Impact of New Digi-Banking Services on Customer Satisfaction in Private Sector Banks in The City of Coimbatore

 

Prashant Debnath¹, P. Chellasamy²

¹Research Scholar, School of Commerce, Bharathiar University, Coimbatore - 641046, Tamil Nadu, India.

²Professor, School of Commerce, Bharathiar University, Coimbatore - 641046, Tamil Nadu, India.

 

ABSTRACT:

In the current context, digital banking has completely revolutionized the banking structure as it was before. Now customers are searching for an alternative where they do not need to visit banks to execute their financial transactions. The old banking model has been altered by digital banking, which has given the banking sector new dimensions. The phrase "digital banking" means all the banking activities that are done with the help of electronic communication channels, i.e., online platforms. It not only makes things easier for the clients but also eliminates the use of paper, especially for making demand drafts, pay slips, and cheque leaves. Now, customers can have access to all standard banking activities without physically visiting a bank branch 24x7 for 365 days. Digital banking provides numerous kinds of products and services such as pos terminals, internet and mobile banking, e-transfer of funds, bill payments, e-wallets, e-cheques, UPI payment mechanism etc. Based on the previous review of literature it has been identified that there is no research related to new digital banking services and its impact on customer satisfaction with special reference to Coimbatore city has been done. Therefore, this study has been plays a crucial role in filling up this gap. The data is collected through purposive sampling method; a structured questionnaire was used to conduct a primary survey of customers' satisfaction. Based on the different sources five new digital banking services i.e., Digital Chatbot, Digital Wallet, Digital Card management, Digital Payment application and Digital Insurance commonly used by private sector banks account holders has been identified, which leads to measure the customer satisfaction. Collected Data was analyzed using correlation and regression technique using SPSS (20) software. The findings implicated that new digital banking services have a significant and positive impact on the customer satisfaction. Since each of these is positively associated to the customer satisfaction, the research provides empirical evidence to indicate that new digital banking services are a significant component in satisfying customers. The most recent digital banking services were included in this study, which has given academic insight into a fresh research model.

 

 

 

INTRODUCTION:

Digital banking is basically the delivery of various banking products and services via electronic and communications networks¹.

 

It is critical in the digitization of traditional banking products and services. A consumer who uses Digital banking can access his account at any time and make a variety of transactions using his computer, mobile phone, or other smart device². Prior to these revolutionary changes, doing banking transactions required a lot of effort and time. On those days, if you require cash, you must visit your branch, get a token number, give the branch staff a drawn check, and then wait for your turn. All of that is, thankfully, no longer the case^3-5. The way things are done has changed as a result of digital banking, now you may transact with more speed, ease and comfort with digital banking⁶. Through its digital India campaign, the Indian government has also made certain important moves to turn India into a society where the use of information technology is widespread^7,8. Therefore, this study has incorporated new digital banking products and services offered by private sector banks and how it has influence over the satisfaction level of the customers.

 

REVIEW OF LITERATURE:

Worku et al. (2016) have studied the impact of e-banking on customers’ satisfaction in Ethiopian banking industry. According to the results, men and young, educated people account for the majority of e-banking users, while women are the least active participants. After implementing e-banking services, it has been discovered that consumer satisfaction levels have dramatically increased. Furthermore, there is a strong correlation between e-banking and demographic traits.

 

Hammoud et al. (2018) have investigated the effect of the e-banking servqual model on customer satisfaction levels. The results show that a number of factors have a significant impact on consumer satisfaction, including simplicity of use, privacy, responsiveness, reliability, efficiency, safety, and communication.

 

Firdous and Farooqi (2018) have looked into the effectiveness of online banking services and how that affects customer satisfaction. The outcome demonstrates that each factor—fulfillment, privacy, efficiency, responsiveness, system accessibility, and contact has greatly raised the degree of customer satisfaction.

 

Hadid et al. (2020) have investigated how customer satisfaction levels in Malaysian banks are impacted by the quality of digital banking services. The findings indicate that while empathy has a negligible impact on customer satisfaction, tangibility, responsiveness, assurance, and reliability are the elements that positively improve consumer satisfaction level.

 

Alabsy, N. (2018) The goal of the study is to identify the various factors that influence customer satisfaction with electronic banking services. According to the study, there is a statistically significant difference in the degree of customer satisfaction for e-banking services offered by Sudanese banks, and the services provided by e-banking have a favorable effect on customer satisfaction. The study also discovered that consumer satisfaction has been greatly impacted by the quality of e-banking services such ATMs, phone banking, and web-based services.

