E-ISSN:2583-0074

Research Article

NPCI

Social Science Journal for Advanced Research

2026 Volume 6 Number 3 May
Publisherwww.singhpublication.com

A Study on the Customers’ Perception towards Use of Unified Payment Interface in India

Nayak S1*
DOI:10.54741/SSJAR/6.3.2026.374

1* Sayanee Nayak, Assistant Professor, Department of Commerce, Vivekananda Mission Mahavidyalaya (Under Vidyasagar University), Chaitanyapur (Haldia), Purba Medinipur, West Bengal, India.

The National Payments Corporation of India (NPCI) established the instant real-time payment system which is governed by the Reserve Bank of India. The paper traces the evolution of Unified Payment Interface (UPI) in India among the other digital payments systems from 2016 to 2025 and analyses the reasons behind the traction of UPI among the users of the technology using the constructs as enumerated in the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. This study reveals a strong relationship between performance of UPI and effortlessness and risk-free nature of the payment system. Delving into the impact of external factors like age, occupation and income on the consumers’ intention to use UPI, it is found that younger age groups show a strong inclination towards adoption of UPI owing to their tech-savviness and exposure to digital environment.

Keywords: digital payments, unified payment interface, UTAUT model, NPCI

Corresponding Author How to Cite this Article To Browse
Sayanee Nayak, Assistant Professor, Department of Commerce, Vivekananda Mission Mahavidyalaya (Under Vidyasagar University), Chaitanyapur (Haldia), Purba Medinipur, West Bengal, India.
Email: 		Nayak S, A Study on the Customers’ Perception towards Use of Unified Payment Interface in India. Soc Sci J Adv Res. 2026;6(3):71-79.
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https://ssjar.singhpublication.com/index.php/ojs/article/view/374

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2026-04-14 2026-04-29 2026-05-19
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
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© 2026 by Nayak S and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

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Download PDFBack To Article1. Introduction2. Literature Review3. Research Gap4. Objectives of the
Study5. Research
Methodology6. Analysis &
Findings7. Summary of
Observations8. Recommendations9. Limitations of the
StudyReferences
Nayak S. A Study on the Customers’ Perception

1. Introduction

In the recent years, it has been observed that there has been a sea change in the way people transact with one another using the digital payments. The traditional ways of digital payments like use of debit and credit cards, internet banking, National Electronic Funds Transfer (NEFT), Real Time Gross Settlement (RTGS), and Immediate Payment Service (IMPS) have been replaced by Unified Payments Interface (UPI) which was officially launched in April, 2016 by the National Payments Corporation of India (NPCI).

UPI have enabled instant money transfer all round the clock using one single mobile application for multiple bank accounts. Every time a customer initiates a transaction, there is no longer a need to feed sensitive information like bank account details. Instead, Virtual Payment Address (VPA) is used which is the technical term for the commonly used term ‘UPI ID’. UPI has been built over the IMPS infrastructure that has not only enabled a wide array of transactions for clients but has also reduced the infrastructure costs of banks. Three parties make up the UPI ecosystem, - banks that manage the accounts for both the payer and the payee, payment service providers that serve as the intermediary and NPCI, the central regulator managing the virtual payment address. Following the two-factor authentication rule of Reserve Bank of India (RBI), this system uses the mobile number linked to the user’s bank and a UPI Pin as the two factors to enable safe and secure transactions, thereby gradually eliminating the need to conduct business with liquid cash or through a bank.

The International Monetary Fund (IMF) has reported India to dominate the global real-time payments as 84 per cent of the electronic transactions real-time (“Prime Time for Real-Time Report 2023.” by ACI Worldwide). UPI accounts for 85 per cent of all digital transactions in India (India’s UPI Revolution, Press Information Bureau, Government of India, July 20, 2025). This shows the wide acceptance of UPI system among the Indian population that is helping India achieve its goal of cashless society by promoting digital revolution across the country. Exhibit 1 shows the increasing trend of UPI transactions surpassing VISA in handling daily transactions.

ssjar_374_01.PNG
The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh, et al., (2003) is a widely accepted model used to understand individuals' behaviour towards adopting and using new technologies. It was as an extension of previous technology acceptance models, with the aim of incorporating various factors that influence technology acceptance.

