Factors Influencing the Adoption of FinTech for the Enhancement of Financial Inclusion in Rural India Using a Mixed Methods Approach

Abstract

The swift expansion of financial technology (FinTech) can substantially improve financial inclusion, especially in the rural regions of emerging nations such as India. FinTech has the potential to drive inclusive growth, reduce inequalities, and foster sustainable economic development. This research examines the determinants affecting the adoption of FinTech services in rural India by synthesizing three theoretical frameworks: The Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Technology Readiness Index (TRI). A mixed methods approach that combines partial least squares structured equation modeling (PLS-SEM) and fuzzy set comparative qualitative analysis (fsQCA) was used to evaluate the suggested framework. The integrated PLS-SEM and fsQCA offer a comprehensive, elegant, and resilient method for data analysis. While fsQCA addresses more intricate patterns within the data, PLS-SEM effectively identifies the relationships among significant factors. This makes the mixed method approach more judicious and advantageous than the single method approach. The findings showed that attitude (β = 0.35), perceived behavioral control (β = 0.28) from the Theory of Planned Behavior (TPB), perceived ease of use (β = 0.31) from the Technology Acceptance Model (TAM), and perceived insecurity (β = −0.19) from the Technology Readiness Index (TRI) all have a big impact on how people use FinTech. The findings also indicate that the desire to adopt FinTech positively influences financial inclusion among rural residents. These research findings enhance the debate on sustainable development by demonstrating how specific FinTech interventions can close the financial inclusion gap, empower rural populations, and achieve various Sustainable Development Goals (SDGs). The study’s findings could help governments, banks, and FinTech firms aiming to enhance the accessibility and use of digital financial services in rural India.

1. Introduction

Financial inclusion ensures that people and companies have responsible and sustainable access to reasonably priced financial products and services, including banking, credit, insurance, and payment systems (World Bank, 2018). It seeks to lower inequality by providing financial access to underprivileged and marginalized groups and encouraging economic development (Demirgüç-Kunt et al., 2021). Financial inclusion is a crucial catalyst for economic growth and poverty reduction, particularly in emerging nations such as India, where a substantial population is underbanked. Notwithstanding various government attempts and the expansion of digital financial services, financial exclusion remains a significant issue in rural India. Introducing financial technology can revolutionize this situation by facilitating access to financial services via digital platforms, mobile banking, and financial technology (FinTech). FinTech has become a crucial catalyst in reshaping the global financial landscape. In emerging economies, it possesses considerable potential to improve financial inclusion by offering affordable, accessible, and user-friendly financial services. Characterized by its extensive rural populace, India is a principal beneficiary of this technological transformation.

Notwithstanding the government’s various measures to enhance financial inclusion, a significant segment of the rural populace remains unbanked or underbanked (Demirgüç-Kunt et al., 2021). FinTech technologies, like digital wallets, mobile banking, and peer-to-peer lending platforms, provide novel methods to address this disparity by surmounting conventional obstacles such as geographical limitations, inadequate banking infrastructure, and elevated transaction costs (Kishor et al., 2024). FinTech services help to attain the SDGs, primarily in three dimensions, i.e., financial, economic, and environmental (Hasan et al., 2024). In the finance dimension, FinTech services enhance financial inclusion by expanding digital payment options, promoting crowdfunding, facilitating peer-to-peer (P2P) lending, supporting microlending, encouraging personal deposit accounts, integrating FinTech with e-commerce, and streamlining lending processes (Bayram et al., 2022). All these facilities help us attain SDG 8 (promote sustained, inclusive, and sustainable economic growth). In the economic dimension, FinTech contributes to the achievement of SDG 10 and SDG 11 by boosting market competition, generating job opportunities in the service sector, expanding financial activities, and promoting sustainable agribusiness development (Begum et al., 2023). FinTech mitigates poverty by aiding households in budget management, encouraging savings, fostering entrepreneurial initiatives, and facilitating the efficient operation of new and existing businesses. FinTech mitigates income inequality by reducing financial and economic activities via digital connectivity. FinTech can effectively mitigate income inequality within the formal financial sector. On the environmental front, FinTech enhances renewable energy production and consumption, boosts green financing for environmentally supportive projects, and encourages investment in low-carbon-emitting production technologies (Lisha et al., 2023). FinTech reduces carbon emissions and improves environmental quality by promoting low-carbon practices among users and facilitating and incentivizing reforestation efforts to reduce carbon output. Thus, FinTech contributes to the achievement of SDG 11 and SDG 13.

Over the past ten years, India has made significant progress in financial inclusion; however, certain challenges still exist. Financial inclusion in rural India presents significant socioeconomic, infrastructure, and technological challenges, notwithstanding various government programs. The challenges include (1) individuals in rural regions possessing limited knowledge of formal financial services, resulting in lower usage (M. Sarma & Pais, 2011); (2) insufficient access to smartphones, the internet, and digital payment systems complicating financial transactions (Ghosh & Sahu, 2021); (3) women in rural areas facing barriers to accessing financial services due to patriarchal norms and restricted mobility (Dahiya & Kumar, 2020); and (4) many rural inhabitants, particularly farmers and laborers, experiencing unstable incomes, hindering their ability to maintain bank accounts or secure loans (Barik & Sharma, 2019). Although digital technologies, including mobile banking, digital wallets, and biometric identification systems like Adhaar, have enhanced the accessibility of financial services, their acceptance and effects differ significantly.

As of 2022, around 43% of India’s population is unbanked due to insufficient access to financial institutions, according to the World Bank’s Global Findex database (Owusu-Peprah, 2024). Moreover, merely 28% of the population utilizes debit or credit cards, frequently attributable to the lack of nearby ATMs (Owusu-Peprah, 2024). Furthermore, by the conclusion of 2021, India was positioned 67th among 166 nations regarding the number of commercial bank branches per 100,000 adults worldwide (Owusu-Peprah, 2024). India was placed 119th out of 166 countries for the number of ATMs per 100,000 adults by the conclusion of 2021, marking one of the lowest standings globally. Recently, a study by the Reserve Bank of India (RBI) showed considerable progress in access, use, and quality dimensions of financial services, which rose from 53.9 in March 2021 to 64.2 in March 2024 (Khan & Sahu, 2025). Further, reflecting the success of initiatives, including Pradhan Mantri Jan Dhan Yojana (PMJDY) in increasing banking access, adult bank account ownership in India grew to 77% by 2024 (Economic Survey, 2024). While these initiatives have considerably raised financial inclusion in India, continuous work is needed to address the remaining problems, particularly concerning reducing the gender gap in financial access and increasing the active use of financial services. Comprehending the determinants of technology uptake in rural regions is crucial for formulating targeted interventions to close the financial inclusion gap and foster sustainable development (Muthukannan et al., 2020).

