1 Introduction

Digital finance, which includes internet banking, mobile payments, and fintech solutions, has grown in popularity since COVID-19 because to its convenience and accessibility (Anifa et al. 2022; Babbar et al. 2023). The pandemic has expedited the spread of contactless payments and digital transactions, reducing physical contact. Improved security features and real-time processing have strengthened trust in digital financial services. Businesses and consumers alike benefit from lower transaction costs and more effective financial management tools (Jourdan et al. 2023; Kang 2018; Kruse et al. 2019). Ultimately, in today's fast changing world, digital finance has become critical to sustaining economic resilience and promoting financial inclusion. A revolution in digital banking (Singh & Srivastava 2020; Mbama & Ezepue 2018; Chauhan et al. 2022) could potentially impact rural India's access to financial services (Breidbach et al. 2019; Hentzen et al. 2021). The rural population of India is among the world's largest (The World Bank 2023), yet a significant number of them lack fundamental financial services (Siddiqui & Siddiqui 2020). The widespread usage of digital phones and the growing internet utilization in rural areas make offering financial services through digital channels highly viable (Bansal 2014). Through digital banking, unbanked individuals can access a range of financial services (Siddiqui & Siddiqui 2020), including savings, loans, insurance, and investment products, facilitating their integration into the formal banking system.

Digital banking serves as a practical alternative for rural Indians, negating the need to physically travel to a bank branch. It also provides round-the-clock access to financial services, catering to those whose working hours align with standard business hours. The cost-effective nature of digital banking (Deb & Agrawal 2017), devoid of the expenses associated with physical infrastructure like bank branches and ATMs, might result in lower fees for users.

Even rural Indians without conventional collateral or credit history can access credit through online banking. However, several substantial challenges hinder the success of digital banking in rural India. Many lack the requisite digital literacy (Guerra-Leal et al. 2021) to effectively navigate online banking services, although efforts such as digital literacy training programs and peer support aim to address this. Insufficient internet connectivity remains a major obstacle, necessitating collaboration between the public and commercial sectors to improve rural connectivity.

Distrust in digital services (Kesharwani & Singh Bisht 2012; Vaithilingam et al. 2013) may hinder rural Indians from adopting digital banking, making it crucial to build trust through effective interactions and training. Trust serves as a complete mediator between security and privacy concerns and repurchase intention, while e-satisfaction acts as a mediator between security and ease of use. (Trivedi & Yadav 2020; Sarkar et al. 2020). Digital banking holds the potential to revolutionize rural India by enhancing financial access and inclusion (Shen et al. 2009; Singh and Sinha 2020). The current study aims to identify existing issues and concerns related to technology adoption to ensure the success of digital banking in rural India.

2 Theoretical perspective and hypotheses formulation

The evolution of digital technologies has revolutionized the banking sector, enabling customers to access financial services with greater convenience and efficiency (Gerrard and Barton Cunningham 2003; Alkhowaiter 2020). The substantial expansion of digital banking adoption owes itself to technological advancements, evolving consumer preferences, governmental initiatives, and heightened competition. The extent of digital banking's adoption is significantly shaped by the perceived usefulness (PU) (Behl & Pal 2016) of its services, a crucial notion reflecting customers' recognition of the utility digital banking offers for their financial needs. Moreover, this perception is intertwined with elements like trust, satisfaction, and ease of use (Liébana‐Cabanillas et al., 2013).

As per previous studies, it was found that consumer behavior normally influences the adoption of digital banking (Tan & Teo 2000). Factors such as age, gender, income, and educational levels influence consumers' propensity to adopt digital banking services (Barik & Sharma 2019; Lal 2019). The foundation of digital banking hinges on trust (Vaithilingam et al. 2013); a positive perception of the bank and its technology increases the likelihood of adopting digital banking. Customer loyalty (Amin 2016) is also pivotal in the digital banking realm, influenced by perceived ease of use, perceived usefulness, and customer satisfaction.

This study utilizes a co-word mapping approach to examine the essential factors that can influence digital banking. It analyzes titles, keywords, and abstracts from 225 articles in the WoS database using the VOS viewer software, which generates a network image illustrates keyword relationships and clusters (Singh and Misra, 2021). Based on the VOS viewer output, the study has identified four clusters: "Digital Banking: Roles of IT and Social Media," "Digital Banking: Customer's Acceptance and Adoption," "Digital Banking: Customer Satisfaction," and "Digital Banking: Determinants". Figure 1 depicts the research model, that incorporated the primary predictor variables selected from these clusters.