Four key factors have been proposed to drive individuals' acceptance and use of technology: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). PE is a crucial factor that shows the usefulness and efficiency of UPI payment applications for consumers. EE demonstrates the degree of convenience in using UPI technology and measures the users’ experience. SI measures the degree of influence the peers have on the non-user of the technology and what influences UPI adoption. FC is the presence of necessary resources and knowledge to access the UPI service like, customer support, mobile devices, an internet connection, and QR codes.

Apart from the above mentioned four constructs, there are outcome variables namely Perceived Risk & Safety (PRS), Habit (H) and Behavioural Intention (BI). PRS refers to the inherent risk in the use of technology like threat to data, lack of security and rapid transaction failure or absence of proper grievance redressal mechanism. H is the degree of dependence on a particular technology measuring the frequency of use. BI is the willingness of a person to use the technology.

2. Literature Review

The following literature have been studied during the course of the work:

Fahad, Shahid, M., (2022): In their paper titled ‘Exploring the determinants of adoption of Unified Payment Interface (UPI) in India: A study based on diffusion of innovation theory’, the authors have studied the factors that influence UPI adoption by Indian customers based on the Diffusion of Innovation (DOI) theory.


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Jha, R., Kumar, R., (2021): In their paper titled ‘UPI -An Innovative step for making Digital Payment Effective and factors affecting Consumer Perception On the use of UPI’, the authors cover the architecture, technologies, operations, parties engaged in UPI transactions, advantages and disadvantages, and factors affecting customer satisfaction to get a specific result on usage and recommendation intents.

Kamalasaravanan, S., Aishwariya, K., (2022): Their paper titled ‘A Study on Usage of UPI Payments Services towards Merchants in Madurai’ analyses the awareness of UPI Payments services towards merchants and the level of satisfaction towards usage of UPI Payments services. The paper also identifies the risks involved in the system.

Kaur, S., (2022): In his research paper, ‘Factors Influencing Continuance Intentions of Unified Payment Interface (UPI) users’, author evaluates user satisfaction and UPI continuation intentions. Based on the Unified Theory of Acceptance and Application of Technology. This study proposes a research model combining personal innovativeness and rate of innovation to predict the continuing intention towards UPI.

Kolte, D.M., Humbe, V.R., (2019): In their paper titled, ‘Study of UPI/BHIM Payment System in India’, authors have discussed the impact of BHIM and UPI on the electronic payment system. This study focuses on how users adapt to the BHIM or UPI apps.

Ranpariya, T., Joshi, A., Rajdev, A., (2021): Their paper titled ‘Factors Driving the Adoption of UPI Services Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model’, analyses the factors influencing the adoption of UPI services. Also, the relationship between demographic demographics and elements influencing the use of UPI services was examined.

Saha, P., Kiran, K.B., (2022): In their paper titled, ‘Effect of COVID 19 on adoption of Unified Payment Interface: A study on behavioural intention of baby boomers’, authors have attempted to investigate COVID19's impact on baby boomers' adoption of UPI by understanding the behavioural aspects that lead baby boomers to choose the Unified Payment Interface (UPI) as their preferred payment method.

3. Research Gap

This research paper tries to find out whether the efficient performance of UPI technology has made consumers’ transactions effortless and risk-free. A new technology will be better accepted in the market by the consumers, if its seamless performance allows the users to transact effortlessly and reduce the risk level to an acceptably low level. This area has not been addressed in any of the above literatures.

4. Objectives of the Study

This study aims to:

1. Draw the relationship between performance of UPI and effortlessness and risk-free nature of the payment system.
2. Find out whether the external factors like age, occupation and income affect the consumers’ intention to use UPI.
3. Analyse the users’ perception towards UPI interface.

5. Research Methodology

Time Frame of the Study

The research has been conducted on the data available from 2016 to 2025. This timeframe has been selected since the Government of India launched UPI in 2016.

