INDIAN JOURNAL OF FINANCE AND BANKING 13(1) (2023), 12-27 12 FINANCE AND BANKING IJFB VOL 13 NO 1 (2023) P-ISSN 2574-6081 E-ISSN 2574-609X Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/ijfb Published by CRIBFB, USA INDIAN MOBILE BANKING IN POST COVID-19: AN ANALYTICAL STUDY AND GRATIFICATION FROM THE ASPECT OF KANO MODEL Bhadane Jaywant (a) Rajiv Nayan (b) Gaikar Vilas Bhau (c)1 Kanwal Jeet Singh (d) Joshi Bharat (e) (a) Head of Department, Economics/Banking, KRA College, Deola, Nasik, Savitribai Phule Pune University, India; E-mail: jaywantrekha@gmail.com (b) Assistant Professor, Department of Commerce, Ramanujan College, University of Delhi, India; E-mail: rajivnayan32@gmail.com (c) Professor, Dept. of Economics, Smt. CHM. College, Ulhasnagar, University of Mumbai, India; E-mail: gaikar_vilas@rediffmail.com (d) Associate Professor, Department of Commerce, Ramanujan College, University of Delhi, India; E-mail: kanwaljeet89@gmail.com (e) Department of Commerce, K. P. B. Hinduja College of Commerce, Mumbai, India; E-mail: j28bharat54@gmail.com A R T I C L E I N F O Article History: Received: 4th November 2022 Revised: 28th December 2022 Accepted: 30th January 2023 Published: 7th February 2023 Keywords: Virtual Banking, Gratification, Kano Model, Marketing Research Theory. JEL Classification Codes: A1, A30, C1, G17, Y8 A B S T R A C T The purpose of the present research paper is to understand the research questions related to M-banking. It is the time call to take up Virtual Banking (VB) with Zero Contact Banking (ZCB) as a preventive measure to COVID-19. The study also admits the comparative analysis on the gratification of M-banking users considering factors/attributes of the Kano Model. The researcher has undertaken Integrative Approach (IA) for both, related to literature reviewed and survey so far observed. Both primary data through well-structured questionnaires from 900 M-banking users of SBI, HDFC, and Citi Bank (300 from each) and secondary data from published sources have been cantered and cited to understand the syntactic research gap. The researcher has followed Stratified Random Sampling for sample banks considering the date of establishment, volume and value of M-banking transactions, number of employees, and Convenient Random Sampling for M-banking users, to make the sample representative. The objectives were studied thoroughly and hypotheses were tested in SPSS. The researcher has used Kolmogorov-Smirnov (D-Statistic) and Shapiro-Wilk test (W-Statistic) to test data normality, Cronbachs’ Alpha to test Data Reliability, Descriptive Statistics i.e. frequency and per cent count to describe data and Chi-square to measure significant associations and differences if any. The researcher has drawn an epilogue purely on the basis of data collection and analysis. The researcher has conducted Pearson’s Product Movement Correlation, to suggest a correlation on Y-intercept Model to show an association between volume and value of M-banking transactions of SBI, HDFC, and Citi Bank and suggested a model fit to regression equation. This paper gives a unique insight into KANO model. © 2023 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION Technology has such a large influence on our lives that it is difficult to imagine a life without it. Innovations taking place all over the world in various fields have made our lives much easier and more relaxed. Mobile banking is characterized as “A channel whereby the customer interacts with a bank via a mobile device, such as a mobile phone or personal digital assistant. Mobile banking has given users more flexibility in terms of time and space, which is also seen as a major drawback of traditional banking. It has also supported banks in lowering their operating costs and expanding their customer base. It has also aided banks in offering a variety of other related services to their existing customers at little or no cost. Due to the sheer size of its population, the number of internet users, the government’s drive for financial inclusion, and public awareness of the ease and convenience of mobile banking, India’s prospects for mobile banking appear to be very bright. Banking Sector Reforms in 1991 and 1998, technological advancement, changes in banking policy and further initiative taken by Government of India i.e. the demonetization policy in November, 2016 and digital banking and its services have hit up the Indian banking industry and make them more tech-savvy. Further, the COVID-19 has pushed an economy towards physical, social and mental distancing, results into technological advancement and dependency and implementation of e-banking products and services such as plastic money i.e. Debit and credit card, RTGS, M-banking, 1Corresponding Author: ORCID ID: 0000-0001-5251-6656 © 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA. https://doi.org/10.46281/ijfb.v13i1.1928 To cite this article: Jaywant, B., Nayan, R., Bhau, G. V., Singh, K., & Bharat, J. (2023). INDIAN MOBILE BANKING IN POST COVID-19: AN ANALYTICAL STUDY AND GRATIFICATION FROM THE ASPECT OF KANO MODEL. Indian Journal of Finance and Banking, 13(1), 12-27. https://doi.org/10.46281/ijfb.v13i1.1928 https://orcid.org/0000-0003-3964-608X http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://doi.org/10.46281/ijfb.v13i1.1928 https://orcid.org/0000-0003-0211-8259 https://orcid.org/0000-0001-5251-6656 https://orcid.org/0000-0002-9166-8510 https://orcid.org/0000-0003-3617-1541 Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 13 NEFT etc. (Suoranta, 2003, Tiwari & Buse, 2007). Mobile banking is characterized as “A channel whereby the customer interacts with a bank via a mobile device, such as a mobile phone or personal digital assistant” (Barnes & Corbitt, 2003). Mobile banking is a service provided by a bank or other financial institutions that allows its customers to conduct financial transactions from anywhere anytime geographically, using a mobile device such as a smartphone or tablet. Thus, Mobile banking has removed the difficulty of physical access to bank during COVID-19, provided flexibility to banking customers. Unlike internet banking, it uses software, usually called a mobile banking app, usually designed and backed by the financial institutions. As on 30th October, 2020; 542 banks (includes Public Sector Banks, Private Sector Banks, Foreign banks, Co-operative banks and Sahakari and Gramin banks) were permitted by RBI to provide Mobile Banking Services in India. Users of mobile banking have more flexibility in terms of time and space, which is sometimes overlooked. Assumed to be a major drawback of