Fig. 1
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(Source: VOS viewer)

Co-keywords mapping

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Perceived usefulness (PU) emerges as a pivotal determinant across diverse domains of technology adoption (Yeh & Teng 2012; Davis 1989; Venkatesh et al. 2003). Individuals are more inclined to embrace technology if they believe it serves their goals and objectives (Venkatesh et al., 2007, 2012; Venkatesh 1999; Plewa et al. 2012). Various factors, both individual and contextual, shape the effects of PU on technology adoption. Moreover, PU intertwines with constructs like perceived ease of use, attitude, and intention to use. These constructs form an interconnected web, influencing each other and shaping inclinations toward technology, as exemplified by Venkatesh's (2008) discovery of PU and perceived ease of use (PEOU) impacting beliefs and intention to use (IU) of technology. Notably, perceived usefulness holds significance for technology developers and managers alike. Insights into PU's influencing factors can aid designers in crafting technology aligned with user expectations, while managers can utilize PU as a performance indicator to evaluate technology implementation's effectiveness (Baptista and Oliveira 2015; Oliveira et al. 2014; Watjatrakul 2013; Willyanto 2021).

Numerous studies underscore perceived usefulness as a key predictor of digital banking adoption in India. Customers' inclination to use digital banking services is driven by their anticipation of time and effort savings (Bankuoru Egala et al. 2021; Liébana‐Cabanillas et al., 2013). Various personal and social factors (Zhou et al. 2010) influence perceived usefulness's impact on digital banking adoption, with educational attainment regulating the link between perceived usefulness and adoption. A history of prior technology use also heightens customers' perception of digital banking's utility. Several interconnected concepts, including perceived ease of use, trust, and customer satisfaction, closely align with perceived usefulness. Notably, George & Kumar, (2013) identify a positive relationship between perceived usefulness and customer satisfaction in the context of digital banking adoption. Furthermore, Alalwan et al., (2016) highlight the tandem influence of trust and perceived usefulness intention of customer on digital banking adoption in India (Singh and Srivastava 2018).

For the purpose of this study, trust has been defined as the willingness of one party to expose themselves to potential risk from the actions of another party with the anticipation that the other party will carry out a particular action important to the trustor (Mayer et al. 1995). Prior studies have established role of trust in modifying user intention and have classified it into two phases i.e., trust before the employment of a technology (pre-use trust) and trust after its employment (post-use trust) (Hernández-Ortega 2011). However, current study has analysed trust as a post use construct in order to evaluate users’ intention to continue using digital banking in long run.

Based on outcome oriented approach to anticipate users’ intention to continue using digital banking services, user satisfaction has been measured as an attribute derived from usage of technology (Raza et al. 2020; Senali et al. 2022). User satisfaction positively affects continuance intention to use (Wixom & Todd 2005).

Practical implications emerge from the perceived usefulness of digital banking providers in India. Understanding the determinants of perceived usefulness empowers providers to design services catering to customers' demands (Wang & Li, 2019). Strategies aimed at enhancing usefulness and trustworthiness can boost adoption rates. Perceived usefulness serves as a pivotal performance metric for evaluating the effectiveness and impact of digital banking services (Ananda et al. 2020). In accordance with these empirical research findings, the hypotheses have been framed as below:

H1 – There is a significant association between perceived usefulness and intention to use

H2 – There is a significant association between perceived usefulness and user satisfaction

H3 – There is a significant association between perceived usefulness and user trust

In the realm of technology adoption, perceived ease of use (PEOU) has garnered substantial attention for its pivotal role. PEOU, a cornerstone of the Technology Acceptance Model (TAM) (Davis 1989; Venkatesh and Davis 1996, 2000; Venkatesh and Bala 2008). This perception of simplicity strongly inclines users toward utilization.

In parallel, the Diffusion of Innovation (DOI) theory (Rogers & Cartano 1962) aligns with TAM, suggesting that a technology's perceived ease of use shapes its adoption rate. De Leon (2019) highlights that technologies perceived as user-friendly tend to witness rapid, widespread adoption. Effective user interface design significantly contributes to enhancing a technology's perceived ease of use by promoting user engagement, easing cognitive load, and providing clear feedback.