Data Collection

Primary Data

The data has been collected through a structured questionnaire comprising of two sets of data. In the first one, demographic details and data regarding UPI usage patterns among respondents were put together. In the second part, seven constructs of the UTAUT model have been taken to assess the factors behind the rapid adoption of UPI using the Likert Scale.


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Table 1: Questionnaire based on the constructs of the UTAUT Model

ConstructCodeQuestion
Effort ExpectancyEE1Navigating through the UPI app and understanding the features is effortless for me
EE2It is very easy to make a transaction through UPI.
Performance ExpectancyPE1UPI is an efficient way to make bills and utility payments.
PE2All my transactions get recorded in the passbook, which keeps me organized.
PE3Transactions done through UPI is quicker as compared to other digital payments
Social InfluenceSI1Most retail shops have the option to pay through UPI.
SI2Most of my friends use it and I feel it has an influence on me.
Facilitating ConditionsFC1I have a smartphone with a stable internet connection.
FC2QR codes are available in most stores
FC3Customer support of UPI service providers exists to address grievances.
Perceived Risk & SafetyPRSUPI payment comes with a lot of safety and my data is secure.
HabitH1For most of my transactions, I use UPI.
H2For me, UPI is an efficient substitute for cash.
Behavioural IntentionBIAre you looking forward to make UPI as your primary mode of payment

Secondary Data

The secondary data has been collected from different journals, research papers, NPCI website, RBI bulletin, and PIB.

Hypothesis Used:

H0: The relationship between the performance of UPI application and it is effortless and risk-free is not significant.

H1: The relationship between the performance of UPI application and it is effortless and risk-free is significant.

Models Used

Unified Theory of Acceptance and Use of Technology Model (UTAUT MODEL)

Tools and Techniques

The following statistical models have been used in the study:

  • Factor Analysis have been performed to simplify the items into a single factor to be able to explore relationship between the multiple variables.
  • Correlation Analysis is used to a linear relationship exists between two or more variables and helps to determine the strength of the relationship.
  • Regression Analysis is used to determine the relationship between dependent variable and a series of independent variables.
  • Crosstabs Analysis is used to compare one variable with another from the nominal scale data.

All the above analyses have been carried out on IBM SPSS Statistics Version 25 and Excel Office 2019.

6. Analysis & Findings

The questionnaire has been analysed as follows:

Table 2: Descriptive Data of the respondents who filled the questionnaire

FrequencyPercentage
Gender
Male14655.7%
Female11644.3%
Age
18-2523991.2%
25-35197.3%
35-5020.8%
Above 5020.8%
Occupation
Student21883.2%
Salaried Employee3212.2%
Self Employed/Business/Professional124.6%
Annual Household Income
Below 3LPA5320.2%
3-6 LPA3312.6%
6-10 LPA6524.8%
Above 10LPA11142.4%

Table 3: Preferred Mode of Payment for the respondents

Mode of PaymentFrequencyPercent
Debit Card / Credit Card16964.5%
Mobile Banking9335.5%
NEFT/ RTGS4818.3%
UPI20176.7%
Prepaid Cards, Mobile Wallets5320.2%
Do not use Digital Payments83.1%

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Table 4: Factors that make UPI transactions attractive to the respondents

ReasonFrequencyPercent
Convenience24292.4%
Cashback & offers11242.7%
Transfer funds at no cost11443.5%
Suitable for small transactions12848.9%
Enhanced Security7026.7%
All services under one head (Bills & utility payment)9937.8%

Tables 3 and 4 show that UPI is the most preferred mode of payment among 201 respondents and 242 respondents find UPI convinient to use.

Table 5: Possibility of making UPI as primary mode of payment for the respondents

Are you open to making UPI, your primary mode of payment?FrequencyPercent
Yes21682.5%
No114.2%
Maybe3513.3%

Table 6: Understanding the usage pattern of UPI payment system

How often do you use UPI for making transactions?FrequencyPercent
Very Often16462.6%
Often7930.2%
Not Often145.3%
Not at all51.9%

Those respondents who chose ‘Not at all’ as their option were removed from the analysis of the study and the rest 257 respondents have been considered.