the traditional banking system. It has also aided banks in reducing costs. Lowering their operating costs and broadening their customer base (Cherian, Gaikar, Paul, & Pech, 2021). Despite the benefits of mobile banking, there are many risks associated with it that must be taken into account. The most serious of these risks is the protection of mobile banking transactions, as both the internet and mobile transactions are vulnerable to phishing, account theft, and the leakage of sensitive information, among other things. Competition from mobile wallet companies such as Paytm, Phonepe, and others is another notable obstacle for mobile banking. For a variety of factors, two-thirds of online banking subscribers tend to use nonbanking companies' mobile wallets rather than their banks' mobile banking apps, according to one survey (Durkin, O'Donnell, Mullholland, & Crowe, 2007). According to academic model, “Mobile Banking is a proviso and expediency of banking products and financial services with the help of mobile telecommunication devices”. Thus, M-banking is a digital form of banking linked with a bank account to carry out banking financial transactions such as Account Balance Check, Fund Transfer, Request to Bank for Availing Various Banking Services, Online Shopping and Payments, Loans, Investments and Deposits, Mudra Loan etc. The rationale of the problem statement is available as follows: COVID-19 has created panic and made society and people at mental, physical and social distance. The different banks are providing m-banking services on different platform offering disparate services to accountholders such as debit card add-on services, account check, investments and deposits, fund transfer, loan avail, m-passbook, mudra loan etc. Private and Foreign Banks were the foremost to adopt and implement technology in banking business, which has created competitive environment for public sector banks not only to satisfy existing banking customers but also to retain them for long adopting technological up gradation. Hence, there is a need to compare, explore and analyze the present research. Following are the objectives of study:  To study the meaning and use of mobile banking.  To study about the demographic profile of mobile banking users SBI, HDFC and Citi Bank.  To study about the gratification of mobile banking users SBI, ICICI and Citi Bank.  To study the aftermath COVID-19 on m-banking use of SBI, ICICI and Citi Bank. The significance of the study is as follows:  The present research study will be helpful to understand the concept and use of M-banking use of SBI, HDFC and Citi Bank.  It will be helpful to study the gratification of m-banking use of SBI, HDFC and Citi Bank only.  It will be helpful to study the concept of impact of COVID-19 on M-banking use.  The present research study will be helpful to examine and analyze the comparative M-banking use in terms of volume and value of SBI, HDFC and Citi Bank.  The study will be useful to the bank to target M-banking users applying Artificial Intelligence (AI). LITERATURE REVIEW This literature review aims to investigate the most important contributions of the Kano methodology and in which way researchers have used, interpreted and modified the methodology of Kano at the same time how this model fits for the mobile banking. The discussions about use of WAP services in GSM mobile phones, which enables the users to interact with the bank to carry out internet – based content and advance value-added banking and financial services provided by bank (Cherian, Jacob, Qureshi, & Gaikar, 2020). The application-based m-banking and its studies showed how the internet banking has given rise to mobile banking, which includes facilities to conduct bank transactions, to administer accounts and to access customized information via internet using mobile based application (Durkin et al., 2007). Most of the Indian m- banking users are concerned about security issues like financial frauds and account misuse. To overcome these difficulties, the user uses different codes for banking transactions, installation and updating of application. Hence, lacks standardization. The mobile banking is defined as “The provision of banking services to customers on their mobile devices”. It is the innovations in banking sector, which facilitates to carry out banking and other financial transactions with the help of mobile phones using internet (Laforet, & Li, 2005). The Mobile phone is an electronic channel capable of giving customers more low-cost service options such as access to banking information, funds management and making online payments. M-banking transactions are economical compared to the traditional banking channels. To gain in long term benefits, bank has to encourage m-banking services by specific mobile application and individual platform which plays major role in building brand loyalty (Matzler & Hinterhuber, 1998). Many empirical studies on electronic banking and mobile banking have applied TAM. For identifying the important drivers having a bearing on the mobile banking adoption intention of users. A few other studies have also used demographic variables along with the behavioral factors as drivers of technology adoption intention (Poddar, Erande, Chitkara, Bhansal, & Kejriwal, 2016). According to previous research, simplicity, access to the https://en.wikipedia.org/wiki/Bank https://en.wikipedia.org/wiki/Financial_institution https://en.wikipedia.org/wiki/Mobile_device https://en.wikipedia.org/wiki/Smartphone https://en.wikipedia.org/wiki/Tablet_computer https://en.wikipedia.org/wiki/Internet_banking https://en.wikipedia.org/wiki/Mobile_app Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 14 service at any time and from any location, anonymity, and time and effort savings are all factors that contribute to mobile banking adoption (Reserve Bank of India, 2023, Zhao & Roy Dholakia, 2009). MATERIALS AND METHODS Research Framework: A Kano Model Approach The model based on customer satisfaction, was developed by the Japanese Professor Noriaki Kano in 1984. This model seeks to explain how to assign the priorities to fulfill operational objectives, which results into long lasting improvements in customer service delivery (Zhao & Roy Dholakia, 2009). The Kano Model classifies the products and services knowledge, wants to and the nature and ways it leads to customers’ satisfaction. The model divides product/service attributes into three categories; threshold or must be, performance and excitement or delighter. These attributes distinguish the product or services requirement, which has direct impact on