1. INTRODUCTION:

Banking sector is one of the crucial pillars of an economy; the stronger the banking sector is the stronger will be the economy. So it becomes very important to study this sector in this technological oriented century. The pace of technological evolution is immense, leading to drastic growth and development of this sector. Along with the effectiveness of service, efficiency will be enhanced of service encounters on account of use of these new technologies (Chowdhury, 2014).

Moreover in order to face the tough competition in this competitive scenario of banking sector, banks are forced to adapt to new technologies and they should concentrate on class banking instead of mass banking (Nair, 2014). Digital banking is the outcome of this scenario.

Thus ‘Digital banking’ is defined as offering of various banking services with the use of electronic machine or technology without time and place constrain without any direct involvement of Banks’ employees. It is a very wide concept since it covers all possible banking service and all alternate banking channels like Automated Teller Machines (ATM), Point of Sale (POS), Internet Banking, Mobile Banking, Banking through Application, Phone Banking etc. except branch banking.

The main aim of banks using this non-conventional ways of banking is to offer faster and better services to customers leading to their satisfaction and retention while customers have started preferring this way of banking. Thus slowly and gradually this non-conventional way of banking is increasing.

Many studies have been conducted taking into consideration traditional banking, but lack of research in this area with regard to digital banking motivated the researcher to carry out study in this aspect. It becomes quite interesting to study the service quality aspect of digital banking in this technological era. According to (Patricio, 2003) offering of satisfactory services by traditional banking enhances the chances of acceptance of other alternative channels like digital banking by the customers. Hence service quality is one of the crucial aspects.

In this study, the researcher tried to identify the factors that contribute towards the service quality of digital banking. So here the main aim of the researcher is to explore the impact of various factors that contribute towards the service quality of digital banking and influences the satisfaction level of customer with regard to digital banking

2. THEORETICAL BACKGROUND:

Service quality has been defined in different ways by different researchers. Service quality is “the outcome of an evaluation process, where the customers compare their expectations with the service they have received” defined by (Gronroos, 1984), while, Bitner et al. (1994) defined service quality as ‘the consumer’s overall impression of the relative inferiority / superiority of the organisation and its services’.

Customer satisfaction:

Customer satisfaction and their retention are important aspects for banks in order to get success (Vijay Kumar T, Velu R 2007); absence of which leads to customers’ bank switching behaviour (Vyas and Raitani 2014). Moreover customer satisfaction is influenced by seven factors: employee responsiveness, appearance of tangibles, social responsibility, innovative services, positive word-of-mouth, competence, and reliability (Jaspal Singh and GagandeepKaur).Further (Nupur, 2010) had confirmed Reliability, Responsiveness, Assurance, Empathy and Tangibles as the core service quality dimensions for customer satisfaction in e-banking during investigation of the impact of variables of e-banking on customer satisfaction in Bangladesh. For survival of any banking company depends a lot on customer satisfaction for Digital Banking services.

Service Quality

Service quality is another element that contributes towards the success of any business organization. Since services are non-tangible so parameters of quality differ for non-tangible things. It can help banks in attaining increased levels of customers’ loyalty towards bank, readiness to pay, commitment and trust by paying extra attention to the service quality (Hazra and Srivastava, 2009). Many studies concluded that perceived service quality have direct link with behavioral intentions of customers (Boulding et al., 1993; Parasuraman et al., 1988, 1991; Zeithaml et al., 1996). Thus, the role of service quality should not be neglected in service oriented sectors particularly in banking sector.

Customer Satisfaction in E-Banking:

Customer satisfaction is very crucial for success of any kind of business activities. The same is applicable to applicable to e-banking also. Customer loyalty towards bank has a direct relation with the customer satisfaction of the service quality Levy (2014). Convenience and satisfaction with service quality directly influences the use of online services as customers incur cost to use these online services when compared to offline services. Thus, e-satisfaction is basically displayed as the customers’ attitude toward the e-portals delivering e-services (Chen and Chen, 2009). While (MahtabAlam andAnkitaSoni, 2012) in their study of customer satisfaction of internet banking, revealed that the satisfaction of internet banking user depend upon Reliability, Responsiveness, Security, and Ease of use and Tangibility. So far as online banking adoption is concerned, security, trust and privacy concerns are being considered as exceptionally significant from the customers’ point of view (Benamati and Serva 2007).

