1. To study the various factors influencing the Electronic Payment system in India
2. To investigate the sorts of problems and issues regarding the e-Payment system

Singhal & Padhmanabhan (2008) explored the major factors responsible for internet banking based on respondents’ perception of various internet applications. They also examined whether there is any relation between the demographic variable (e.g., gender) and respondents’ perception of internet banking and whether the user and non-user perceptions differ.

Ally (2010) added essential points to the current literature and offered a few answers to the problems associated with Australian consumers and businesses adopting alternative payment methods. The thesis examines the influences and challenges related to evaluating and selecting payment methods for goods and services, specifically within the context of traditional and alternative payment methods and the Internet environment. Researchers advocated that there is a need for widespread take-up of trusted, secure, privacy-protected and low-cost electronic payment methods by Australian consumers, organizations, and businesses, which will provide opportunities to help drive online payments and produce significant productivity gains that benefit the Australian economy.

The e-payment system’s significance is accepted worldwide as Ayo & Ukpere (2010) proposed a study relating to a unified (single) smart card-based ATM card with a biometric-based cash dispenser for all banking transactions. A set of questionnaires was designed to evaluate the acceptability of this concept among users, and the architecture of the proposed system is presented. They found that the ICT adoption level in Nigeria’s banking sector is on the increase. Yet, the amount of cash in circulation is equally increasing, attributable to the lack of safety, security, privacy, and reliability in the e-payment instruments.

Geetha & Malarvizhi (2011) identified the factors affecting the acceptance of e-banking services and also tried to look into the significant issues of privacy and security in India. For this purpose, data was used through a structured questionnaire and descriptive statistics; factor and regression analyses were used to determine the factors affecting e-banking services among Indian customers. They concluded that customers are opposed to joining those technologies that might contain insecurity, so to overcome these issues banks should ensure that e-banking is safe and secure, convenient and also put efforts to educate customers regarding use of e-banking by providing them with computer knowledge.

Oyewoleet al. (2013) analyzed the relationship between the e-payment system and economic growth as the Nigerian economy shifted to the cashless payment mechanism. The result indicates a significant positive relationship between the e-payment system and economic development in terms of real GDP per capita and trade per capita of the study period of 7 years 2005-2012. The statistical observation through OLS and TSLS revealed that only ATMs positively contributed to economic growth while other e-payment channels contributed negatively.

Roy & Sinha (2014) studied the factors influencing consumer’s adoption of Electronic Payment Systems in the light of the TAM (Technology Acceptance Model) given by (Davis, 1989). The TAM model is analyzed based on three essential factors: perceived Usefulness (PU) Perceived Ease of Use (PEOU), and Risk, Trust, Security, and Privacy. Questionnaires and factor analysis were used in their study to find reliable factors. The main objectives of their research are to find out the most popular electronic payment system among various electronic payment options, awareness and usage of e-payment techniques, and reasons behind the unpopularity or constraints of e-payment acceptance, as well as to provide suggestions for further improvement. They concluded that Perceived ease of use (PEOU) is the most significant predictor, and customer attitude was found to have the least important effect on adopting E-payment.

Kaur & Pathak (2015) synthesized the E-payment system with reference to electronic commerce in India. The research was carried out to explore the areas of quality customer services, the relationship between bank and customer, working of the online payment system, usage of various payment methods, and knowing the economic requirements for a business to operate an effective E-payment system. They concluded that because each mode of payment has its own merits and demerits, it is quite challenging to decide which payment method is best. They found that E-payments modes depend upon a number of factors such as consumer preferences, ease of use, cost, industry agreement, authorization, security, authentication, non-refutability, accessibility reliability, anonymity, and public policy.

