# Netizens’ Perspective towards Electronic Money and Its Essence in the Virtual Economy: An Empirical Analysis with Special Reference to Delhi-NCR, India

Authors: Mohammed Arshad Khan
Journal: Complexity
 (2021)

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## Abstract

This research attempts to evaluate the ongoing position of the modalities exercised in India concerning digital payments. Largely appraised as a success story in the making, this research examines several instrumental factors in India’s digital payment systems. It further extends to identifying the impact of demographic attributes and constructive variables, such as service quality, reliability, satisfaction, and security on digital payment system, Delhi NCR, India. A Google form questionnaire was adopted to collect data through online mode. The researcher collected the primary data from 165 respondents. Purposive sampling method was exercised, along with CFA technique and parameter test used through SPSS (version 25), reliability issue, validity issue, and model fitness achieve through SPSS-AMOS (version 24). The significant peculiarities and analysis are presented further in this research contributing to a precise depiction of findings and then, based on it, the conclusion.


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## Body

## 1. Introduction

Digital payment, quite simply, is a form of carrying out transactions devoid of any physical exchange of money. However, the said transaction is mandatorily performed via the Internet or applications necessitating its employment. It is the principal branch of digitization, comprising the transition of the natives from the physical exchange of money to digital transactions. The parties involved in the said scenario include a payer and a payee. The individual sending the money is the payer, whereas the recipient or the person to whom the money is transferred is the payee. This contemporary form of exchange of money, mandating the Internet for both parties and eliminating hard currency give-and-take in its entirety, is also known as electronic payment. It is, hence, a rapid and highly convenient method of carrying out transactions [1].This modern-day method of money exchange comprises several modalities in its application of the Internet. These are debit card, credit card, net banking, Unified Payments Interface (UPI), digital wallets, etc. Compared with the typical form of carrying out transactions, which is, through currency notes, the employment of the Internet (hence, the word “digital”) separates digital payment from the earlier forms of payments, particularly the barter system. The barter system did not have a justifiable give-and-take format along with the previously exercised forms of payments. Coins and currency notes, later on, amended the problems, which is now superseded by, or surging by and large, electronic payments. One of the logical reasons for amelioration is the recurrent issues found in the system, involving cash falsification, cheque bounce, and signature forgery. However, the transition to digital payment is also not infallible. It raises several doubts about frauds happening due to compromised privacy when carrying out transactions digitally on the Internet.It is also worth mentioning the intensified Internet use in the present time arising from widespread availability, also accommodating cybercriminals. This decade is witnessing extensive use of smartphones (again, differentiating from regular phones for having Internet applications of all kinds) and enormous access to many products and services via e-commerce applications, which natives greatly enjoy as they happily make payments digitally online [2]. The substantial improvements happening continually in the mobile and broadband industry, leading to cutting-edge technology in rapid succession, and Internet speed, thought of unimaginable some time ago, are also instrumental in the greater use of the Internet and the migration of natives from the currency-based exchange of money to digital payments. The movement from paper to digital, however, was not as smooth [3]. The element of trust was sorely missing initially as people only relied on the cash on delivery method, which they were accustomed to. The lack of proper awareness about the various forms of digital payments largely hindered people from using them [4].Additionally, various myths acted against the use of digital payment methods. Many believed for the banks to deduct high transaction charges for performing transactions digitally, whether by debit card, credit card, or net banking. Some even criticized the means of payment for the likelihood of overspending, provided the extreme ease of convenience in buying products and services. These myths are still largely present amongst the populace of India, calling for the need for adequate literacy on digital payment systems [5]. It is all more prominent when considered India is undergoing a substantial digital revolution. Appropriate digitization of the payment mechanism will be highly rewarding in the definite possibility of an entirely cashless future economy.It is also imperative to acknowledge the need for the rightful progression of the Indian digital payment system. It is expectedly the most significant factor that defines how the industry looks in the future [6]. With India shifting to digital means, having an agreeable regulatory environment, the advent of next-generation payment service providers and an improved customer experience are the rudimentary factors contributing to India’s overall progression in digital payment systems. That is not to say, various facilitators are encouraging in boosting the means of digital payments, allowing the switch from cash-based to the wholly digital economy [7]. Acting as a bridge to solidifying the Indian digital payment systems, these facilitators comprise the successive advancements done in bolstering Internet connectivity on smartphones, nonbanking financial institutions expediting digital means of carrying out transactions, the emergence of financial technology sectors, and the gradual push by the government through various tax incentives [8]. Hence, all of these factors are helping with an assertive situation for the progression of digital payment systems in India.The various digital payment systems currently exercised in India comprise banking cards (debit/credit card), digital wallets (Paytm, PhonePe, etc.), National Electronic Fund Transfer (NEFT), Unified Payment Interface (UPI), mobile banking, Real Time Gross Settlement (RTGS), Unstructured Supplementary Service Data (USSD), and Aadhar Enabled Payment System (AEPS) [9].


### 1.1. Types of Digital Payment

#### 1.1.1. Banking Cards

The most common types of payment cards are credit cards and debit cards. They are the most easily found and commonly used methods of digital payment. The primary feature of the cards is that they are electronically linked to the holder’s bank account and serve as a medium to enable the account holder to purchase commodities without using cash or withdrawing money from an ATM if needed. The cardholder can perform the said actions via a secret code embedded on the backside of the cards, also known as the CVV number (card verification value). The stated number establishes the cardholder’s identity and minimises fraud risk [10].The most influential factors that make banking cards so broadly exercised comprise the ease of convenience and the greater security in making payments. Securitywise, banking cards are quite assuring, most notably, when used in other forms of online payments, such as digital wallets or online transactions [11]. A user may save his or her information on a specific digital app for making digital transactions. The benefit of OTPs (one-time password) ensures, even in the app, either web or mobile-based, at minimal risk. The most recognized banking card payment systems comprise Visa, MasterCard, RuPay, etc. [12]. The usefulness of the cards further extends to making instore purchases via card swiping or a simple tap in POS machines.


#### 1.1.2. Credit Card

In today's times, the credit cards are widely used by the customers as they are providing them the facility of making transactions without debiting their account balances. It is the kind of digital banking card that allows the customer to make purchases against a credit amount, which is lent to the customer by the bank, based on various factors, and hence, it needs to be paid off after a month to 45 days [13]. This variable credit amount lent to the customer is generally called the credit limit. If the customer makes purchases from the card, as per his given credit limit and fails to repay the amount, the bank then begins to charge money on the unpaid credit amount, with interest charges and late fees. However, the bank also rewards loyal customers by increasing the credit limit when they are regularly paying the credit amount. Those having a good credit history also benefit with ease in taking bigger loans from a bank.


#### 1.1.3. Debit Card

The successor to credit card, and currently, the most found banking card amongst the populace, the debit cards, enjoy simple mechanics. As opposed to spending a credit limit provided by the bank, customers can pay as much money in making a digital purchase as per the amount in their account. It is, otherwise, a very convenient method of utilising the saved amount in a bank, akin to a cheque or cash. For the same reasons, these banking cards are also denoted as “cheque cards” or “bank cards.” One differentiating advantage of debit cards over credit cards is the greater cash withdrawal limit. A customer can only withdraw cash of a specific limitation, as provided by the bank, from an ATM with a credit card. However, debit cards allow customers to draw a much more significant amount (ranging from ₹25,000 to ₹1,00,000) or more, depending on the amount of savings they have in their bank account [14].


#### 1.1.4. Internet Banking

While debit and credit cards are convenient and more regularly used, their most significant limitations include spending a substantial amount, or quite precisely, for corporate use. Internet banking, in this case, becomes the most useful, allowing the customers to make online transactions through a web browser, on a bank’s official website, with the help of a login ID and a password. Also, denoted as online banking, virtual banking, or more lately, electronic banking (e-banking), this form of digital payment warrants a steady Internet connection to ensure the process of carrying out transaction is not interrupted. While transaction through debit and credit cards is much quicker, Internet banking has a more elaborate approach, more so for security reasons, and hence, it requires a good Internet connection. Necessary details such as the account number of the person or entity to whom the amount is to be sent are needed, along with the IFSC (Indian Financial System Code). For regular transactions, customers can add beneficiaries to simplify the process of adding account number and IFSC daily [15]. In purchasing through e-commerce websites and opting for Internet banking, the portal automatically redirects to the preferred bank’s website, and the banking details mentioned above are not required. At present, nearly every bank has an Internet banking service, and all the payment gateways provide the option of virtual banking options. As specified earlier, NEFT, RTGS, and IMPS are the most commonly exercised ways to transact via online banking [16].


#### 1.1.5. Mobile Banking

Though introduced two decades ago, the application of mobile banking is still scarce, especially when compared with banking cards and Internet banking. However, their usefulness is equally substantial. Also denoted as M-banking, or SMS banking, this is a kind of digital payment service that incorporates almost all Internet banking features (such as issuing a new cheque book or requesting a new debit/credit card, beyond merely paying money) and is exercised via an official banking software application that needs to be installed via app store of a smartphone or tablet [17]. Nearly every bank has its own application, which, as of late, has been used more than Internet banking, provided similar features but significant ease of accessibility. Many people regard this branch of digital payment services as the future of banking, considering the lot of features, and it provides in a clean user interface [18]. The necessity of a smartphone for using M-banking further bolsters digitization.