E- Banking Service Quality:

Effective qualitative services are needed by customers in every aspect and this is applicable with regard to E-Banking services also. Since in E-Banking, staffs’ interaction with the customers is minimal, so for E-Banking service quality is more important than to off-line services. (Quereshi et al.,2008) had evaluated factors influencing customer’s inclination towards online banking and discovered that almost 50% of the banking customers had switched over to online banking systems due to perceived usefulness, security and privacy provided by them. (Ibrahim EE, 2006) tried to find out the dimensions of electronic service quality and found out that Convenient and accurate electronic banking operations, accessibility and reliability, good queue management, service personalization, friendly and responsive customer service and targeted customer service are dimensions of electronic service quality.

3. MODEL DEVELOPMENT:

Based on the rigorous literature review and study of several model, seven factors have been identified to evaluate service quality. After the rigorous process the model with seven (7) factors have been derived and discussed below.

3.1 Materiality:

The quality of the contents and information of the Digital Banking Channel is being evaluated in the factor named “Materiality” basis the six constructs

3.2 Accessibility:

It is important that Digital Banking services are available and accessible to the customers when they need to avail the services in order to bring more and more customers to the Digital Banking platform.

3.3 Complexity:

In Digital Banking Services customers uses the banking services on their own without any direct interaction with the Bank staff. It is a kind of self – service. Rogers (1995) defined complexity as difficulties faced by persons in using new technologies. Black et.al, (2001) stated customers’ perceptions regarding difficulties and complications while using internet banking as the complexity.

3.4 Competence:

Competences of Digital Banking Services for accessing the bank account and executing transactions determine the service quality and affect the customer satisfaction. Qualities of basic proficiencies are being checked in the factor “Competence” basis six constructs.

3.5 Assistance:

Assistance is the support provided by the banks from the back end on various matters. It includes the support for pre and post operating support as well as support for operating Digital Banking Services.

3.6 Security:

Digital Banking services are basically offered through information technology and internet services. The entire data of banks is now online and it is possible that hackers may hack that data. Security factor checks the level of safety of customers’ data and money. Security factor is evaluated basis five constructs.

3.7 Connectivity:

Digital Banking services are based on internet technology where in the devices being used to get the services get connected with the banks servers through internet. Though banks do not have any direct control over the factor of connectivity, it has significant impact over the customers in terms of satisfaction as well as intention to use the Digital Banking services.

Fig. 3.1 – Model to evaluate services quality of digital banking services

4. RESEARCH METHODOLOGY:

4.1. Research Design:

(Aaker et al, 2001) defined research design as the detailed outline of the study which helps in achievement of the research objectives and decisions related to research process and data collection methods used. It is basically the conceptual structure of the research within which the entire research work is to be carried out. Research design shall answer the six Ws and one H (What, Why, When, Where, Who, Which and How) for the research work. Descriptive research design is mainly applied where the researches wants to improve on existing work. So here in this study, the researcher has used descriptive research design.

4.2. Sampling Design:

Sampling Design refers to the methods and techniques used for selection of samples. It shows the detailed plan for selection of samples from the universe

4.2.1. Universe and Target Population:

The research is to identify the factors influencing the service quality of the Service Quality of Digital Banking services; hence the universe of the population is the customers using the Digital Banking services across the world

4.2.2. Sampling Techniques:

In this study, the researcher has used quota sampling in which the population is divided in different segments according geographic location and selected banks

4.2.3. Sample Size:

Sample size has been determined by using the standard model for sample size;

Sample size = z²*(p)*(1-p)/c²

Where,

z = z value (1.96) for 95% confidence level

p = percentage of probability of picking up a choice expressed as decimal c = confidence interval.

Hence the total sample size has been decided 900 divided in to 400 customers from public sector, 400 customers from private sector banks and 100 customers from foreign banks as number banks as well as customers are very less in number for foreign banks.