Alsomali (2015) analyzed the perception of risk in the Saudi e-banking sector in detail, identified the factors that affected the perception of risk, and suggested some ways to resolve this to reduce the perception of risk and increase the adoption of e-banking. The critical contribution of this research is the inclusion of culture as a factor affecting individuals’ perception of risk in e-banking. The researcher gave a few recommendations: banks should improve user server-side technologies to boost security, increase personal interaction in e-banking, develop a straightforward and intuitive web interface, and avoid changing the web interface too frequently.

Sinha (2016) studied the benefits and risks associated with the electronic services provided by banks and their penetration level. They revealed in their study that despite the hundreds of benefits of Internet Banking, its associated risks ought to be recognized and controlled by banks in a mindful methodology in view of the simple characteristics and issues of web or internet-based transactions. E-banking or internet banking dangers ought to be founded on the class of customer, the association’s value-based capacities, the significance and worth of the amassed information to the association and customer, the straightforwardness of utilizing the plan, and the degree and extent of exchanges.

Khan et al. (2017) evaluated online payment systems’ present status and growth in worldwide markets and analyzed the prospective for future growth. Based on the statistics, their study concludes that the number of customers employing online modes of payment and making online transactions is continuously growing. It suggests that better integration of online payment systems with the present financial and telecommunication infrastructure is necessary for a promising future of this payment mode.

Lundh & Svensson (2018) researched the adoption factors within alternative e-payment methods and the reasons why these factors affect the consumer’s adoption. The Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory (DIT) are applied to this exploratory research. The findings of the research point towards that benefits E-Commerce are classified as ease of accessibility, not having to put in an effort or provide extensive information, as well as the time-saving advantage and cost was found to be a factor of adoption. Still, due to the competitive nature of alternative e-payments where actors do not currently compete on price, and given that cost was seen as relative to the sum of the transaction, it is appropriate to have the cost factor as a construct of Perceived Usefulness (PU).

The study by Suhas and Ramesh (2019) is based on the growth of Electronic banking and its products used in India’s banking sector. The study observed from data analysis that ATM deployment, debit and credit card issuing, and transactions relating to NEFT, RTGS, Mobile banking (Values and Volumes) have grown significantly in recent years. Our young generation has accepted the changes in the banking system more as a convenience than a challenge.

They verify that E-banks are easy to set up, so many new entrants will arrive, and ‘Old-world’ systems, cultures, and structures will not encumber these new entrants. E-banking is a borderless entity permitting banking anytime, anywhere, and anyhow. This provides us with all the functions and many advantages of traditional banking services. During this process step, controls that could mitigate or eliminate the identified risks, as appropriate to the organization’s operations, are provided. The recommended controls aim to reduce the risk level to the IT system and its data to an acceptable level.

Research Design- The research design for this study is descriptive.

Sample Unit, Size, & Technique- The sample unit for the study is all persons older than 18 years, and both consumer and business market users are approached for the data collection. A random sampling technique was used for the data collection, and a total of 400 respondents were interviewed. Three hundred ninety-six (396) responses were found correct and considered for the analysis.

A total of 396 respondents were contacted, of which 235 were male and 161 were female. Most of the respondents were in the age category of 18-35 years. More than 70 % of the persons in the sample were unmarried.

Reliability test

The reliability statistics tables show that Cronbach’s alpha value is .944, which is more than satisfactory in the social sciences research criteria for reliability testing.

Assumption Checks

The table presents the results of Bartlett’s Test of Sphericity, a statistical test used in factor analysis to assess whether the correlation matrix of observed variables is significantly different from the identity matrix. The test yielded a chi-square value of 5357 with 231 degrees of freedom, resulting in a highly significant p-value (< .001). The p-value’s significance indicates evidence to reject the null hypothesis of Sphericity, suggesting that the observed variables are not uncorrelated in the population.

Overall, the KMO value is .946, which is more than the standard value of 0.5, which supports that factor analysis can be used. Therefore, using factor analysis, three factors were extracted, all with Eigen values more than 1, suggesting that all factors are important.