their gratification (Saeidipour, Vatandost, & Akbari, 2012). The Kano Model graphically shows the combination of two axis - the x axis and the y axis, the x axis defines the customer needs were met and to what extent; which is referred as a product/service performance or function and the y axis is defines the customer response to the product/service; whether the customer is delighted or disappointed. On the basis of this the customer expectations and its achievement are categories into three; Basic Needs, is called as “Must be Requirements”, which are essential; if met customers are delighted and if not, they are disappointed and not preferred by them. Performance Needs, are define by customers and discussed by manufacturer, are called as “More is Better”. This need makes product/services different from competitors. Attractive needs, the unspoken or unexpected needs which the customer cannot define. If such needs provided, they feel excited and if not remains neutral. Zhao & Dholakia using Kano model and multi-criteria decision models to evaluate the measurement of customer satisfaction (Sharma & Sharma, 2019). Figure 1. The Basic Kano Model Thus, the Kano model is viewed in the perspective of mobile banking service via customer product/service delivery. In present research study, the researcher has thought-out mobile banking as one of the ways to interact with bank customers during COVID-19 in which physical and social distancing is must and hence measured and compared the gratification of SBI, HDFC and Citi Bank M-banking users with idiosyncrasy i.e. Basic Needs, Performance Requirements, Excitements Requirements, Neutral Attributes and Reverse Attributes (Sulaiman, Jaafar, & Mohezar, 2007). Following are the hypotheses of the study: The formulated affirmative statement in research study is called as hypothesis. It explains an association between two or more dependent and\or independent variable under study, which is tested using statistical tools and techniques and thereby study the objectives and to accept/reject the statements. The researcher has considered following hypotheses of the study. In the form of qualitative and quantitative hypotheses: Qualitative Hypotheses  H0: There is no significant difference in gratification related to Basic Needs of mobile banking users of SBI, HDFC and Citi Bank.  H0: There is no significant difference in gratification related to Performance Requirements of mobile banking users of SBI, HDFC and Citi Bank.  H0: There is no significant difference in gratification related Excitement Requirements of mobile banking users of SBI, HDFC and Citi Bank.  H0: There is no significant difference in gratification related to Neutral Attributes of mobile banking users of SBI, HDFC and Citi Bank. Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 15  H0: There is no significant difference in gratification related to Reverse Attributes of mobile banking users of SBI, HDFC and Citi Bank.  H0: There is no association between demographic profile (gender, age, marital status, educational qualification, occupation and income level) and gratification (BPENR) of mobile banking users of SBI, HDFC and Citi Bank.  H0: There is no significant difference in prior experience of M-banking use.  H0: There is no significant difference in frequency of using M-banking services. Quantitative Hypotheses  H0: There is no significant difference in volume (i.e. number) of m-banking transactions of SBI, HDFC and Citi Bank aftermath COVID-19. (H0:µvolSBI = µvolHDFC = µvol’Citi Bank)  H0: There is no significant difference in value (i.e. amount) of m-banking use of SBI, HDFC and Citi Bank aftermath COVID-19 (H0: µvalSBI = µvalICICI = µval’Citi Bank)  H0: There is no association between volume and value of M-banking transactions of SBI, HDFC and Citi Bank. Participant (Subject) Characteristics The present research study is qualitative and quantitative in nature. The approach to the present research study is Particularistic. The research study is of exploratory and conclusive type. The universe and population for the present research study is public sector banks, Private Sector Banks and Foreign Banks in India. The respondents were Mobile banking users. The population for the present research study is verbal for mobile banking users and measurable for sample banks in terms of number of banks, its branches, volume and value of mobile banking and its use (Cherian, Jacob, Qureshi, & Gaikar, 2020). The data for the present research study has been collected from 900 M-banking users of the SBI, HDFC and Citi Bank. For the present study the researcher has used Cochran’s formula to determine the size of the sample of M-banking users. Cochran (1977) has developed a formula to determine the representative sample in both ways when population infinite and finite. Hence, for the present study the researcher has decided to apply both formulas considering Level of Precision, Confidence Level Desired and Degree of Variability to determine representative sample population for the present research study. Sampling Procedures – Size, Power and Precision Assuming large infinite population whose variability not known, assuming maximum variability i.e. 50% at p = 0.5 and taking 95% confidence level with ± 5 precision, the sample size for the present research study shall be 666. To study and probe into detail the researcher found such size of sample little less representative of population. Further, it is said that larger the size of sample, more the surety of their responses to truly represent the population. Thus, to buffer, the researcher has increased the total size of sample to 900 numbers of M-banking users in Mumbai city i.e. 300 M-banking users of each sample bank i.e. SBI, HDFC and Citi Bank respectively. The sample banks were selected by Stratified Random Sampling. Three banks from each of the public sector, private sector and foreign banks have been selected considering their Date of Establishment, Volume and Value of Mobile Banking Use, Number of Working Branches\Offices and Number of employees. Table 1. Details of sample bank as on 31st March, 2020 in India Particular Head\Bank Name SBI HDFC Citi Bank Date of Establishment July, 1955 August, 1994 June, 1902 Number\Volume of Mobile Banking (Actual) 322432111 120519634 1898161 Value\Amount of Mobile Banking (in Rs’000) 1054325130.37 696795439.29 43713103.50 Number of Branches\Offices 24000 4787 42 Number of Employees 249448 104154 204000 Source: Compiled and calculated from Secondary Source It was found that the SBI, HDFC and Citi Bank lead in above criteria. Hence, Public Sector Bank - State