#### 1.1.6. Digital Wallet

Similar to having a physical wallet, a digital wallet is a virtual case for people to store their money. The application of such wallets is quite identical to modern-day debit and credit cards. However, they come conducive in carrying out online and instore transactions with a mere tap, saving plenty of time and inconvenience. Also denoted as an E-wallet, or mobile wallet, this digital payment service needs to be connected with an individual’s bank account for carrying out transactions. Some service providers (Paytm, PhonePe, MobiKwik, etc.) allow the customers to exercise the services by adding the amount to their digital wallet by debit or credit cards, hence not necessitating linking their bank account. These wallets comprise two vital components: software, which feeds personal data, ensuring its security and encryption [19]. The second component, information, is a database of data rendered by the users, including their name, shipping address, the preferred mode of payments, amount to be paid, and debit/credit card details [20].


## 1.1. Types of Digital Payment

### 1.1.1. Banking Cards

The most common types of payment cards are credit cards and debit cards. They are the most easily found and commonly used methods of digital payment. The primary feature of the cards is that they are electronically linked to the holder’s bank account and serve as a medium to enable the account holder to purchase commodities without using cash or withdrawing money from an ATM if needed. The cardholder can perform the said actions via a secret code embedded on the backside of the cards, also known as the CVV number (card verification value). The stated number establishes the cardholder’s identity and minimises fraud risk [10].The most influential factors that make banking cards so broadly exercised comprise the ease of convenience and the greater security in making payments. Securitywise, banking cards are quite assuring, most notably, when used in other forms of online payments, such as digital wallets or online transactions [11]. A user may save his or her information on a specific digital app for making digital transactions. The benefit of OTPs (one-time password) ensures, even in the app, either web or mobile-based, at minimal risk. The most recognized banking card payment systems comprise Visa, MasterCard, RuPay, etc. [12]. The usefulness of the cards further extends to making instore purchases via card swiping or a simple tap in POS machines.


### 1.1.2. Credit Card

In today's times, the credit cards are widely used by the customers as they are providing them the facility of making transactions without debiting their account balances. It is the kind of digital banking card that allows the customer to make purchases against a credit amount, which is lent to the customer by the bank, based on various factors, and hence, it needs to be paid off after a month to 45 days [13]. This variable credit amount lent to the customer is generally called the credit limit. If the customer makes purchases from the card, as per his given credit limit and fails to repay the amount, the bank then begins to charge money on the unpaid credit amount, with interest charges and late fees. However, the bank also rewards loyal customers by increasing the credit limit when they are regularly paying the credit amount. Those having a good credit history also benefit with ease in taking bigger loans from a bank.


### 1.1.3. Debit Card

The successor to credit card, and currently, the most found banking card amongst the populace, the debit cards, enjoy simple mechanics. As opposed to spending a credit limit provided by the bank, customers can pay as much money in making a digital purchase as per the amount in their account. It is, otherwise, a very convenient method of utilising the saved amount in a bank, akin to a cheque or cash. For the same reasons, these banking cards are also denoted as “cheque cards” or “bank cards.” One differentiating advantage of debit cards over credit cards is the greater cash withdrawal limit. A customer can only withdraw cash of a specific limitation, as provided by the bank, from an ATM with a credit card. However, debit cards allow customers to draw a much more significant amount (ranging from ₹25,000 to ₹1,00,000) or more, depending on the amount of savings they have in their bank account [14].


### 1.1.4. Internet Banking

While debit and credit cards are convenient and more regularly used, their most significant limitations include spending a substantial amount, or quite precisely, for corporate use. Internet banking, in this case, becomes the most useful, allowing the customers to make online transactions through a web browser, on a bank’s official website, with the help of a login ID and a password. Also, denoted as online banking, virtual banking, or more lately, electronic banking (e-banking), this form of digital payment warrants a steady Internet connection to ensure the process of carrying out transaction is not interrupted. While transaction through debit and credit cards is much quicker, Internet banking has a more elaborate approach, more so for security reasons, and hence, it requires a good Internet connection. Necessary details such as the account number of the person or entity to whom the amount is to be sent are needed, along with the IFSC (Indian Financial System Code). For regular transactions, customers can add beneficiaries to simplify the process of adding account number and IFSC daily [15]. In purchasing through e-commerce websites and opting for Internet banking, the portal automatically redirects to the preferred bank’s website, and the banking details mentioned above are not required. At present, nearly every bank has an Internet banking service, and all the payment gateways provide the option of virtual banking options. As specified earlier, NEFT, RTGS, and IMPS are the most commonly exercised ways to transact via online banking [16].


### 1.1.5. Mobile Banking

Though introduced two decades ago, the application of mobile banking is still scarce, especially when compared with banking cards and Internet banking. However, their usefulness is equally substantial. Also denoted as M-banking, or SMS banking, this is a kind of digital payment service that incorporates almost all Internet banking features (such as issuing a new cheque book or requesting a new debit/credit card, beyond merely paying money) and is exercised via an official banking software application that needs to be installed via app store of a smartphone or tablet [17]. Nearly every bank has its own application, which, as of late, has been used more than Internet banking, provided similar features but significant ease of accessibility. Many people regard this branch of digital payment services as the future of banking, considering the lot of features, and it provides in a clean user interface [18]. The necessity of a smartphone for using M-banking further bolsters digitization.


### 1.1.6. Digital Wallet

Similar to having a physical wallet, a digital wallet is a virtual case for people to store their money. The application of such wallets is quite identical to modern-day debit and credit cards. However, they come conducive in carrying out online and instore transactions with a mere tap, saving plenty of time and inconvenience. Also denoted as an E-wallet, or mobile wallet, this digital payment service needs to be connected with an individual’s bank account for carrying out transactions. Some service providers (Paytm, PhonePe, MobiKwik, etc.) allow the customers to exercise the services by adding the amount to their digital wallet by debit or credit cards, hence not necessitating linking their bank account. These wallets comprise two vital components: software, which feeds personal data, ensuring its security and encryption [19]. The second component, information, is a database of data rendered by the users, including their name, shipping address, the preferred mode of payments, amount to be paid, and debit/credit card details [20].


## 1.1.1. Banking Cards

The most common types of payment cards are credit cards and debit cards. They are the most easily found and commonly used methods of digital payment. The primary feature of the cards is that they are electronically linked to the holder’s bank account and serve as a medium to enable the account holder to purchase commodities without using cash or withdrawing money from an ATM if needed. The cardholder can perform the said actions via a secret code embedded on the backside of the cards, also known as the CVV number (card verification value). The stated number establishes the cardholder’s identity and minimises fraud risk [10].The most influential factors that make banking cards so broadly exercised comprise the ease of convenience and the greater security in making payments. Securitywise, banking cards are quite assuring, most notably, when used in other forms of online payments, such as digital wallets or online transactions [11]. A user may save his or her information on a specific digital app for making digital transactions. The benefit of OTPs (one-time password) ensures, even in the app, either web or mobile-based, at minimal risk. The most recognized banking card payment systems comprise Visa, MasterCard, RuPay, etc. [12]. The usefulness of the cards further extends to making instore purchases via card swiping or a simple tap in POS machines.


## 1.1.2. Credit Card

In today's times, the credit cards are widely used by the customers as they are providing them the facility of making transactions without debiting their account balances. It is the kind of digital banking card that allows the customer to make purchases against a credit amount, which is lent to the customer by the bank, based on various factors, and hence, it needs to be paid off after a month to 45 days [13]. This variable credit amount lent to the customer is generally called the credit limit. If the customer makes purchases from the card, as per his given credit limit and fails to repay the amount, the bank then begins to charge money on the unpaid credit amount, with interest charges and late fees. However, the bank also rewards loyal customers by increasing the credit limit when they are regularly paying the credit amount. Those having a good credit history also benefit with ease in taking bigger loans from a bank.


## 1.1.3. Debit Card

The successor to credit card, and currently, the most found banking card amongst the populace, the debit cards, enjoy simple mechanics. As opposed to spending a credit limit provided by the bank, customers can pay as much money in making a digital purchase as per the amount in their account. It is, otherwise, a very convenient method of utilising the saved amount in a bank, akin to a cheque or cash. For the same reasons, these banking cards are also denoted as “cheque cards” or “bank cards.” One differentiating advantage of debit cards over credit cards is the greater cash withdrawal limit. A customer can only withdraw cash of a specific limitation, as provided by the bank, from an ATM with a credit card. However, debit cards allow customers to draw a much more significant amount (ranging from ₹25,000 to ₹1,00,000) or more, depending on the amount of savings they have in their bank account [14].


## 1.1.4. Internet Banking

While debit and credit cards are convenient and more regularly used, their most significant limitations include spending a substantial amount, or quite precisely, for corporate use. Internet banking, in this case, becomes the most useful, allowing the customers to make online transactions through a web browser, on a bank’s official website, with the help of a login ID and a password. Also, denoted as online banking, virtual banking, or more lately, electronic banking (e-banking), this form of digital payment warrants a steady Internet connection to ensure the process of carrying out transaction is not interrupted. While transaction through debit and credit cards is much quicker, Internet banking has a more elaborate approach, more so for security reasons, and hence, it requires a good Internet connection. Necessary details such as the account number of the person or entity to whom the amount is to be sent are needed, along with the IFSC (Indian Financial System Code). For regular transactions, customers can add beneficiaries to simplify the process of adding account number and IFSC daily [15]. In purchasing through e-commerce websites and opting for Internet banking, the portal automatically redirects to the preferred bank’s website, and the banking details mentioned above are not required. At present, nearly every bank has an Internet banking service, and all the payment gateways provide the option of virtual banking options. As specified earlier, NEFT, RTGS, and IMPS are the most commonly exercised ways to transact via online banking [16].