The significance of perceived ease of use extends notably to digital technology (Gefen & Straub 2000; Shen & Chiou 2010), especially pertinent in the context of mobile devices (Alalwan et al. 2016) frequently used in dynamic, unpredictable environments. Simplified applications tend to be readily decrypted and used. Additionally, cultural nuances play a role (Im et al. 2011; Phillips et al. 1994; Lee et al. 2013); certain cultures lean toward intuitive, less training-intensive technologies, while others may invest time and effort in learning. This cultural understanding becomes imperative in developing and disseminating technology products globally.

Perceived ease of use holds pivotal importance in various domains. E-commerce relies significantly on PEOU (Awa et al., 2015), as per the Technology Acceptance Model, with PEOU substantially influencing users' intent to make online purchases. Likewise, in digital learning, PEOU affects students' intention (Songkram et al. 2023) to use educational websites, well-designed platforms enhance satisfaction and engagement. Moreover, the acceptance of mobile health (mHealth) applications and emerging technologies like virtual reality (VR) is deeply tied to PEOU. Ease of use significantly influences users' intention to adopt and utilize mHealth apps and VR, fostering better health outcomes and technology adoption.

Crucially, PEOU, along with perceived usefulness (PU), forms the crux of TAM. PEOU gauges system understanding and simplicity, while PU reflects user expectations of system performance. This interplay becomes particularly relevant in digital wallet adoption, where PEOU translates to effortless learning and PU pertains to enhanced user performance. (Mew & Millan 2021; Chawla & Joshi 2020). Within contexts like e-commerce and website usage, PEOU and PU work in tandem, and hence contributes to online trust and user confidence. An easy-to-navigate platform fosters transparency, building trust during online transactions.

Notably, in the realm of digital banking, PEOU significantly shapes users' attitudes and adoption intentions. Indian digital banking users' attitudes were positively influenced by PEOU (Chawla & Joshi 2019), accentuating its impact on digital banking acceptance. As a recurrent factor across multiple technology channels, PEOU is expected to play a similar role in the context of digital wallets. The perceived ease of use plays a fundamental role in technology adoption, influencing users' decisions across diverse domains. Its impact extends from e-commerce to digital learning, mobile health applications, and emerging technologies. Recognizing and leveraging PEOU can substantially enhance technology adoption, user engagement, and overall outcomes. In light of these empirical research findings, the following hypotheses are proposed:

H4 – There is a significant association between perceived ease of use and intention to use

H5 – There is a significant association between perceived ease of use and user satisfaction

H6 – There is a significant association between perceived ease of use and user trust

Intention to Use (IU) stands as a pivotal factor in technology adoption. According to the Technology Acceptance Model (TAM), two key influencers of IU are Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). PU gauges a user's perception of a technology's contribution to performance enhancement, while PEOU assesses its user-friendliness.

Intention to use has been extensively examined across contexts, revealing its central role in shaping real-world usage behavior, particularly evident in online shopping platforms (Natarajan et al. 2017) where enhanced intent translates to increased sales and revenue. In social networking sites (SNSs), PU and PEOU significantly influence users' intent to engage with platforms (Hussein & Hassan, 2017). A perception of Facebook's utility and user-friendliness positively correlated with users' intent to use and engage socially (Luarn et al., 2015).

Similarly, intent to use holds significance in the realm of digital applications (Oliveira et al. 2016). PU, PEOU, and subjective norms exert substantial influence on users' intention to use digital apps. Favorable perceptions of usefulness, simplicity, and social acceptance propel willingness to adopt and utilize such apps. This intention-based paradigm extends to emerging technologies like blockchain, where PU, PEOU, and perceived risk (Kesharwani & Singh Bisht 2012) influence users' intent to adopt. Blockchain applications perceived as useful, straightforward, and low-risk heighten willingness to embrace the technology.

In light of these insights, researchers and practitioners collaborate to develop effective strategies for technology acceptance and utilization, acknowledging the interplay of factors that influence users' intention to use. In accordance with these empirical research findings, the following hypotheses are framed:

H7 – There is a significant association between intention to use and user satisfaction

H8 – There is a significant association between intention to use and user trust

3 Research methodology

This section is divided into four key segments: data collection process, respondent profiles, item measurement, and introduction to data analysis.