Bank of India, Private Sector Bank - The Housing Development Finance Corporation Limited and Foreign Bank - Citi Bank has constituted the sample bank for the present research study. The primary and secondary data has been organized and anlysed to study the objectives and to test the hypotheses of the present research study. The researcher has collected primary data from actual mobile banking users of SBI, HDFC and Citi Bank. 300 actual M-banking users from each of the sample banks has been collected and reported. Measures and Covariates The researcher has collected secondary data related to mobile banking use from the published source the Reserve Bank of India (2023). The researcher has collected secondary data related to mobile banking use of SBI, HDFC Bank Ltd. and Citi Bank for aftermath, pre COVID-19 from November, 2019 to March, 2020 and post COVID-19 from April, 2020 to August, 2020. Primary data from the actual M-banking users and the secondary data from the published sources, by The Reserve Bank of India. Just to balance the data the researcher has collected 5 months of pre (i.e. from November, 2019 to March, 2020) and 5 months of post (i.e. from April, 2020 to August, 2020) COVID-19 of each sample bank data related to mobile baking use in terms of volume and value has been cited and analyzed. The result of normality of data using Kolmogorov-Smirnov Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 16 and Shapiro-Wilk is as follows: Table 2. Tests of Normality by Kolmogorov-Smirnov = (D) and Shapiro-Wilk = (W) Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. BN1 .228 900 .000 .889 900 .000 BN2 .253 900 .000 .877 900 .000 BN3 .216 900 .000 .890 900 .000 BN4 .248 900 .000 .871 900 .000 BN5 .252 900 .000 .861 900 .000 BN6 .247 900 .000 .861 900 .000 PR1 .201 900 .000 .901 900 .000 PR2 .260 900 .000 .874 900 .000 PR3 .223 900 .000 .892 900 .000 PR4 .261 900 .000 .869 900 .000 PR5 .259 900 .000 .851 900 .000 ER1 .228 900 .000 .889 900 .000 ER2 .253 900 .000 .877 900 .000 NA1 .223 900 .000 .888 900 .000 NA2 .256 900 .000 .872 900 .000 RA1 .261 900 .000 .862 900 .000 RA2 .248 900 .000 .871 900 .000 TOB .223 900 .000 .793 900 .000 a. Lilliefors Significance Correction Source: Compiled and calculated from primary data The researcher has considered variables to study, understand and compare gratification of M-banking use of sample bank. To verify whether all variable measure the same construct-scale, all variables are correlated and could form into some type of scaling, the Cronbach’s Alpha - The Test of Reliability was conducted (Table 4). Table 3. Reliability statistics of M-banking Use Variable Head Cronbach’s Alpha No of Items Internal Consistency Basic Needs .864 06 Good Internal Consistency Performance Requirements .719 05 Acceptable Internal Consistency Excitement Requirements .598 02 Poor Internal Consistency Neutral Attributes .739 02 Acceptable Internal Consistency Reverse Attributes .544 02 Poor Internal Consistency Overall Reliability .729 17 Acceptable Internal Consistency (for all attributes) Source: Compiled and calculated from primary data The researchers applied SPSS 21 to study the objectives and to test the hypotheses of the present research. The researcher has used Kolmogorov-Smirnov and Shapiro -Wilk test of normality, to test data Normality. The researcher has used Cronbachs’ Alpha, to test data Reliability. Descriptive Statistics-frequency and per cent count, Kruskal Wallis 1–Way ANOVA, Mean Rank, (to make gratification comparative), Chi-square test, Z-test (to calculate z-score to measure aftermath COVID-19 of M-banking use of sample bank). RESULTS To understand the behavior toward m-banking use, the respondents were asked questions based on their demographic profile and m-banking services by sample banks. Further, to measure their gratification, questions-based Likert Five Point Scale was asked and the same has been analyzed using descriptive statistics and inferential analysis as follow. Descriptive analysis describes the collected data in logical order. The researcher has described data as follow: Demographic Profile Table 4. Demographic Profile Demographic Head Type of Bank Total (900) Total Per cent SBI HDFC Citi Bank Gender Male 98 97 105 300 33.33 % Female 202 203 195 600 66.67 % Female dominates the M-banking use among all three sample banks. Age Up to 25 Year 84 95 97 276 30.67 % Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 17 26 to 35 Year 90 84 82 256 28.44 % 36 to 45 Year 77 75 69 221 24.56 % 46 Year and Above 49 46 52 147 16.33 % The age group up to 25 ears found to be highest number of M-banking users among all three sample banks. Education Graduation 59 60 65 184 20.44 % Postgraduation 86 78 86 250 27.78 % Diploma/Technical 46 46 43 135 15.00 % Professional 76 82 70 228 25.33 % Other 33 34 36 103 11.45 % Postgraduation M-banking users are more in all three sample banks. Occupation Business 74 84 77 235 26.11 % Government Employee 86 75 83 244 27.11 % Private Employee 87 78 89 254 28.22 % Professional 53 62 51 167 18.56 % Most of the M-banking users are private employees. Monthly Income Up to Rs. 24999 87 89 88 264 29.33 % Rs. 25000 to Rs. 49999 71 69 71 211 23.44 % Rs. 50000 to Rs. 74999 81 84 85 250 27.78 % Rs. 75000 and Above 61 58 56 175 19.45 % M-banking users having monthly income up to Rs. 249999 found to be more. Prior Experience of Using M-banking Yes 175 183 180 538 59.78 % No 125 117 120 362 40.22 % Most of the M-banking users are having prior experience of using M-banking services. Frequency of Using M-banking Atleast once in a day 80 77 78 235 26.11 % Once in 2 to 4 days 91 91 99 281 31. 