INDIAN MOBILE BANKING IN POST COVID-19: AN ANALYTICAL STUDY AND GRATIFICATION FROM THE ASPECT OF KANO MODEL

) 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.


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, 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 30 th 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. 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 internetbased 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 mbanking 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 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.
 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.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 and Shapiro-Wilk is as follows: 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).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: .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.

Demographic Profile
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.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.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.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.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 Reverse Attributes of mobile banking users of SBI, HDFC and Citi Bank.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.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.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.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)   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.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.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.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 mbanking transactions, as follow; .000N 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 R 2 Linear = 1 = 1 (in graph).
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: 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)

Figure 3 .
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).

Table 1 .
Details of sample bank as on 31 st March, 2020 in India

Table 3 .
Reliability statistics of M-banking Use

Table 4 .
Demographic Profile The age group up to 25 ears found to be highest number of M-banking users among all three sample banks.users having monthly income up to Rs. 249999 found to be more.

Table 6 .
Calculation of Chi-Square Value -to measure statistical significance difference in gratification related to basic needs (BN)

Table 7 .
Kruskal-Wallis 1-Way ANNOVA mean rank related to performance requirements (PR) 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)

Table 12 .
Calculation of Chi-Square Value -to measure statistical significance difference in Gratification Related to Neutral Attributes (NA)

Table 14 .
Calculation of Chi-Square Value -to measure statistical significance difference in Gratification Related to Reverse Attributes (RA)

Table 15 .
Calculation of Chi-Square Value -To Measure Statistical Significance Difference between Demographic Profile and Gratification Related to BPENR

Table 16 .
Calculation of Chi-Square Value -to measure statistical significance difference in prior experience of using Mbanking Services

Table 17 .
Calculation of Chi-Square value -to measure statistical significance difference in frequency of using M-banking Services

Table 18 .
Descriptive Statistics: Related to Volume of M-banking Transactions From the calculated Minimum, Maximum, Mean and Standard Deviation value (in table19), the researcher has found Z-Score using Z-table negative and positive value for sample SBI bank as follow:

Table 19 .
Z-Score: Aftermath COVID-19 Related to Volume of M-banking

Table 20 .
Descriptive statistics related to value of M-banking transactionsFrom the calculated minimum, maximum, mean and standard deviation value (in table19), 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 value of M-banking transactions Citi Bank:
Table 21.Z-Score: Aftermath COVID-19 Related to Value of M-banking

Table 22 .
Correlations Statistics: Volume and Value of M-banking transactions 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

Table 24 .
One-Way ANOVA: Volume and Value of M-banking Transactions

Table 25 .
Number of Credit Cards and Point-of-Sale Transactions

Table 26 .
Correlations Statistics: Unstandardized predicted value volume and value of M-banking transactions