3.1 Method of data collection

The study's dataset comprises account holders from public, private, or cooperative banks with rural Karnataka branches. Due to unavailability of the entire customer list and resource limitations, a survey questionnaire was administered using convenience sampling. This method, widely used in studies on digital adoption across various nations, facilitated sample selection. The initial phase involved a pilot survey for feedback, refining the questionnaire for data collection. Participants formally agreed through consent forms and information sheets, including some who responded in regional languages. Surveys were distributed physically at bank branches or ATMs, yielding a total of 500 issued surveys. Participation was voluntary, and 405 surveys were gathered (27 via Google form), with 360 (fully completed responses) used for analysis, resulting in a 72% response rate.

3.2 Respondents’ profile

Table 1 offers a comprehensive snapshot of respondents' demographic characteristics and profiles, indicating a notable representation of male participants. The respondents' attributes are summarized in a similar tabular layout for easy comprehension.

Table 1 Demographic profile of the respondents
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3.3 Measurement items

To assess the theoretical constructs and model in this study, established multi-item scales from prior research were employed, with necessary adjustments to suit the study's specific context. The evaluation of underlying theoretical constructs utilized five-point Likert scales, a well-established psychometric tool in social science research. Rigorous validity and reliability assessments were conducted on the adapted scales. Internal consistency and reliability were determined using Cronbach's alpha coefficient, retaining items with a Cronbach's value of 0.7 or higher for further analysis. Additionally, content validity and appropriateness of the psychometric scale were verified through expert review of the English questionnaire. Table 2 presents the sources and selected constructs in detail.

Table 2 Convergent validity, internal reliability, and descriptive statistics
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Survey data underwent analysis using SPSS 28.0, with structural equation modeling (SEM) analysis conducted using AMOS 28.0. Construct validity of the research instrument was assessed through exploratory factor analysis, employing principal components analysis for extraction and Varimax rotation for rotation. The criterion for acceptance was factor loading exceeding 0.5. Detailed outcomes of the factor analysis are illustrated in Table 2.

4 Results

Cronbach's alpha was employed to assess the constructs' validity, a widely used approach in social science research that gauges the consistency of items measuring a shared concept. The analysis demonstrated satisfactory reliability, with alpha values ranging from 0.70 to 0.80, confirming the study's constructs.

In addition to reliability, the study evaluated convergent and discriminant validity of the collected data. Convergent validity examines the closeness of multiple measurements of a single concept, while discriminant validity gauges’ dissimilarity between distinct ideas. Results of these validity analyses revealed that all measurements were valid, displaying significant associations within themselves and minimal correlation with unrelated variables. To enhance the accuracy of findings, several measures were employed. Confirmatory factor analysis (CFA) was utilized to identify cross-loadings and insignificant components. Cross-loadings, where an item loads onto multiple factors, can impact factor analysis outcomes. The CFA findings verified the absence of cross-loadings, confirming the independence of components within the study's data.

4.1 Measurement model

The analysis of convergent validity confirmed that each element of the measurement model met the prescribed criteria. All factor loadings, Cronbach's alphas, average variance extracted (AVEs), composite reliabilities, and factor loadings exceeded the 0.60 benchmark, attesting to their satisfactory convergent validity. These outcomes affirm the measurement model's dependability and consistency, as the scale's items consistently appraise the same constructs. The study's measurements are thus deemed precise, and the results are not mere chance occurrences. This lends confidence to the study's findings.

The examination of convergent validity adhered to established guidelines proposed by Hair et al. (2013, 2019), reinforcing its relevance within an appropriate framework. These standardized criteria have proven effective in identifying valid and reliable scales in the realm of psychometrics. The current study's results endorse the compliance of its measurement technique with these criteria, reinforcing the trustworthiness and credibility of its conclusions.

For the evaluation of discriminant validity and inter-construct relationships, the average square root method advocated by Fornell and Larcker (1981) was employed. The results, as depicted in Table 3, showed that diagonal elements (representing average square roots) exceeded off-diagonal elements, establishing discriminant validity. To prevent multicollinearity, construct correlation coefficients ideally should be below 0.85 (Kline 2015). Examination of the correlation coefficients among constructs, as detailed in Table 3, revealed values below 0.85, indicating significant correlations among survey items without multicollinearity concerns. Hence, Table 3 succinctly showcases the constructs' discriminant validity, affirming both convergent and discriminant validity of the measurement scales.