22 % Once in 5 to 7 days 96 93 87 276 30.67 % Once in a fortnight 19 26 22 67 07.44 % Once in a month 14 13 14 41 04.56 % It is found that there is insignificant difference of using M-banking services between once in 2 to 4 days and once in 5 to 7 days. Source: Compiled and calculated from primary data Inference from Analyses of Mobile Banking Use To test and verify above hypothesis, the researcher has collected primary data from 900 respondents (300 from each) sample banks related to basic needs, performance requirements, excitement requirements, neutral attributes and reverse attributes. The researcher has also collected Pre (from November, 2019 to March, 2020) - Post (April, 2020 to August, 2020) COVID- 19 monthly data related to volume and value of M-banking transactions of sample banks. Analyses and Interpretations Based On Qualitative Hypotheses H0: There is no significant difference in gratification related to Basic Needs of mobile banking users of SBI, HDFC and Citi Bank. Table 5. Kruskal-Wallis 1-Way ANNOVA mean rank related to basic needs (BN) Ranks Results/ Ranks TOB N Mean Rank BN1 SBI 300 470.25 1st HDFC 300 412.33 3rd Citi Bank 300 468.92 2nd Total 900 SBI bank dominates. BN2 SBI 300 432.20 3rd HDFC 300 479.32 1st Citi Bank 300 439.98 2nd Total 900 HDFC bank dominates. BN3 SBI 300 504.28 1st HDFC 300 416.00 3rd Citi Bank 300 431.22 2nd Total 900 SBI bank dominates. BN4 SBI 300 462.82 1st HDFC 300 448.86 2nd Citi Bank 300 439.82 3rd Total 900 SBI bank dominates. BN5 SBI 300 433.37 3rd HDFC 300 470.47 1st Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 18 Citi Bank 300 447.66 2nd Total 900 HDFC bank dominates. BN6 SBI 300 444.64 3rd HDFC 300 445.06 2nd Citi Bank 300 461.81 1st Total 900 Citi Bank bank dominates. Source: Compiled and calculated from primary data Table 6. Calculation of Chi-Square Value - to measure statistical significance difference in gratification related to basic needs (BN) Test Statistics a,b BN1 BN2 BN3 BN4 BN5 BN6 Chi-Square 10.485 6.220 21.091 1.294 3.372 .924 Df 2 2 2 2 2 2 Table Value 5.99 5.99 5.99 5.99 5.99 5.99 Asymp. Sig. .005 .045 .000 .524 .185 .630 Results P(Χ2(10.485) > 5.99) = .005 < 0.05 P(Χ2(6.220) > 5.99) = .045 < 0.05 P(Χ2(21.091) > 5.99) = .000 < 0.05 P(Χ2(1.294) < 5.99) = .524 > 0.05 P(Χ2(3.372) < 5.99) = .185 > 0.05 P(Χ2(0.924) < 5.99) = .630 > 0.05 Sig.\Insig. Significant Ha Accepted Significant Ha Accepted Significant Ha Accepted Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 a. Kruskal Wallis Test b. Grouping Variable: TOB Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 2 and its significance value @ 5% level of significance. It shows either acceptance of Ha or failure to reject H0 H0: There is no significant difference in gratification related to Performance Requirements of mobile banking users of SBI, HDFC and Citi Bank. Table 7. Kruskal-Wallis 1-Way ANNOVA mean rank related to performance requirements (PR) Ranks Results/ Ranks TOB N Mean Rank PR1 SBI 300 434.81 2nd HDFC 300 483.94 1st Citi Bank 300 432.75 3rd Total 900 HDFC bank dominates. PR2 SBI 300 467.89 1st HDFC 300 428.79 3rd Citi Bank 300 454.81 2nd Total 900 SBI bank dominates. PR3 SBI 300 438.22 3rd HDFC 300 470.35 1st Citi Bank 300 442.93 2nd Total 900 HDFC bank dominates. PR4 SBI 300 435.50 3rd HDFC 300 468.46 1st Citi Bank 300 447.54 2nd Total 900 HDFC bank dominates. PR5 SBI 300 443.81 3rd HDFC 300 450.36 2nd Citi Bank 300 457.34 1st Total 900 Citi Bank bank dominates. Source: Compiled and calculated from primary data Table 8. Calculation of Chi-Square Value - to measure statistical significance difference in gratification related to Performance Requirements (PR) Test Statistics a,b PR1 PR2 PR3 PR4 PR5 Chi-Square 7.983 3.873 2.863 2.707 .443 Df 2 2 2 2 2 Table Value 5.99 5.99 5.99 5.99 5.99 Asymp. Sig. .018 .144 .239 .258 .801 Results P(Χ2(7.983) > 5.99) = .018 < 0.05 P(Χ2(3.873) < 5.99) = .144 > 0.05 P(Χ2(2.963) < 5.99) = .239 > 0.05 P(Χ2(2.707) < 5.99) = .258 > 0.05 P(Χ2(.443) < 5.99) = .801 > 0.05 Sig.\Insig. Significant Ha Accepted Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 a. Kruskal Wallis Test b. Grouping Variable: TOB Source: Compiled and calculated from primary data Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 19 The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 2 and its significance value @ 5% level of significance. It shows either acceptance of Ha or failure to reject H0. H0: There is no significant difference in gratification related to Excitement Requirements of mobile banking users of SBI, HDFC and Citi Bank. Table 9. Kruskal-Wallis 1-Way ANNOVA mean rank related to Excitement Requirements (ER) Ranks Results/ Ranks TOB N Mean Rank ER1 SBI 300 470.25 1st HDFC 300 412.33 3rd Citi Bank 300 468.92 2nd Total 900 SBI bank dominates. ER2 SBI 300 432.20 3rd HDFC 300 479.32 1st Citi Bank 300 439.98 2nd Total 900 HDFC bank dominates. Source: Compiled and calculated from primary data Table 10. Calculation of Chi-Square Value - to measure statistical significance difference in Gratification Related to Excitement Requirements (ER) Test Statistics a,b ER1 ER2 Chi-Square 10.485 6.220 Df 2 2 Table Value 5.99 5.99 Asymp. Sig. .005 .045 Results P(Χ2(10.485) > 5.99) = .005 < 0.05 P(Χ2(6.220) < 5.99) = .045 > 0.05 Sig.\Insig. Significant Ha Accepted Significant Ha Accepted a. Kruskal Wallis Test b. Grouping Variable: TOB Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 2 and its significance value @ 5% level of significance. It shows the acceptance of Ha. H0: There is no significant difference in gratification related to Neutral Attributes of mobile banking users of SBI, HDFC and Citi Bank. Table 11. Kruskal-Wallis 1-Way ANNOVA Mean Rank Related to Neutral Attributes (NA) Ranks Results/ Ranks TOB N Mean Rank NA1 SBI 300 468.34 2nd HDFC 300 478.85 1st Citi Bank 300 404.31 3rd Total 900 HDFC bank dominates. NA2 SBI 300 473.36 1st HDFC 300 419.68 3rd Citi Bank 300 458.46 2nd Total 900 SBI bank dominates. Source: Compiled and calculated from primary data Table 12. Calculation of Chi-Square Value - to measure statistical significance difference in Gratification Related to Neutral Attributes (NA) Test Statistics a,b NA1 NA2 Chi-Square 15.486 7.470 Df 2 2 Table Value 5.99 5.99 Asymp. Sig. .000 .024 Results P(Χ2(15.486) > 5.99) = .000 < 0.05 P(Χ2(7.470) > 5.99) = .024 < 0.05 Sig.\Insig. Significant Ha Accepted Significant Ha Accepted a. Kruskal Wallis Test b. Grouping Variable: TOB Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 2 and its significance value @ 5% level of significance. It shows the acceptance of Ha. Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 20 H0: There is no significant difference in gratification related to Reverse Attributes of mobile banking users of SBI, HDFC and Citi Bank. Table 13. Kruskal-Wallis 1-Way ANNOVA mean rank related to Reverse Attributes (RA) Ranks Results/ Ranks TOB N Mean Rank RA1 SBI 300 458.65 1st HDFC 300 442.20 3rd Citi Bank 300 450.65 2nd Total 900 SBI bank dominates. RA2 SBI 300 460.16 1st HDFC 300 451.30 2nd Citi Bank 300 440.05 3rd Total 900 SBI bank dominates. Source: Compiled and calculated from primary data Table 14. Calculation of Chi-Square Value - to measure statistical significance difference in Gratification Related to Reverse Attributes (RA) Test Statistics a,b RA1 RA2 Chi-Square .500 .974 Df 2 2 Table Value 5.99 5.99 Asymp. Sig. .779 .614 Results P(Χ2(0.500) > 5.99) = .779 < 0.05 P(Χ2(0.974) > 5.99) = .614 < 0.05 Sig.