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The study attempts to explore the determinants of financial inclusion. Subsequently, it examines the effect of financial inclusion on financial well-being of marginalized street vendors in India.

The demand-side analysis of measuring financial inclusion with a sample of 371 marginalized street vendors is adopted. Both exploratory and descriptive research designs are employed in this study. The primary data collection is done by administering the structured interview schedule by using a convenience sampling technique. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) are performed to describe the latent constructs and their hypothetical relationships with adequate empirical evidence.

Out of five dimensions of financial inclusion considered for the study, accessibility, availability, usage and affordability are found to be significant determinants of financial inclusion; however, the financial literacy dimension is found statistically insignificant. Further, the study results confirm that financial inclusion contributes substantially to the well-being of marginalized street vendors.

The outcome of the study will facilitate all the stakeholders including policymakers and financial institutions to enact policy guidelines to ensure financial well-being of the marginalized street vendors through financial inclusion initiatives.

Financial well-being through financial inclusion is possible even without the effect of financial literacy from the unorganized sector perspective specifically marglianized street vendors. Thus, it adds new dimension to the existing literature on demand side analysis of measuring financial inclusion.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.

The purpose of this paper is to measure the financial inclusion dimensions from the perspective of beneficiaries (demand-side).This research additionally attempts to assess the impact of financial inclusion on socio-economic status of primitive tribal groups (PTGs) in India.

Using an exploratory factor analysis, the financial inclusion dimensions are identified for a sample of 520 families belonging to PTGs in India. In the next step, reliability and validity test were used to establish the financial inclusion dimensions and socio-economic status factors through confirmatory factor analysis using analysis of moment structures. Finally, structural equation modeling (SEM) had been applied for testing the hypothetical relationships.

The study documented five-dimensions of measuring financial inclusion from demand side perspective. The dimensions are as follows: physical proximity, availability, ease of access, affordability and usage. It was found that these factors represent the various dimensions of financial inclusion, which has a significant impact on socio-economic status of the beneficiaries.

The research results focus on dimensions of financial inclusion, which have an impact on the socio-economic status among the most PTGs in India. The research offers ideas to policymakers to outline strategies to enhance the access to and usage of financial services.

The present research is one of the first few studies that present the demand-side perspective to assess the financial inclusion dimensions of the “financially most excluded” groups – PTGs in India using a SEM approach.

This study aims to investigate the antecedents of behavioral intention and actual usage of mobile payment (m-payment) services during the COVID-19 pandemic among Indian consumers.

The proposed research model of this study is based on the extended framework of the Unified Theory of Acceptance and Use of Technology (UTAUT2) by using two additional variables, namely, perceived security (PS) and perceived trust (PT). In total, 436 sample respondents are chosen from Indian consumers with experience using m-payment services through the online survey method. The data analysis and proposed hypothetical relationships were tested using confirmatory factor analysis and structural equation modeling techniques.

The results confirm that performance expectancy, effort expectancy, facilitating conditions, PS, PT, habit and price value are antecedents of consumer intention toward adopting m-payment services. Furthermore, behavioral intention significantly influences the actual usage of m-payment services during the COVID-19 pandemic.

Though the impact of COVID-19 has been observed during the research period in getting responses from m-payment service users, the constructs used in the study are confined to the UTAUT2 model, and dimensions related to COVID-19 are not directly included in the measurement scale. The study’s findings propose valuable insights for service providers and policymakers.

This study’s results offer valuable insights to the service providers and policymakers to achieve the Government of India digital India objective of “Faceless, Paperless and Cashless” transactions.

This study’s results contribute to extending the empirical research literature on m-payment as antecedents of behavioral intention toward the adoption of m-payment services during the COVID-19 pandemic. Furthermore, this study assumes important interrelationships among UTAUT2 constructs with the additional incorporation of PS and PT.

Recently, the Government of India has emphasized digital financial inclusion for promoting cashless transactions with a vision to transform India from a traditional cash-based economy into a cashless economy. Technology-driven payment apps are facilitated greater access to cashless financial services and improve the speed, efficiency, accuracy and effectiveness of financial transactions. This study aims to explore the determinants of quick response (QR) code mobile payment (m-payment) adoption intention among marginalized street vendors in India.

The proposed research model was tested using 320 responses from QR code m-payment users. An interview schedule was performed using the structured questionnaire from marginalized street vendors by adopting a purposive sampling technique. The proposed research framework of this study developed on the Unified Theory of Acceptance and Use of Technology (UTAUT). In addition to the existing variables proposed in the UTAUT model, three more variables have been added, namely, digital financial literacy (DFL), personal innovativeness (PI) and perceived trust (PT). Besides, the study used confirmatory factor analysis and structural equation modeling techniques to analyze the data.

This study confirms that factors such as performance expectancy, effort expectancy, facilitating conditions, PT and customers’ DFL are significant determinants of street vendors’ intention to use QR code m-payment services. However, social influence and PI have shown an insignificant relationship with adopting a QR code m-payment system.

The results provide insights for policymakers and service providers. Specifically, government and bankers design promotional campaigns emphasizing the ease of use, perceived benefits, security and faster business transactions to accept and use the QR code m-payment system to encourage prospective users to achieve a cashless economy.

Many prior studies have widely concentrated on m-payment adoption intention in India. However, only a few studies have attempted to examine the factors influencing the adoption of QR code m-payment services among merchants from emerging economies. There is a dearth of studies on QR code adoption from an unorganized sector perspective, specifically marginalized street vendors. Therefore, this study explicitly examines the extent to which the determinants of adoption intention toward QR code-based m-payment services among marginalized street vendors within the framework of the extended UTAUT model by incorporating DFL, PI and PT. The findings of this study contribute, theoretically and practically, to the existing literature.