‘Very Often’ is defined as using UPI every day to more than once a week, ‘Often’ means using it more than once a month, ‘Not Often’ means using it just once a month and lastly ‘Not at all’ is defined as Nil. From the response, it can be said that more than 50% of the respondents use the UPI technology very often.

With the help of Factor Analysis, items in Table 1, have been simplified to a single factor to verify how related the item is to the construct. Further Cronbach’s Alpha shows their aptness as a group.

Rotated Component Matrixa
Component
123
EE1.717
EE2.672
PE1.634.512
PE2.672
PE3.719
SI1.644
SI2.727
FC1.755
FC2.651
FC3.655
PRS.695
H1.818
H2.653
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.a
a. Rotation converged in 8 iterations.

In Principal component analysis (PCA), the huge data set with numerous variables per observation is condensed into a smaller number of summary indices.

The Rotated Component Matrix, also known as the factor loadings, is the key output of principal components analysis. The estimated components allow us to interpret the traits our components might reflect and estimate of the correlations between each of the variables.

Component 1 correlates strongly with EE1, EE, H1, H2, and PRS. If we inspect the variable labels of these items, we see that the EE, H, and PRS items all relate to the Effortlessness, Habit, and Perceived risk & safety traits of UPI.

Component 2 correlates strongly with PE1, PE2, PE3 and FC1, FC2. If we inspect the variable labels of these items, we see that PE and FC items all relate to the Performance Expectancy and Facilitating Conditions traits of UPI.

Component 3 correlates strongly with SI1 and S12. Thus, the component reflects the Social Influence trait.

The Factor Loadings are extracted using the Principal Component Analysis and from each construct, the maximum value of loading is chosen to carry out the analysis. The ones highlighted in green will be used for further analysis.


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Table 7: Factor loadings and Cronbach’s Alpha result

ItemsFactor LoadingsCronbach’s Alpha
EE10.7170.790
EE20.672
PE10.6340.815
PE20.672
PE30.719
SI10.6440.676
SI20.727
FC10.7550.609
FC20.651
FC30.655
PRS0.6950.724
H10.8180.781
H26.653

The constructs or the above items in Table 7 demonstrates satisfactory level of internal consistency and reliability criteria as the values of Cronbach’s Alpha lies within the threshold level.

Table 8: Correlation among Effort Expectancy (EE1), Social Influence (SI2) & Habit (H1)
ssjar_374_Table08.PNG

This is a correlation table that shows the Pearson correlation coefficients among three variables (Effort Expectancy, Social Influence, Habit). The table also shows that the significance level of the correlation coefficient, as well as the sample size (N).

It is to be noted that all the correlation coefficients indicate, that there is a positive relationship between each pair of variables. Additionally, all the correlation coefficients are statistically significant at the 0.01 level, indicating that the correlations are unlikely to have occurred by chance.

The strongest correlation in this table is between Effort Expectancy (EE1) and Habit (H1), with a Pearson correlation of 0.503.

This indicates that higher the effortlessness in using UPI, the higher are the chances that consumers develop a habit to use it more often. In other words, the voluntariness to use it increases. The ease of use of UPI system promotes the users to adopt the technology holistically.

Another positive correlation exists between Effort Expectancy (EE1) and Social Influence (SI2), with a Pearson correlation coefficient of 0.181. This one indicates a moderate correlation between the two variables. It shows that, higher the effortlessness is using UPI, the higher will be its influence to use.

Table 9: Correlation between Perceived Risk & Safety (PRS) & Facilitating Condition (FC1)
ssjar_374_Table09.PNG

This is a correlation table that shows the Pearson correlation coefficients among two variables (Perceived Risk & Safety and Facilitating Conditions).

It is to be noted that all the correlation coefficients indicates that there is a positive relationship between the pair of variables. Additionally, all the correlation coefficients are statistically significant at the 0.01 level, indicating that the correlations are unlikely to have occurred by chance.

