Factors influencing the usage of mobile payment services during the COVID-19 pandemic
1Symbiosis School of Banking and Finance, Pune, India.
2Faculty Member, ICFAI Business School, Pune, India.
3School of Business, Skyline University College, Sharjah, UAE.
4Department of Management and Marketing, College of Business, Austin Peay State University, Kimbrough 237, Clarksville, Tennessee, USA.
ABSTRACT
This study aims to identify and examine the factors that affected users' behavioral intentions, attitudes, and perceptions toward mobile payment services during COVID-19. It also attempts to determine the impact of demographic variables on the ease of use of mobile payment services. A survey approach was adopted to collect primary data and capture attitudes and behaviors on the use of mobile payment services during and after COVID-19. Being a quantitative study, a multiple regression analysis was used to analyze the primary data. The results indicate that education was the main reason for the adoption of mobile payment services, and security was the leading factor contributing to user satisfaction. Ease of use of the payment app was important for continued usage of the app. The statistical analysis explains that ease of use is an important variable for the usage of payment wallets or mobile payment services by users, followed by familiarity, safety, and security of the mobile payment wallet. Based on the findings of this study, it is suggested that mobile payment companies should concentrate more on the performance and ease of use of their applications. Mobile payment companies should also concentrate on meeting their users’ needs, which will indirectly lead to the retention of their users for a long period.
Keywords:COVID-19, Digital payment, E-commerce, Mobile payment services, Perception, Regression analysis , Unified payment interface.
ARTICLE HISTORY: Received:8 April 2023, Revised:1 June 2023, Accepted:4 August 2023, Published:17 August 2023
JEL Classification:C130; E420; G230; I120; O300.
Contribution/Originality: The originality of the paper is that it focuses on different generations and explains the role of education as a major contributor to the adoption of mobile payment services.
Mobile-based payments have become a necessity since the COVID-19 pandemic rather than a process of convenience. The use of mobile-based payments can be considered a preventive behavior (adoption of social distancing) during pandemic times. Individuals’ perceptions of the severity of the pandemic and their feelings of the extent of susceptibility to the disease can determine the level of adoption of preventive behavior (e.g., applying social distancing by shifting from physical banking to digital banking). Digital payments are mobile payments made via applications (apps) or payments made via internet banking and act as instruments to promote social distancing and allow customers to make transactions during lockdown periods. The present study analyzes and determines the impact of different demographic factors, such as age, gender and income, on the usage of mobile payment systems.
The Reserve Bank of India (RBI) advised using digital payment modes in order to minimize the use of currency/coins and face-to-face contact to contain the virus and reduce the chances of infection (Auer, Cornelli, & Frost, 2020). The evidence of the presence of the virus and it being “alive” on nonporous materials means that virus transmission is not limited to notes and coins, there is also a risk of infection from handling debit and credit cards, which are also frequently used by customers (Lopez et al., 2013). However, to date, there have been no reported cases via this mode of transmission, but the panic has created chaos, and in order to reduce transmission, people have shifted to mobile payment systems.
Mobile payments are a segment of fintech whose evolution started in 1886 with the first transatlantic cable and Fedwire, which allowed the electronic transfer of funds in the United States with the help of Morse code and telegraph. This further gave rise to Fintech 2.0 (1967–2008), highlighted by ATMs (automatic teller machines) starting with Barclays Bank. This is known as the transition from the analog mode of transfer to digitization. This particular phase of the fintech revolution is also marked by the formation of NASDAQ (National Association of Securities Dealers Automated Quotations) and the Society for Worldwide Interbank Financial Telecommunication (SWIFT). This phase is further characterized by the development of mainframe computers and the initial movements toward digital banking. The subprime mortgage crisis in 2008 led to the loss of trust in traditional banking services. This was the emergence of Fintech 3.0, which is characterized by cryptocurrency, the use of mobile phones, and open banking. Additionally, it accelerated the process of the formation of the UPI (Unified Payments Interface), which is a real-time payment system that allows the transfer of funds from one bank account to another via a mobile-based platform (Arner, Barberis, & Buckley, 2017).