## 1.1.5. Mobile Banking

Though introduced two decades ago, the application of mobile banking is still scarce, especially when compared with banking cards and Internet banking. However, their usefulness is equally substantial. Also denoted as M-banking, or SMS banking, this is a kind of digital payment service that incorporates almost all Internet banking features (such as issuing a new cheque book or requesting a new debit/credit card, beyond merely paying money) and is exercised via an official banking software application that needs to be installed via app store of a smartphone or tablet [17]. Nearly every bank has its own application, which, as of late, has been used more than Internet banking, provided similar features but significant ease of accessibility. Many people regard this branch of digital payment services as the future of banking, considering the lot of features, and it provides in a clean user interface [18]. The necessity of a smartphone for using M-banking further bolsters digitization.


## 1.1.6. Digital Wallet

Similar to having a physical wallet, a digital wallet is a virtual case for people to store their money. The application of such wallets is quite identical to modern-day debit and credit cards. However, they come conducive in carrying out online and instore transactions with a mere tap, saving plenty of time and inconvenience. Also denoted as an E-wallet, or mobile wallet, this digital payment service needs to be connected with an individual’s bank account for carrying out transactions. Some service providers (Paytm, PhonePe, MobiKwik, etc.) allow the customers to exercise the services by adding the amount to their digital wallet by debit or credit cards, hence not necessitating linking their bank account. These wallets comprise two vital components: software, which feeds personal data, ensuring its security and encryption [19]. The second component, information, is a database of data rendered by the users, including their name, shipping address, the preferred mode of payments, amount to be paid, and debit/credit card details [20].


## 2. Review of Literature

In their article, Jayakumar and Vincent Sahayaraj [21] have comprehended the factors that make a customer a happy customer. The paper shows that the customers prefer online banking, ATM etc., but some factors like alertness and consistency affect their level of satisfaction. Customer satisfaction increases towards current banking services if the bank provides more consistent services with value as promised and on time. SBI has successfully implemented substantial factors such as modern equipment, infrastructural amenities, and quality of equipment used. They have been flourishing in achieving an amicable relationship with customers. Most of the article respondents felt that the employees of the SBI are very intense to satisfy their customers.An electronic wallet (E-wallet) is one of the most current digital payment services that provides its users with the comfort of smooth and simplified transactions via allowing them to store amount or otherwise link their bank account or save credit card details faster and easier checkout or instore payments. The benefits associated with E-wallets, mainly saving time and inconvenience, make the mode of payment favourable among the populace [22]. M-wallet, otherwise known as mobile wallet, is distinguished for various advantages over other modes of payments. The most promising benefits comprise customization and instant communication. However, E-wallets have gained a more significant following their usefulness, particularly in the riding industry and food delivery and bill payments. Hence, they are more used than M-wallet and the other forms of digital payment systems [23]. The peculiarities of E-wallets are not merely advantageous to buyers but also for the traders, who have begun to exercise the mode of payment for its ease of convenience and efficacious cash management, ultimately leading to a reduction in the cost of labour. Physical stores, for instance, have witnessed a steep increase in the exercising of E-Wallets, where the customers can make payments swiftly by scanning the QR code, accommodating for a smooth paperless, cashless experience [24]. Near field communications, otherwise known as NFC-supported devices, are placed close by the payment terminals in physical stores to make the transactions convenient. Henceforth, based on the literature mentioned above, it can be surmised that the usefulness of E-Wallets over the other modes of digital payments, mainly for their high flexibility and adaptability and extremely user-friendly process of carrying out transactions via smartphones or tablets.Abhijit M. Tadse & Harmeet Singh Nannade [25] tried to analyse the use of Paytm by users of mobile phones and also to find out various issues faced by users of Paytm. The research categorized on the basis of age, purpose of usage, frequency of usage, and average monthly spending on Paytm by the respondents. The study further elaborates that Paytm is quite convenient owing to wide network of partners. This paper concludes that, to improve the transaction efficiency, it needs to work upon the payment gateway as 70% people face issues with it. In order to cater the requirement of maximum customers, the service needs to improvise as indicated by only 5% people respondents to have got help every time they encounter a problem in it.Revathy and Balaji emphasized the crucial role of the rural populace in the rightful progression of the economy, provided they acquire two-third of the total population. The study highlighted how the IT and Communication sector surge is turning fruitful for the nation, surmising a total of 50% Internet users in India by the end of 2020. The researcher further necessitated the application of digital wallets, underlining the rural populace and the neediness to spread rightful awareness regarding the usage and benefits of using E-wallets amongst them. The study further specified how the Indian Government took the initiative of spreading appropriate awareness amongst the rural populace towards the usefulness of E-wallets, thoroughly covered under their digitization campaign. The timely implementation of advanced and upcoming technology has been unceasingly low in India instead of the other nations. Though the situation has been highly different in this scenario, the country is progressing shoulder to shoulder with other countries to digitize the economy, explicitly exercising E-wallets. Provided two-thirds of the Indian population constitutes the people residing in the rural areas, the timely exercising of digital payment services will ensure the country becomes a cashless economy in mere few years to come. The urban populace is already well-versed with the usefulness of digital payment systems and hence exercising it. By converting, the rural public towards a cashless system of carrying out business will complete the mission of the Indian Government of having a digitized economy. The best step taken forward in this regard is by the National Payments Corporation of India, accommodating for the E-wallets to function on all mobiles, with or without the availability of the Internet [26].The researcher highlighted how the rapid increase in the number of smartphone, or otherwise, Internet users gave a rise in the total users exercising digital payment services, who found the alternative of cash convenient and more appealing. This research furthermore emphasized the increasing competition in the domain of digital payment services and the willingness to dominate the massive Indian market, specifying those who entered the territory during the phase of demonetization with the prospect of establishing the company at the right time. Additionally, the study speculated India to become a cashless economy with the appropriate digitization, managing to convert people’s behaviour from traditional to the modern-day system of digital payments. The research employed ANOVA to prove no significant variance in consumer perception regarding its demographic factors.Routray et al. endeavoured to recognize the significant predictors of digital payment services in Middle Eastern Country, Oman. The researcher administered an empirical study for the same and developed a hybrid model through an SEM-neural network model. The study’s outcome exhibited that perceived trust, perceived usefulness, and perceived security have a notable impact on the users’ intention to exercise digital payment services, specifically M-wallets. In contrast, perceived ease of use does not have a structural effect on the users’ intention to exercise M-wallets. Additionally, the study recommended that the service providers maximize social media platforms to produce the rightful awareness among the people for increasing the number of time users spend exercising mobile-based payment services [27].Tamil Selvi and Balaji [28] strived to recognize whether the demographic profiles of the respondents have any substantial impact on the behavioural intent of the mobile users towards exercising mobile banking services. The researchers, henceforth, carried out an exploratory study for the same in the city of Chennai and Hyderabad. By devising structured questionnaires and distributing them amongst the customers of both public and private banks, the researchers acquired the primary data concerning their viewpoints on the employment of mobile banking. The outcome of the research, thereon, showcased that the performance expectancy, effort expectancy, hedonic motivation, trust, and loyalty are substantially impactful in the behavioural intent of the customers towards exercising mobile banking services.


## 3. Research Gap

The literature review showcased how most of the research is carried out to distinguish the customers’ opinion towards the banking payment systems. However, the empirical studies undertaken solely examined the perception of customers towards the digital payment system. This research, however, considered relevant factors such as reliability, service quality, satisfaction, user-friendliness, security, and trustworthy and how they impact the customers.


## 4. Objectives of the Study

The study has the following objectives:(1)
To examine customer opinion concerning the advantageousness of digital payment system in the perspective of service quality, reliability, satisfaction, trustworthy, user-friendly, and security(2)
To analyse customers’ (on the basis of their demographics) perception towards digital payment system and its importance in the banking sector


## 5. Hypotheses of the Study

The study has formulated the following hypotheses:H01: the mean score of digital payment system does not differ with genderH02: the mean score of digital payment system does not differ with ageH03: the mean score of digital payment system does not differ with educational qualificationH04: the mean score of digital payment system does not differ with occupational statusH05: the mean score of digital payment system does not differ with income level


## 6. Research Methodology

This research study is based on descriptive-cum-cross section. Primary data are used to obtain an accurate result. The Google Form of the questionnaire was designed and shared online to acquire users’ response in Delhi NCR. The questionnaire was divided into two parts: the first part relevant to the respondent background and the second part based on specific statements pertinent to the digital payment system. All statements are further classified into major 6 sets. These sets (reliability, satisfaction, service-quality, user-friendly, trustworthy, and security) established a relationship with the digital payment system. Apart from this, to check the credibility of the constructs was analysed using Cronbach’s alpha, which signifies to which the items in the questionnaire were associated with one another [29]. The online data obtained were collected from February to April 2021. A total of 167 replies were obtained through judgement or purposive sampling technique, out of which 165 respondents were selected, and those 165 responses were utilized further for data analysis. For better result, confirm variables were clearly explaining their interrelated construct. CFA technique was accompanied in the study with the help of AMOS (version 24) software. The investigator employed the relevant statistical tools and methods through SPSS (version 25) software to achieve the study’s main objectives.


