Introduction

Post-COVID-19 pandemic, the Unified Payments Interface (UPI) has emerged as an innovative real-time digital payment system characterized by it “paperless and cashless” nature. This platform facilitates the instant transfer of funds between bank accounts, not only for peer-to-peer transactions but also for bill payments and merchant transactions conducted through smartphone devices (Jena, 2023; Jha & Kumar, 2020). UPI integrates multiple bank accounts under a single application using virtual payment addresses (VPA) as unique identifiers (Ranpariya et al., 2021; Tungare, 2019). This system is based on a “disruptive technology” framework (Vivek & Selvan, 2021) and has become a significant component of the Indian fintech industry (Rajeswari & Vijai, 2021). Such a ground-breaking approach was developed by the not-for-profit “National Payments Corporation of India” (NPCI) in 2016 and is regulated by the Reserve Bank of India (RBI) (Chawla et al., 2019). UPI has revolutionized the scenario of financial services in India (Priya & Anusha, 2019; Raghavendra & Veeresha, 2023), and it has positioned India as a global leader in digital payments by reshaping conventional payment systems (Dhivya et al., 2023). Its widespread adoption can be attributed to its user-friendly interface, robust security features, and widespread acceptance across various sectors, including small retailers, roadside vendors, and unorganized sectors (Chawla et al., 2019; Ligon et al., 2019; Sinha & Singh, 2023). However, certain prerequisites are essential for its adoption, such as having a bank account, a smartphone, access to the Internet, and technological literacy (Ligon et al., 2019). Key unique characteristics of UPI include seamless money transfer via mobile devices, initiation of payment by both payees and payers, and one-click, two-factor authentication (Gochhwal, 2017; Tungare, 2019). Unlike traditional Internet banking, UPI transactions are free for personal use, regardless of the transaction amount (Pal et al., 2023). Various applications support UPI transactions, with prominent players including Bharat Interface for Money (BHIM), CRED, PhonePe, Google Pay, MobiKwik, Paytm, etc. (Neema & Neema, 2018; Vivek & Selvan, 2021). Among these, Google Pay has emerged as the most preferred platform, followed by PhonePe, Paytm, and BHIM, reflecting a rapid expansion in UPI’s consumer base (Jakhiya et al., 2020). In the latest estimate from Statista, about 84 billion UPI transactions worth 139 trillion Indian rupees were made throughout India in the fiscal year 2023, almost doubling up from 2022, when the number of UPI transactions across India was just 46 billion (Keelery, 2024). Projections indicate that UPI transactions may exceed 456 billion by the fiscal year 2028. The highest UPI usage has been recorded in the states of Maharashtra, Tamil Nadu, and Karnataka, while Hyderabad, Bengaluru, and Chennai lead among cities (Dhivya et al., 2023). The key drivers for the adoption of digital payments among merchants include demonetization, customer demand, and ease of use (Ligon et al., 2019). Among consumers, UPI usage has witnessed exponential growth (Singh & Singh, 2022) due to the rapid Internet expansion and high mobile penetration rates in India (Jena, 2023). Moreover, UPI is a highly “safe and secure payment option” for conducting transactions at any time of the day throughout the year (Chaudhari & Chaudhari, 2019; Neema & Neema, 2018; Vivek & Selvan, 2021). Other influential factors are the absence of fund transfer fees, compatibility with small transactions, cashback offers, privacy protection, secured payments, instant transfers, multi-account linkage, options of choosing apps, and widespread bank support (Jha & Sharma, 2022; Vivek & Selvan, 2021). Addtionally, considerations such as perceived risks, compatibility of applications and operating systems, comparative advantages over other payment platforms, and digital financial literacy (knowledge, awareness, skills, and experience) significantly impact UPI usage, along with concerns over external threats (Patel & Datta, 2020; Shehadeh et al., 2024). Promoting cashless transactions through UPI has furthered participation in the formal economy, reducing tax evasion, increasing financial savings, and facilitating equitable conditions for both individuals and businesses (Nigam & Kumari, 2018).