The spread of the virus through proximity or physical contact created panic, and precautionary measures were introduced that include wearing masks, sanitizing hands at regular intervals, and social distancing (Chang et al., 2020). One of the main causes of infection was assumed to be the exchange of notes and coins as it involves physical proximity; hence, the need to use mobile payments or other contactless means of transfer was emphasized as one of the solutions (Angelakis et al., 2014). After assessing the unfavorable circumstances, The World Bank predicted that this might result in another recession, with countries such as India and other developing countries taking a major hit (World Bank, 2020). However, the development of vaccines and India’s large vaccination drive has helped to boost business and rebuild the economy to its original state.
UPI was introduced in April 2016, and by December 2018 it had seen an astonishing growth of 913 million transactions worth INR 1.09 trillion. During the 2019 fiscal year, UPI payments saw a growth of 490% in terms of volume and 700% in terms of transaction value. In the 2020 financial year, UPI payments witnessed growth of 132% and 143% in terms of volume and transactions, respectively. Considering the continuous inclination toward mobile-based payments, the adoption of mobile payment apps due to the threat of COVID-19 infection may be partly responsible for the increase in the number of people opting for contactless payments. Hence, the present study attempts to understand the relationship between various demographic factors and COVID-19 and their respective influence on the adoption of mobile-based payment systems. It also aims to understand if there will be continuity in the use of mobile-based payments in the post-COVID era.
The goal of this study is to identify and assess the key variables that may have had an impact on consumers' intentions, behavior, and use of mobile payment services during COVID-19.
The findings of some studies show that attitudes, rather than utility or risk, are the primary determinants of how mobile apps are meant to be used. Such studies highlight the key managerial ramifications and outline specific ways to support digital payment systems in light of contemporary technical developments(Munoz-Leiva, Climent-Climent, & Liébana-Cabanillas, 2017).
According to one study's findings, performance expectations, effort expectations, and perceived severity all have a significant positive influence on consumers' attitudes. Additionally, effort expectations are significantly influenced by facilitating conditions, self-efficacy, attitude, and behavioral intention, all of which significantly positively influence user behavior. Social impact failed to provide any meaningful connection (Upadhyay, Upadhyay, Abed, & Dwivedi, 2022). Another study uses the Technology Readiness Index (TRI) and the Technology Acceptance Model (TAM) to examine how mobile payment apps were adopted during the COVID-19 pandemic. The perceived usefulness and usability of the TRI components were affected, with the exception of discomfort in using the technology, which had no impact on perceived benefit. Perceived usefulness and perceived ease of use are two other important TAM characteristics that are influenced by attitude. Meanwhile, attitude has an impact on one's inclination to use mobile payment apps (Rafdinal & Senalasari, 2021).
The results of the multiple regression analyses conducted for a study on "Behavioral intention to use mobile banking among the millennial generation" showed that performance expectancy (PE) was the best predictor, followed by effort expectancy (EE), perceived risk, and social influence. The prediction model was able to account for 68.3% of the variation in respondents' intentions to use mobile banking. The partial mediation impact of PE on the association between EE and intention to embrace mobile banking was supported by the mediation analysis. Instead of focusing on respondents' actual behavior, this study also looked at their intention to use mobile banking. Although understanding the intent of the activity is important, it cannot fully reflect actual behavior. Additionally, due to selection bias and lack of information about the sampling frame, the findings from this study may not be applicable to the entire population of Generation Y college or university students (Tan & Lau, 2016). The researchers looked at payment methods before and after demonetization to see if they changed and the results revealed that retailers can benefit if they primarily use digital payment methods post demonetization. Most retailers accept payments through various apps (Manickam, Vinayagamoorthi, Gopalakrishnan, Sudha, & Mathiraj, 2022).