## 7. Findings and Discussion

The essential purpose of this study was to collect primary data by using a closed-ended questionnaire, which was shared with the help of Google Forms. An adequate number of participants showed interest in this online survey. For this study, the purposive sampling method was used. The questionnaire was designed in English for spread and diversification. It consisted of 29 questions divided into two parts. One part related to the respondent’s demographic profile, and the other part divided into six factors: viz., reliability, service quality, satisfaction, user friendly, trustworthy, and security. Summated method of rating scale was applied to assess the data from participants. It was performed on a five-point scale basis, starting from “strongly disagree (1) to strongly agree (5).” A total of 167 responses were received, but for the study, 165 responses settled to facilitate data analysis. The SPSS (version 25) and SPSS -AMOS software were used to investigate the collected data quantitatively. This segment comprises the results and findings of the research.


### 7.1. Background Information of the Respondents

The details of the respondents who filled the questionnaire are provided in this segment. Table1 demonstrates the responses of the questions connecting to several demographic variables preferred for the study. The statistics showcased here is accumulated from the primary data.Table 1
Baseline data of the participants (N = 165).
BasisCategoriesFC.F%GenderM11611670.3F4916529.7Age groupUp to 20 years191911.521–30 years71904331–40 years5314332.141 and above2216513.3Educational qualificationU.G313118.8G376822.4P.G4411226.7Dr2313513.9P.D.H3016518.2Occupational statusGovt. employees373722.4Private. employees498629.7Business and self employees3211819.4Students4716528.5Monthly income≤Rs. 10000383823Rs. 10000–Rs. 25000327019.4Rs. 25000–Rs. 500006913941.8>Rs. 500002616515.8Using the digital paymentLess than 1 year232313.91–5 years9611958.26–10 years4416326.7Above 10 years21651.2Table1 represents the participants’ demographic information based on their gender, age group, educational qualification, occupational status, and monthly income. It indicates that most of the sample respondents (70.3%) were males (M), whereas 29.7% were females (F). The above data also indicate that most of the respondents (43%) belong to the age group of 21–30 years, 32.1% were between 31 and 40 years, and 13.3% belonged of age above 40 years, and the last 11.5% fall within the age bracket of up to 20 years.Educational qualification signifies that 18.8% of respondents belong to undergraduate (U.G), 22.4% represent graduation (G), 26.7% belong to postgraduation (P.G), 13.9% represent doctorate (Dr), and 18.2% represent professional degree holder (PDH). Occupational status represents that 22.4% of respondent belong to government employees, 29.7% represent private employees, 19.4% belong to business or self-employees, and 28.5% represent students.The monthly income of the respondents illustrates that 23% respondents belong to income of Rs. ≤10000, 19.4% respondents belong to income of Rs. 10000–25000, 41.8% respondents belong to income of Rs. 25000–50000, and 15.8% respondents belong to income of Rs. <50000.


### 7.2. Reliability of the Latent Constructs

According to [30], “Cronbach’s alpha is the standard measure of internal correspondence between items in a scale, facilitating its widespread use with Likert Scale-based questions used in the survey. The fundamental objective of reliability testing was to examine the attributes of the scales of measurement and the items for getting the overall index of internal consistency of the scales.”The result of this test is summarized in the Table2 provided above. The table represents the reliability analysis of the latent constructs used in the study. The consistency interprets the “high internal reliabilities” as the value of Cronbach’s alpha ranges between 0.70 and 0.90. Because of this, it outpaces the threshold limit of 0.70 [31]. Table 2 of reliability result complementary denotes that the coefficient alpha of each latent construct is more than 0.8, revealing that there is strong internal consistency between the variable in a scale elect for the study.Table 2
Reliability analysis.
ConstructαNo. of itemsPerception towards digital payment system (PDPS)0.81215Bartlett’s test is used to resolve the requirement for reducing many statements into a smaller number of factors. KMO describes the ratio of respondents to number of statements. If the KMO value is greater than 0.6, it is accepted for the study. This study revealed 0.813, which was highly accepted [32]. On the other hand, if Bartlett test p value is less than 5% alpha, it shows that sampling is adequate. The results of the KMO and Bartlett's test are provided in Table 3.Table 3
Sampling adequacy with the help of KMO and Bartlett’s test.
ConstructKMONo. of ItemsBartlett’s test of sphericityp valueApprox. Chi-square (χ2)dfPerception towards digital payment system (PDPS)0.81315509.536105≤0.001