Implementing financial technology (FinTech) presents a disruptive solution to this problem by delivering economical, accessible, and scalable financial services (Najib et al., 2021; Ozili, 2023). A complex interaction of elements, including personal attitudes, societal norms, and technological preparedness, affects the adoption and utilization of FinTech in rural regions (Madan & Yadav, 2021). The Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Technology Readiness Index (TRI) offer comprehensive frameworks for comprehending these processes. The Technology Acceptability Model (TAM) emphasizes the perceived ease of use and perceived usefulness of technology, which are essential for assessing user acceptability (Davis, 1989). The Theory of Planned Behavior (TPB) improves its framework by including social variables and perceived behavioral control. This makes it easier to see how societal norms and trust in technology use affect the use of FinTech in rural areas (Ajzen, 1991). Simultaneously, the TRI assesses consumers’ inclination to adopt new technologies based on optimism, innovativeness, discomfort, and insecurity (Colby & Parasuraman, 2001). This study seeks to amalgamate these three theoretical models to examine the determinants affecting FinTech uptake in rural India thoroughly. The study aims to clarify the effective utilization of FinTech to enhance financial inclusion in these communities.

FinTech adoption is receiving more and more attention in the academic world. However, few studies have used the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Technology Readiness Index (TRI) to look into this phenomenon, especially in rural India. Much research has predominantly concentrated on urban populations or examined a singular theoretical framework, resulting in a disjointed comprehension of the determinants affecting FinTech adoption (Irimia-Diéguez et al., 2023; Sharma & Munjal, 2024; G. Wu & Peng, 2024).

The economy and financial system can only grow with fair economic policies and long-term financial inclusion that help people to make social and economic progress (Lusardi, 2019; Koomson et al., 2020; Nsiah et al., 2021). However, the factors that cause this to occur in rural India remain unknown, necessitating additional investigation. The economic behavior of individuals is elucidated in the literature through several hypotheses, including the absolute income hypothesis, the permanent income hypothesis, the life cycle hypothesis, and the relative income hypothesis (Mohanta & Dash, 2022). However, these traditional models do not consider important demographic and personal traits. Instead, they focus on earnings, income, and life cycles to explain what makes people save and spend money. Evaluating the scope and impact of many initiatives, research of this nature can aid in establishing benchmarks and objectives for politicians, financial institutions, and economic development. Despite the extensive examination of technological factors, there has been insufficient focus on psychological and behavioral dimensions, especially in rural settings where digital literacy and technological infrastructure are often inadequate (M. Ali et al., 2021). Also, not many real-world studies look at the combined effects of these factors on different age, gender, and income groups in rural areas. The impact of FinTech on improving access to financial services, as well as enhancing financial literacy and long-term financial well-being, is, as of yet, inadequately examined (Morgan & Trinh, 2020). Given the preceding debate, the study aimed to address the following research questions:

• What are the important technological, psychological, and socio-cultural factors affecting the adoption of FinTech in rural India, as elucidated by the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Technology Readiness Index (TRI)?
• How do these factors influence the intention to accept and utilize FinTech services across various demographic segments within the rural population?
• How can policymakers and financial institutions utilize these findings to improve financial inclusion in rural India?

This research offers significant contributions to the current literature. Initially, it amalgamates three significant theoretical frameworks, e.g., TAM, TPB, and TRI, to provide a thorough understanding of the determinants affecting FinTech uptake in rural India. This comprehensive method overcomes the shortcomings of earlier studies that depended on a singular framework, providing a more intricate examination of the factors influencing FinTech adoption (Kumari, 2024). Secondly, while previous research on FinTech investigates various factors (technical, psychological, and socioeconomical) influencing its adoption and use separately, limited studies have assessed these elements collectively (G. Wu & Peng, 2024; Musa et al., 2024; S. Singh et al., 2020; Somesanook, 2024). Furthermore, no studies in India have simultaneously examined the technological, psychological, and socioeconomic factors. Thus, this study examines how technological, psychological, and socio-cultural factors affect FinTech adoption among rural people in India. This gives specific information that can help to make FinTech solutions fit the needs of other rural communities. The research advances the discussion on financial inclusion by examining FinTech’s role in facilitating access to financial services while improving financial literacy and promoting long-term financial well-being (Morgan, 2022). These findings hold practical significance for policymakers, financial institutions, and technology providers in formulating ways to enhance sustained financial inclusion in rural India.

2. Literature Review

Given the significant expectations from organizations like the World Bank and the UN regarding mobile financial services’ potential to reduce poverty and foster economic development, it is essential to understand the technological and behavioral factors influencing the adoption of this innovation. FinTech services face adoption challenges compared to cash systems (Grohmann et al., 2018). Additionally, online banking poses risks that can result in financial losses, and the digital nature of FinTech transactions makes individuals hesitant to embrace the technology (Baganzi & Lau, 2017). Many of the banked population lack formal education, complicating the use of FinTech innovations (Demirgüç-Kunt et al., 2020). Furthermore, mobile services often use formal languages like English, which does not benefit many individuals with low educational levels in emerging nations. In India, most FinTech services are available in English and Hindi, creating barriers for local illiterate or semi-literate users. As a result, many individuals in developing countries remain without access to financial services. Adopting financial technology has emerged as an essential tool for increasing financial inclusion, particularly in developing economies with insufficient traditional banking infrastructure.

In India, the rapid expansion of mobile networks in rural areas has significantly contributed to this cause over the past decade (Chauhan et al., 2022; Asif et al., 2023). Payment banks have emerged as an alternative to online and mobile banking, enhancing operational efficiency and reducing costs related to service provision for customers in rural and semi-urban areas (Schuetz & Venkatesh, 2020; Chauhan et al., 2022). The Reserve Bank of India’s open regulatory framework and the government’s policies encouraging new businesses have benefited digital finance companies. Indian customers, known for their prudence in financial decisions, want increased confidence in FinTech enterprises. Key issues include meeting their needs and changing how people handle their money, as well as creating a strong and flexible regulatory framework that can keep up with the speed of technological progress (Asif et al., 2023; Chauhan et al., 2022). The Reserve Bank of India (RBI) uses computer algorithms to support the Unified Payments Interface, Bharat Bill Payments System, digital payments, peer-to-peer lending, and financial advice. The RBI also permitted eleven FinTech companies to launch savings, deposit, and remittance payment banks in various parts of India, focusing on rural areas. To address these socioeconomic disparities, it is crucial to understand the factors influencing FinTech utilization, enabling service providers to adapt their offerings for broader acceptance and improved financial inclusion (Demir et al., 2022). This literature review examines key studies on FinTech usage in rural areas through three theories: The Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Technology Readiness Index (TRI).

The Technology Acceptance Model (TAM), introduced by Davis in 1989 (Davis, 1989), is a prominent paradigm for comprehending technology uptake. It asserts that perceived usefulness and perceived ease of use are the principal factors influencing technology adoption. Multiple studies have utilized the Technology Acceptance Model (TAM) to investigate FinTech uptake in diverse situations. To and Trinh (2021) examined mobile payment uptake and determined that perceived ease of use and perceived utility greatly affected users’ desire to use the technology. A study by Shaikh and Karjaluoto (2015) emphasized that trust and perceived risk are essential factors in the FinTech adoption process, especially in environments with poor digital literacy. Despite the well-established use of the Technology Acceptance Model (TAM) in urban environments, its application in rural regions still needs to be improved. Conventional TAM conceptions may need to address distinct issues that rural populations frequently encounter, such as low digital literacy, restricted internet access, and socio-cultural obstacles (Kumari, 2024). In this setting, augmenting the Technology Acceptance Model by incorporating additional elements such as trust, perceived risk, and socio-cultural impacts can yield a more thorough comprehension of FinTech acceptance in rural India (Madan & Yadav, 2021).

