s increasing participation in gig-based employment.