Despite its rapid adoption, the usage of UPI has not been uniform across various population segments (Ligon et al., 2019), and its adoption is yet to reach “full scale” due to consumers’ preference for cash (Nigam & Kumari, 2018; Ranpariya et al., 2021). Other challenges include inadequate internet infrastructure, technological limitations, low levels of digital literacy, limited awareness, operational complexities, and concerns related to trust and privacy (Gupta et al., 2022; Nigam & Kumari, 2018). In an effort to expand adoption, UPI applications offer various supplementary services and functionalities apart from basic money transfers, such as access to bank account statements, transaction records, expenditure history, and balance inquiries (Kuriakose et al., 2022a). In addition, promotional incentives, including rewards, coupons, cashback offers, loyalty programs, and discounts, are offered to enhance consumers’ engagement (Kapoor et al., 2022; Madan & Yadav, 2016). However, despite these efforts, consumer awareness and willingness to adopt UPI applications at a broader scale remain limited (Rana et al., 2023). Therefore, it is important to understand how UPI users perceive the add-on services or promotional benefits in an emerging digital economy like India. Existing studies have not explored the effect of add-on services or promotional strategies on consumers’ adoption intentions in the Indian context. Besides, demographic profiles such as age, gender, education, income, and occupation significantly influence UPI adoption and active usage (Patel & Datta, 2020). Research indicates that younger individuals are more inclined to use UPI compared to senior citizens (Jena, 2023; Saini & Khasa, 2023). Understanding these demographic determinants is essential for stakeholders, including retailers, entrepreneurs, policymakers, financial institutions, and technology providers, to develop more inclusive and effective digital financial solutions (Gupta et al., 2023; Raghavendra & Veeresha, 2023).

This study seeks to examine the adoption and usage of UPI through the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT framework identifies four key determinants of technology adoption: performance expectancy, effort expectancy, facilitating conditions, and social influence (Venkatesh et al., 2003). These factors are critical in shaping users’ behavioral intentions towards adopting UPI (Jena, 2023; Ranpariya et al., 2021). Additionally, this study extends the UTAUT model by incorporating additional factors, such as perceived trust (Joshi & Chawla, 2023; Saha & Kiran, 2022), perceived promotional benefits, and add-on services, which are highly relevant in the context of digital payments in the contemporary technological scenario.