The Theory of Planned Behavior (TPB), formulated by Ajzen (Ajzen, 1991), enhances the Technology Acceptance Model by integrating subjective standards and perceived behavioral control as supplementary factors influencing behavioral intention. The Theory of Planned Behavior (TPB) has been employed to examine various activities, including adopting technology. In FinTech adoption, subjective norms denote the impact of societal pressure on an individual’s choice to embrace financial innovations. In contrast, perceived behavioral control pertains to the perceived ease or difficulty of utilizing these technologies (Ajzen, 1991). Numerous studies have underscored the significance of social influences in adopting technology. Martins (Martins et al., 2014) discovered that subjective standards greatly influence the adoption of Internet banking in Portugal. In rural environments, where community impact and social interactions are significant, subjective norms may be relevant (Alharbi & Sohaib, 2021). Also, perceived behavioral control, including resource accessibility and self-efficacy, is very important in rural areas where educational and infrastructure problems may make it hard to use technology (Raza et al., 2019).

The Technology Readiness Index (TRI), developed by Colby and Parasuraman (2001), evaluates individuals’ inclination to accept and utilize new technologies across four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism and innovation facilitate technology adoption, whereas discomfort and insecurity hinder it. TRI offers a comprehensive view of technology adoption by including psychological elements affecting an individual’s readiness to accept new technologies. Research on the Technology Readiness Index (TRI) concerning FinTech adoption is notably limited, especially in rural areas. Various studies have demonstrated a significant impact of technology readiness on adopting self-service technologies and mobile banking (Chang & Chen, 2021; Liljander et al., 2006). However, research on the implementation of TRI in rural India still needs to be improved. Rural consumers may demonstrate varying degrees of technological readiness, influenced by differences in exposure to digital technologies, socioeconomic position, and cultural factors (Madan & Yadav, 2018). Integrating TRI into the study of FinTech uptake in rural regions can provide significant insights into the psychological impediments and motivators of technology utilization in these communities.

The exclusion from the formal economic system is widely acknowledged as an impediment to the future eradication of poverty. Numerous cultural and procedural limitations constrain the stakeholders in the FinTech ecosystem. Scholars disagree on whether mobile money systems can fully realize their growth potential in rural regions (Ozili, 2023; Asif et al., 2023). A study on financial inclusion at the bottom of the pyramid (B.O.P.) in Indian society requires transformation, focusing on developing technological solutions for underserved communities (Asif et al., 2023; Schuetz & Venkatesh, 2020). Consequently, developing creative methods for delivering financial services to economically disadvantaged individuals is imperative.

There have not been many real-world studies in developing economies that try to figure out what makes mobile technology an important tool for financial inclusion (Wang & Zhang, 2025; Asif et al., 2023). These studies seem to have a more contextual approach to alleviating poverty in less developed places. It is also important to create good practices for policymakers in this complex and constantly evolving market (Schuetz & Venkatesh, 2020). Research indicates that mobile banking and digital payment platforms can enhance access to financial services in rural areas (Bateman et al., 2019). Issues such as insufficient digital literacy, lack of faith in digital platforms, and inadequate infrastructure frequently obstruct the implementation of these technologies (Kishor et al., 2024). Current research mostly emphasizes technological and economic considerations while neglecting the psychological and behavioral dimensions of FinTech adoption in rural settings (G. Wu & Peng, 2024). Although the Technology Acceptance Model (TAM) considers perceived ease of use and usefulness, it inadequately encompasses the impact of societal norms and individual preparedness for technology adoption, which are essential in rural contexts. Likewise, although the Theory of Planned Behavior includes social and behavioral control elements, it may need to consider rural users’ psychological preparedness for adopting new technology. Similarly, TRI offers a more comprehensive understanding of personal readiness, but its application in rural FinTech adoption still needs to be examined. This paper aimed to fill a gap in the current research about financial inclusion and financial technology’s impact on financial services provision. This study utilized an empirical method to assess the impact of technology on fostering entrepreneurship in impoverished areas. The study also analyzed the critical success factors for the future implementation of financial technology in rural regions (Wang & Zhang, 2025; Asif et al., 2023; Ji et al., 2021).

This study employed a mixed method approach using PLS-SEM and fsQCA to predict essential factors for adopting FinTech in rural India. Researchers recognize the synergistic advantages of using integrated PLS-SEM and fsQCA methodologies to address complex research problems. The variance-based PLS-SEM methodology predicts and identifies linear relationships. In contrast, the set-theoretic FSQCA methodology discerns linkages through configurations (Ragin, 2008). PLS-SEM assumes that relationships are additive and symmetric, so it is not efficient to handle asymmetric relations among variables (J. Hair & Alamer, 2022). Beyond PLS-SEM’s linear limitations, fsQCA exposes other strategies for an outcome. Hossain et al. (2024) found that PLS-SEM gives full model fit and effect sizes, while fsQCA gives configurational insights that improve theoretical and empirical validity. PLS-SEM also thinks that effects are the same across a dataset, but fsQCA can look at subgroups and find different causal configurations (Kaya et al., 2020). The mixed method can look at the complexity of predictors using fsQCA and the ability to predict using PLS-SEM (Pappas & Woodside, 2021).

2.1. Theoretical Framework

The proposed theoretical framework integrates the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Technology Readiness Index (TRI) to analyze the factors influencing FinTech adoption in rural India comprehensively (Figure 1). This multidimensional approach allows for an in-depth understanding of the technological, psychological, and socio-cultural determinants of FinTech adoption, which are essential for promoting financial inclusion in rural areas. This approach enables a thorough analysis of the technological, social, and psychological factors influencing adoption (Negm, 2023).

The framework is structured around three core constructs derived from the TAM, TPB, and TRI models and their respective sub-constructs. It also includes financial inclusion as the outcome variable, influenced by the behavioral intention to use FinTech services.

2.2. Hypothesis Development

This section discusses the relationship between study constructs and the formulation of hypotheses based on the proposed hypothesis.

2.2.1. Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) consists of two key components: perceived usefulness (PU) and perceived ease of use (PEOU). PU indicates how much an individual believes that using FinTech services can enhance financial activities such as savings, credit access, and transactions (Davis, 1989). Perceived usefulness is crucial in technology adoption (Yan et al., 2022). This research evaluates how perceived usefulness influences FinTech adoption among rural Indians by addressing user needs such as time efficiency and benefits. Furthermore, the study conducted by Somesanook (2024) revealed that it is unsurprising that individuals will enhance their acceptance of FinTech upon recognizing its use. Multiple studies consistently show a positive relationship between perceived usefulness and the intention to adopt FinTech (Baba et al., 2023; Igamo et al., 2024; J. Singh & Singh, 2023). However, some studies found that perceived usefulness had little effect on adopting electronic financial services (Mufarih et al., 2020). PEOU reflects the perception that using FinTech services requires minimal effort, which is particularly relevant in rural areas with lower digital literacy (Davis, 1989). PEU relates to an individual’s effort in utilizing new technology (Davis, 1989). PEOU evaluates the interfaces of FinTech services in terms of their user-friendliness and accessibility across various devices. Research has shown a positive relationship between perceived ease of use (PEOU) and FinTech adoption (Baba et al., 2023; Igamo et al., 2024; J. Singh & Singh, 2023; G. Wu & Peng, 2024). The research by Darnida et al. (2024) indicated that a user’s perception of a system’s usability greatly influences their intention to utilize FinTech for financial services. Additionally, S. Singh et al. (2020) concluded that customers were more inclined to accept FinTech when they perceived it as more user-friendly. Based on the above evidence, the following hypotheses are proposed.