Gani and Adeoti (2011) used a pre-tested, standardized questionnaire on electronic payment system adoption to collect primary data, and a multistage sampling technique and the probit model was used for the analysis. The study looked into the impact of motivational factors on customers’ decisions to use point of sale (POS) terminals and it found that nativity, security, ease of use, availability, convenience, intention to use, and technology complexity are among the factors influencing the use POS terminals. In India, the electronic payment (e-payment) system has seen tremendous development, but there is still much work to be done to increase its use. Cash also accounts for 90% of all transactions. The Technology Acceptance Model (TAM) was used in the analysis carried out by Roy and Sinha (2014), who discovered that four factors contribute to the strength of the e-payment system—innovation, motivation, consumer comfort, and legal structure.
To explore the acceptance and use of technology in a consumer environment, researchers have developed the unified theory of acceptance and use of technology (UTAUT) model. Hedonic motivation, price value, and habit are three constructs included in the revised UTAUT2 model. The impacts of these dimensions on behavioral intention and technology use are predicted to be moderated by individual characteristics, including age, gender, and experience. Our strategy was supported by the results from a two-stage online survey of 1,512 mobile internet users that collected information on technology use four months after the initial study. In comparison to the UTAUT model, the extensions proposed in the UTAUT2 model significantly increased the variation explained in behavioral intention (from 56% to 74%) and technology use (from 40% to 52%) (Venkatesh, Thong, & Xu, 2012). This study developed and empirically evaluated a model to predict the factors impacting students' behavioral intentions toward using mobile learning (m-learning). In order to analyze consumers’ intent to utilize mobile learning, this study applied the UTAUT model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk as moderators (Chao, 2019).
When the perception of security and trust is higher, people tend to have a positive attitude toward mobile payments, indicating a positive effect of payment services and trust on the adoption of mobile payments. Of all the variables studied in the research on mobile payments in the USA and China, security responsibility commitment (SRC) was found to have a significantly positive impact on perceived security (PS) and trust, with the exception that the positive impact of security on trust is not supported (Fan, Shao, Li, & Huang, 2018). In a study to examine the gratifications that lead to positive attitudes and the continued use of mobile payment services in a developing country context, the authors discovered that integration, ease of use, and usefulness have a significant impact on attitudes toward the use of mobile payment services. Additionally, the user's mindset has a big impact on whether they want to keep using mobile payment services. The use of mobile payment technology can be increased by encouraging an atmosphere that is favorable for it (Alhassan, Kolog, & Boateng, 2020).
A study found PE to be the best predictor, followed by EE, perceived danger, and social impact, according to the multiple regression analysis. The prediction model was able to account for 68.3% of the variation in respondents' intentions to use mobile banking. The partial mediation impact of PE on the association between EE and the intention to embrace mobile banking was supported by the mediation analysis. This study identifies the variables that influence Generation Y college or university students' intentions to use mobile banking. To increase the rate of acceptance of mobile banking among students, bank operators can use the findings to enhance their marketing plans and the services they provide(Tan & Lau, 2016).
The effective usage of mobile payments during COVID-19, the characteristics of technology users, and their behavior toward payment systems were investigated. It was identified that the stronger the knowledge of technology users, the higher the influence on their behavior to adopt the payment system. Due to lower costs involved, the users feel that the benefit is higher and are motivated to adopt more mobile payment systems (Sunarjo, Nurhayati, & Muhardono, 2021). To analyze users’ acceptance of mobile payment systems on social networks, trust and perceived risk can be integrated into a traditional Technology Acceptance Model. External influences have the highest effect on the acceptance of mobile payment systems as they also affect people’s social image and subjective rules. Apart from this, the usefulness of the payment system also positively influences the adoption, whereas perceived risk is negatively related. When comparing gender-wise usability, men find it more useful, and the younger generation is highly predisposed in the adoption of new technologies (Munoz-Leiva et al., 2017).