Literature review and hypothesis development

Previous research on the adoption of mobile payment systems

Previous studies have extensively examined the factors influencing the adoption of mobile-based payment services (Chakraborty et al., 2022; Singu & Chakraborty, 2022). Various information system theories and models are introduced to explain the behavioral intention of consumers to adopt new technologies (Chakraborty et al., 2023; Chakraborty, 2023; Verma et al., 2023), especially with respect to digital payment apps (Aliu, 2024; Belmonte et al., 2024). By applying the technology acceptance model (TAM), previous studies have suggested that perceived ease of use and perceived usefulness significantly influence customers’ intention (Hasan et al., 2021; Nelwan et al., 2021; Norng, 2022). However, TAM was limited in certain aspects, and more factors had to be added to explain the adoption behavior thoroughly (Belmonte et al., 2024; Putri et al., 2023). Some studies used an extended version of the TAM model by including factors, such as trust and perceived enjoyment (To & Trinh, 2021), data security and privacy (Putri et al., 2023), subjective norms (Gumussoy et al., 2018), observability and social image (Yang et al., 2023). These additional factors demonstrated a significant effect on the intention to adopt technological advancements. The UTAUT model was introduced by Venkatesh et al. (2003) to understand the adoption intention factors. The UTAUT model and its variants are most frequently employed to examine the intention and behavior toward UPI adoption (Kuriakose et al., 2022a; Ranpariya et al., 2021; Saha & Kiran, 2022; Shah, 2021; Sinha & Singh, 2023) and e-money services (Giri et al., 2019). The UTAUT model has 70% more prediction efficacy than the TAM (Gupta et al., 2023). The outcome variables are behavioral intention and continuous use of the technology being adopted. Under the UTAUT model, existing studies showed that performance expectancy (Oliveira et al., 2016; Patil et al., 2020a; Shaikh & Amin, 2023), effort expectancy (Khalilzadeh et al., 2017; Upadhyay et al., 2022), social influence (Hamzah et al., 2023; Yan et al., 2023) and facilitating conditions (Hassan et al., 2023) significantly influence adoption intention of mobile payments. However, contradictory studies showed no influence of these variables on consumer intention (Alkhwaldi et al., 2022; Bajunaied et al., 2023; Kurniasari et al., 2022). Recent studies have utilized the UTAUT 2 model by including factors, such as price value, hedonic motivation, and habit (Migliore et al., 2022; Tang & Tsai, 2024; Wu & Liu, 2023). Empirical studies extended the UTAUT and UTAUT 2 models to comprehend the significant drivers impacting the behavioral intention to adopt mobile payment systems by adding factors and theories, such as avoidance and ownership (Yang et al., 2023), awareness, security, and privacy (Al-Okaily et al., 2024), perceived trust (Gupta et al., 2023), perceived enjoyment (Nur & Panggabean, 2021), and uncertainty avoidance (Alkhwaldi et al., 2022). Apart from using TAM and UTAUT models in explaining the intention of adopting mobile payment systems, few studies have used the theory of consumption values (Chakraborty et al., 2022; Karjaluoto et al., 2021), which has depicted a significant influence on mobile payment adoption. Although the UTAUT model has been extensively used to explain mobile payment adoption behavior, it is criticized for not including variables addressing individual differences (Patil et al., 2020b; Razi-ur-Rahim et al., 2024). In addition, the impact of factors such as add-on services or promotional benefits provided by the service providers is not studied in the context of Indian UPI users (Kuriakose et al., 2022b). Besides, the true effect of promotional benefits on consumers’ behavior often remains unnoticed, as it is not observed from the consumers’ perspective. Furthermore, studies on the role of demographic factors in UPI adoption are less studied and are contradictory (Banerjee & Pradhan, 2024; Chauhan et al., 2022).

Conceptual model and hypotheses development

Figure 1 presents the conceptual framework for the study. This study is based on the UTAUT model to examine the factors influencing adoption intention. The model is expanded to include perceived trust, perceived promotional benefits, and add-on services provided by UPI to consumers. The UTAUT theory, as suggested by Venkatesh et al. (2003) and its variants, behavioral intention refers to the psychological state that indicates one’s readiness or plan to engage in a specific behavior (in this case, using UPI). This intention serves as a precursor to actual behavior, whereas UPI usage behavior pertains to the tangible adoption and incorporation of the technology into routine financial transactions (Jha & Kumar, 2020; Shah, 2021). Furthermore, the theory proposes the moderating role of demographics on the impact of the UTAUT factors on the behavioral intention to adopt. When applied in the context of UPI, it was found that facilitating conditions imply the degree to which an individual user believes that organizational and technical infrastructure will support the usage of UPI, while performance expectancy is the degree to which the consumer believes that the use of UPI will help in enhancing their performance (Jena, 2023; Kuriakose et al., 2022a). Effort expectancy refers to the perceived ease of using UPI, while social influence pertains to the extent to which individuals believe that significant others expect them to adopt UPI. All four factors are expected to have a significant positive impact on the consumers’ behavioral intention of adopting UPI (Gulia & Singh, 2023; Kuriakose et al., 2022a). On the contrary, facilitating conditions (Ranpariya et al., 2021; Saha & Kiran, 2022) along with effort expectancy (Gulia & Singh, 2023; Saha & Kiran, 2022) or social influence (Gulia & Singh, 2023; Jha & Kumar, 2020), were found to be insignificant for the consumers’ intention to adopt UPI. In the study of Gupta et al. (2022), none of the four factors were found to be statistically impacting behavioral intention. Therefore, with such varying responses, it becomes critical to validate each factor in the current context. Based on this, the first hypothesis, H1: The factors of the UTAUT model (facilitating conditions, performance expectancy, effort expectancy, and social influence) significantly influence the intention to adopt UPI, constituting several sub-hypotheses individually testing their adoption intention, was proposed.