Table – 4.1 Sampling Frame

	Public Sector Banks				Private Sector Banks				Foreign Banks
City	SBI	BOB	PNB	BOI	HDFC	AXIS	ICICI	Kotak	Citi Bank	Standard Chartered
Ahmedabad	25	25	25	25	25	25	25	25	20	15
Vadodara	25	25	25	25	25	25	25	25	20	15
Surat	25	25	25	25	25	25	25	25	10	10
Rajkot	25	25	25	25	25	25	25	25	NA	10
Total	100	100	100	100	100	100	100	100	50	50

4.3. Data Collection:

Data have been collected through survey method, where structured questionnaire is used as an instrument

4.4. Reliability of scale:

Reliability of the scales was checked by computing cronbach alpha, a measure of reliability which found to be satisfactory. All the variables’ coefficient of alpha was above 0.6 specifying a satisfactory internal consistency (Nunnally, 1978).

Table 4.1. Reliability for Factors

Factor	No. of Items (Constructs)	Cronbach’s Alpha (α)
Applicability	6	0.894
Accessibility	8	0.888
Complexity	8	0.899
Competence	6	0.890
Assistance	6	0.886
Security	5	0.853
Connectivity	5	0.926

4.5. Sample Characteristics:

Sample characteristics specifies that majority of the respondents were male (68%) and falls in the age group of 20-30 years (46%). When occupation is considered almost 39% of the respondents were private job. Educational qualification specified that majority had U.G. level education (38%) and income level showed that majority have income up to Rs.25000 (38%). Further majority of respondents have their account SBI bank and use their digital platform and mostly use the digital platform on weekly basis.

Table 2: Sample Characteristics of the study

Variable	Categories	Frequency	Percentage
City	Ahmedabad	578	56.17%
	Vadodara	248	24.10%
	Rajkot	96	9.33%
	Surat	107	10.40%
Age	20-30 years	469	45.58%
	30-40 years	342	33.24%
	40-50 years	143	13.90%
	50years and above	75	7.29%
Gender	Male	696	67.6%
	Female	333	32.4%
Occupation	Govt. job	297	28.86%
	Private Job	400	38.87%
	Business	112	10.88%
	Professional	40	3.89%
	Retired	18	1.75%
	Others	162	15.74%
Education	U.G.	386	37.51%
	Graduate	146	14.19%
	Post graduate	301	29.25%
	Professional	168	16.33%
	Others	28	2.72%
Personal Income (Monthly)	Up to 25000	386	37.51%
	Rs.25001-50000	146	14.19%
	Rs.50001-75000	301	29.25%
	Rs.75001-100000	168	16.33%
	Above 100000	28	2.72%
Banking with (for digital banking platform)	SBI	133	12.93%
	BOB	104	10.11%
	PNB	80	7.77%
	BOI	73	7.09%
	HDFC	116	11.27%
	ICICI	134	13.02%
	AXIS	168	16.33%
	KMBL	91	8.84%
	CITI	57	5.54%
	SCB	73	7.09%
Vintage with the Bank	Less than 1 year	45	4.37%
	1 year to 2 years	482	46.84%
	More than 2 years	502	48.79%
Frequency of usage of digital banking channel	Daily	80	10%
	Twice a week	153	7.77%
	Weekly	617	14.87%
	Fortnightly	152	15%
	Monthly	27	12%

5. DATA ANALYSIS:

Data was analysed using SPSS.21 version. Based on the variables different types of test were applied for testing the hypothesis which are as follows:

5.1. Multiple Regressions Analysis:

Regression analysis gives the understanding and estimation of the relationships among variables. It helps in understanding how the distinctive value of dependent variable changes with the change in one of the independent variable when other independent variables are unchanged. Now when the researcher have established the linear relationship among the dependent variable and independent variables, the researcher wish to estimate the degree of change in the value of dependent variable due to change in independent variable. Further, he also need to know the most influencing factor out of all seven independent variables. As the researcher has developed a new model which is being used for the first time, he need to confirm the fitness of the model and relative contribution of each of the predictors to the total variance explained.