 

T.S.L.W Gunawardana (2020) has looked into how electronic banking services affect customer satisfaction levels in Sri Lankan private commercial banks. The research showed that while telephone and mobile banking had a negative effect on consumer satisfaction, internet banking, credit and debit cards, as well as atm and online banking, had a favorable effect.

 

OBJECTIVES:

1.    To investigate the association between demographic characteristics and the satisfaction level of customers towards new digital banking services.

2.    To study the impact of new digital banking services on the customer satisfaction level.

 

HYPOTHESIS:

H01:      There is no significant association between demographic characteristics and the satisfaction level of customers towards new digital banking services.

H02:      Digital Chatbot has no significant impact on the level of customer satisfaction.

H03:      Digital Wallet has no significant impact on the level of customer satisfaction.

H04:      Digital Card management has no significant impact on the level of customer satisfaction.

H05:      Digital Payment application has no significant impact on the level of customer satisfaction.

H06:      Digital Insurance has no significant impact on the level of customer satisfaction.

 

RESEARCH METHODOLOGY:

The study's population, the new digital banking service users of private sector banks ICICI, HDFC, and AXIS in the city of Coimbatore, has been studied using both descriptive and exploratory research approaches. The study has used both primary and secondary data. While the secondary data was taken from secondary sources like journals, annual reports, and websites, the primary data was collected via an online Google survey form. A purposive sample technique was used to choose respondents from the population. A total of 75 responses were received, indicating that a sizable portion of respondents filled out the Google survey form. The data acquired from primary data sources has been analysed using SPSS software. Here, there are six key variables in the questionnaire: five independent and one dependent, as well as demographic factors (education, area, income, age, gender, and profession). Then, with the exception of demographic factors, each variable is assessed by having participants rate various statements on a Likert scale of 1 to 5 (1 being strongly disagree, and 5 being strongly agree).

 

RESULTS AND DISCUSSIONS:

Table 1 represents the demographic data of respondents, a total of 75 responses were collected through questionnaire and used for analysis. From the table, 45 respondents which represent 60% were belongs to male category and 30 of them which represent 40% were from female category. In which majority 32% respondents were between the age group of 25-35, 26.7% were between 35-45 and only 5.3% were 55 and above. In the context of area of residence, Majority numbering, 63 respondents were belonging to urban area, only 12 respondents from semi urban area and no respondents have been noted down from rural area. In considering the income of the respondents, majority of them are falling between 40000-60000, that is 54%, while only 14 respondents had average monthly income of 20000-40000. Most of the respondents were self-employees, which is 34.7% of the total respondents, 33.3% and 24% were private and government employees respectively. Of all respondents around 35% are holding graduation degree while only 5.3% of respondents belong to other category of education.

 

Table 1 Demographic Characteristics of Respondents

Variables	Categories	Frequency Distribution	%
Gender	Male	45	60
	Female	30	40
Age	Below 25	17	22.7
	25-35	24	32
	35-45	20	26.7
	45-55	10	13.3
	55 and above	4	5.3
Residence Area	Rural	0	0
	Urban	63	84.0
	Semi Urban	12	16.0
Monthly Income	20000 or less	11	14.7
	20000-40000	14	18.7
	40000-60000	41	54.7
	60000-80000	3	4.0
	80000 or more	6	8.0
Profession	Student	6	8.0
	Private Employees	25	33.3
	Government Employees	18	24.0
	Self Employees	26	34.7
Education	+2	13	17.3
	UG	26	34.7
	PG	17	22.7
	Professional	15	20.10
	Others	4	5.3

 

Table 2: Independent Sample t test

		Levene’s Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2- tailed)	Mean Difference	Std. Error Dofference	95% Confidence Interval of the Difference
									Lower	Upper
Intention to Buy	Equal variances assumed	0.11	0.918	-0.131	73	0.896	-0.15556	1.18807	-2.52337	2.21226
	Equal variances not assumed			-0.130	61.330	0.897	-0.15556	1.19348	-2.54180	2.23069

 

Table 2 reveals that P = 0.918, above 0.05 at the 5% significance level, and the F = 0.11 with 73 df. infer no statistically significant difference in customer satisfaction with new digital banking services between genders. Hence, the researcher has sufficient proof to accept the null hypothesis.

 

Table 3 Customer Satisfaction Level and Age of the Respondents

ANOVA
Total satisfaction
	Sum of Squares	Df	Mean Square	F	Sig.
Between Groups	541.176	4	135.294	7.208	.000
Within Groups	1313.971	70	18.771
Total	1855.147	74

 

Table 3 reveals that P = 0.000*, below 0.05 at the 5% significance level, and the F = 7.208 with 4 df, indicates that there is a statistically significant difference in the customer satisfaction level with respect to the age of the respondents. Therefore, the researcher does not accept the null hypothesis.