Fig. 1
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Proposed conceptual model developed for the study for assessing UPI adoption and usage.

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H1a: Facilitating conditions substantially boost UPI adoption by consumers.

H1b: Performance expectancy positively influences UPI adoption.

H1c: Effort expectancy leads to enhanced UPI adoption by consumers.

H1d: Social influence significantly dictates the adoption of UPI by consumers.

The UTAUT theory also implies that the behavioral intention towards the adopted technology will directly impact the actual usage and adoption. This has been reported to be true in the context of UPI, where intention has been found to drive the usage behavior of UPI users (Gupta et al., 2022; Saha & Kiran, 2022). A strong intention will most likely lead to the actual usage of the UPI mode of payment. Moreover, the prior experience of UPI usage positively influences the usage of Central Bank Digital Currency (CBDC) (Gupta et al., 2023). Therefore, to validate the impact of the behavioral intention of UPI adoption on actual usage in the current context, the next hypothesis was formulated as follows:

H2: The intention to adopt UPI dictates the UPI usage behavior.

According to Kuriakose et al. (2022b), the add-on services imply the supplementary services and functionalities that are provided alongside the core UPI payment services. In the current context, these services extend beyond basic money transfers, improving users’ experience and providing added value to them. Such services include bill payments, mobile and direct-to-home TV services, QR code payments, loans and credit facilities, investment and financial services, subscriptions, auto payment, account balance checks, account history, mini statements, merchant offers, cashback, split bills, etc. Based on this, the next hypotheses intend to validate the following.

H3a: The add-on services provided by UPI directly influence the intention to adopt UPI among consumers.

It is believed that add-on services provided by UPI improve performance expectancy by expanding the platform’s utility beyond the simple payment functions by increasing utility and versatility, saving time, facilitating convenience, rendering promotional offers, and offering cashback benefits (Dhivya et al., 2023; Kuriakose et al., 2022a). Thus, it can be expected that add-on services will have a certain impact on the performance expectancy of UPI (Kuriakose et al., 2022a). To test this, the following hypothesis was formulated,

H3b: The add-on services provided by the UPI significantly enhance the performance expectancy of UPI.

Apart from improving performance, add-on services may improve effort expectancy by simplifying multiple functions and making the platform more user-friendly through centralized platforms, seamless integration, user-friendly features, intuitive design, and support (Kuriakose et al., 2022a). Providing incentives and automating tasks can increase perceived value and ease of use. Based on this, the next hypothesis was proposed,

H3c: The add-on services also improve the effort expectancy of UPI.

Promotional benefits can be described as the “financial incentives” provided to attract potential users and retain them (Kuriakose et al., 2022a). Such benefits include cashback, coupons, discounts, and rewards, which are offered to users (Khanra et al., 2020). These perks can motivate users to ensure the availability of the necessary conditions at both technical and operational levels and enable compatibility of their devices. The perceived availability of support and assistance will strengthen the perceived facilitating conditions. Therefore, the next hypothesis validates this relationship.

H4a: The perceived promotional benefits provided by UPI have a significant impact on the facilitating conditions.

Promotional benefits tend to increase the perceived financial value to users, reduce perceived risks, induce positive reinforcement, encourage social influence, boost trust, and incentivize repeat usage. All of these factors collectively make mobile payment adoption more attractive and economically efficient, thereby providing a competitive edge to users and capturing their attention (Al-Saedi et al., 2019; Kukreja et al., 2020). It has been proposed that promotional benefits may positively influence the intention of UPI adoption (Jha & Kumar, 2020; Kuriakose et al., 2022b; Shah, 2021). To validate that in the current context, the next hypothesis was proposed.

H4b: The perceived promotional benefits provided by UPI significantly lead to the intention to adopt UPI.