One study discovered that merchants' adoption of mobile payments is influenced significantly more by their faith in technology and service providers than by perceived usefulness or convenience of use. The study advises service providers to take advantage of the chance to foster merchant confidence because it serves as a crucial enabler for the adoption of mobile payments (Yeboah, Boateng, Owusu, Afful-Dadzie, & Ofori-Amanfo, 2020).
According to a study by Sreelakshmi and Prathap (2020), the Health Belief Model components of perceived severity, perceived vulnerability, and self-efficacy have a substantial impact on whether or not mobile-based payment services are adopted. Usefulness and satisfaction substantially predicted the intention to continue using the service. Additionally, confirmation, perceived utility, and satisfaction all have an indirect impact on the intention to continue use when there is a perceived health hazard (which includes perceived severity and perceived vulnerability).
The existing literature highlights the importance and shift from liquid cash to digital payments. Also, recent data from the National Payments Corporation of India (NPCI) website suggests that there are 313 banks associated with UPI platforms, resulting in 5405.65 million transactions in terms of volume and accumulating a total of INR 9,6,581.66 crores in March 2022 (National Payments Corporation of India (NPCI), 2022).
The primary objective of the study is to find the impact of demographic variables on the ease of use of mobile payment services. The study also focuses on the impact that level of education and age have on familiarity with mobile payment services. The level of education on their intention to purchase and their consideration of safety and security were analyzed.
Hypothesis 1: Education has no effect on attitude toward and use of mobile payment services.
Hypothesis 2: Age has no effect on attitude toward and use of mobile payment services.
Hypothesis 3: Occupation has no effect on attitude toward and use of mobile payment services.
Hypothesis 4: Gender has no effect on attitude toward and use of mobile payment services.
This study aims to identify the influencing factors of people’s intention to use and recommend digital wallets based on a multi-generation preference perspective. The random convenience sampling technique was employed to collect data. The link to the survey was shared through email and social media platforms, such as WhatsApp, to solicit participation from users in India. A cover letter was enclosed with each questionnaire explaining the study’s objective, the need for voluntary participation, and a promise to maintain anonymity. This study extends the Technology Acceptance Model (TAM), and the study was carried out between December 2021 to April 2022.
The survey was used to collect primary data to capture attitudes and behaviors regarding the use of mobile payment services during and after the COVID-19 period. The survey methodology is by far the most used, as it allows for the collection of both opinions/attitudes and facts. In the Indian context, empirical research, or obtaining data from real-world or naturally occurring circumstances, has been employed to collect data across different demographics.
A total of 214 valid surveys were analyzed by conducting a t-test and one-way analysis of variance (ANOVA) in SPSS (Statistical Package for the Social Sciences) software to check the hypotheses and derive inferences to arrive at the conclusion. The question the research aims to answer is: Has COVID-19 increased the usage of mobile payment services among the respondents? The research also aims to identify the demographic factors which impact the change in usage of mobile payment services by asking the following question: Do variables such as age, education level and gender have an impact on the usage of mobile payment services? It also examines which aspects of mobile payment services (such as ease of use, familiarity, safety, and security) have increased the usage of mobile payment services.
The respondents were divided into three generation categories based on age (Generations X, Y and Z). Generation X (Gen X) is generally defined as people born between 1965 and 1980, Generation Y (Gen Y) is defined as people born between 1981 and 1995, and Generation Z (Gen Z) are people born between 1996 and 2010 (McCrindle & Wolfinger, 2009).
The first part of the questionnaire contains questions to measure the demographic variables (age group, education, income level, gender and marital status). The second part includes 24 questions regarding purchase intention and factors impacting use, such as ease of use, familiarity, and security/safety due to COVID-19. The responses were collected on a five-point Likert scale from 1 – strongly disagree to 5 – strongly agree.