 

Table 4 Customer Satisfaction Level and Residence Area of the Respondents

ANOVA
Total satisfaction
	Sum of Squares	Df	Mean Square	F	Sig.
Between Groups	81.829	1	81.829	3.369	.071
Within Groups	1773.317	73	24.292
Total	1855.147	74

 

Table 4 reveals that P=0.071, above 0.05 at the 5% significance level, and the F=3.369 with 1 df, indicates that there is no statistically significant difference in the customer satisfaction level with respect to the residence area of the respondents. Therefore, the researcher has to accept the null hypothesis.

 

 

Table 5 Customer Satisfaction Level and income level of respondents

ANOVA
Total satisfaction
	Sum of Squares	Df	Mean Square	F	Sig.
Between Groups	621.695	4	155.424	8.821	.000
Within Groups	1233.451	70	17.621
Total	1855.147	74

 

Table 5 reveals that P=0.000*, below 0.05 at the 5% significance level, and the F=8.821 with 4 df, shows that there is a statistically significant difference in the customer satisfaction level with respect to the income level of respondents. Therefore, the researcher does not accept the null hypothesis.

 

Table 6 Customer Satisfaction Level and profession of the respondents

ANOVA
Total satisfaction
	Sum of Squares	Df	Mean Square	F	Sig.
Between Groups	264.883	3	88.294	3.942	.012
Within Groups	1590.264	71	22.398
Total	1855.147	74

Table 6 reveals that P = 0.012, below 0.05 at the 5% significance level, and the F = 3.942 with 3 df, shows that there is a statistically significant difference in the customer satisfaction level with respect to profession of the respondents. Therefore, the researcher does not accept the null hypothesis.

 

Table 7 Customer Satisfaction Level and education level of the respondents

ANOVA
Total satisfaction
	Sum of Squares	Df	Mean Square	F	Sig.
Between Groups	422.375	4	105.594	5.159	.001
Within Groups	1432.772	70	20.468
Total	1855.147	74

 

Table 7 reveals that P = 0.001, below 0.05 at the 5% significance level, and the F = 5.159 with 4 df, shows that there is a statistically significant difference in the customer satisfaction level with respect to education level of the respondents. Therefore, the researcher does not accept the null hypothesis.

 

 

Table 8 Relationship between Digital Banking Services and Customer Satisfaction

Correlations
		Total satisfaction	Chatbot	Wallet	card management	Payment application	Insurance
total satisfaction	Pearson Correlation	1	0.864**	0.847**	0.796**	0.851**	0.906**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000
	N	75	75	75	75	75	75
Chatbot	Pearson Correlation	0.864**	1	0.841**	0.854**	0.851**	0.820**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000
	N	75	75	75	75	75	75
Wallet	Pearson Correlation	0.847**	0.841**	1	0.848**	0.868**	0.871**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000
	N	75	75	75	75	75	75
card management	Pearson Correlation	0.796**	0.854**	0.848**	1	0.855**	0.826**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000
	N	75	75	75	75	75	75
payment application	Pearson Correlation	0.851**	0.851**	0.868**	0.855**	1	0.892**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000
	N	75	75	75	75	75	75
Insurance	Pearson Correlation	0.906**	0.820**	0.871**	0.826**	0.892**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000
	N	75	75	75	75	75	75

 

Digital Chatbot and Customer Satisfaction:

The Digital Chatbot is the first variable in this study's frame of reference. Chatbot in banking are primarily used to improve the customer experience and engagement⁹. Its technology, combined with a human touch, aims to improve service and make routine procedures more convenient and smoother^10,11. The association between a digital chatbot and customer satisfaction is calculated to be 0.864. This score suggests that there is a strong and positive connection between the variables and the correlation exists at both confidence level i.e., 95% and 99%.

 

 

Digital Wallet and Customer Satisfaction:

Digital Wallet is the second variable in this study, which is another mode of e-payment system. It stores all the payment related information of customers in a safe and compact form and allows them to transact electronically without carrying the physical wallets with them^12-14.  According to analysis, there is a 0.847 association between digital wallets and customer satisfaction. This result shows that the variables have a significant and strong association. The outcome exhibits a positive correlation between these two variables with a 95% and 99% degree of confidence.

 

 

Digital Card management and Customer Satisfaction:

Digital Card management is the third variable in this study, through digital card management customers can have a better control over their cards (e.g., debit card, credit card and Atm card) and their financial life^15,16. Now they can easily set up alerts for their expenditures and can ensure card security through regular pin changes¹⁷. As per the analysis, there is a substantial and positive association between digital card management and customer satisfaction, with a correlation value of 0.796. At both 0.01 and 0.05 levels of significance.