### 7.3. Perception of Customer towards Digital Payment System

To analyze whether all the manifest variables are clearly explaining their respective latent construct, the CFA technique was applied by the researcher via AMOS (v-24) software in the present study. Conferring to this research study, to scrutinize the perception of customers towards digital payment system, the leading latent construct that is “perceptions towards digital payment system (PDPS)” has been categorized into five subconstructs. Additionally, each is examined by various statements chosen by the analyst to collect responses from the contributors. It is revealed in Figure1.The aforesaid CFA measurement model (Figure1) portrays the “perception of customer towards digital payment system,” the prime latent variable, consistent of six subconstructs: “reliability, service quality, satisfaction, user-friendly, trustworthy, and security.” Reliability, the first subconstruct, is consistent with three statements (DPS1, DPS2, and DPS3) expressed by rectangles known as observed variables. Service quality, the second subconstruct, is consistent with two items implied as DPS4 and DPS6. Satisfaction, the third subconstructs, is consistent with two items denoted as DPS7 and DPS8. User-friendly, the fourth subconstruct, measured by three statements implied as DPS10, DPS11, and DPS12. Trustworthy, the fifth subconstruct, is analysed through two items implied as DPS14 and DPS16. Security, the last six subconstructs, is examined by three items denoted as DPS19, DPS20, and DPS21. Fifteen statements were selected out of twenty-one, implying six items were deleted. The reason being it creates a model fitness problem.Figure 1
CFA measurement model for PDPS.Table4 describes the “Chi-square (χ2)” value, i.e., 0.067, which is greater than 5% limit, and the “CMIN/DF” value, i.e., 1.238, which is less than the prescribed limit of 3. These values denote that the deposit sample input is fitting for the model fit. The four indices of goodness, i.e., GFI = 0.927, AGFI = 0.897, CFI = 0.952, and TLI = 0.941, are more than their recommended limits. It shows that the collected sample dataset is suitable for the model fitness. These values surpass their adequate limits, describing that the model is well-fitted. The two “badness-of-fit” indices denoted by RMSEA = 0.038 and SRMR = 0.062 are under the approved limit; it represents that the collected sample input fits the model properly. Hence, it supports that the measurement mentioned above is a well-fitted model.Table 4
Model fit analysis.
Name of categoryRequired fit indicesAcceptable limitsValues obtainedAbsolute fit indicesχ2p value >0.050.067RMSEA<0.050.038SRMR<0.090.062GFI>0.900.927Incremental fit indicesAGFI>0.800.897CFI>0.900.952TLI>0.900.941Parsimonious fit indexCMIN/DF<31.238The term “e” denotes error terms that represent how many parts of variation are unexplained. The standardised regression coefficient represented the arrow leading to the corresponding item, although value over each response item design the squared multiple correlations (R2) of manifest/measured variables. Tables 4 and 5 serve the analysis portion of the earlier model by analysis of moment structure (v-24).Table 5
Analysis summary of scalar estimates.
PathΒCRp valueDPS ⟶ reliability0.905.691<0.001DPS ⟶ service quality0.614.051<0.001DPS ⟶ satisfaction0.794.452<0.001DPS ⟶ user-friendly0.724.051<0.001DPS ⟶ trustworthy0.764.301<0.001DPS ⟶ security0.794.452<0.001Reliability ⟶ DPS 10.616.432<0.001Reliability ⟶ DPS 20.576.118<0.001Reliability ⟶ DPS 30.744.151<0.001Service quality ⟶ DPS 40.523.682<0.001Service quality ⟶ DPS 60.734.131<0.001Satisfaction ⟶ DPS 70.624.716<0.001Satisfaction ⟶ DPS 80.503.996<0.001User-friendly ⟶ DPS 100.503.996<0.001User-friendly ⟶ DPS 110.644.430<0.001User-friendly ⟶ DPS 120.514.072<0.001Trustworthy ⟶ DPS 140.544.112<0.001Trustworthy ⟶ DPS 160.665.229<0.001Security ⟶ DPS 190.544.112<0.001Security ⟶ DPS 200.584.768<0.001Security ⟶ DPS 210.655.001<0.001The upstair examination shows that all the measured variables are significantly connecting to their conforming constructs since theirp values are lower than 5% alpha approved limit. Besides, the standardized regression weight (β) of each item is above 0.40 which confirm that the “convergent validity” of the earlier discussed that the CFA measurement model is achieved, and it also illustrates that each manifest variable is highly correlated with its respective latent construct [33].Table6 shows the responses of customer towards the digital payment system. Draw out the five-point summated scale that stretches from “strongly disagree (1) to strongly agree (5).” Each statement percentage calculated as per the response provided by customers. This table proffers those which statements more positive feedback received from respondent and vice versa. Statements DPS1, DPS2, DPS3, DPS4, DPS6, DPS7, DPS8, DPS10, DPS11, DPS12, DPS14, DPS16, DPS18, DPS19, and DPS21 shows that respondents are more favourable towards digital payment system. On the other hand, DPS5, DPS9, DPS13, DPS15, DPS17, and DPS20 show that respondent least favourable towards digital payment systems.Table 6
Responses of customers towards digital payment system.
CodeVariablesS. D (%)D (%)N (%)A (%)S. A (%)DPS1Digital payment system is more user-friendly than previous conventional ones0.60.621.84037DPS2Digital payment system is better than the traditional payment system0.62.413.352.131.5DPS3I am more aware related to digital payment system15.23.60.0646.733.9DPS4Helpful in saving time, cost, and processing fees4124.639.440DPS5Update regarding digital payment service available from time to time2630132110DPS6I find that it is easier to conduct financial transaction through digital mean3.98949.130.3DPS7Digital payment system provides good mechanism, so I feel more comfortable while performing digital transaction5.323852.711DPS8There is well-regulated law relating to digital payment system5.113.45.252.723.6DPS9I trust on digital payment system that it will not lead to transaction fraud302221128DPS10It follows simple steps for completing the particular transactions4.113223030.9DPS11An electronic payment system is easy to use1021203217DPS12Confidential information is delivered safely to customers621134119DPS13Track and maintain the digital record of every transaction3120.12115.612.3DPS14I trust on the ability of a digital payment system to protect my privacy2112133321DPS15Effective complaints and redressal mechanism.332462314DPS16Digital payment system provides good mechanism, so I am not concerned regarding security and data protection issues15.219.8113321DPS17It is providing speedy refund mechanism4113171811DPS18Heavily regulated and protected from any risk1310193127DPS19Transaction take place in immediate time1421134111DPS20The structure and contents of the web site/app are easy to understand312192118DPS21Digital payment offers cash back and reward points to the users1019114321Consistent with the results of the independent samplet-test (Table 7), H01 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that gender does not significantly differ from the digital payment system. In some variables such as DPS7, DPS9, DPS12, DPS17, DPS18, and DPS20, p value is less than 0.005, which means gender significantly differs from the digital payment system.Table 7
Gender as independent variable by using independent samplet-test.
VariableGenderNStd. errort-valued.fp valueDPS1M1160.0731.8951630.568F490.1221.83183.896DPS2M1160.070−0.3001630.248F490.177−0.29083.896DPS3M1160.0722.0251630.800F490.1311.89183.896DPS4M1160.0810.1151630.944F490.1180.11583.896DPS5M1160.0651.7061630.974F490.1071.66283.896DPS6M1160.0720.7681630.350F490.1290.72383.896DPS7M1160.0652.041163<0.001F490.1211.88583.896DPS8M1160.0710.4211630.293F490.1160.41083.896DPS9M1160.0731.321163<0.001F490.1291.24783.896DPS10M1160.0730.1821630.631F490.1220.17683.896DPS11M1160.0652.3761630.213F490.1032.34983.896DPS12M1160.0652.361163<0.005F490.1462.03583.896DPS13M1160.0671.1771630.761F490.1131.13583.896DPS14M1160.0711.8091630.161F490.1371.65383.896DPS15M1160.0821.8371630.814F490.1191.88183.896DPS16M1160.0790.6641630.079F490.1370.63283.896DPS17M1160.0550.747163<0.001F490.1090.67583.896DPS18M1160.0712.781163<0.001F490.1292.60283.896DPS19M1160.0661.0441630.678F490.1260.95883.896DPS20M1160.0691.544163<0.001F490.1491.35283.896DPS21M1160.0690.7621630.659F490.1180.73183.896Consistent with the results of ANOVA (Table8), H02 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that age group does not significantly differ from the digital payment system. In some variables such as DPS6, DPS9, DPS11, and DPS14, the p value is less than 0.005, which means the age group significantly differs from the digital payment system.Table 8
Age as independent variable by using one-way ANOVA.
VariableAge groupSum of squared. fF valuep valueBetween groupsWithin groupsDPS1Up to 20 years2.367105.20931.2070.30921–30 years31–40 years16140 and aboveDPS2Up to 20 years3.340109.10930.1610.92221–30 years31–40 years16140 and aboveDPS3Up to 20 years2.534116.67831.6430.18221–30 years31–40 years16140 and aboveDPS4Up to 20 years1.19983.61431.1660.32521–30 years31–40 years16140 and aboveDPS5Up to 20 years7.446101.06330.7690.51321–30 years31–40 years16140 and aboveDPS6Up to 20 years7.446101.06333.954<0.00121–30 years31–40 years16140 and aboveDPS7Up to 20 years0.23092.47430.1330.94021–30 years31–40 years16140 and aboveDPS8Up to 20 years1.63397.07030.9030.44121–30 years31–40 years16140 and aboveDPS9Up to 20 years11.88398.92936.446<0.00121–30 years31–40 years16140 and aboveDPS10Up to 20 years1.613104.36230.8300.47921–30 years31–40 years16140 and aboveDPS11Up to 20 years4.71879.07533.202<0.00121–30 years31–40 years16140 and aboveDPS12Up to 20 years3.698106.20531.8690.13721–30 years31–40 years16140 and aboveDPS13Up to 20 years0.07690.43330.0450.98721–30 years31–40 years16140 and aboveDPS14Up to 20 years5.543108.23932.748<0.00121–30 years31–40 years16140 and aboveDPS15Up to 20 years5.642113.78232.5470.05821–30 years31–40 years16140 and aboveDPS16Up to 20 years1.270125.85830.5410.65521–30 years31–40 years16140 and aboveDPS17Up to 20 years1.67367.14031.3370.26421–30 years31–40 years16140 and aboveDPS18Up to 20 years4.318108.13032.1430.09721–30 years31–40 years16140 and aboveDPS19Up to 20 years1.57694.56930.8950.44521–30 years31–40 years16140 and aboveDPS20Up to 20 years5.447113.06232.5850.05521–30 years31–40 years16140 and aboveDPS21Up to 20 years0.85296.78530.4720.70221–30 years31–40 years16140 and aboveConsistent with the results of ANOVA (Table9), H03 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that educational qualification does not significantly differ from the digital payment system. In some variables such as DPS9, DPS14, and DPS18, p value is less than 0.005, which means educational qualification significantly differ from the digital payment system.Table 9
Educational qualification as independent variable by using one-way ANOVA.
VariableEducational qualificationSum of squared. fF valuep valueBetween groupsWithin groupsDPS1U. G2.144105.43230.8130.518GP. GDr161P.D.HDPS2U. G3.30993.50331.4150.231GP. GDr161P.D.HDPS3U. G3.072109.37731.1230.347GP. GDr161P.D.HDPS4U. G0.294118.91830.0990.983GP. GDr161P.D.HDPS5U. G3.85480.95831.9040.112GP. GDr161P.D.HDPS6U. G5.966102.54332.3270.059GP. GDr161P.D.HDPS7U. G1.02391.68030.4460.775GP. GDr161P.D.HDPS8U. G5.55693.14732.3860.053GP. GDr161P.D.HDPS9U. G6.466104.34732.478<0.001GP. GDr161P.D.HDPS10U. G3.649102.32731.4260.228GP. GDr161P.D.HDPS11U. G1.20782.58730.5840.674GP. GDr161P.D.HDPS12U. G1.213108.69130.4460.775GP. GDr161P.D.HDPS13U. G0.79589.71530.3540.841GP. GDr161P.D.HDPS14U. G11.372102.41034.442<0.001GP. GDr161P.D.HDPS15U. G4.685119.82531.5640.187GP. GDr161P.D.HDPS16U. G5.004122.12331.6390.167GP. GDr161P.D.HDPS17U. G2.15166.66131.2910.276GP. GDr161P.D.HDPS18U. G7.493104.95532.856<0.001GP. GDr161P.D.HDPS19U. G1.33394.81230.5620.690GP. GDr161P.D.HDPS20U. G5.091113.41831.7960.132GP. GDr161P.D.HDPS21U. G4.38593.25131.8810.116GP. GDr161P.D.HConsistent with the results of ANOVA (Table10), H04 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that occupational status does not significantly differ with the digital payment system. In some variables such as DPS3, DPS9, DPS14, DPS15, DPS18, and DPS20, p value is less than 0.005, which means occupational status significantly differ with the digital payment system.Table 10
Occupational status as independent variable by using one-way ANOVA.
VariableOccupational statusSum of squared. fF valuep valueBetween groupsWithin groupsDPS 1Govt. employee2.115105.46131.0760.361Private employeeBusiness/self employee161StudentsDPS 2Govt. employee1.46395.34930.8230.483Private employeeBusiness/self employee161StudentsDPS 3Govt. employee5.502106.94632.761<0.001Private employeeBusiness/self employee161StudentsDPS 4Govt. employee0.898118.31430.4070.748Private employeeBusiness/self employee161StudentsDPS 5Govt. employee1.83382.97931.1860.317Private employeeBusiness/self employee161StudentsDPS 6Govt. employee4.418104.09132.2780.082Private employeeBusiness/self employee161StudentsDPS 7Govt. employee1.50991.19430.8880.449Private employeeBusiness/self employee161StudentsDPS 8Govt. employee2.06496.63931.1460.332Private employeeBusiness/self employee161StudentsDPS 9Govt. employee8.592102.22034.511<0.001Private employeeBusiness/self employee161StudentsDPS 10Govt. employee1.210104.76630.6200.603Private employeeBusiness/self employee161StudentsDPS 11Govt. employee0.77483.02030.5000.683Private employeeBusiness/self employee161StudentsDPS 12Govt. employee2.203107.70031.0980.352Private employeeBusiness/self employee161StudentsDPS 13Govt. employee1.13489.37530.6810.565Private employeeBusiness/self employee161StudentsDPS 14Govt. employee11.833101.94936.229<0.001Private employeeBusiness/self employee161StudentsDPS 15Govt. employee6.994117.51533.194<0.001Private employeeBusiness/self employee161StudentsDPS 16Govt. employee5.134121.99432.2580.084Private employeeBusiness/self employee161StudentsDPS 17Govt. employee1.73267.08031.3860.249Private employeeBusiness/self employee161StudentsDPS 18Govt. employee6.435106.01433.257<0.001Private employeeBusiness/self employee161StudentsDPS 19Govt. employee0.12396.02230.0690.976Private employeeBusiness/self employee161StudentsDPS 20Govt. employee5.807112.70232.765<0.001Private employeeBusiness/self employee161StudentsDPS 21Govt. employee3.07694.56131.7460.160Private employeeBusiness/self employee161StudentsConsistent with the results of ANOVA (Table11), H05 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that monthly income does not significantly differ with the digital payment system. In some variables such as DPS6, DPS7, DPS9, DPS14, DPS15, and DPS18, p value is less than 0.005, which means monthly income significantly differs from the digital payment system.Table 11
Monthly income as independent variable by using one-way ANOVA.
VariableMonthly incomeSum of squared. fF valuep valueBetween groupsWithin groupsDPS 1≤Rs. 100001.269106.30730.6410.590Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 2≤Rs.100001.47595.33730.8310.479Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 3≤Rs. 100002.872109.57631.4070.243Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 4≤Rs. 100002.194117.01831.0060.392Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 5≤Rs. 100003.55281.26132.3460.075Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 6≤Rs. 100009.79398.71635.324<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 7≤Rs. 100004.59288.11132.797<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 8≤Rs. 100001.47197.23230.8120.489Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 9≤Rs. 1000010.336100.47635.521<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 10≤Rs. 100000.761105.21530.3880.762Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 11≤Rs. 100001.21982.57530.7920.500Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 12≤Rs. 100003.102106.80131.5590.202Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 13≤Rs. 100001.11589.39430.6700.572Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 14≤Rs. 100008.521105.26134.344<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 15≤Rs. 100007.666116.84333.521<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 16≤Rs. 100002.053125.07530.8810.452Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 17≤Rs100000.25968.55330.2030.894Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 18≤Rs. 100008.334104.11434.296<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 19≤Rs. 100000.76195.38530.4280.733Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 20≤Rs. 100003.927114.58231.8390.142Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 21≤Rs. 100003.54594.09132.0220.113Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000