H1: 

PU significantly impacts users’ intentions to use FinTech services.

H2: 

PEOU positively influences people’s intentions to use FinTech-based financial transactions.

2.2.2. Theory of Planned Behavior (TPB)

The main constructs of the Theory of Planned Behavior (TPB) are Attitude (ATT), Subjective Norms (SN), and Perceived Behavioral Control (PBC). Attitude reflects an individual’s favorable or unfavorable evaluation of using FinTech services (Ajzen, 1991). A growing body of literature has explored the positive impact of attitudes on the behavioral intention to adopt FinTech tools for mobile payment (Giovanis et al., 2020; Verma et al., 2021; Yadegari et al., 2024). Musa et al. (2024) also affirmed that attitude affects the adoption of FinTech among Nigerians. A study by Rai et al. (2019) established that attitude is a crucial factor influencing intentions toward financial inclusion in India. Similarly, Maune & Mundonde (2024) demonstrated that attitude significantly influences engagement behavior in financial inclusion in Zimbabwe. Subjective norms capture individuals’ social pressure regarding FinTech use, which is particularly influential in rural areas where community dynamics significantly affect behavior (Ajzen, 1991). Previous research (Giovanis et al., 2020; Verma et al., 2021) indicates that subjective norms substantially enhance the intention to use FinTech applications for financial transactions, such as mobile and NFC-based transactions. Panchasara and Sharma (2019) identified a substantial positive correlation between subjective norms and the intention toward financial inclusion in India. Musa et al. (2024) have affirmed the significant correlation between subjective norms and Nigerians’ intention to adopt FinTech. Perceived behavioral control pertains to the perceived ease or difficulty of adopting FinTech services, influenced by access to technology, financial literacy, and self-efficacy with digital tools (Ajzen, 1991). Perceived behavioral control over FinTech usage refers to an individual’s ability to effectively manage payment transactions through various FinTech application channels (Verma et al., 2021). Several studies have highlighted the positive impact of perceived behavioral control on the intention to use FinTech applications for financial transactions (Ajouz et al., 2021; Giovanis et al., 2020; Verma et al., 2021). Maune & Mundonde (2024) found a significant positive correlation between perceived behavioral control and involvement in financial inclusion in Zimbabwe. Musa et al.’s (2024) research revealed the substantial impact of perceived behavioral control and behavioral intention on the financial inclusion participation of Nigerian individuals. Panchasara and Sharma (2019) identified a negligible correlation between perceived behavioral control and intention toward financial inclusion in India. Thus, the study formulated the following hypotheses:

H3: 

The individual’s attitude positively influences their intention to use FinTech solutions for financial transactions.

H4: 

The subjective norms have a significant positive influence on FinTech use.

H5: 

Individuals’ perceived behavioral controls significantly influence their desire to use FinTech solutions for transactions.

2.2.3. Technology Readiness Index (TRI)

Optimism (OPT), Innovativeness (INN), Discomfort (DIS), and Insecurity (INS) are the core components of TRI. Optimism reflects a positive view of technology, believing it enhances control, flexibility, and efficiency in financial management (Colby & Parasuraman, 2001). As previous studies indicate, customers are more likely to adopt technology they view as empowering and flexible (Liljander et al., 2006). Individuals with a positive outlook on technology are expected to favor using it (G. Wu & Peng, 2024). Innovativeness refers to individuals acting as technology pioneers and thought leaders, crucial for early FinTech adoption (Colby & Parasuraman, 2001). Innovators experiment with new technologies and related services (Flavián et al., 2022). Those with high innovation levels are generally open minded and more likely to adopt innovations, such as mobile payments (Ramírez-Correa et al., 2020). Additionally, innovativeness influences adoption intentions; innovative consumers usually have a positive view of technological capabilities, especially when faced with uncertain potential value (Prodanova et al., 2021). Studies demonstrate that innovativeness predicts the propensity to adopt inventive technologies (G. Wu & Peng, 2024). Extensive research indicates that innovativeness positively influences technology acceptance and utilization (Ashrafi & Easmin, 2023; G. Wu & Peng, 2024), as persons with robust, innovative abilities and awareness are more inclined to adopt new technologies.

Discomfort arises from a perceived lack of control over technology and overwhelming feelings, which can impede FinTech uptake, particularly in rural areas with limited digital exposure (Colby & Parasuraman, 2001). Individuals uncomfortable with technology view it as complex and inadequate (Lu et al., 2023). Those facing significant discomfort in unfamiliar technological settings may hesitate to adopt new tech-based products and services (Flavián et al., 2022). Feelings of inadequacy in using technology can lead to rejecting new methods. People who are uncomfortable with ceding control to automated systems may be reluctant to use FinTech-based financial transactions. Insecurity involves distrust in technology and skepticism about its functionality, potentially affecting the perceived security of digital financial transactions (Colby & Parasuraman, 2001). McCloskey (2021) highlighted that security and privacy concerns are significant obstacles to technology acceptance. Dong et al. (2024) further established that insufficient security heightens apprehensions about using new technologies. C. Wu and Lim (2024) discovered that insecurity adversely impacts the adoption of new technology. Customers with high technological insecurity may avoid using such technologies (Flavián et al., 2022). Research shows that in the finance sector, apprehensive clients are likely to resist adopting technology-driven services (Caldeira et al., 2021).

Further, a study indicates that the use of new technology induces anxiety and negatively affects individuals while also hindering their acceptance of self-service technology (Abis et al., 2024). Another investigation indicated that perceived nervousness regarding technology is inversely related (Abis et al., 2024), while technology anxiety negatively affects the time spent on modern technology (Sabir et al., 2023). The preceding discussion resulted in the formulation of the following hypotheses:

H6: 

People’s technology optimism significantly influences their intention to utilize FinTech.

H7: 

Users’ technical innovation significantly influences their intention to use FinTech applications.

H8: 

People’s discomfort with technology negatively impacts their intention to use FinTech tools.

H9: 

Users’ insecurity negatively impacts their intention to use FinTech applications.

2.2.4. Financial Inclusion (FI)

Financial inclusion is an increasingly important issue attracting global attention from financial and regulatory authorities. Financial inclusion can be defined as enhancing the access to and use of financial products and services for all societal segments through formal channels while addressing their needs swiftly and affordably, protecting their rights, and promoting financial literacy for informed decision-making. Numerous studies have explored the factors driving financial inclusion (Ahmad et al., 2022; Bongomin et al., 2023; Chowdhury & Chowdhury, 2024). Mobile payment utilization plays a significant role in enhancing financial inclusion, albeit requiring careful evaluation. This study examines financial inclusion through a model incorporating country-specific factors. The model consists of three components: accessibility, availability, and usage of financial services, as proposed by Sarma (S. Sarma, 2015). Various empirical and theoretical research has analyzed the relationship between mobile payment adoption and financial inclusion. Therefore, we have the following hypothesis:

H10: 

The intention to adopt FinTech significantly and positively impacts financial inclusion.