Table 3 Discriminant validity of constructs
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4.2 Structural model testing

The structural model's fit measures indicate an adequate fit to the data, evidenced by X2/df = 2.675, CFI = 0.955, TLI = 0.943, GFI = 0.905, NFI = 0.930, RMR = 0.031, SRMR = 0.043, and RMSEA = 0.068 (Table 4). These results robustly endorse the research model depicted in Fig. 2. Hypotheses test outcomes are presented in Table 5. Results reveal the highly significant nature of all ten hypothesized paths within the structural model, at a significance level of 0.01. Moreover, the path coefficients (beta weights) were appraised to ascertain the robustness and pertinence of relationships between dependent and independent variables, aligned with anticipated outcomes (Streukens & Leroi-Werelds 2016) (Fig. 3).

Table 4 Structural model testing
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Fig. 2
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Proposed model

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Table 5 Results of hypotheses testing
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Fig. 3
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Results of the structural model

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Squared multiple correlations were computed for dependent variables PU, PEOU, and ITU, provides values of 0.49, 0.76, and 0.50, respectively. These values denote the extent of variance in dependent variables explicable by independent variables, signifying the predictive capacity of the model. Specifically, the attitude variable clarifies 76% of PEOU factor variance, indicating a robust association. The measurement model's reliability is comprehensively summarized in Table 2. Importantly, all components herein fulfilled convergent validity criteria, with composite reliability and Cronbach's alpha values surpassing 0.70. Furthermore, average extracted variance exceeded 0.50, and factor loadings surpassed 0.60. In conclusion, the model's measurement elements demonstrate trustworthiness and adherence to convergent validity requisites.

Hypotheses H1, H2, and H3 suggest that perceived usefulness (PU) has a positive influence on intention to use (ITU), user satisfaction (US), and user trust (UT), respectively. The positive and statistically significant estimates (PU-ITU: 0.179, PU-US: 0.146, PU-UT: 0.173) confirm that rural consumers who perceive digital banking as useful are more likely to intend to use it, experience satisfaction, and find it trustworthy. This underscores the importance of demonstrating the utility of digital banking services to rural customers.

The R2R^2R2 values indicate the proportion of variance in the dependent variable that is explained by the independent variable in each hypothesis path. For example, the path "PEOU-ITU" with an R2R^2R2 of 0.76 has the highest explanatory power among the paths, meaning that Perceived Ease of Use (PEOU) explains 76% of the variance in Intention to Use (ITU). In contrast, paths like "PU-US" and "PEOU-US," each with an R2R^2R2 of 0.35, have lower explanatory power, indicating that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) explain only 35% of the variance in Usage Satisfaction (US). Thus, the R2R^2R2 values help identify which paths have a stronger impact on the dependent variables, with higher R2R^2R2 values reflecting greater explanatory power.

Similarly, hypotheses H4, H5, and H6 propose that perceived ease of use (PEOU) positively affects ITU, US, and UT. The substantial estimates (PEOU-ITU: 0.203, PEOU-US: 0.168, PEOU-UT: 0.218) indicate that an intuitive and user-friendly digital banking experience contributes to increased intention to use, user satisfaction, and convenience. This highlights the significance of designing digital banking platforms that are easy for rural consumers to navigate and use effectively.

Hypotheses H7 and H8 suggest that there is a positive relationship between intention to use (ITU) and both user satisfaction (US) and user trust s(UT). The significant estimates (ITU-US: 0.135, ITU-UT: 0.162) indicate that rural consumers who express stronger intention to use digital banking services tend to experience higher levels of satisfaction and convenience. This implies that fostering positive attitudes and intentions toward digital banking can lead to enhanced user experiences and convenience.

5 Findings and discussion

The main aim of conducting the study was to examine rural consumers' adoption of digital banking services and user experiences based on a behavioral model and the factors that determine the model. The trust based technology acceptance model proposed by the study integrates user satisfaction and user trust variables as an extension to TAM model.