\Insig. Not-Significant Fails to reject H0 Not-Significant Fails to reject H0 a. Kruskal Wallis Test b. Grouping Variable: TOB Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 2 and its significance value @ 5% level of significance. It shows the failure to reject H0. H0: There is no significant difference between demographic profile (gender, age, marital status, educational qualification, occupation and income level) and gratification (BPENR) of mobile banking users of SBI, HDFC and Citi Bank. Table 15. Calculation of Chi-Square Value - To Measure Statistical Significance Difference between Demographic Profile and Gratification Related to BPENR Test statistics Gender Age Education Occupation Monthly Income Chi-Square 100.000a 42.942b 84.300c 21.280d 21.520b Df 1 3 4 3 3 Table Value 3.84 7.82 9.49 7.82 7.82 Asymp. Sig. .000 .000 .000 .000 .000 Results P(Χ2(100.00) > 3.84) = .000 < 0.05 P(Χ2(42.942) > 7.82) = .000 < 0.05 P(Χ2(84.300) > 9.49) = .000 < 0.05 P(Χ2(21.280) > 7.82) = .000 < 0.05 P(Χ2(21.520) > 7.82) = .000 < 0.05 Sig.\Insig. Significant; Ha Accepted Significant; Ha Accepted Significant; Ha Accepted Significant; Ha Accepted Significant; Ha Accepted Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a different degree of freedom and its significance value @ 5% level of significance. It shows the acceptance of Ha. H0: There is no significant difference in prior experience of M-banking use. Table 16. Calculation of Chi-Square Value - to measure statistical significance difference in prior experience of using M- banking Services Test Statistics Prior Experience Chi-Square 34.418a Df 1 Table Value 3.84 Asymp. Sig. .000 Results P(Χ2(34.418) > 3.84) = .000 < 0.05 Sig.\Insig. Significant; Ha Accepted Source: Compiled and calculated from primary data Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 21 The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 1 and its significance value @ 5% level of significance. It shows the acceptance of Ha. H0: There is no significant difference in frequency of using M-banking services. Table 17. Calculation of Chi-Square value - to measure statistical significance difference in frequency of using M-banking Services Test Statistics Chi-Square 302.956a Df 4 Table Value 9.49 Asymp. Sig. .000 Results P(Χ2(302.956) > 9.49) = .000 < 0.05 Sig.\Insig. Significant; Ha Accepted Source: Compiled and calculated from primary data The table above shows that the calculated Chi-square value is compared with its table value at a degree of freedom 4 and its significance value @ 5% level of significance. It shows the acceptance of Ha. Based On Quantitative Hypotheses H0: There is no significant difference in volume (i.e. number) of m-banking transactions of SBI, HDFC and Citi Bank aftermath COVID-19. (H0:µvolSBI = µvolHDFC = µvol’Citi Bank) Table 18. Descriptive Statistics: Related to Volume of M-banking Transactions Descriptive Statistics N Minimum Maximum Mean Std. Deviation VOLPRESBI 5 300516767 337113205 322469099 14716214.15 VOLPOSTSBI 5 287963313 424607423 370776973 55013702.67 VOLPREHDFC 5 108376705 129307244 121382721 8062225.05 VOLPOSTHDFC 5 86337417 150214245 119898353 24522352.72 VOLPRE’CITI BANK 5 1835063 1973294.00 1903472 53708.78 VOLPOST’CITI BANK 5 1460133 1681857.00 1558342 96246.13 Zscore (VOLPRESBI) 5 -1.49171 .99510 .0000000 1.00000000 Zscore (VOLPOSTSBI) 5 -1.50533 .97849 .0000000 1.00000000 Zscore(VOLPREHDFC) 5 -1.61320 .98292 .0000000 1.00000000 Zscore (VOLPOSTHDFC) 5 -1.36859 1.23626 .0000000 1.00000000 Zscore (VOLPRE’CITI BANK) 5 -1.27371 1.30000 .0000000 1.00000000 Zscore (VOLPOST’CITI BANK) 5 -1.02040 1.28332 .0000000 1.00000000 Valid N (listwise) 5 Source: Compiled and calculated from primary data From the calculated Minimum, Maximum, Mean and Standard Deviation value (in table 19), the researcher has found Z-Score using Z-table negative and positive value for sample SBI bank as follow: Calculation Z-Score and Pre-Post Per cent changes in Volume of M-banking Transactions SBI Pre: Minimum Maximum Z1 = 300516767 ─ 322469099 Z2= 337113205 ─ 322469099 14716214.15 14716214.15 Z1= -1.49 Z2= 0.99 Z1= -0.0681 Z2= 0.8389 Pre - Per cent (SBI) = (Z2-Z1) = (0.8389-0.0681) = 0.7708 = 77.08% Minimum Maximum Post: Z1 = 287963313 ─ 370776973 Z2= 424607423 ─ 370776973 55013702.67 55013702.67 Z1= -1.5 Z2= 0.97 Z1= -0.0648 Z2= 0.834 Post - Per cent (SBI) = (Z2-Z1) = (0.8340-0.0648) = 0.7692 = 76.92% Calculation Z-Score and Pre-Post Per cent changes in Volume of M-banking Transactions HDFC Pre: Minimum Maximum Z1 = 108376705 ─ 121382721 Z2= 129307244 ─ 121382721 8062225.05 8062225.05 Z1= -1.61 Z2= 0.98 Z1= -0.0537 Z2= 0.8365 Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 22 Pre - Per cent = (Z2-Z1) = (0.8365 - 0.0537) = 0.7828 = 78.28% Maximum Maximum Post: Z1 = 86337417 ─ 119898353 Z2= 150214245 ─ 119898353 24522352.72 24522352.72 Z1= -1.368 Z2= 1.2362 Z1= -0.08534 Z2= 0.8925 Post - Per cent (SBI) = (Z2-Z1) = (0.8925 - 1.368) = 0.80716 = 80.72% Calculation Z-Score and Pre-Post Per cent changes in Volume 0f M-banking Transactions CITI BANK Pre: Minimum Maximum Z1 = 1835063 ─ 1903472 Z2= 1973294 ─ 1903472 53708.78 53708.78 Z1= -1.273 Z2= 1.3 Z1= -0.10027 Z2= 0.9302 Pre - Per cent = (Z2-Z1) = (0.9302 - 1.273) = 0.82993 = 82.99% Minimum Maximum Post: Z1 = 1460133 ─ 1558342 Z2= 1681857 ─ 1558342 96246.13 96246.13 Z1= -1.02 Z2= 1.2833 Z1= -0.15386 Z2= 0.8997 Post - Per cent (SBI) = (Z2-Z1) = (0.8997 - 0.15386) = 0.74584 = 74.58% Source: Compiled and calculated from primary data Table 19. Z-Score: Aftermath COVID-19 Related to Volume of M-banking Name of The Bank (Volume of M-banking) Pre -Percent Post Percent Difference (% Increase\% Decrease) SBI 77.08 % 76.92 % 00.16 % (Decrease) HDFC 78.28 % 80.72 % 02.44 % (Increase) Citi Bank 82.99 % 74.58 % 08.41 % (Decrease) Source: Compiled and calculated from primary data Hence, the Alternate Hypothesis, there is a significant difference in volume (i.e. number) of m-banking transactions of SBI, HDFC and Citi Bank aftermath COVID-19. (H0:µvolSBI = µvolHDFC = µvol’Citi Bank), is accepted. H0: There is no significant difference in value (i.e. amount) of m-banking use of SBI, HDFC and Citi Bank aftermath COVID-19 (H0: µvalSBI = µvalHDFC = µval’Citi Bank) Table 20. Descriptive statistics related to value of M-banking transactions Descriptive Statistics N Minimum Maximum Mean Std. Deviation VALPRESBI 5 910042961 1054325130.37 981478286.54 59670139.53 VALPOSTSBI 5 925024539.83 1373117981.00 1229123774.56 185372952.98 VALPREHDFC 5 660154034.10 726268897.70 699673177.02 25745493.54 VALPOSTHDFC 5 427146398.30 826220988.90 673413205.10 162568589.94 VALPRE’CITI BANK 5 37749396.82 43713103.50 41109833.76 2515407.30 VALPOST’CITI BANK 5 29215948.74 38735733.45 34128391.652 3886039.31 Zscore (VALPRESBI) 5 -1.19717 1.22083 .0000000 1.00000000 Zscore (VALPOSTSBI) 5 -1.64047 .77678 .0000000 1.00000000 Zscore (VALPREHDFC) 5 -1.53499 1.03302 .0000000 1.00000000 Zscore (VALPOSTHDFC) 5 -1.51485 .93996 .0000000 1.00000000 Zscore (VALPRE’CITI BANK) 5 -1.33594 1.03493 .0000000 1.00000000 Zscore (VALPOST’CITI BANK) 5 -1.26413 1.18561 .0000000 1.00000000 Valid N (listwise) 5 Source: Compiled and calculated from primary data Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 23 From the calculated minimum, maximum, mean and standard deviation value (in table 19), the researcher has found Z- Score using Z-table negative and positive value for sample SBI bank as follow: The Calculation of Aftermath COVID-19 of Value of M-banking of SBI bank Calculation Z-Score and Pre-Post Per cent changes in value of M-banking Transactions SBI: Pre: Minimum Maximum Z1 = 910042961 ─ 981478286.5 Z2= 1054325130 ─ 981478286.5 59670139.53 59670139.53 Z1= -1.2 Z2= 1.22 Z1= -0.11507 Z2= 0.88877 Pre - Per cent (SBI) = (Z2-Z1) = (0.88877-0.11507) = .7737 = 77.37 % Minimum Maximum Post: Z1 = 925024539.8 ─ 1229123775 Z2= 1373117981 ─ 1229123775 185372953 185372953 Z1= -1.64 Z2= 0.78 Z1= -0.0505 Z2= 0.7823 Post - Per cent (SBI) = (Z2-Z1) = (0.78230-0.05050) = 0.7318 = 73.18 % Calculation Z-Score and Pre-Post Per cent changes in value of M-banking Transactions HDFC: Pre: Minimum Maximum Z1 = 660154034.1 ─ 699673177 Z2= 726268897.7 ─ 699673177 25745493.54 25745493.54 Z1= -1.53 Z2= 1.03 Z1= -0.06301 Z2= 0.84849 Pre - Per cent = (Z2-Z1) = (0.84849 - 0.06301) = 0.7883 = 78.83 % Maximum Maximum Post: Z1 = 427146398.3 ─ 673413205.1 Z2= 826220988.9 ─ 673413205.1 162568589.9 162568589.9 Z1= -1.51 Z2= 0.94 Z1= -0.06552 Z2= 0.82639 Post - Per cent (SBI) = (Z2-Z1) = (0.82639 - 0.06552) = 0.7609 = 76.09% Calculation Z-Score and Pre-Post Per cent changes in value of M-banking transactions Citi Bank: Pre: Minimum Maximum Z1 = 37749396.82 ─ 41109833.76 Z2= 43713103.5 ─ 41109833.76 2515407.3 2515407.3 Z1= -1.34 Z2= 1.03 Z1= -0.09012 Z2= 0.84849 Pre - Per cent = (Z2-Z1) = (0.84849 - 0.09012) = 0.7584 = 75.84 % Minimum Maximum Post: Z1 = 29215948.74 ─ 34128391.65 Z2= 38735733.45 ─ 34128391.65 3886039.31 3886039.31 Z1= -1.26 Z2= 1.19 Z1= -0.10383 Z2= 0.88298 Post - Per cent (SBI) = (Z2-Z1) = (0.88298 - 0.10383) = 0.7792 = 77.92 % Source: Compiled and calculated from primary data Table 21. Z-Score: Aftermath COVID-19 Related to Value of M-banking Name of The Bank (Volume of M-banking) Pre-Percent Post Percent Difference (% Increase\% Decrease) SBI 77.37 % 73.18 % 04.19 % (Decrease) HDFC 78.83 % 76.09% 02.74 % (Decrease) Citi Bank 75.84 % 77.92 % 02.08 % (Increase) Source: Compiled and calculated from primary data Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 24 Hence, the alternate hypothesis, there is no significant difference in value (i.e. amount) of m-banking use of SBI, HDFC and Citi Bank aftermath COVID-19 (H0: µval SBI = µval HDFC = µval’ Citi Bank), Thus this hypothesis is accepted. H0: There is no association between volume and value of M-banking transactions of SBI, HDFC and Citi Bank. To test above hypothesis, the researcher has collected secondary data related to volume and value of m-banking transactions of SBI, HDFC and Citi Bank from November, 2019 to August, 2020. Table 22. Correlations Statistics: Volume and Value of M-banking transactions Correlations VOL VAL VOL Pearson Correlation 1 .951** Sig. (2-tailed) .000 N 30 30 VAL Pearson Correlation .951** 1 Sig. (2-tailed) .000 N 30 30 **. Correlation is significant at the 0.01 level (2-tailed). Source: Compiled and calculated from primary data In the table above, a Pearson’s Data Analysis shows a Very High Positive Correlation, r (30) = 0.951, which clearly states that the increase in number of m-banking transactions results into increase in values of transactions of SBI, HDFC, Citi Bank m-banking transactions. Table 23. Model Summary: Volume and Value of M-banking Transactions Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson R Square Change F Change df 1 df 2 Sig. F Change 1 .951a .905 .902 145067623 .48663 .905 266.770 1 28 .000 .408 a. Predictors: (Constant), VOL b. Dependent Variable: VAL Source: Compiled and calculated from primary data The above Model states that 95.10 (0.951*100) per cent of the Dependent Variable i.e. value of m-banking transactions of SBI, HDFC and Citi Bank by the independent variable i.e. volume of m-banking transactions of SBI, HDFC and Citi Bank. Calculated value of Durbin-Watson is 0.408 (it is between 0 and less than 2) indicates Positive Autocorrelation between value of and volume of m-banking transactions of SBI, HDFC and Citi Bank. Table 24. One-Way ANOVA: Volume and Value of M-banking Transactions ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 5614080707056142300.000 1 5614080707056142300.000 266.770 .000b Residual 589249230753630590.000 28 21044615384058228.000 Total 6203329937809772500.000 29 a. Dependent Variable: VAL b. Predictors: (Constant), VOL Source: Compiled and calculated from primary data The Calculated Fisher Value Fcrit (1, 28) = 266.770 is greater than its critical value 4.20 (at df1 1 and df 2 28) and its Significance Value is 0.000 (i.e. p = 0.000), which is less than 0.05, and therefore there is an association between value and volume of m-banking transactions of SBI, HDFC and Citi Bank. Table 25. Number of Credit Cards and Point-of-Sale Transactions Coefficient sa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 145109729.501 38871708.825 3.733 .001 VOL 2.973 .182 .951 16.333 .000 a. Dependent Variable: VAL Source: Compiled and calculated from primary data Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 25 Figure 2. Graphical Presentation of Regression Equation From the table above and a graph; following regression equation has been formed. Yi = Assumed to be volume of m-banking transactions of SBI, HDFC Citi Bank. b0 = Constant Value from Table bi = Dependent Variable - Value of m-banking Xi = Assumed to be Independent Variable - Volume of m-banking Yi = b0 + bi (Xi) Yi = 145109729.501 + 2.973 (Xi)........ …………………………(1) The above regression coefficient shows that for every unit of increase in volume of m-banking use of each sample bank, it is expected that the value of m-banking of sample bank transactions increase by 2.973 in a month. Further, to check the fitness of the above regression model, the researcher has found the unstandardized predicted volume and value of m- banking transactions, as follow; Table 26. Correlations Statistics: Unstandardized predicted value volume and value of M-banking transactions Correlations VAL Unstandardized Predicted Value VAL Pearson Correlation 1 .951** Sig. (2-tailed) .000 N 30 30 Unstandardized Predicted Value Pearson Correlation .951** 1 Sig. (2-tailed) .000 N 30 30 **. Correlation is significant at the 0.01 level (2-tailed). Source: Compiled and calculated from primary data The table above prove the fitness of above regression model, with respect to R2 Linear = 1 = 1 (in graph). Figure 3. Graphical Presentation of Regression Equation Based on Unstandardized Predicted Value Therefore, the alternate hypothesis, “There is an association between volume and value of M-banking transactions of SBI, HDFC and Citi Bank.”, is accepted. (H0: µSBI ≠ µHDFC ≠ µ’CITI BANK). Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 26 DISCUSSIONS The findings of the study on the research ‘Mobile Banking: An Analytical Study on Aftermath COVID-19 and Gratification from the Aspect of KANO Model’ are as follows:  Overall female M-banking users found to be more aware and benefited: Female M-banking users were more (66.67%) as compared to male (33.33%) in SBI, HDFC and Citi Bank. However, there is negligible difference in number of female M-banking users in SBI (N=202) and HDFC (N=203) and male M-banking users in SBI (N=98) and HDFC (N=97).  Most of the m-banking users belong to the age group of up to 25 years. This is because they found ease and prefer flexibility in carrying out banking transactions.  The data reveals that around 27.11% government employees are using M-banking from SBI, HDFC and Citi Bank. However, there is negligible difference between government employee and business doing M-banking users.  It is also found that, majority 29.33% M-banking users belong to the income group up to Rs. 24999. This shows M-banking users with higher income (Rs. 75000 and above) is comparatively less because of hacking and security concern.  Most of the M-banking users have prior experience of M-banking services. It is found that there is a significant difference in prior experience of using M-banking services.  Daily use of M-banking services enjoyed by 31.22% customers. The consistency in Providing M-Banking Services shows that HDFC M-banking users were more satisfied than SBI and Citi Bank. The bank has good reputation and provides reliable method of M-Banking shows that SBI M-banking users more satisfied than HDFC and Citi Bank.  The M-Banking Services are totally secured reveals that HDFC M-banking user are more satisfied than SBI and Citi Bank. The easy portability of M-Banking Services reflected that, Citi Bank M-banking users more satisfied than HDFC and SBI. The support to customers shows that, HDFC M-banking users more satisfied than SBI and Citi Bank.  The comprehensive services provided by banks i.e. M-Banking Services shows that SBI M-banking users more satisfied than HDFC and Citi Bank. M-Banking provides flexibility in acceptance of different plastic cards\Payments, shows that HDFC M-banking users are more satisfied than SBI and Citi Bank.  The interface of M-Banks Services were users friendly, shows that HDFC M-banking users are more satisfied than SBI and Citi Bank. M-Banking services highly secured, shows that SBI M-banking user more satisfied than HDFC and Citi Bank. It is found that there is decrease in volume of M-banking transactions of SBI (00.16% decrease) and Citi Bank (05.54% decrease). However, there is 01.94% increase in M-banking transactions of HDFC bank. It is found that there is decrease in value of M-banking transactions of SBI (04.29% decrease) and HDFC (02.72% decrease). However, there is 02.21% increase in M-banking transactions of Citi Bank. CONCLUSIONS From the present research study, it has been observed that the M-banking is a financial service providing online and over internet mobile based platform, found to be immediate substitute to plastic money, which helps to reduce the risk of carrying cash and boost up money in digital form for bank customers’ convenience and safety.  Telecommunication and cellular service provider should extend their network coverage to remote rural area (Time call in COVID-19). This promotes the banks to provide services to areas were physical branch access not possible. Hence, bank can expand their banking services in rural area with m-banking coverage, it should be made users friendly and with regional language option.  For every wrong data input for online transactions, reversal procedure should be made immediate. Bank should expand their merchant tie-up for payments and associations. Bank should make synchronization ease with different m-wallets platforms. This helps to push an economy towards digital banking.  There should be professionals and technicians at bank branch to educate users about its usage. Bank should allow use of m-banking for non-core banking purpose also.  Mobile platform should be well networked with bank server, to avail easy and error free access to account.  The bank should monitor transactions to implement Artificial Intelligence (AI). Security concern and anti- hacking measures must be undertaken by banks to promote On-line Transactions using m-banking.  The bank should direct their branches in rural and urban areas to have some definite number of mobile banking accounts. Mobile banking has transmogrified the banking from brick-mortar to virtual banking-online, using smartphones called as M-banking. M-banking provides flexibility, convenience, safety and security to bank customers and further offer them account related utility services. M-banking provides bank based mobile application to carry out financial transactions. There is a threat of hacking, cloning and safety-security concern also. Hence, m-banking users should be made aware on time about its secured use and passcode change and re-change. Technological advancement and banking policy amendments has made it financial inclusive. This is the time to understand and resolve with M-banking problems to make it more dependable and efficient. Jaywant et al., Indian Journal of Finance and Banking 13(1) (2023), 12-27 27 Author Contributions: Conceptualization, V.G. and B.J.; Methodology, B.J. and V.G.; Software, B.J. and K.J.S.; Validation, V.G. and R.N.; Formal Analysis, V.G.; Investigation, R.N., K.J.S. and V.G.; Resources, V.G. and B.J.; Data Curation, K.J.S. and J.B.; Writing – Original Draft Preparation, V.G., B.J. and K.J.S.; Writing – Review & Editing, V.G., B.J. and K.J.S.; Visualization, J.B. and B.J.; Supervision, R.N.; Project Administration, V.G.; Funding Acquisition, K.J.S. and R.B.N. Authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgments: Not applicable. 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