3. Research Methodology

This research employs the positivist paradigm, applying a clearly defined conceptual framework with explicit and direct linkages. The positivist perspective is mostly associated with the quantitative methods typically employed in the deductive process. A mixed method approach was used to analyze the data using PLS-SEM and fsQCA. PLS-SEM is a robust technique that does not strictly require normality testing like other statistical methods (J. Hair & Alamer, 2022). fsQCA is used to further validate the PLS-SEM results the in case of non-linear relations among variables. The research is carried out in the following steps:

• Step 1: Questionnaire development based on a literature review and theoretical framework;
• Step 2: Sampling processes and data collection;
• Step 3: Data pre-processing for non-response bias and common method variance test;
• Step 4: Symmetric data analysis using PLS-SEM techniques;
• Step 5: Asymmetric data analysis using the fsQCA method.

3.1. Study Instruments

A quantitative research methodology was employed, as the study relies on survey research (Bhattacherjee, 2012). The measurement items were adapted from established scales in the prior literature with minor language changes to fit the study context. Respondents rated items on a five-point Likert scale, with ‘1’ indicating strong disagreement and ‘5’ indicating strong agreement. Sixteen items for the Technology Readiness Index (TRI) were adapted from (Parasuraman & Colby, 2015), ten items for the Technology Acceptance Model (TAM) (perceived usefulness and perceived ease of use) were taken from Devis (Davis, 1989), nine items for the Theory of Planned Behavior (TPB) (attitude, subjective norms, and perceived behavioral control) were adapted from Ajzen (Ajzen, 1991), three items on FinTech Adoption Intention were sourced from Bian and Moutinho (2011), and fifteen items on FinTech inclusion were derived from Sarma (S. Sarma, 2015).

Before administering the survey, a pilot study was conducted to improve the clarity and precision of the measurement scales (J. F. Hair et al., 2019). This study collected 30 valid responses from elderly individuals in Nagpur district, India, with Cronbach’s alpha values ranging from 0.71 to 0.83. All constructs received Cronbach’s alpha values above 0.7, confirming the reliability of the scale items (J. F. Hair et al., 2019). Additionally, several items were revised based on participant feedback to enhance clarity and reduce response time.

3.2. Participants and Data Collection

This study necessitated the collection of appropriate, representative data from the population to facilitate generalization and to implement a methodology consistent with the research design. A cross-sectional survey using a stratified sampling strategy was used to collect the data. Using a stratified sampling approach often improves the accuracy of statistical estimates (Creswell & Creswell, 2017). The study’s sample size was established according to SEM specifications. Boomsma and Hoogland (2001) assert that a minimum sample size of 200 is essential to mitigate bias in structural equation modeling (SEM) outcomes. The minimum sample size for structural equation modeling (SEM) should be no less than ten times the free variables. A sample size of no less than 200–250 is sufficient to mitigate bias in the study results. Between March and August 2024, 1500 questionnaires were distributed to the rural residents of four states (e.g., Maharastra, Chattisgarh, Madhya Pradesh, and Odisha) from central India through social media (WhatsApp, Facebook, etc.) and in-person interactions. Before the survey, all participants were informed about the project, and their responses were guaranteed to remain confidential and utilized exclusively for research purposes. Five hundred seventy-two responses were collected, although only four hundred thirty-three were appropriate for further analysis after initial screening. An initial screening was conducted based on the following criteria: participants’ bank accounts (yes), missing values, and incomplete responses.

3.3. Data Pre-Processing

In addition to utilizing several data-screening processes for missing values and outlier detection, common method variance and non-response bias tests were used to ensure data quality.

• Non-Response Bias Test

Non-response bias is a major concern with data from self-administered instruments. To assess potential non-response biases, we applied an extrapolation method based on Armstrong and Overton’s theory (Armstrong & Overton, 1977). We compared responses from early and late respondents to identify any mean value differences. A t-test was conducted to compare the means of the first 50 participants with those of the last 50. The results revealed no significant variation in average values (t = 11.6, p = 0.00). Thus, the data showed no response bias.

• Common Method Variance Test

Common technique variance poses a significant challenge in cross-sectional studies (J. F. Hair et al., 2019). To address this, Podsakoff’s (Podsakoff et al., 2003) strategy was used to mitigate potential common method variance in our investigation. The author utilized the varimax rotation method to consolidate all 53 components into a single factor. After five iterations, the one-factor test yielded six factors: PEOU, PU, ATT, SN, PBC, OPT, INS, DIS, INN, FAI, and FI, explaining 41% of the total variation, which is below the 50% threshold (Harman, 1976). These results indicate an absence of common technique variance in the data.

3.4. Data Analysis Tools

The study used structural equation modeling (SEM) with a Partial Least Squares (PLS) approach to analyze existing theories and identify key determinant variables through construct projection. Data analysis was performed using R packages (plspm and QCA). Initially, the assumptions of multivariate normality, outliers, and missing data were assessed to ensure readiness for path analysis. The subsequent phase involved measurement model assessment. This was followed by hypothesis testing, mediation analysis, and model validation. Further, Fuzzy Set Qualitative Comparative Analysis (fsQCA) was used to tackle causal complexity and identify the optimal combination of predictors for the desired outcome. PLS-SEM shows general trends, while fsQCA uncovers multiple pathways to achieve desired levels of inventive performance. The fsQCA analysis aligns with configuration theory, enabling the exploration of complex, non-linear interactions among variables. This study employs the fsQCA method because it accommodates outcome and predictor variables on a continuous fuzzy scale. In conclusion, integrating PLS-SEM and the fuzzy sets theory’s configurational approach makes a novel contribution to this research domain.

4. Results and Analysis

Various theoretical frameworks have examined the relationships among the mentioned variables. Low R² values from regression and PLS-SEM analyses may lead to incorrect conclusions about the extent to which different measures explain variation in dependent variables (P.-L. Wu et al., 2014). FsQCA was combined with PLS-SEM to identify multiple pathways and explore complex, non-linear interactions among variables for optimal outcomes to address these limitations. Symmetric analysis utilized PLS-SEM, while asymmetric analysis employed fsQCA.

Table 1. Respondents’ profiles.

Variables	Labels 433	Count
Gender	Male	252
	Female	181
Education	10th and below	124
	Graduation and below	197
	Above graduation	112
Age	Gen X	182
	Gen Y	251
Income *	INR 7000 and above	265
	Below INR 7000	168

* The average income of rural India is approximately INR 7000 per month.

provides the demographic profile of the participants before detailed data analysis using PLS-SEM and fsQCA. These groups are Generation X (Gen X), born 1965–1980 (≥42 years old now), and Generation Y (Gen Y), born 1981–1994 (=41 years old now) (McCrindle & Wolfinger, 2009).