In this study, perceived usefulness and ease of use emerged as pivotal factors influencing attitudes toward digital banking. Both were affected by user satisfaction levels. The survey's outcomes revealed that the primary drivers motivating rural banking customers to embrace digital banking services were PU & PEOU(Wamba et al. 2021; Willyanto & Sfenrianto, 2021). The robust prediction of user satisfaction was attributed to perceived ease of use (PEOU; ß = 0.39).

Both PU (ß = 0.36) and PEOU (ß = 0.33) significantly influenced the intention to use (ITU) construct, encompassing adoption intentions, feature usage, and future adoption considerations. Moreover, user experience-related factors, namely user satisfaction (US) (ß = 0.38) and user trust (UT) (ß = 0.37), exerted considerable impact on digital banking uptake(Lara-Rubio et al. 2021; Sathiyamurthi et al. 2021; Sobaih & Elshaer 2023; Willyanto & Sfenrianto, 2021). These findings hold implications for policymakers, suggesting that initiatives aimed at encouraging rural Indian bank customers to adopt digital banking should prioritize enhancing connectivity, promoting financial literacy, and reinforcing the utility and security of digital banking offerings.

The structural equation modeling (SEM) results provide strong support for the hypotheses proposed in this study, revealing significant relationships among the key constructs. These findings offer valuable insights into the factors driving digital banking adoption and acceptance among rural Indian consumers.

The increasing smartphone usage in rural India, as indicated by the ASER poll, presents an opportunity for policymakers to leverage technology in promoting digital banking adoption among remote residents. Additionally, the survey unveiled psychological benefits of online banking, exemplified by the propensity of satisfied customers in rural areas to recommend it to peers, underscoring the potential influence of word-of-mouth marketing.

5.1 Theoretical implications

This study focused on user experience and vulnerabilities of digital banking among rural customers. It has several theoretical implications. The study adds to the body of literature on the adoption of digital banking in emerging nations like India by using a methodical approach and integrating known theoretical notions. In contrast to earlier studies in this field, the study proposes a novel methodology called trust based technology acceptance model to analyze the acceptability of digital banking. The TAUE model offers a thorough framework for analyzing the factors impacting the adoption of digital banking among rural clients in India by combining aspects of perceived usefulness (PU), perceived ease of use (PEOU), intention to use (IU), user satisfaction (US) and user trust (UT). Future study in this area may benefit from using this model as a solid theoretical base.

According to the survey, perceptions of usefulness and usability are crucial influences on rural clients' attitudes towards digital banking. These results support earlier studies on technology adoption and emphasize the significance of speed, effectiveness, and convenience in encouraging users to adopt digital banking services and products. Policymakers and financial organizations can develop strategies to increase the adoption rates of digital banking in rural areas through making a focus on these elements. According to the study, user satisfaction (US) and user trust (UT) have a significant impact on digital banking usage. This suggests that encouraging adoption depends on providing favorable user experiences, such as smooth transactions, simple access to services, and satisfaction with the entire digital banking experience. Users' convenience and satisfaction should be increased by policymakers and service providers through initiatives including better connectivity, financial literacy initiatives, and greater security measures.

6 Conclusion

This study aimed to explore the factors driving the digital banking adoption among rural Indian consumers and their experiences. Unlike prior research focusing on adoption or user experience separately, this study uniquely integrated both aspects using the Trust based TAM model. The findings highlight the significance of elements like PU, PEOU, ITU, and their linkages with US and UT for enhancing digital banking adoption (Goswami et al. 2022; Hübenbecker, 2023; Lee et al. 2023; Yang et al. 2021). Analyzing the both direct and indirect impacts into this study allows for a more comprehensive understanding of how digital banking adoption influences financial behaviors and outcomes. Direct effects show that digital banking has an immediate impact on consumer involvement and happiness, whereas indirect effects highlight the role of intermediate characteristics like trust and technology literacy in influencing these interactions. Recognizing these consequences not only broadens our understanding, but also has important implications for practitioners looking to enhance digital banking tactics and improve consumer experiences. While the study has limitations due to sample size and model coverage, it provides insights for secure infrastructure and improved services. Further research can delve into additional variables and the unique challenges of rural adoption. Overall, the Trust based TAM offers a valuable foundation for future investigations into digital banking acceptance and experience in emerging economies.