Generation-related maturity and exposure are always implicit in several situations. Generation also reflects a person’s level of adaptability and their ability to accept changes; therefore, generation is identified as one of the criteria for analysis in this study, and the breakdown of the respondents’ generations is presented below:
| Parameter | Generation | % of count |
| Generation | Gen X | 21.5% |
| Gen Y | 34.11% |
|
| Gen Z | 44.39% |
A total of 214 responses were received, of which 21.5% were classified as Generation X, 34.11% as Generation Y, and 44.39% as Generation Z.
Gender plays an important role in the need to adapt to the changing circumstances and hence we have collected the data for gender, which is summarized in Table 2.
| Parameter | Gender | % of count |
| Gender | Male | 61.68% |
| Female | 38.32% |
Education could be an important factor as it might increase the adaptability of a person toward changes in technology. Hence, we have collected responses on the same, which is summarized below in Table 3.
| Parameter | Level | % of count |
| Education | Graduate | 24.53% |
| Post-Graduate | 66.51% |
|
| Doctoral | 8.96% |
Occupation dictates the in-practice accommodability of the individuals with respect to changing norms and conditions and hence we have taken occupation as one of the factors which can influence the adaptability to mobile payment.
| Parameter | Option | % of count |
| Occupation | Business owner | 5.66% |
| Retired | 1.89% |
|
| Salaried | 39.15% |
|
| Self-employed | 8.02% |
|
| Student | 45.28% |
As per the results presented in Table 4, 45% of the respondents are students, followed by 39% who are salaried. Only 8% are self-employed, 6% are leading a business, and 2% are retired.
For the analysis of each of the four factors (ease of use, familiarity, safety/security due to COVID-19, and purchase intention), factor analysis, multiple regression, independent t-test, and ANOVA were used to prove if the comparisons were relevant or not. Factor analysis was done to determine the loading of 24 items and to establish factor scores needed for further analysis. This was done using Principal Component Analysis with varimax rotation. One of the vital considerations before conducting a factor analysis is the sample size. There are many views regarding a suitable sample size for factor analysis. According to Hair, Black, Babin, and Anderson (2006), a minimum of five subjects per variable is a must for factor analysis to achieve good results. A multiple regression analysis was carried out to check the impact of individual predictors on the dependent variable. The t-test and the ANOVA were conducted to check Hypotheses 1 to 4.
In the present study, the sample size is 214, which is greater than the minimum requirement of 120 (24 variables*5) and is therefore adequate for factor analysis. Further, the Kaiser–Meyer–Olkin (KMO) measure verified the sampling adequacy for analysis (KMO = 0.929), which is well above the acceptable limit of 0.6 (Field, 2009; Hair et al., 2006). Bartlett‘s test of sphericity χ2 (153) = 3179.129, p < .001, is significant, which indicates that correlations among the items/variables are sufficiently large for factor analysis.
For the factor analysis, it is suggested that four factors or components are adequate to represent the data. These four factors had an eigenvalue above the Kaiser criterion of 1 and together explained 75.4% of the variance. Next, factor loadings were obtained for each item. The loadings reveal the strength of the relationship between an item and a particular constructor factor. In interpreting the factor loadings, a value over 0.45 is considered fair, over 0.55 is good, over 0.63 is very good, and over 0.71 is excellent. For this study, a loading of 0.50 or greater was considered, which is considered fair by Hair et al. (2006) and Field (2009). Four items were dropped due to low factor loading and two items due to cross-loading. Finally, five items were loaded on factor 1 (ease of use), three on factor 2 (familiarity), four on factor 3 (security/safety due to COVID-19), and the remaining six items on purchase intention.