Perceived trust seems to have a direct influence on the willingness of users to adopt digital payments, as it tends to reduce perceived risks, decrease transaction anxieties, and increase confidence in the reliability of transaction systems, banks, regulatory framework, and app providers (Khan & Abideen, 2023; Manrai & Gupta, 2020). Perceived trust was found to influence the adoption of e-banking among the aged population (Jena, 2023). This leads to the cultivation of positive experiences, which would gradually result in sustainable usage (Al-Saedi et al., 2020). Hence, we hypothesized that:

H5: Perceived trust in UPI tends to significantly influence the adoption intention of consumers.

One of the major factors that influences the usage of UPI among users is demographics (Patel & Datta, 2020). Significant differences have been observed in the adoption of UPI based on demographics, such as age, gender, monthly income, and occupation (Tungare, 2019). The adoption of UPI was much higher in males compared to females and adults over young adults. As a part of the UTAUT theory, as suggested by Venkatesh et al. (2003), demographic factors such as age, gender, education, experience, and voluntariness have been proposed to influence adoption and subsequent usage. However, it was found that neither age nor gender moderated the impact of performance expectancy on behavioral intention to adopt e-money services in Indonesia (Giri et al., 2019) or e-banking services, including UPI in India (Ranpariya et al., 2021). Therefore, for the present study, age, gender, income, and occupation were considered under demographics.

H6: Demographic factors considerably moderate the impact of UTAUT factors on the adoption of UPI.

Research methods

A quantitative research design, based on the positivistic research philosophy and employing a deductive research approach, was adopted for this study, wherein a survey was used as the primary research instrument.

Research instrument

A closed, structured questionnaire comprising four sections was developed. Section I enquired about the demographics of respondents, such as gender, age, occupation, and monthly income. Section II focused on understanding the sustained UPI usage by consumers through six items (Khan & Abideen, 2023; Patil et al., 2020b; Sharma & Sharma, 2019). Section III investigated the four factors influencing UPI usage based on the UTAUT model, such as facilitating conditions, performance expectancy, effort expectancy, and social influence. Facilitating conditions comprised four subfactors, namely Customer support (Barbu et al., 2021; Miadinovic & Xiang, 2016), Speed (Barbu et al., 2021), Assurance (Barbu et al., 2021), and Redressal of grievances (Chawla et al., 2019; Narayan & Prasadi, 2023), with five items each. Performance expectancy was measured through subfactors such as Monetary savings (Ryu, 2018b) with four items, Economic efficiency (Bajunaied et al., 2023; Ryu, 2018b) with five items, and Seamless transactions (Chao, 2019; Ryu, 2018b) with four items. Effort expectancy comprises Ease of use (Hu et al., 2019; Nigam & Kumari, 2018; Sarmah et al., 2020), Convenience (Pal et al., 2023; Ryu, 2018b; Tungare, 2019), and Complexity (Fahad, 2022; Thompson et al., 1991) with four items each. Social influence comprised two subfactors: Interpersonal influence with five items and External influence with three items (Nur & Panggabean, 2021; Tungare, 2019). Other variables, such as Perceived trust (Amnas et al., 2023; Khan & Abideen, 2023; Manrai & Gupta, 2020; Patil et al., 2020a; Roh et al., 2022), perceived promotional benefits (Kuriakose et al., 2022a), and Add-on services (Kuriakose et al., 2022a), were evaluated using five items each. Finally, Section “Results” comprised five items assessing the intention to use UPI among consumers (Amnas et al., 2023; Khan & Abideen, 2023; Manrai & Gupta, 2020; Sharma & Sharma, 2019). All items were designed as a 5-point Likert scale, ranging from Strongly Disagree (1) to Strongly Agree (5), which was further used to evaluate the construct items. The questionnaire was presented in English and administered online via platforms like Google Forms.

Sample population and sample size

The study adopted a cross-sectional survey period to gather data at a specific point in time from UPI users across diverse demographic backgrounds. The target population for this study consisted of individuals in India who are current or potential users of UPI for digital payments, selected via random sampling. The sample size was determined using Cochran’s formula for large populations, ensuring statistical power and generalizability of the results. The estimated sample size was calculated to be 385 respondents, which will allow for meaningful subgroup analyses. The questionnaire was distributed to 450 consumers, out of which 416 responses were found to be valid, with a response rate of 92%.