The correlation between Perceived Risk & Safety (PRS) and Facilitating Conditions (FC1) is positive, with a Pearson correlation coefficient of 0.302. This shows that presence of proper facilitating conditions for the usage of UPI reduces the risk level and ensures safety for the users.

Now, that the relationship has been established between the two sets of variables through correlation analysis, further analysis is carried out. The relationship between Performance Expectancy and UPI being effortless and risk-free promotes UPI transactions can be addressed through Regression analysis.


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The following hypothesis shall help in determining if the Unified Payments Interface (UPI) has an ease of transaction, with the help of the constructs.

H0: The relationship between the performance of UPI payment system and it is effortless and risk-free is not significant.

H1: The relationship between the performance of UPI payment system and it is effortless and risk-free is significant.

To define ‘effortless’ in the above hypothesis, Effort Expectancy (EE), Social Influence (SI), and Habit(H) constructs have been considered. Perceived Risk & Safety (PRS) and Facilitating conditions (FC) have been taken to define ‘risk-free’.

In regression analysis, PE is considered the dependent variable, and the rest, EE, SI, FC, H, and PRS, are considered the independent variables.

This table gives the values of R, R Square, Adjusted R Square that helps determine the model fit for the data.

Table 10: Model Summary from Regression Analysis

Model Summary
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.581a.338.325.83929
a. Predictors: (Constant), H1, SI2, FC1, EE1, PRS

Table 11: ANOVA table from Regression Analysis
ssjar_374_Table11.PNG

The ANOVA table shows that the regression model is significant, with an F-value of 25.599 and a p-value of .000. This indicates that at least one of the independent variables is significantly related to the dependent variable.

Since the p value (Sig) <0.005, we will reject the null Hypothesis. Thus, the independent variables predict the dependent variables are statistically significantly. It can be said that the relationship between Performance of UPI application and its being effortless and risk free is significant.

ssjar_374_Table12.PNG

Crosstabs Analysis has been used to achieve the second objective of identifying the external factors like the age, occupation and income that have any influence on the behavioural adoption of UPI by consumers. Question No. 9 is referred to in all the tables below, with respect to Behavioural Intention (BI) for performing the analysis.
ssjar_374_Table12BI.PNG

As the income level of the respondents increase, the more they are willing to make UPI as their primary mode of payment. The respondents who have an annual household income of more than 10LPA (43%, 103/236) have shown a much stronger inclination towards the behavioural intention than the respondents within income bracket of 3-6LPA (11%,28/236).
ssjar_374_Table11BI.PNG

The respondents belonging to the age group 18-25 are the ones most willing to make UPI as their primary mode of payment. In that segment, 92% youth community (217/235) have shown a strong inclination, whereas, the other age groups show a weaker inclination towards making UPI as a primary mode of payment.

Table 13: BI * Occupation Crosstabulation
Count
OccupationTotal
StudentSelf Employed/Business OwnerSalaried Employee
BINo200121
Yes1941131236
Total2141132257

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The respondents in the study were mostly students (214/257). So, it can be inferred that, the UPI payment system has gained more traction from the student community and young working professionals as they are more tech-savvy and tend to follow the bandwagon much faster than the other class of occupations. It may indicate their exposure to the digital environment and the internet connectivity that shows such behaviour.

7. Summary of Observations

This study shows that UPI is the most preferred mode of payment for the respondents owing to its convenience in using the technology. More than 80% of the respondents are willing to make UPI as their primary mode of payment in their future. There exists a strong relationship between effortlessness to use UPI and it becoming a habit. The easier consumers find the technology to use, the more readily it transforms into a habit for making transactions. Existence of efficient facilitating conditions like stable internet connection, access to smart phone, QR code availability and presence of customer support reduces the risk involved in making payments. Higher the performance efficiency of UPI payment system, the more effortless and risk-free it becomes to carry out transactions using UPI. Students and younger age groups have shown a strong inclination towards adoption of UPI technology as they are considered more tech-savvy and are more connected to this digital environment.