Further reliability analysis was done using Cronbach’s alpha, which is a widely used measurement of the internal consistency of a multi-item scale (Field, 2009). Table 5 shows the number of items loaded on each factor and the reliability analysis for all four factors. The values are well above the minimum acceptable value of 0.70 (Flynn, Sakakibara, Schroeder, Bates, & Flynn, 1990; Nunnally, 1978).
| Construct | No. of items |
Cronbach’s α |
| Ease of use | 5 |
0.913 |
| Familiarity | 3 |
0.803 |
| Security/safety due to COVID-19 | 4 |
0.905 |
| Purchase intention | 6 |
0.915 |
Multiple regression analysis was used to test the hypotheses regarding the impact of individual predictors, i.e., ease of use, familiarity, and security/safety due to COVID-19 on the dependent variable of purchase intention and to assess their relative significance. The model summary and results of the analysis are detailed in Table 6.
| Model | Purchase intention (PI) (Dependent variable) |
Collinearity statistics |
|||
| Independent variables | Unstandardized coefficient |
Β |
Sig. (p) |
Tolerance |
VIF |
| (Constant) | 0.261 |
NA |
0.025 |
NA |
NA |
| Ease of use | 0.178 |
0.184 |
0.000 |
0.677 |
1.476 |
| Familiarity | 0.525 |
0.525 |
0.000 |
0.577 |
1.733 |
| COVID-19 Security/Safety | 0.219 |
0.248 |
0.000 |
0.606 |
1.651 |
| Model summary | |||||
| R2 | 0.670 |
||||
| Adjusted R2 | 0.665 |
||||
| F change | 140.512 |
||||
| Sig. F change | 0.000 |
||||
| Durbin–Watson | 1.822 |
||||
As the model summary shows, the R2 value is 0.670 and the F value is significant, which indicates that 67% of the purchase intention is explained by the influencing factors of ease of use, familiarity, and security/safety due to COVID-19 at a 1% level of significance. There is no autocorrelation problem in that data as the Durbin–Watson index is at 1.822, which is near to 2 (Watson & Durbin, 1951). Also, as shown in Table 2, each of the variables has a tolerance value of more than 0.10 and a variance inflation factor (VIF) of less than 10. These results stipulate that the model had no multicollinearity problem (Hair et al., 2006). Thus, from these investigations, one can infer that all the assumptions required to ensure the validity of the multiple regression model’s significance tests are met. Further, β indicates that familiarity has a stronger effect on predicting purchase intention, followed by safety/security from COVID-19 and ease of use. This shows that usage of mobile payments by family and friends along with adoption of the same by businesses has played an important role in pushing people toward mobile payment. The security and safety concerns due to COVID-19 and fear of catching contagious diseases has further driven the usage. In such a scenario, the ease of use was secondary.
The multiple regression equation is PI = 0.261+ 0.178 EU+ 0.525 FM + 0.219 SSC.
EU: Ease of use.
FM: Familiarity.
SSC: Safety and Security.
The multiple regression equation explains that ease of use is an important variable for usage of payment wallets or mobile payment services, followed by familiarity and safety and security of the mobile payment wallet.
Education becomes an important parameter as it refers to literacy and knowledge about mobile-based payment options. Education was broadly divided into three categories—Graduate, Postgraduate, and Doctoral studies. The one-way ANOVA was run to compare the significant differences among the four variables, viz., familiarity, ease of use, safety/security, and purchase intention. However, there were no significant differences observed between different education groups and adaptability of mobile payments (see Table 7). Post hoc comparisons to evaluate pairwise differences among the group means were conducted with the use of Tukey’s honestly significant difference (HSD) test since equal variances were tenable. However, there were also no significant differences observed. This suggests that the education level of the respondent does not affect their use of mobile payment services.
| Education | ||||
| Variable | DF (Degree of freedom) |
F value |
Sig. |
Post hoc comparison |
| Ease of use | 211 |
0.892 |
0.412 |
No significance |
| Purchase intention | 211 |
0.208 |
0.812 |
No significance |
| Safety and security | 211 |
0.542 |
0.583 |
No significance |
| Familiarity | 211 |
0.347 |
0.707 |
No significance |
Table 8. Results of the one-way ANOVA test and post hoc comparison of generations and variables. |
| Generation | ||||
| Variable | DF |
F value |
Sig. |
Post hoc comparison |
| Ease of use | 211 |
3.067 |
0.049 |
Gen Y and Gen Z |
| Purchase intention | 211 |
2.238 |
0.109 |
No significance |
| Safety and security | 211 |
2.247 |
0.108 |
No significance |
| Familiarity | 211 |
0.344 |
0.709 |
No significance |
Table 8 presents the adaptability of each generation, and it is evident from the statistical analysis that there is a significant difference when it comes to the ease of use of mobile payments between different generations. Interestingly, the post hoc comparison shows a significant difference in ease of use between Generation Y and Generation Z, but there is no difference between Gen Z from the other generation groups.