Principal Component Analysis

The table presents the component loadings resulting from a factor analysis with varimax rotation. Three components were extracted: convenience enhancement, efficiency ease factor, and digital trust and security. This suggests that these variables are closely related and contribute significantly to the formation of Component 1. The variables Q16, Q17, and Q15 also have loadings on Component 1, but they are negative, indicating an inverse relationship. The variables Q16, Q17, and Q15 have strong positive loadings on Component 2. This implies that these variables are highly correlated and play a significant role in defining Component 2. Other variables have negligible loadings on Component 2. The variable Q18 shows a strong favorable loading on Component 3, while Q16, Q17, and Q15 have negligible loadings on this component. This suggests that Q18 is primarily associated with Component 3. In summary, the factor analysis reveals three distinct components with specific sets of variables strongly contributing to each component. The scree plot below also shows the three components based on the Eigen value.

Component Statistics

The table summarizes the results of a factor analysis, presenting component-wise information. The sum of squares loadings in the first component is 5.94, explaining 27.0% of the variance. Moving to the second component, it has a sum of squares loadings of 5.23, contributing to 23.8% of the variance. Together with the first component, these components cumulatively account for 50.7% of the total variance. The third component adds an additional 10.7% to the variance, bringing the cumulative explained variance to 61.4%. Each component represents a set of variables that share common underlying factors, and their respective loadings indicate the strength of association between the variables and the components. The results suggest that these three components collectively capture a substantial portion of the variability in the data, providing valuable insights into the underlying structure of the analyzed variables.

The table presents the frequencies of responses to the questions about the problems and issues regarding the e-payment system. In analyzing the data, it’s evident that the majority of respondents selected either “Neutral” or “Agree” for (lack of updated technology/application), constituting 40.7% and 36.4% of the total, respectively. This indicates a relatively balanced distribution of opinions among the respondents, with a notable concentration in the middle categories of “Neutral” and “Agree.”

Regarding the poor network problem, most participants (43.2%) agreed with the statement, while a significant portion (33.2%) chose the neutral option. Nearly half of the participants (45.2%) had a neutral or negative stance, while 54.8% expressed agreement or strong agreement. This distribution suggests a relatively balanced range of opinions among the participants regarding the statement in question (for poor network), with a notable portion holding neutral views.

There is a chance of misuse of the information available on the platform. While answering this question, the respondents emphasized agreeing with the statement with 48.7% and 24.6% of respondents choosing the “Neutral” option, suggesting a considerable proportion with no strong inclination towards agreement or disagreement.

The issues regarding employment are replaced by technology; the majority of participants chose the “Agree” option, constituting 52.3% of the total responses, while 23.6% selected “Neutral.” Additionally, the data indicates a relatively balanced distribution across the response categories, with no extreme skewness or kurtosis noted in the distribution of responses.

The next question is related to the process and steps in advance to operate the e-payment application. The data shows that a considerable portion of the participants chose the “Agree” option, with 53.3% of the total responses falling into this category. The cumulative percentages provide insights into the overall trend of responses, showing the progressive accumulation of participants’ choices. This means that most respondents agree that one should know the steps and process in advance when operating e-payment platforms.

The next question concerns the risk of leakage of the PIN and password of the application; provide the favourable response in the agree section. Firstly, most respondents either agreed or strongly agreed with the statement, as evidenced by the combined percentage of 61.8% for Agree and Strongly Agree categories. The table reveals a predominantly positive sentiment among respondents toward the statement coded as Q33, with a noteworthy proportion expressing neutrality. The cumulative percentages offer a comprehensive view of the overall distribution of responses, emphasizing the prevalence of agreement among the survey participants.

The next question is that there is a chance of misuse of the information on the platform. While answering this question, the respondents emphasized agreeing with the statement with 48.7% and 24.6% of respondents choosing the “Neutral” option, suggesting a considerable proportion with no strong inclination towards agreement or disagreement.

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