## 7.1. Background Information of the Respondents

The details of the respondents who filled the questionnaire are provided in this segment. Table1 demonstrates the responses of the questions connecting to several demographic variables preferred for the study. The statistics showcased here is accumulated from the primary data.Table 1
Baseline data of the participants (N = 165).
BasisCategoriesFC.F%GenderM11611670.3F4916529.7Age groupUp to 20 years191911.521–30 years71904331–40 years5314332.141 and above2216513.3Educational qualificationU.G313118.8G376822.4P.G4411226.7Dr2313513.9P.D.H3016518.2Occupational statusGovt. employees373722.4Private. employees498629.7Business and self employees3211819.4Students4716528.5Monthly income≤Rs. 10000383823Rs. 10000–Rs. 25000327019.4Rs. 25000–Rs. 500006913941.8>Rs. 500002616515.8Using the digital paymentLess than 1 year232313.91–5 years9611958.26–10 years4416326.7Above 10 years21651.2Table1 represents the participants’ demographic information based on their gender, age group, educational qualification, occupational status, and monthly income. It indicates that most of the sample respondents (70.3%) were males (M), whereas 29.7% were females (F). The above data also indicate that most of the respondents (43%) belong to the age group of 21–30 years, 32.1% were between 31 and 40 years, and 13.3% belonged of age above 40 years, and the last 11.5% fall within the age bracket of up to 20 years.Educational qualification signifies that 18.8% of respondents belong to undergraduate (U.G), 22.4% represent graduation (G), 26.7% belong to postgraduation (P.G), 13.9% represent doctorate (Dr), and 18.2% represent professional degree holder (PDH). Occupational status represents that 22.4% of respondent belong to government employees, 29.7% represent private employees, 19.4% belong to business or self-employees, and 28.5% represent students.The monthly income of the respondents illustrates that 23% respondents belong to income of Rs. ≤10000, 19.4% respondents belong to income of Rs. 10000–25000, 41.8% respondents belong to income of Rs. 25000–50000, and 15.8% respondents belong to income of Rs. <50000.


## 7.2. Reliability of the Latent Constructs

According to [30], “Cronbach’s alpha is the standard measure of internal correspondence between items in a scale, facilitating its widespread use with Likert Scale-based questions used in the survey. The fundamental objective of reliability testing was to examine the attributes of the scales of measurement and the items for getting the overall index of internal consistency of the scales.”The result of this test is summarized in the Table2 provided above. The table represents the reliability analysis of the latent constructs used in the study. The consistency interprets the “high internal reliabilities” as the value of Cronbach’s alpha ranges between 0.70 and 0.90. Because of this, it outpaces the threshold limit of 0.70 [31]. Table 2 of reliability result complementary denotes that the coefficient alpha of each latent construct is more than 0.8, revealing that there is strong internal consistency between the variable in a scale elect for the study.Table 2
Reliability analysis.
ConstructαNo. of itemsPerception towards digital payment system (PDPS)0.81215Bartlett’s test is used to resolve the requirement for reducing many statements into a smaller number of factors. KMO describes the ratio of respondents to number of statements. If the KMO value is greater than 0.6, it is accepted for the study. This study revealed 0.813, which was highly accepted [32]. On the other hand, if Bartlett test p value is less than 5% alpha, it shows that sampling is adequate. The results of the KMO and Bartlett's test are provided in Table 3.Table 3
Sampling adequacy with the help of KMO and Bartlett’s test.
ConstructKMONo. of ItemsBartlett’s test of sphericityp valueApprox. Chi-square (χ2)dfPerception towards digital payment system (PDPS)0.81315509.536105≤0.001