Table 9. Results of the one-way ANOVA test and post hoc comparison of occupation and variables. |
| Occupation | ||||
| Variable | DF |
F value |
Sig. |
Post hoc comparison |
| Ease of use | 211 |
1.052 |
0.381 |
No significance |
| Purchase intention | 211 |
1.401 |
0.235 |
No significance |
| Safety and security | 211 |
2.247 |
0.108 |
No significance |
| Familiarity | 211 |
0.358 |
0.839 |
No significance |
Table 9 presents the results based on source of income, and it dictates the awareness among local vendors, businesspeople, and others, who should be familiar with mobile payment options. Hence, the different occupations were checked to determine if there is any significant effect on the four variables. Interestingly, there were no significant differences observed in any of the occupations concerning ease of use, familiarity, safety, and purchase intention. A post hoc test was also conducted to see if there are any pairwise differences; however, no significant relation was observed.
To check Hypothesis 4 concerning gender, the two-tailed t-test was used since the independent factors had values less than 3. The t-test scores are shown in Table 10.
| T-test: Two-tailed with unequal variance | P-value |
|
| Gender | Purchase intention | 0.429 |
| Gender | Familiarity | 0.025 |
| Gender | Ease of use | 0.649 |
| Gender | Safety and security | 0.072 |
The results in Table 10 indicate that there is a strong significant relation between gender and familiarity as the p-value is less than 0.05, while the other options, viz., purchase intention, ease of use, and safety did not have a significant relation concerning adaptability to mobile payments. The significant difference that was observed might be attributed to the fact that both genders are equally inclined to make day-to-day purchases, while the other variables showed no significant difference when it comes to gender.
This research aimed to examine the determinants of mobile payment service usage during COVID-19 and analyze user satisfaction and the continuous usage of such payment methods. It was found that education was the main factor influencing mobile payment usage. The factor of security had the strongest relationship with user satisfaction. Regarding adaptability to mobile payment among the different generations, it is evident from the statistical analysis that there is a significant difference when it comes to the ease of use of mobile payments. Interestingly, the post hoc comparison shows a significant difference in ease of use between Generation Y and Generation Z, but there is no difference between Gen Z and the other generation groups. The multiple regression equation explains that ease of use is an important variable for the usage of mobile payment services, followed by familiarity and safety and security of the mobile payment wallet. Based on the findings of this study, it is suggested that mobile wallet companies should concentrate on the performance and ease of use of their mobile wallet applications. Mobile payment companies should focus on user satisfaction, which indirectly leads to the retention of users for a long period.
This study was conducted in India; therefore, the results can be generalized globally with caution as conditions may vary across countries in terms of infrastructure, economic conditions, and acceptability of digital payment systems. Also, very little data was collected from businesspeople. Therefore, future studies can focus on the acceptability of mobile payment in business. To make mobile payment services more effective, usage and factors influencing mobile payment services are required to be studied on a regional basis within a country so that specific measures can be taken in each region to improve mobile payment services. The impact of COVID-19 on various services is a current hot topic, and there are many opportunities to bring different perspectives to various financial products post-COVID-19 to improve usage. Scholars may study the impact of digitization on payment systems, and research on the causal relationship of various factors affecting different payment systems can also be conducted.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Authors’ Contributions: Conceived and designed the analysis, N.P.; contributed data or analysis tools, S.G.; performed the analysis, A.B.; wrote the paper, R.S. All authors have read and agreed to the published version of the manuscript. |
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