8. Recommendations

  • Due to the increasingly heavy reliance on UPI, the service providers like Google Pay, Paytm etc. should ensure data security and keep making the technology robust so that it continues to be a safe option for transferring funds.
  • UPI should aim at financial inclusion in the rural areas as well. Since penetration of mobile phones and internet connectivity is so high, that making the UPI service available in the rural areas will promote digital literacy and India’s aim of cashless society.
  • The KYC service should be improved for the individuals opting to open new UPI accounts. This ensures smooth transfer of funds from e-wallets to bank accounts and cross checks the identity to avoid fraudulent cases.

9. Limitations of the Study

  • The respondent’s data is mostly filled by the age group of 18-25 that may have a major influence on the results of this study.
  • The data has been collected from primary sources and the opinion provided by the respondents are individual opinion which may include bias.
  • The selection of the sample may not represent the entire population of consumers, limiting the generalization of the results.
  • The study may not consider external factors such as economic conditions or political changes, which may have an impact on one’s behavioural intention to use UPI.

References

1. Jha, R., & Kumar, R. (2021). UPI—An innovative step for making digital payment effective and factors affecting consumer perception on the use of UPI. Journal of Emerging Technologies and Innovative Research (JETIR), 8(7).

2. Kolte, D. M., & Humbe, V. R. (2019). Study of UPI/BHIM payment system in India. International Journal of Science and Research (IJSR).

3. Kaur, S. (2022). Factors influencing continuance intentions of Unified Payment Interface (UPI) users. Research Square.

4. Kamalasaravanan, S., & Aishwariya, K. (2022). A study on usage of UPI payment services towards merchants in Madurai. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 2(9).

5. Saha, P., & Kiran, K. B. (2022). Effect of COVID-19 on adoption of Unified Payment Interface: A study on behavioural intention of baby boomers. Social Science Research Network (SSRN).

6. Fahad, & Shahid, M. (2022). Exploring the determinants of adoption of Unified Payment Interface (UPI) in India: A study based on diffusion of innovation theory. Digital Business, 2(2).

7. Ranupriya, T., Joshi, A., & Rajdev, A. (2021). Factors driving the adoption of UPI services based on UTAUT model. In Conference Proceedings, 4–7.


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8. Kumar, A., Choudhary, R. K., Kumar, S., & Kar, S. K. (2022). The growth trajectory of UPI-based mobile payments in India: Enablers and inhibitors. Indian Journal of Finance and Banking, 11.

9. Shah, V. (2021). Adoption intention of UPI payment method using unified theory. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 9(12).

10. https://www.npci.org.in/

11. https://www.imf.org/

12. https://www.pib.gov.in/


ANNEXURE

The following structured questionnaire was used to collect the primary data from 262 respondents.

1. Name _________________________

2. Gender

i) Female ii) Male iii) Prefer Not to Say

3. Age

i) 18-25 ii) 25-35 iii) 35-50 iv) Above 50

4. Occupation

i) Salaried Employee ii) Self Employed/Business/Professional iii) Retired Person iv) Student v) Homemaker

5. Annual Household Income

i) Below 3LPA ii) 3-6 LPA iii) 6-10 LPA iv) Above 10 LPA

6. Do you use Digital Payment for your purchases?

i) YES ii) NO

7. Your preferred mode of Digital Payment

i) Debit Card/ Credit Card ii) Mobile banking iii) NEFT/RTGS iv) UPI v) Prepaid Cards, Mobile Wallets v) Do not use Digital Payments

8. How Often do you use UPI?

i) Very Often ii) Often iii) Not Often iv) Not at all

9. Are you open to making UPI as your primary mode of payment?

i) Yes ii) No iii) May be

10. Why do you prefer paying through these apps?

i) Convenience ii) Cashback and offers iii) Transfer funds at no cost iv) Suitable for small transactions v) Enhanced security vi) All services under one head

11. Any hindrance faced while using UPI?

i) Frequent Payment failure ii) Difficulty in getting KYC done iii) Exceeding daily limit of UPI payments iv) Less trust in transaction v) I did not face any major problem

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