Data analysis

Partial Least Squares Structural Equation Modeling (PLS-SEM) was chosen to employ SmartPLS software (version 3.3.3), as it is a robust method suitable for predictive modeling when dealing with complex models and small sample sizes (Ringle et al., 2015), as also highlighted by Jena (2023) in the context of digital payments in India. Subsequently, measurement models and structural models were estimated, leading to a comprehensive evaluation of the hypothesized relationships. The measurement model assessed the reliability and validity of the constructs used in the study, ensuring that the items appropriately measured the theoretical constructs. The structural model used path coefficients for hypotheses testing, wherein 5000 bootstrapped resamples were applied to obtain the standard errors and t-statistics (Hair et al., 2014). Moreover, the study adhered to ethical guidelines for research involving human participants. Informed consent was obtained from all respondents, assuring them of the voluntary nature of participation, anonymity, and confidentiality of their data. Respondents had the right to withdraw from the study at any stage without penalty.

Results

Sample characteristics

Table 1 presents the socio-demographic features of the study population for the current study. More than half (56.7%) of the users considered in this study were females aged 25 years or younger (60.6%) who were either employed in private organizations (39.4%) or were self-employed and had a monthly income of Rs. 750,000 and above (56.3%).

Table 1 Demographics of the participating users.
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Measurement model

The measurement model was assessed using construct reliability, internal consistency, and convergent validity (Table 2, Fig. 2). Cronbach’s Alpha and composite reliability (CR) were used to evaluate the internal consistency of the latent variables. All the values exceeded the threshold of 0.70 to indicate satisfactory reliability. Average variance extracted (AVE) was computed to assess convergent validity, ensuring that each construct explained more than 50% of the variance in its indicators. An AVE value of 0.50 or higher was deemed to be acceptable (Hair et al., 2017). Moreover, each indicator’s loading on its respective construct was 0.70 or higher, which confirmed convergent validity, though loadings between 0.50 and 0.70 are acceptable if AVE is above the required threshold.

Table 2 Construct reliability and validity.
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Fig. 2
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Measurement model for the study.

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The discriminant validity was studied using the Fornell–Larcker criterion and Heterotrait–Monotrait ratio (HTMT) elaborated in Supplementary Table A3 and Supplementary Table A4, respectively. For the discriminant validity, it was ensured that the square root of the AVE for each construct was higher than the correlation of that construct with any other construct in the model. In the case of the HTMT values, values were less than 0.85 (Henseler et al., 2015), which indicated good discriminant validity.

Assessment of structural model

The evaluation of model fitness was conducted using the goodness of fit for the model, coefficient of determination (Supplementary Table A5), effect size (Supplementary Table A6), predictive relevance (Supplementary Table A7), and collinearity assessment (Supplementary Table A8). The goodness of fit for the model was estimated, and it was found that the fit indices such as standardized root mean square residual (SRMR) = 0.083, squared Euclidean distance (d_ULS) = 7.067, geodesic distance (d_G) = 93.727, chi-square (χ2) = 16588.683, and normed fit index (NFI) = 0.114 were found to be within the stipulated values indicating that the proposed measurement model had a good fit. The R2 values are crucial in PLS-SEM to assess the model’s explanatory power, which indicates the proportion of variance in the endogenous constructs explained by the exogenous constructs. It varied from 7.1% (Performance expectancy) to 44.3% (Intention to adopt UPI). As suggested by Hair et al. (2017), the effect size (f2) shows the relative impact of each independent or exogenous construct on dependent or endogenous constructs (Supplementary Table A6). According to the estimation given by Cohen (1992), the impact sizes of the associations ranged from medium to large. The blindfolding procedure was used to calculate Stone–Geisser’s Q2 to assess the predictive relevance of the model, presented in Supplementary Table A7 (Geisser, 2012; Stone, 1974). A Q2 value greater than zero indicates that the model has predictive relevance for the dependent variable (Richter et al., 2016). All the variables had the Q2 value more than zero. Therefore, it can be implied that these values were quite satisfactory.