H0: The multiple regression model developed in this study is not significant.:

In order to evaluate the model fitness and finding out the most influencing factor the researcher has used stepwise multiple regression analysis. Seven models based on the degree of its influence on the dependent variable are derived by the analysis.

All the seven factors “Assistance”, “Accessibility”, “Security”, “Connectivity”, “Competence”, “Complexity” and “Materiality” together influence the service quality/ customer satisfaction. The model is constituted to indicate overall impact of all seven factors on the service quality / customer satisfaction. The value of correlation coefficient of 0.931 shows strong correlation between four independent variables and the dependent variable. Value of coefficient of determination is 0.868 which indicates that approximately 87% of changes in the service quality / customer satisfaction are resulted due to the factors of Assistance, Accessibility, Security, Connectivity, Competence, Complexity and Materiality.

The significant value of 0.035 (< 0.05) mandates us to reject the null hypothesis and accept the alternate hypothesis.

Table - 5.5 Co-efficient

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.	Correlations			Collinearity Statistics
Model		B	Std. Error	Beta	T	Sig.	Zeroorder	Partial	Part	Tolerance	VIF
1	(Constant)	1.346	.605		2.223	.026
	OV_AS	.408	.019	356	21.639	.000	.794	.562	.247	.480	2.082
	OV_AC	.177	.026	.130	6.881	.000	.726	.211	.078	.356	2.741
	OV_SC	.354	.022	.264	16.439	.000	.759	.459	.187	.55	1.982
	OV_CY	.375	.026	.221	14.243	.000	.718	.408	.162	.540	1.850
	OV_CT	.152	.023	.104	6.570	.000	.580	.202	.075	.523	1.914
	OV_CX	.077	.022	.066	3.496	.000	.754	.109	.040	.364	2.746
	OV_MT	.045	.022	.036	2.108	.035	.655	.066	.024	458	2.184
a. Dependent Variable: OV_IM

Now we will evaluate the multiple regression models developed above using the values generated

Y= a + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + €

[Y= Customer satisfaction / Service Quality (Dependent variable),

a = intercept/constant,

β=the regressions coefficient of y on x, impact on dependent variable because of independent

variable,

X1 = Assistance, X2 = Accessibility, X3 = Security, X4 = Connectivity,

X5 =Competence, X6 = Complexity, X7 = Materiality, € = the error term]

As per the result for the Model Beta (β) for Assistance is 0.356, for Accessibility it is 0.130, for Security it is 0.264, for Connectivity it is 0.221, for Competence it is .104, for Complexity it is 0.066 and for Materiality it is 0.036 which means change of one unit in Assistance will have 0.356 unit changes in service quality where other six factors are unchanged, change of one unit in Accessibility will have 0.130 unit changes in service quality where other six factors are unchanged, change of one unit in Security will have .264 unit changes in service quality where other six factors are unchanged, change of one unit in Connectivity will have .221 unit changes in service quality where other six factors are unchanged, change of one unit in Competence will have .104 unit changes in service quality where other six factors are unchanged, change of one unit in Complexity will have .066 unit changes in service quality where other six factors are unchanged and change of one unit in Materiality will have .036 unit changes in service quality where other six factors are unchanged.

T values for Assistance, Accessibility, Security, Connectivity, Competence, Complexity and Materiality are 21.639, 6.881, 16.439, 14.243, 6.570, 3.496 and 2.108 respectively with Significant value sig. = .000 (< .05) for all except Materiality which is .035 (<0.05) which validates the model with significant impact. When we look at the values of Tolerance, it is 0.480, 0.365, 0.505, 0.540, 0.523, 0.364 and 0.458 for Assistance, Accessibility, Security, Connectivity, Competence, Complexity and Materiality (all the values are < 1) respectively and VIF is 2.082, 2.741, 1.982, 1.850, 1.941, 2.746 and 2.184 for Assistance, Accessibility, Security, Connectivity, Competence, Complexity and Materiality respectively (all the values are ≥ 1 and ≤10), which means there is no problem of multi colinearityin the model. Hence, the model can be framed as,

Service Quality = 1.346 + 0.356 (Assistance) + 0.130 (Accessibility) + 0.264 (Security) +

0.221 (Connectivity) + 0.104 (Competence) + 0.066 (Complexity) + 0.036 (Materiality) + €

6. FINDINGS:

In order to study the impact of all seven factors identified i.e. Materiality, Accessibility, Complexity, Competence, Assistance, Security and Connectivity on overall service quality step wise multiple regression was conducted. It was found out that “Assistance”, “Accessibility”, “Security”, “Connectivity”, “Competence”, “Complexity” and “Materiality” were identified as most influencing factors for service quality and all the seven factors together have significant impact on the service quality of Digital Banking Services suggesting 87% changes due to these factors.