4.1. Symmetric Analysis

This paper employed variance-based structural equation modeling (PLS-SEM) for symmetric modeling. The first step in PLS route modeling is to assess the measurement model for internal consistency (composite reliability), convergent validity (indicator reliability and average variance extracted), and discriminant validity. The second step involves evaluating the structural model, which includes checking for the components’ collinearity and assessing the proposed hypotheses’ significance and relevance.

4.1.1. Descriptive Analysis

Table 2. Analysis of demographic variables.

Constructs	Demographic Variables	Levels	Mean	T-Statistics (p-Values)
FinTech adoption intention	Gender	Male	3.89	3.78 (0.00)
		Female	3.62
	Age	X-Gen	3.47	−4.73 (0.01)
		Y-Gen	3.76
	Income	Below Average	3.59	−2.19 (0.00)
		Above Average	3.43
Financial inclusion	Gender	Male	3.65	3.17 (0.03)
		Female	3.41
	Age	X-Gen	3.52	−3.41 (0.03)
		Y-Gen	3.87
	Income	Below Average	3.39	−1.21 (0.12)
		Above Average	3.37

revealed statistically significant differences in key demographic variables (gender, income, and age group) for the major dependent variables: FinTech adoption intention and financial inclusion.

4.1.2. Measurement Model Evaluation

Table 3. Measurement properties.

	PU	PEOU	ATT	SN	PBC	OPT	INS	DIS	INN	FAI	FI
PU	0.88
PEOU	0.46	0.89
ATT	0.32	0.34	0.88
SN	0.29	0.31	0.36	0.87
PBC	0.30	0.32	0.35	0.38	0.86
OPT	0.28	0.27	0.31	0.32	0.31	0.88
INS	−0.32	−0.31	−0.29	−0.28	−0.31	−0.36	0.88
DIS	−0.25	−0.29	−0.24	−0.33	−0.28	−0.29	0.24	0.87
INN	0.31	0.33	0.32	0.27	0.32	0.31	−0.37	−0.33	0.88
FAI	0.47	0.43	0.39	0.37	0.41	0.39	−0.33	−0.31	0.42	0.88
FI	0.32	0.34	0.33	0.28	0.32	0.35	−0.29	−0.30	0.33	0.44	0.88
Mean	3.89	3.92	3.67	3.58	3.41	3.51	3.29	3.19	3.45	3.67	3.71
SD	1.19	0.98	1.38	1.09	1.34	0.89	1.21	1.27	1.07	1.13	1.29
Factor Loading	0.72–0.81	0.74–0.81	0.72–0.79	0.69–0.74	0.70–0.75	0.72–0.81	0.74–0.79	0.73–0.79	0.72–0.80	0.70–0.82	0.69–0.83
Cronbach’s α	0.83	0.86	0.81	0.79	0.79	0.84	0.81	0.79	0.81	0. 82	0.81
AVE	0.79	0.80	0.78	0.76	0.75	0.79	0.78	0.77	0.78	0.79	0.79
CR	0.91	0.93	0.89	0.87	0.86	0.91	0.90	0.87	0.90	0.89	0.91

Note: Bold values are the mean of the squared root of AVE values.

presents the correlations between constructs along with the factor loadings for each item, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) for the constructs. The values in Table 2 indicate that our data are valid and reliable at construct levels. The result includes Cronbach’s alpha and composite reliability (CR) in order to assess internal consistency. Although Cronbach’s alpha is less commonly used than CR in structural equation modeling, it remains a conservative measure of internal consistency (J. F. Hair et al., 2019). Values above 0.70 for both metrics signify acceptable reliability, though values exceeding 0.95 for CR are undesirable (J. F. Hair et al., 2019). In our analysis, the CR values are found to be satisfactory.

The Fornell and Larcker criterion evaluates discriminant validity by comparing the square root of each construct’s average variance extracted (AVE) to its bivariate correlations with other constructs (Grégoire & Fisher, 2006). Discriminant validity is established when the square root of AVE for each construct exceeds its bivariate correlation values (Grégoire & Fisher, 2006). Table 3 shows that the square root of AVE of all the constructs exceeds the bivariate correlations with all competing constructs, confirming its discriminant validity. However, Henseler, Ringle, and Sarstedt (Henseler et al., 2015) introduced the more sensitive Heterotrait–Monotrait Ratio of Correlations (HTMT) for detecting discriminant validity. HTMT measures the mean of heterotrait–heteromethod correlations against the mean of monotrait–heteromethod correlations (Henseler et al., 2015). An HTMT value significantly less than 1 indicates distinct constructs. The conservative HTMT_.85 criterion requires HTMT values between constructs to be below 0.85.

Table 4. Heterotrait–Monotrait Ratio (HTMT).

	PU	PEOU	ATT	SN	PBC	OPT	INS	DIS	INN	FAI	FI
PU
PEOU	0.86
ATT	0.81	0.84
SN	0.67	0.71	0.72
PBC	0.71	0.77	0.79	0.81
OPT	0.79	0.81	0.83	0.74	0.78
INS	0.74	0.74	0.81	0.77	0.81	0.64
DIS	0.71	0.69	0.79	0.76	0.79	0.71	0.76
INN	0.78	0.80	0.81	0.79	0.72	0.81	0.63	0.62
FAI	0.74	0.81	0.76	0.80	0.77	0.80	0.62	0.63	0.69
FI	0.81	0.82	0.79	0.78	0.79	0.81	0.71	0.67	0.72	0.68

suggests that all HTMT values fall below 0.85, except between PU and PEOU. The more lenient HTMT_.90 criterion accepts acceptable values between 0.85 and 0.90 (Henseler et al., 2015). Both HTMT_.85 and HTMT_.90 criteria confirm discriminant validity.

4.1.3. Structural Model Assessment

Before hypothesis testing, it is crucial to evaluate collinearity among predictor variables (J. F. Hair et al., 2019). The variance inflation factor (VIF), which should not exceed 5, is a standard metric for collinearity assessment (J. F. Hair et al., 2019). All VIF values of the study constructs ranged from 1.78 to 3.01, indicating no collinearity among the predictors. The structural model was assessed using R2, beta (β), and t-value. Further, the predictive relevance (Q2), effect sizes (f2), and standardized root mean square (SRMR) were determined to assess the strength and prevalence of the model. The relevance and significance of the structural model links were subsequently evaluated using a bootstrapping procedure. This routine involved generating 5000 subsamples and conducting a two-tailed test using bias-corrected and accelerated bootstrapping.

Table 5. Hypotheses test results.