Hypothesis testing

The structural model (Fig. 3) was estimated using the bootstrapping method, which is a resampling approach in which several subsamples, such as 1000 or 5000, are obtained from the original data (Vinzi et al., 2010). The route coefficients and their significance were therefore evaluated. To validate the hypotheses, the p-value must be less than 0.05, while the t-standard statistic must be more than 1.96 (Hair et al., 2021). The results of direct and indirect effects between the constructs are presented in Table 3. It was observed that there is a statistically significant positive effect of the factors of UTAUT, such as facilitating conditions, performance expectancy, effort expectancy, and social influence, thus supporting H1. Along with this, the intention to adopt UPI among users had a positive impact on the usage behavior, thereby supporting H2. Even though add-on services significantly impacted performance expectancy and effort expectancy, they did not have any impact on the intent to adopt UPI. Thus, it can be derived that only H3b and H3c are supported. Coming to the impact of perceived promotional benefits of UPI on facilitating conditions and intention to adopt UPI, both the relationships were found to be significant, thereby suggesting that H4a and H4b both stand accepted. Further, the perceived trust was found to significantly influence the intent of UPI adoption, thereby suggesting the acceptance of H5. The direct effect of demographics on intention to adopt UPI showed that age, gender, and occupation had a direct impact on intention to adopt UPI, while income did not have any role. The indirect effects of each of the demographics showed that it was a mixed bag for age and occupation of users, with only age and occupation showing a moderating impact on the impact of performance expectancy on intention to adopt UPI, and occupation also playing a moderating role on the influence of occupation on facilitating conditions. Moreover, the gender and income of the users had no moderating role on the impact of any of the factors of UTAUT on the intention to adopt UPI. Overall, it can be construed that H6 was partially accepted.

Fig. 3
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Structural model for the study.

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Table 3 Path Coefficients.
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Discussion

The rapid adoption of the UPI in India has revolutionized the digital payments landscape, establishing it as one of the most prominent modes of transactions for individuals and businesses alike. This study adopted the UTAUT theory to examine the adoption and utilization of UPI among users in India, with demographic factors serving as moderating variables. The analysis revealed that performance expectancy significantly influenced behavioral intention to adopt UPI among Indian consumers. Specifically, when individuals perceive UPI applications as highly efficient—offering user-friendly interfaces, financial savings, enhanced payment efficiency, improved financial management, and seamless transaction experiences—they are more inclined to adopt UPI. These findings are in agreement with previous studies (Gupta, 2022; Hanafiah et al., 2024; Martinez & McAndrews, 2021; Nguyen et al., 2022). Furthermore, the present study demonstrated that effort expectancy positively influenced the intention to adopt UPI apps and eventually increased usage behavior. This suggests that when consumers find the application easy to use, intuitive, and free from complexity, they are more likely to adopt it. This finding corroborates with prior studies (Hussain et al., 2019; Upadhyay et al., 2022). Furthermore, the present study identified that facilitating conditions significantly influence consumers’ intention to adopt UPI. Factors, such as a responsive customer support system, service speed and accessibility, UPI app reliability, and efficient grievance redressal mechanisms, contribute to increased adoption behavior, corroborating previous findings (Alswaigh & Aloud, 2021; Bailey et al., 2022; Xu et al., 2025). Furthermore, social influence positively influences consumer behavior. This signifies that individuals are more likely to adopt UPI when family members, friends, or influential figures, such as celebrities, endorse or actively use the platform. This observation is in line with previous research (Hamzah et al., 2023; Yan et al., 2021). Apart from validating the factors in the UTAUT model, the study showed that the add-on services of UPI did not influence the adoption intention; however, it influenced the performance expectancy and effort expectancy of UPI. To the best of our knowledge, this study is the first to study the effect of add-on services, such as bill payment service and credit service provided by UPI apps, on their adoption intention in India. The limited influence of add-on services on UPI adoption can be explained by the fact that Indian consumers are not used to handling different add-on services, such as online payment of utility bills, managing multiple bank accounts digitally, or using UPI for loan EMI payments. More studies are needed to understand the effectiveness of these services. The present study also depicted a significant effect of perceived promotional benefits on the adoption intention of UPI consumers. Prior research has similarly established the impact of promotional strategies on consumer adoption and switching behavior in mobile payment applications (Wang et al., 2019; Wei et al., 2021). The findings suggest that when consumers perceive promotional incentives positively, their likelihood of adopting UPI increases. With respect to the moderating role of demographic factors, the study found that gender, income, and occupation did not moderate the relationship between UTAUT model variables and adoption intention. This is in contrast to the earlier findings by Banerjee and Pradhan (2024), where consumers’ mobile adoption was found to be varied based on gender and educational qualification.