7. LIMITATIONS OF THE STUDY:

· The study was carried out in four cities of Gujarat due to constraint of time and expenses.

· The study has been conducted only for those who are using Digital Banking services for more than six months. Thus, difficulties that a new customer may face while using Digital banking services have not been covered under the study.

· Customers of selected banks only have been contacted in order to collect data.

· Customers in rural areas have not covered under the study.

8. FUTURE SCOPE OF RESEARCH:

The model has been validated in the selected cities of Gujarat state. There is scope of further validation of the model from different geographical locations and with different banks.

· Comparative study of service quality and customer satisfaction with respect to the Digital Banking services can also be carried out know the difference between or rate Digital Banking services provided by different banks.

· Technological developments are an on-going process and it will continue forever. Technological developments will continue to push banks to develop more and Digital Banking services and platforms. Hence, the research work does not stop here. The model will have to continuously update with the development of new products and services.

9. IMPLICATION OF THE STUDY:

This study will help in understanding the important aspects of service quality with regard to digital banking. Strategy formulators will be able to frame the strategies accordingly to get the benefit and retain the customers which will enhance their satisfaction and loyalty. This will help in framing awareness campaign and what aspects should be added in order to increase the usage of digital banking platform.

10. CONCLUSION:

Since last few years’ technology has started challenging the traditional forms of businesses and banking sector is also not an exceptional. Banks in India are investing heavily in technology in order to move many of their services and delivery processes online. In order to survive in the new technologically challenging and highly competitive market the banks need to have superior products and best-in-class technologies. Effective Digital Banking services have been designed and introduced in the market. It is very important that customers not only are aware about these services but also use it on a continuous basis; for this effective service quality offering is mandate. Banks should attract the customers to accept Digital Banking services by improving on service quality aspect. However, to succeed acceptance by the customers is not just enough, complete migration of customers onto Digital Banking platform is required. For this highest level of service quality becomes utmost important to retain customers. Hence, it becomes very important to evaluate service quality of the Digital Banking services.

11. REFERENCES:

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4. Chen and Chen, (2009), “Determinants of Satisfaction and Continuance Intention Towards Self-Service Technologies”, Industrial Management & Data Systems Vol. 109 No. 9, 2009 pp. 1248-1263

5. Gronroos, C. (1984), “A service quality model and its marketing implications”, European Journal of Marketing, 18(4), 36-44

6. Hazra, S.G. and Srivastava, K.B.L. (2009), “Impact of Service Quality on Customer Loyalty, Commitment and Trust in The Indian Banking Sector”, ICFAI Journal of Marketing Management, Vol. 3 Nos 3/4, pp. 75-95.

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8. Jaspal Singh, Gagandeep Kaur (2011), “Customer Satisfaction and Universal Banks: An Empirical Study”, International Journal of Commerce and Management Vol. 21 No. 4, 2011

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Received on 31.10.2018 Modified on 14.11.2018

Int. J. Rev. and Res. Social Sci. 2018; 6(4): 479-485.

DOI: 10.5958/2454-2687.2018.00046.1

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	.932g	.868	.867	2.73378	.001	4.4441	1	1015	.0351	1.518
a. Predictors: (Constant), AS, AC, SC, CY, CT, CX, MT
b. Dependent Variable: IM

Model		Sum of Squares	Df	Mean Square	F	Sig. Value
1	Regression	49962.688.000h	7	7137.527	955.040	.000b
	Residual
	Total
a. Dependent Variable: IM
b. Predictors: (Constant), AS, AC, SC, CY, CT, CX, MT