Path	Coefficient (β)	Effect Size (f2)	Sig. (p)	R2	SRMR
PU→FAI	0.29	0.41	0.00	0.51	0.06
PEOU→FAI	0.31	0.39	0.00
ATT→FAI	0.35	0.29	0.02
SN→FAI	0.06	0.09	0.12
PBC→FAI	0.28	0.32	0.03
OPT→FAI	0.06	0.03	0.23
INS→FAI	−0.19	0.28	0.04
DIS→FAI	−0.03	0.08	0.19
INN→FAI	0.09	0.11	0.23
FAI→FI	0.34	0.33	0.00	0.57

summarizes the estimation results, emphasizing the significance and magnitude of the path coefficients. All pathways, except SN→FAI, OPT→FAI, DIS→FAI, and INN→FAI, are statistically significant (p < 0.05), indicating that hypotheses H4, H6, H7, and H8 were not supported. Conversely, hypotheses H1, H2, H3, H5, H9, and H10 were supported. The effect sizes of the supported hypotheses, assessed using f2, range from medium to high, as detailed in Table 5. The model accounts for 51% of the variation in FinTech adoption intention (R² = 0.51) and 57% in financial inclusion (R² = 0.57), demonstrating moderate predictive capability (J. F. Hair et al., 2019). Henseler (Henseler et al., 2015) advised that a Standardized Root Mean Square (SRMR) value below 0.08 indicates better model adequacy. This threshold was confirmed by Cho (Cho et al., 2020) for sample sizes exceeding 100. The study result demonstrated an adequate fit with an SRMR score of 0.06.

The assessment of R² values, reflecting predictive accuracy, was complimented by calculating Stone–Geisser’s Q2 value, H2, indicating predictive relevance. Wold’s blindfolding approach was used to compute the cv-communality index (H²) and the cv-redundancy index (Q2) for constructs and indicators (Wold, 1982). Garson (2021) states that H² and Q² values above zero validate the structural and measurement models for prediction.

Table 6. H² and Q² indices.

Constructs	H2	Q2
FAI	0.39	0.36
FI	0.41	0.37
Average	0.40	0.36

shows all H² values exceeding 0, with a mean of 0.40, and all Q² values also above 0, with a mean of 0.36, indicating the measuring model’s superior quality over the structural model. Alharbi and Sohaib (2021) note that 0.02 represents a modest effect size, 0.15 is a medium effect size, and 0.35 is a large effect size. This information suggests that the proposed model has significant predictive power.

4.2. Asymmetric Analysis

Fuzzy sets QCA (fsQCA) employs an asymmetric modeling approach that integrates fuzzy sets and logic grounded in complexity theory. This methodology is crucial as the non-linear relationships between independent and dependent variables often make correlation and beta coefficients inadequate for capturing their association. Fuzzy sets overcome this limitation by providing diverse methods that yield consistent results (Olya & Altinay, 2016; Ruffoni & Reichert, 2024). Consequently, symmetric methods like multiple regression analysis and structural equation modeling (SEM) fail to deliver accurate results in models with highly correlated independent variables (Olya & Altinay, 2016). In a symmetric framework, high values of an independent variable (X) can suggest high values of a dependent variable (Y), but they do not ensure them. In contrast, in an asymmetric relationship, high values of X are necessary and sufficient for predicting high values of Y. Therefore, examining all combinations where X influences Y positively or negatively is beneficial (Olya & Altinay, 2016). This study investigates how independent variables—PU, PEOU, ATT, SN, PCB, OPT, INS, DIS, and INN—collectively impact FinTech adoption intention.

The standardized scores from the PLS-SEM results were used as inputs for the fsQCA analysis. Rasoolimanesh (Rasoolimanesh et al., 2021) suggested calibrating these scores to a 0 to 1 range, where a score of −3 indicated no membership (0), a score of 0 represented the crossover point (0.5), and a score of 3 indicated full membership (1). A truth table was created to identify combinations of conditions that could produce the desired outcome (Sukhov et al., 2023). Rows with two cases or fewer were removed, and the analysis focused on assessing the consistency and coverage of the configurations. Configurations were selected based on having greater than 0.2 coverage and higher than 0.8 consistency (Pappas & Woodside, 2021). The fsQCA algorithm produced three outputs categorized as complicated, intermediate, and economical, with the criterion that the combination leads to the outcome consistently supporting a solution (Pappas & Woodside, 2021). This study employed the previously recommended intermediate set (Kumar et al., 2022; Rasoolimanesh et al., 2021).

The fsQCA identifies three scenarios that lead to higher FAI (

Table 7. Significant configurations for high FinTech adoption intention.

Configurations	Solutions
	1	2	3
PU
PEOU
ATT
SN
PBC
OPT
INS
DIS
INN
Consistency	0.942	0.941	0.936
Raw coverage	0.798	0.613	0.622
Unique coverage	0.238	0.106	0.103
Solution coverage	0.789
Solution consistency	0.889

Legend: “Black circles” = presence of the variable; “Hollow circles” = absence of the variable; “Blank” = not considered in the solution.

). Solution 1 shows that the simultaneous presence of PEOU, PU, ATT, PBC, INS, and INN, along with the absence of OPT, can significantly increase FAI. Solution 2 indicates that PEOU, PU, ATT, and INS contribute to high FAI. Solution 3 highlights that PEOU, PU, INS, ATT, and DIS enhance the intention to adopt FinTech among individuals in rural India. Based on coverage and consistency, Solution 1 is the most effective combination for boosting FinTech adoption intent. These results align with H1, H2, H3, H5, H9, and H10. The fsQCA analysis yields valuable insights supporting and refining PLS-SEM findings.

5. Discussion

In 2014, the Indian government launched the Jan Dhan Yojana (PMJDY) to improve access to financial services. Since then, research has increasingly highlighted how digital financial services boost financial inclusion and economic development, emphasizing the crucial role of FinTech in sustainable development, particularly in India. Financial inclusion is now recognized as a critical strategy for alleviating poverty, reducing income inequality, and driving economic growth (Demirgüç-Kunt et al., 2021). The rise of digital financial services, fueled by technological advancements, offers new solutions to address the financial needs of individuals (Mendoza et al., 2021). Therefore, three research questions were investigated to predict various significant enablers to increase the FinTech footfall in rural India. To address the first research question, this study utilized an integrated framework of TAM, TPB, and TRI models to identify key factors, i.e., technological, psychological, and socio-cultural factors influencing FinTech adoption and its effects on financial inclusion among rural populations in India. The framework was empirically tested using a mixed methods approach, incorporating symmetric (PLS-SEM) and asymmetric (fsQCA) techniques. This approach addressed the limitations of commonly used methods like PLS-SEM. While SEM focuses on comparing alternative models rather than assessing effect size within each model (J. F. Hair et al., 2019), it captured path coefficients but did not identify indirect effects. Additionally, both methods failed to account for variability in causal conditions leading to similar outcomes (Tóth et al., 2015). Therefore, this study employed fuzzy set qualitative comparative analysis (fsQCA) to identify causal factors, circumventing the limitations of quantitative methods like PLS-SEM.

The findings reveal a significant correlation between various characteristics and rural Indians’ inclination to adopt FinTech. Among the variables in the Theory of Planned Behavior, attitude is the key factor influencing the intention to adopt FinTech among rural respondents in India (hypothesis 3). This aligns with previous research emphasizing the importance of user attitudes toward FinTech services (Setiawan et al., 2024). Additionally, the COVID-19 pandemic has notably impacted individuals’ decisions to adopt FinTech due to increased familiarity with digital technology, contrasting with studies conducted before the pandemic (Shareef et al., 2018). Furthermore, consistent with our findings, prior research indicates that perceived behavioral control (PBC) also significantly and positively influences users’ intentions to adopt FinTech (Setiawan et al., 2024).