 

Digital Payment application and Customer Satisfaction:

Digital Payment application is the fourth variable in this study, which is a new payment method and allows the users of this to transfer money between any two parties. It also facilitates a safe and secure transaction system^18-20. According to the study, there is a high and positive association between the use of digital payment applications and customer satisfaction, with a correlation coefficient of 0.851, presents at both significance level i.e., 1% and 5%.

 

Digital Insurance and Customer Satisfaction:

Digital Insurance is the last variable in this study. Digital insurance means any company which uses technological based business model to sell and manage the insurance is known as digital insurance^21-23. As the analysis shows, there is a 0.906 correlation coefficient between digital insurance and customer satisfaction, which is the largest and strongest positive association among all the variables.

 

Table 9: Regression

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	0.932a	0.868	0.858	1.88424

a. Predictors: (Constant), insurance, chatbot, card management, wallet, payment application

b. Dependent Variable: total satisfaction

 

The dependent variable's overall predictability is shown in the table 9 model summary. The adoption of new digital banking services leads to variation in customer satisfaction, which can be predicted with the help of multiple regression analysis. The adjusted R²=0.858 indicates that all five independent variables, namely Digital Chatbot, Digital Wallet, Digital Card management, Digital Payment application, and Digital Insurance, explaining 85.80% of variance in the dependent variable, implies that the model is able to explain more variability.

 

 

 

 

Table 10: Analysis of Variance

ANOVAs
Model	Sum of Squares	Df	Mean Square	F	Sig.
Regression	1610.171	5	322.034	90.704	0.000b
Residual	244.976	69	3.550
Total	1855.147	74

a. Dependent Variable: total satisfaction

b. Predictors: (Constant), insurance, Chatbot, card management, wallet, payment application

 

Table 10: (ANOVA) shows that the regression model is statistically significant as its P = 0.000*, below 0.05 at the 5% significance level, and the F value is 90.704 with 5 df. It demonstrates that there is a statistically significant difference between all of the independent variables in terms of how they affect customers' levels of satisfaction. Therefore, it is reasonable to infer that all independent variable has a considerable impact on the dependent variable.

 

Table 11: Coefficients of the Variables

Coefficients
Model	Unstandardized Coefficients		Standardized Coefficients
	B	Std. Error	Beta	t	Sig.
Constant	-1.213	0.655		-1.851	0.068
Chatbot	0.438	0.11	0.387	3.979	.000**
Wallet	0.105	0.123	0.091	0.855	0.395
Card management	-0.116	0.114	-0.101	-1.016	0.313
Payment application	0.004	0.119	0.004	0.035	0.972
Insurance	0.624	0.114	0.589	5.449	.000**

a. Dependent Variable: total satisfaction

 

The coefficient values in table 11 indicate the variables that are significant predictors of the dependent variable. From the table, it can be observed that digital insurance (p-0.000*, t-5.449, beta-0.624) was found to be the significant and highest predictor of customer satisfaction, followed by digital chatbots with p-value 0.000*, t-value 3.979, and beta-0.438; this rejects both hypothesis, i.e., H02 and H06 in the study; while digital wallets (p-0.395, t-0.885, beta-0.105); digital card management (p-0.313, t-(1.016), beta-(0.116)); and digital payment applications (p-0.972, t-0.035, beta-0.004) are not considered significant predictors of the dependent variable. Therefore, the study does not reject H03, H04, and H05. This concludes with insurance predicting 62.4% and digital chatbots predicting 43.8%, both playing a significant role in predicting the dependent variable and having a substantial impact on customer satisfaction. This suggests that organisations must give special focus to employing digital insurance and chatbot services for their customers.

 

CONCLUSION AND SUGGESTION:

The paper aims to contribute to novel research in the existing literature by conducting a study on how new digital banking services affect client satisfaction. The following new digital banking services were taken into account when conducting the study: Digital Chatbot, Digital Wallet, Digital Card management, Digital Payment application and Digital Insurance. The study revealed that all the new digital banking services considered in this study have a positive relationship with customer satisfaction. Digital chatbots and insurance are main predictors and have a significant impact on customer satisfaction. Therefore, banks must focus on the most recent products and services, especially digital chatbots and insurance, that have the greatest influence on customer satisfaction. This will lead to the retention of existing clients as well as the acquisition of new ones. This study also allows bank managers and policy makers in increasing public understanding and trust in Digital banking practices and encourage new customers to adopt new digital banking technologies which can increase the banking business in the long run.

 

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

 

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