## 7.3. Perception of Customer towards Digital Payment System

To analyze whether all the manifest variables are clearly explaining their respective latent construct, the CFA technique was applied by the researcher via AMOS (v-24) software in the present study. Conferring to this research study, to scrutinize the perception of customers towards digital payment system, the leading latent construct that is “perceptions towards digital payment system (PDPS)” has been categorized into five subconstructs. Additionally, each is examined by various statements chosen by the analyst to collect responses from the contributors. It is revealed in Figure1.The aforesaid CFA measurement model (Figure1) portrays the “perception of customer towards digital payment system,” the prime latent variable, consistent of six subconstructs: “reliability, service quality, satisfaction, user-friendly, trustworthy, and security.” Reliability, the first subconstruct, is consistent with three statements (DPS1, DPS2, and DPS3) expressed by rectangles known as observed variables. Service quality, the second subconstruct, is consistent with two items implied as DPS4 and DPS6. Satisfaction, the third subconstructs, is consistent with two items denoted as DPS7 and DPS8. User-friendly, the fourth subconstruct, measured by three statements implied as DPS10, DPS11, and DPS12. Trustworthy, the fifth subconstruct, is analysed through two items implied as DPS14 and DPS16. Security, the last six subconstructs, is examined by three items denoted as DPS19, DPS20, and DPS21. Fifteen statements were selected out of twenty-one, implying six items were deleted. The reason being it creates a model fitness problem.Figure 1
CFA measurement model for PDPS.Table4 describes the “Chi-square (χ2)” value, i.e., 0.067, which is greater than 5% limit, and the “CMIN/DF” value, i.e., 1.238, which is less than the prescribed limit of 3. These values denote that the deposit sample input is fitting for the model fit. The four indices of goodness, i.e., GFI = 0.927, AGFI = 0.897, CFI = 0.952, and TLI = 0.941, are more than their recommended limits. It shows that the collected sample dataset is suitable for the model fitness. These values surpass their adequate limits, describing that the model is well-fitted. The two “badness-of-fit” indices denoted by RMSEA = 0.038 and SRMR = 0.062 are under the approved limit; it represents that the collected sample input fits the model properly. Hence, it supports that the measurement mentioned above is a well-fitted model.Table 4
Model fit analysis.
Name of categoryRequired fit indicesAcceptable limitsValues obtainedAbsolute fit indicesχ2p value >0.050.067RMSEA<0.050.038SRMR<0.090.062GFI>0.900.927Incremental fit indicesAGFI>0.800.897CFI>0.900.952TLI>0.900.941Parsimonious fit indexCMIN/DF<31.238The term “e” denotes error terms that represent how many parts of variation are unexplained. The standardised regression coefficient represented the arrow leading to the corresponding item, although value over each response item design the squared multiple correlations (R2) of manifest/measured variables. Tables 4 and 5 serve the analysis portion of the earlier model by analysis of moment structure (v-24).Table 5
Analysis summary of scalar estimates.
PathΒCRp valueDPS ⟶ reliability0.905.691<0.001DPS ⟶ service quality0.614.051<0.001DPS ⟶ satisfaction0.794.452<0.001DPS ⟶ user-friendly0.724.051<0.001DPS ⟶ trustworthy0.764.301<0.001DPS ⟶ security0.794.452<0.001Reliability ⟶ DPS 10.616.432<0.001Reliability ⟶ DPS 20.576.118<0.001Reliability ⟶ DPS 30.744.151<0.001Service quality ⟶ DPS 40.523.682<0.001Service quality ⟶ DPS 60.734.131<0.001Satisfaction ⟶ DPS 70.624.716<0.001Satisfaction ⟶ DPS 80.503.996<0.001User-friendly ⟶ DPS 100.503.996<0.001User-friendly ⟶ DPS 110.644.430<0.001User-friendly ⟶ DPS 120.514.072<0.001Trustworthy ⟶ DPS 140.544.112<0.001Trustworthy ⟶ DPS 160.665.229<0.001Security ⟶ DPS 190.544.112<0.001Security ⟶ DPS 200.584.768<0.001Security ⟶ DPS 210.655.001<0.001The upstair examination shows that all the measured variables are significantly connecting to their conforming constructs since theirp values are lower than 5% alpha approved limit. Besides, the standardized regression weight (β) of each item is above 0.40 which confirm that the “convergent validity” of the earlier discussed that the CFA measurement model is achieved, and it also illustrates that each manifest variable is highly correlated with its respective latent construct [33].Table6 shows the responses of customer towards the digital payment system. Draw out the five-point summated scale that stretches from “strongly disagree (1) to strongly agree (5).” Each statement percentage calculated as per the response provided by customers. This table proffers those which statements more positive feedback received from respondent and vice versa. Statements DPS1, DPS2, DPS3, DPS4, DPS6, DPS7, DPS8, DPS10, DPS11, DPS12, DPS14, DPS16, DPS18, DPS19, and DPS21 shows that respondents are more favourable towards digital payment system. On the other hand, DPS5, DPS9, DPS13, DPS15, DPS17, and DPS20 show that respondent least favourable towards digital payment systems.Table 6
Responses of customers towards digital payment system.
CodeVariablesS. D (%)D (%)N (%)A (%)S. A (%)DPS1Digital payment system is more user-friendly than previous conventional ones0.60.621.84037DPS2Digital payment system is better than the traditional payment system0.62.413.352.131.5DPS3I am more aware related to digital payment system15.23.60.0646.733.9DPS4Helpful in saving time, cost, and processing fees4124.639.440DPS5Update regarding digital payment service available from time to time2630132110DPS6I find that it is easier to conduct financial transaction through digital mean3.98949.130.3DPS7Digital payment system provides good mechanism, so I feel more comfortable while performing digital transaction5.323852.711DPS8There is well-regulated law relating to digital payment system5.113.45.252.723.6DPS9I trust on digital payment system that it will not lead to transaction fraud302221128DPS10It follows simple steps for completing the particular transactions4.113223030.9DPS11An electronic payment system is easy to use1021203217DPS12Confidential information is delivered safely to customers621134119DPS13Track and maintain the digital record of every transaction3120.12115.612.3DPS14I trust on the ability of a digital payment system to protect my privacy2112133321DPS15Effective complaints and redressal mechanism.332462314DPS16Digital payment system provides good mechanism, so I am not concerned regarding security and data protection issues15.219.8113321DPS17It is providing speedy refund mechanism4113171811DPS18Heavily regulated and protected from any risk1310193127DPS19Transaction take place in immediate time1421134111DPS20The structure and contents of the web site/app are easy to understand312192118DPS21Digital payment offers cash back and reward points to the users1019114321Consistent with the results of the independent samplet-test (Table 7), H01 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that gender does not significantly differ from the digital payment system. In some variables such as DPS7, DPS9, DPS12, DPS17, DPS18, and DPS20, p value is less than 0.005, which means gender significantly differs from the digital payment system.Table 7
Gender as independent variable by using independent samplet-test.
VariableGenderNStd. errort-valued.fp valueDPS1M1160.0731.8951630.568F490.1221.83183.896DPS2M1160.070−0.3001630.248F490.177−0.29083.896DPS3M1160.0722.0251630.800F490.1311.89183.896DPS4M1160.0810.1151630.944F490.1180.11583.896DPS5M1160.0651.7061630.974F490.1071.66283.896DPS6M1160.0720.7681630.350F490.1290.72383.896DPS7M1160.0652.041163<0.001F490.1211.88583.896DPS8M1160.0710.4211630.293F490.1160.41083.896DPS9M1160.0731.321163<0.001F490.1291.24783.896DPS10M1160.0730.1821630.631F490.1220.17683.896DPS11M1160.0652.3761630.213F490.1032.34983.896DPS12M1160.0652.361163<0.005F490.1462.03583.896DPS13M1160.0671.1771630.761F490.1131.13583.896DPS14M1160.0711.8091630.161F490.1371.65383.896DPS15M1160.0821.8371630.814F490.1191.88183.896DPS16M1160.0790.6641630.079F490.1370.63283.896DPS17M1160.0550.747163<0.001F490.1090.67583.896DPS18M1160.0712.781163<0.001F490.1292.60283.896DPS19M1160.0661.0441630.678F490.1260.95883.896DPS20M1160.0691.544163<0.001F490.1491.35283.896DPS21M1160.0690.7621630.659F490.1180.73183.896Consistent with the results of ANOVA (Table8), H02 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that age group does not significantly differ from the digital payment system. In some variables such as DPS6, DPS9, DPS11, and DPS14, the p value is less than 0.005, which means the age group significantly differs from the digital payment system.Table 8
Age as independent variable by using one-way ANOVA.
VariableAge groupSum of squared. fF valuep valueBetween groupsWithin groupsDPS1Up to 20 years2.367105.20931.2070.30921–30 years31–40 years16140 and aboveDPS2Up to 20 years3.340109.10930.1610.92221–30 years31–40 years16140 and aboveDPS3Up to 20 years2.534116.67831.6430.18221–30 years31–40 years16140 and aboveDPS4Up to 20 years1.19983.61431.1660.32521–30 years31–40 years16140 and aboveDPS5Up to 20 years7.446101.06330.7690.51321–30 years31–40 years16140 and aboveDPS6Up to 20 years7.446101.06333.954<0.00121–30 years31–40 years16140 and aboveDPS7Up to 20 years0.23092.47430.1330.94021–30 years31–40 years16140 and aboveDPS8Up to 20 years1.63397.07030.9030.44121–30 years31–40 years16140 and aboveDPS9Up to 20 years11.88398.92936.446<0.00121–30 years31–40 years16140 and aboveDPS10Up to 20 years1.613104.36230.8300.47921–30 years31–40 years16140 and aboveDPS11Up to 20 years4.71879.07533.202<0.00121–30 years31–40 years16140 and aboveDPS12Up to 20 years3.698106.20531.8690.13721–30 years31–40 years16140 and aboveDPS13Up to 20 years0.07690.43330.0450.98721–30 years31–40 years16140 and aboveDPS14Up to 20 years5.543108.23932.748<0.00121–30 years31–40 years16140 and aboveDPS15Up to 20 years5.642113.78232.5470.05821–30 years31–40 years16140 and aboveDPS16Up to 20 years1.270125.85830.5410.65521–30 years31–40 years16140 and aboveDPS17Up to 20 years1.67367.14031.3370.26421–30 years31–40 years16140 and aboveDPS18Up to 20 years4.318108.13032.1430.09721–30 years31–40 years16140 and aboveDPS19Up to 20 years1.57694.56930.8950.44521–30 years31–40 years16140 and aboveDPS20Up to 20 years5.447113.06232.5850.05521–30 years31–40 years16140 and aboveDPS21Up to 20 years0.85296.78530.4720.70221–30 years31–40 years16140 and aboveConsistent with the results of ANOVA (Table9), H03 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that educational qualification does not significantly differ from the digital payment system. In some variables such as DPS9, DPS14, and DPS18, p value is less than 0.005, which means educational qualification significantly differ from the digital payment system.Table 9
Educational qualification as independent variable by using one-way ANOVA.
VariableEducational qualificationSum of squared. fF valuep valueBetween groupsWithin groupsDPS1U. G2.144105.43230.8130.518GP. GDr161P.D.HDPS2U. G3.30993.50331.4150.231GP. GDr161P.D.HDPS3U. G3.072109.37731.1230.347GP. GDr161P.D.HDPS4U. G0.294118.91830.0990.983GP. GDr161P.D.HDPS5U. G3.85480.95831.9040.112GP. GDr161P.D.HDPS6U. G5.966102.54332.3270.059GP. GDr161P.D.HDPS7U. G1.02391.68030.4460.775GP. GDr161P.D.HDPS8U. G5.55693.14732.3860.053GP. GDr161P.D.HDPS9U. G6.466104.34732.478<0.001GP. GDr161P.D.HDPS10U. G3.649102.32731.4260.228GP. GDr161P.D.HDPS11U. G1.20782.58730.5840.674GP. GDr161P.D.HDPS12U. G1.213108.69130.4460.775GP. GDr161P.D.HDPS13U. G0.79589.71530.3540.841GP. GDr161P.D.HDPS14U. G11.372102.41034.442<0.001GP. GDr161P.D.HDPS15U. G4.685119.82531.5640.187GP. GDr161P.D.HDPS16U. G5.004122.12331.6390.167GP. GDr161P.D.HDPS17U. G2.15166.66131.2910.276GP. GDr161P.D.HDPS18U. G7.493104.95532.856<0.001GP. GDr161P.D.HDPS19U. G1.33394.81230.5620.690GP. GDr161P.D.HDPS20U. G5.091113.41831.7960.132GP. GDr161P.D.HDPS21U. G4.38593.25131.8810.116GP. GDr161P.D.HConsistent with the results of ANOVA (Table10), H04 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that occupational status does not significantly differ with the digital payment system. In some variables such as DPS3, DPS9, DPS14, DPS15, DPS18, and DPS20, p value is less than 0.005, which means occupational status significantly differ with the digital payment system.Table 10
Occupational status as independent variable by using one-way ANOVA.
VariableOccupational statusSum of squared. fF valuep valueBetween groupsWithin groupsDPS 1Govt. employee2.115105.46131.0760.361Private employeeBusiness/self employee161StudentsDPS 2Govt. employee1.46395.34930.8230.483Private employeeBusiness/self employee161StudentsDPS 3Govt. employee5.502106.94632.761<0.001Private employeeBusiness/self employee161StudentsDPS 4Govt. employee0.898118.31430.4070.748Private employeeBusiness/self employee161StudentsDPS 5Govt. employee1.83382.97931.1860.317Private employeeBusiness/self employee161StudentsDPS 6Govt. employee4.418104.09132.2780.082Private employeeBusiness/self employee161StudentsDPS 7Govt. employee1.50991.19430.8880.449Private employeeBusiness/self employee161StudentsDPS 8Govt. employee2.06496.63931.1460.332Private employeeBusiness/self employee161StudentsDPS 9Govt. employee8.592102.22034.511<0.001Private employeeBusiness/self employee161StudentsDPS 10Govt. employee1.210104.76630.6200.603Private employeeBusiness/self employee161StudentsDPS 11Govt. employee0.77483.02030.5000.683Private employeeBusiness/self employee161StudentsDPS 12Govt. employee2.203107.70031.0980.352Private employeeBusiness/self employee161StudentsDPS 13Govt. employee1.13489.37530.6810.565Private employeeBusiness/self employee161StudentsDPS 14Govt. employee11.833101.94936.229<0.001Private employeeBusiness/self employee161StudentsDPS 15Govt. employee6.994117.51533.194<0.001Private employeeBusiness/self employee161StudentsDPS 16Govt. employee5.134121.99432.2580.084Private employeeBusiness/self employee161StudentsDPS 17Govt. employee1.73267.08031.3860.249Private employeeBusiness/self employee161StudentsDPS 18Govt. employee6.435106.01433.257<0.001Private employeeBusiness/self employee161StudentsDPS 19Govt. employee0.12396.02230.0690.976Private employeeBusiness/self employee161StudentsDPS 20Govt. employee5.807112.70232.765<0.001Private employeeBusiness/self employee161StudentsDPS 21Govt. employee3.07694.56131.7460.160Private employeeBusiness/self employee161StudentsConsistent with the results of ANOVA (Table11), H05 is accepted. The value obtains not significant since the p value (probability value) is more than 0.001. This outcome expresses that monthly income does not significantly differ with the digital payment system. In some variables such as DPS6, DPS7, DPS9, DPS14, DPS15, and DPS18, p value is less than 0.005, which means monthly income significantly differs from the digital payment system.Table 11
Monthly income as independent variable by using one-way ANOVA.
VariableMonthly incomeSum of squared. fF valuep valueBetween groupsWithin groupsDPS 1≤Rs. 100001.269106.30730.6410.590Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 2≤Rs.100001.47595.33730.8310.479Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 3≤Rs. 100002.872109.57631.4070.243Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 4≤Rs. 100002.194117.01831.0060.392Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 5≤Rs. 100003.55281.26132.3460.075Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 6≤Rs. 100009.79398.71635.324<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 7≤Rs. 100004.59288.11132.797<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 8≤Rs. 100001.47197.23230.8120.489Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 9≤Rs. 1000010.336100.47635.521<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 10≤Rs. 100000.761105.21530.3880.762Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 11≤Rs. 100001.21982.57530.7920.500Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 12≤Rs. 100003.102106.80131.5590.202Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 13≤Rs. 100001.11589.39430.6700.572Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 14≤Rs. 100008.521105.26134.344<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 15≤Rs. 100007.666116.84333.521<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 16≤Rs. 100002.053125.07530.8810.452Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 17≤Rs100000.25968.55330.2030.894Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 18≤Rs. 100008.334104.11434.296<0.001Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 19≤Rs. 100000.76195.38530.4280.733Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 20≤Rs. 100003.927114.58231.8390.142Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000DPS 21≤Rs. 100003.54594.09132.0220.113Rs. 10000–Rs. 25000Rs. 25000–Rs. 50000161>Rs. 50000