Implications of the study

The study offers significant theoretical and managerial implications concerning the adoption of mobile payment systems. The study validated the applicability of the UTAUT model in comprehending consumers’ adoption behavior. Along with this, the study introduced critical variables, such as perceived promotional benefits and add-on services, which were not studied in the context of Indian UPI adoption. Moreover, the findings of this study indicate the non-moderating role of demographics and oppose previous studies, opening further avenues in research. Apart from theoretical implications, the study provides several implications from a managerial perspective. The integration of loyalty programs, payment reminders, and multi-bank account management emerged as pivotal factors in enhancing the overall utility of UPI. Consumers who perceived UPI as offering a comprehensive suite of value-added services demonstrated a higher likelihood of adoption and continued usage. Furthermore, promotional benefits, such as cashback offers, discounts, and rewards, were found to significantly boost UPI adoption, as they provided immediate financial incentives and mitigated perceived risks associated with transitioning to a digital payment system. Trust was found to be a critical factor in determining UPI adoption, especially in terms of perceived security and privacy. Consumers who trusted UPI as a secure payment method were more likely to adopt it. Ensuring robust security measures and transparent communication about data protection can therefore enhance trust and encourage wider adoption across demographic segments. These results highlight how essential it is for UPI service providers to provide user-friendly interfaces that appeal to a variety of demographic groups, particularly older and less tech-savvy customers. Adoption of UPI among these groups can be accelerated by providing individualized help and streamlining onboarding procedures. Additionally, sustaining promotional incentives may prove effective in attracting new users, particularly from lower-income segments. To ensure equitable access to UPI, policymakers should concentrate on enhancing digital literacy and on increasing the availability of smartphones and internet connectivity.

Conclusion

All the studied UTAUT factors such as facilitating conditions, performance expectancy, effort expectancy, and social influence directly influence intention to adopt UPI, which in turn significantly affects actual UPI usage, thereby validating the model. These factors emerged as crucial predictors of UPI adoption intention. Moreover, perceived promotional benefits and trust in UPI were identified as key enablers in its adoption as a routine medium for financial transactions. However, the influence of add-on services on the intention to adopt remained inconclusive in the present study.

Limitations and future directions

Despite the valuable insights derived from this study, certain limitations must be acknowledged, particularly those associated with its cross-sectional research design. These constraints form the basis for future research to improve the robustness and generalizability of findings. The reliance on self-reported data presents potential biases, such as social desirability bias or recall inaccuracies, which may have led respondents to overestimate or underestimate their experiences with UPI or their willingness to adopt it. Future research could integrate objective data, such as transaction histories, to corroborate self-reported intentions and behaviors. In addition, future research can extend the UTAUT model by addressing variables related to specific fears, such as data breaches or misuse of personal information or technical barriers, such as low internet infrastructure and limited smartphone access, which will help to understand privacy concerns related to UPI adoption. Furthermore, factors such as local cultural norms, diverse regional economies, and varying levels of digital literacy can be investigated from the perspective of Indian consumers. Longitudinal studies could also be conducted to examine shifts in UPI adoption and usage patterns over time, thereby capturing the evolving behaviors of users in the dynamic digital payment ecosystem.