The perceived ease of use (PEOU), a Technology Acceptance Model (TAM) component, positively influences consumers’ intentions to adopt FinTech, promoting the use of digital financial technologies (hypothesis 2). Research shows that users favor eco-friendly FinTech solutions that are simple and user-friendly (Q. Ali et al., 2021). Technology with a low learning curve and minimal effort boosts perceived ease of adoption (Q. Ali et al., 2021; Davis, 1989; Kamal et al., 2020; Paganin et al., 2023). These findings suggest that banks and FinTech providers focusing on user-friendly, sustainable technology will likely see higher adoption rates and enhanced financial inclusion in rural India. Similarly, perceived usefulness (PU) significantly impacts the likelihood of adopting FinTech services, corroborating earlier studies (hypothesis 1). This result is supported by various past studies (Kamal et al., 2020; Paganin et al., 2023; Rafique et al., 2024). Consumers are drawn to these technologies for their perceived benefits, such as environmental protection and sustainability. Innovations aligning with ecological values and demonstrating benefits in resource conservation increasingly resonate with a societal shift toward sustainability (M. Ali et al., 2021; Davis, 1989; Kamal et al., 2020; Paganin et al., 2023).

Insecurity regarding the utilization of FinTech services adversely affects the propensity to adopt them (Radnan & Purba, 2016). It reflects a lack of trust in emerging technologies, including worries about accuracy and harmful effects. The study found that insecurity (INS) was the only TRI dimension significantly and negatively impacted individuals’ willingness to adopt FinTech (hypothesis 9). Increased exposure to FinTech technologies appears to boost adoption as users gain confidence in the technology’s functionality and experience reduced concerns about adverse effects (hypothesis 10). Previous research supports these findings (Haddad et al., 2020; Hasheem et al., 2022; O’Hern & Louis, 2023). Khadka and Kohsuwan (2018), observing that security and privacy concerns hinder technology adoption, while Pham et al. (2020) found that inadequate security increases anxieties about new technologies. All the aforementioned findings contribute to addressing the second research question.

These findings are especially pertinent to India, considering the nation’s commitment to sustainability as articulated in Vision 2030. The country confronts distinct environmental issues, including water scarcity and increasing energy usage, and is implementing green IT solutions to achieve its sustainability objectives. Economic factors such as resource efficiency and cost reduction are crucial in India, making these insights indispensable. India can advance green IT by shaping user views and using sustainability trends. Furthermore, they enhance technology while decreasing expenses associated with environmentally detrimental activities (Alqublan, 2021).

6. Conclusions

This study highlights the transformative potential of financial technology (FinTech) in promoting financial inclusion in rural India, thoroughly examining the key factors influencing its adoption to advance the sustainable development goals (SDGs). Utilizing the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Technology Readiness Index (TRI), it demonstrates the significance of attitude, perceived behavioral control, and perceived insecurity in shaping individuals’ intentions to use FinTech. The mixed methods approach, employing both PLS-SEM and fsQCA, enhances and broadens the theoretical framework, clarifying the complex factors affecting adoption in rural areas. The findings provide policymakers and FinTech providers with actionable insights for improving digital literacy, strengthening infrastructure, and tailoring services to meet the specific needs of rural communities. The study’s findings also support the achievement of SDGs by illustrating how FinTech adoption fosters financial inclusion, economic empowerment, and reduced inequalities in rural India. By implementing targeted interventions and promoting collaboration, FinTech can effectively bridge the financial inclusion gap and support the obtainment of sustainable development goals in emerging economies. The following subsections discuss the contributions of the research findings to address the third research question.

6.1. Theoretical Contribution

Our research contributes significantly to the literature on financial inclusion and FinTech adoption among rural populations in India. This study is among the first to explore the key factors influencing the intention to adopt FinTech and enhance financial inclusion. By integrating the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Technology Readiness Index (TRI), we create a comprehensive theoretical framework for analyzing FinTech uptake in rural, less technologically advanced communities. This work contributes to knowledge by achieving a prediction accuracy of 57.4%, surpassing the frequently utilized TAM, which typically explains between 32% and 53% of the variance (Liébana-Cabanillas et al., 2021). This synthesis elucidates the interactions between social influences, attitudes, and technological readiness, making it applicable to other disadvantaged areas facing similar digital adoption challenges. Additionally, our research advances the discourse on FinTech’s role in sustainable development by linking technology adoption to financial inclusion and highlighting technological acceptability as a catalyst for economic empowerment aligned with sustainable development goals (SDGs). This connects adoption theories with sustainability literature, demonstrating FinTech’s potential as a transformative tool for financial inclusion. We emphasize the value of mixed methods approaches, including PLS-SEM and fsQCA, for the robust validation of theoretical models, providing nuanced insights into the complex interactions influencing technology adoption that single method approaches might overlook.

6.2. Practical Implications

This study offers practical recommendations for policymakers, FinTech companies, and government agencies seeking to enhance financial inclusion in rural India. Since perceived usefulness and attitude are crucial for FinTech adoption, targeted digital literacy initiatives should highlight benefits such as transaction simplicity, financial security, and time efficiency. This approach can shift rural perceptions, fostering a more positive view of digital financial services. Both government and private sectors must prioritize improving digital infrastructure, including internet connectivity and mobile networks in remote areas. Furthermore, stakeholders must enhance user interfaces to assist individuals with limited digital literacy by incorporating regional languages, voice navigation, and visual elements. Enhanced access to digital tools will reduce perceived behavioral control barriers, encouraging broader FinTech adoption. Additionally, banks and other stakeholders should focus on developing microfinance solutions that offer flexible repayment options aligned with the seasonal income patterns observed in rural India. Incorporating digital literacy modules into rural development initiatives such as the Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA) also can help to boost financial inclusion in rural India. The insecurity associated with digital services, as highlighted by the Technology Readiness Index (TRI), emphasizes the need for strong data protection policies, user-friendly platforms, and transparent financial products. FinTech providers must focus on these aspects to build trust and confidence among rural users, ensuring secure transactions. Given rural residents’ varying readiness levels and financial needs, FinTech companies should tailor their services accordingly. Collaboration among the public sector, private FinTech providers, and community organizations is crucial for achieving sustainable financial inclusion. Such partnerships will improve service implementation, increase accessibility, and foster trust within rural communities, ultimately narrowing the financial inclusion gap. In conclusion, a combination of governmental support, user-centric service design, and robust community participation can facilitate the closure of the rural financial divide and advance India’s sustainable economic development.

6.3. Limitations and Future Directions

This study provides valuable insights into the acceptance and use of FinTech services for financial inclusion among India’s rural population, but it has limitations. The sample primarily comes from India, and while rural populations often show similar technological adoption patterns, the study fails to account for cultural and socioeconomic differences in Western countries, affecting the generalizability of the findings. Future research should explore ways to adapt these models to better meet rural people’s needs and align with the Indian cultural context, including the impact of cultural values on technology acceptance. Additionally, incorporating factors like self-efficacy, perceived risk, and health anxiety may enhance understanding of the predictors of FinTech adoption. Future investigations should take into account variables such as socioeconomic status and education level to explore their influence on financial inclusion. Furthermore, expanding the sample size through cross-cultural comparisons or longitudinal designs, including rural peoples from diverse backgrounds, would improve the generalizability and relevance of the results.