## 8. Limitations and Future Research

This section points out a few limitations of this research study which should be taken into forethought. First, the sample size in this study is relatively moderate to generalize for India’s large population, and in this study, participants of age group response % rate are more or less similar to each other. So, future studies might take a larger sample size and focus on the youth age group of below 30 years as their perception of customers regarding digital payment might be different. Also, the study participants are associated with the urban class of the population who have more connection to technology. Future research might include the rural class of India in the study as the rural class constitutes a bigger section of the Indian population. If the researcher is unable to reach them through due to technological barriers, then the success of digital system becomes a big question mark. Furthermore, purposive sampling was used to determine the respondents taken in the study. So, in future research, if probability sampling techniques could be used to collect data, and then the generalizability of the findings could be improved.


## 9. Future of Digital Payment in India

The research analysis indicates that the Indian economy will take a substantial time in becoming cashless in its entirety, i.e., with the natives confidently relying on exercising digital payment systems and using electronic means of payment. Greater awareness is needed to ensure people in all parts of the country recognize the benefits of carrying out digital transactions through various means. It is imperative to resolve the scarcity of appropriate digital literacy to advance to make India a digitized economy correctly. Despite the currently provided cashback and relevant promotional offers, a steady, dedicated, and genuinely secure payment network is needed to adequately boost digital transactions in the country, which will enable absolute transparency in transactions, ultimately leading to the identification and eradication of black money.


## 10. Suggestions

(1)
Transaction cost plays a crucial role in encouraging and discouraging at the same time in the adoption of digital payment methods.(2)
Education-related programs are the best way to provide knowledge to customers about how it is beneficial, less costly, and user-friendly.(3)
Security and trustiness are complementary. If users feel secure regarding the online platform, it ultimately creates goodwill or trust in service providers.


## 11. Conclusion

This research thoroughly endeavoured towards understanding the viewpoints of the general people towards online means of payment. It is, hence, discovered that a more significant influence is played by the demographic factors in the appropriate adoption of the various digital payment services, which include age, gender, educational qualification, occupational status, and the level of income. On other hand, factors such as reliability, satisfaction, service quality, user-friendly, trustworthy, and security also have positive impact on customer adoption. The study examines that the user provides positive response towards digital payments. Besides, the research outcome emphasises the various benefits of exercising digital payments, or otherwise, necessitating the neediness to increase e-payments. Digital means of transactions are not merely beneficial concerning the give-and-take of money but also comprise additional benefits. These include timely reminders of unpaid bills, recharges, various cashbacks and rewards scheme, and linking of bank accounts for autopay structure, all leading to greater convenience and efficacy. It is also evident that online means of transactions or digital payment services will become necessary soon. Henceforth, it is imperative for most of the populace, mainly of those belonging to the rural areas, to make the required transition from the traditional means of spending money to modern-day methods that rely on the application of the Internet. It is determined that the online mode of transactions is considerably secure and time-saving, which also accommodates for keeping an easy-to-access record of all the transactions carried out. The continual advancements in the IT and Communications sector have also enhanced the reachability of mobile networks, along with greater availability of electricity and Internet, leading to the increased use of digital services. It can, therefore, be unquestionably stated that the ultimate transaction system would be cashless. There are more significant benefits to be reaped from exercising digital means of payments. Whether it is E-wallets or mobile banking, the modern-day method of transacting allows people to carry out business anywhere, anytime, at their fingertips, with absolute ease of operations. The coming years will bring forward a substantial increase in the use of digital transactions, all ultimately leading to the fulfilment of India’s mission of becoming a digitized economy. As more and more advancements are made, specifically in the digital sphere, our habits have continuously evolved. It is high time for the natives to move forward from sticking to the cash-based commerce format and switch to the digitized payment structure. Its several advantages include a substantial reduction in the cost of currency management and detecting various kinds of fraudulent activities, strengthening the economic position. Besides, the government’s continuous efforts in creating adequate awareness, building necessary trust, and providing a concrete cybersecurity framework and infrastructure are likely to fasten the people’s receptiveness towards digital payment systems, which is currently, by and large, facilitated by the constant growth in the number of smartphone users and Internet penetration, in both urban as well as rural areas.



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