Editorial Board

A Refereed Monthly International Journal of Management

A Multiple Comparison of E-banking Quality Dimensions using Tukey’s Post Hoc test – A Study on Selected Banks

1. Deepjyoti Choudhury,

Assistant Professor, Department of Business Administration, Assam University, Silchar

2. Dibyojyoti Bhattacharjee,

Professor, Department of Statistics, Assam University, Silchar

Abstract

The study attempts to investigate and compare the selected banks on Electronic Banking Quality attributes. Later the study prepares an index based on the performance of banks. A sample of 400 respondents were collected from the districts of Southern Assam, India based on post stratified random sampling. Descriptive statistics were used to measure the performance mean scores. For developing the overall performance index simple arithmetic calculations were conducted. Later to find out whether the means obtained are significantly different, one way ANOVA have been calculated and further investigation is done with the help of Tukey’s Post Hoc Test. The results revealed that there is a significant difference of performances of the selected banks on different quality attributes. As a result indexing of the banks based on performance was possible. An overall performance index has been computed to find the overall position of the banks under study based on the quality dimensions .The index obtained is very helpful in identifying the position or overall status of E-banking service of a particular bank under study. From the mean scores obtained the banking organizations can identify and measure the difference in scores from the top position and differences of scores with its competitors.

Key words: E-banking, Performance, Index, Qualitative Dimensions, Post Hoc Test

Introduction

In the context of banking, the distribution channel is known as delivery channel. According to (Kotler & Armstong, 1999), a distribution channel is a set of interdependent organizations (intermediaries) involved in the process of making a product or service available for use or consumption by the consumer or business user.

Electronic banking is a bigger platform than just banking via the Internet (Nasri, 2011). The definition of Electronic Banking varied from time to time. (Nitsure, 2003) defined Electronic Banking as provision of banking products and services through electronic delivery channels. E-banking is defined as the automated delivery of new and traditional banking products and services directly to customers through electronic, interactive communication channels (Salehi, 2010) . The different types of E-banking are internet banking, mobile banking, debit card, credit card, telephone banking, TV based banking etc.

Concept of performance based on quality dimensions

Based on the conceptualization of E- Banking quality dimensions derived from literature review, the closely related parameters are grouped together into four dimensions i.e. E-banking Channel Design, Reliability, Responsiveness and Security.

Table 1: Electronic Banking Services Performance Dimensions

Objective of the study

The objective of the current study is to measure the performance of banks in different dimensions, develop an overall performance index and conduct multiple comparisons from the responses of E-banking users specifically the salaried employees.

Methodology

Here in this paper performance is measured under quality dimensions stated above. Descriptive statistics were used to measure the performance mean scores. For developing the overall performance index simple arithmetic calculations were conducted in excel. Later to find out whether the means obtained are significantly different, one way ANOVA have been calculated to test the difference of means.

Since ANOVA can only tell whether groups in the sample differ, it cannot tell which groups differ, hence to further investigate which pair of groups in the sample are differing, TUKEYS PostHoc Test is conducted. Tukey's method (also known as Tukey's honestly significant difference) is commonly used to determine the minimum difference between means of any two groups before they can be considered significantly different[i].

Tools used

A survey was conducted keeping in mind the study area i.e. Cachar, Hailakandi and Karimganj districts of Assam, India. The top eleven banks were selected based on their presence in the surveyed area. Considering an error of 5 %, sample of 400 units were taken into study based on post stratified random sampling. The sample consists of persons who are salaried employees at the same time E-banking users of their respective banks. The number of respondents collected from each bank was based on proportional allocation that they contribute in the total population.

Table 2: Bank wise sample size collected

Analysis and Results

This section deals with the analysis and results about performance obtained from responses.

7.1 Reliability tests

Reliability tests were conducted to ensure the validity and precision of the statistical analysis and accordingly Cronbach’s Alpha(α) for the main dimensions were calculated as below:

Table 3: Reliability Statistics of the dimensions

7.2 Performance of banks based on responses

The following tables have been obtained by calculating the mean of the responses separately for each bank under consideration for the four individual dimensions. Hence for different dimensions we had different mean scores of different banks under study.

Table 4: Performance on E- Banking Channel Design

Table 5: Performance on Reliability

Table 6: Performance on Responsiveness

Table 7: Performance on Service

[i] http://www.statstodo.com/Posthoc_Exp.php

Table 8: Overall Performance Index

The overall performance index Table 8: suggests that as per the overall ranking of performance is concerned, AXIS bank tops the list with score 6.365 out of 7, which is followed by Bank of Baroda with score 6.034. In the third position is ICICI bank with SBI in fourth position. At the bottom we have Punjab National Bank.

Now we need to find out whether the means obtained are significantly different. Thus we use one way ANOVA to test the difference of means.

After conducting the one way ANOVA we obtain the following result.

Table 9: One Way ANOVA for difference of means

Here since p value is less than 0.05 and at the same time F (Critical value) is less than F value, we reject the null hypothesis and state that the mean scores obtained are not equal, i.e. the differences in mean scores are significant.

Now to further investigate which pair of groups in the sample are differing TUKEYS PostHoc Test is conducted. The table below shows the multiple comparisons of means each of different banks

From the Table 10 it is clear that the differences of means are significant for the pairs such as AXIS-HDFC, AXIS-CANARA, AXIS-VIJAYA, AXIS-UCO and AXIS-PNB. The differences of means in rest of the pairs are insignificant.

Conclusion

The user responses in a likert scale were tapped to identify the performance score of banks in different dimensions. Finally an overall performance index has been computed to find the overall position of the banks under study based on the quality dimensions of electronic banking services. The computation is made by combining the performance of the banks in each category or dimensions of E-banking services. The overall performance index suggests that as per the overall ranking of performance is concerned, AXIS bank tops the list with score 6.365 out of 7, which is followed by Bank of Baroda with score 6.034. In the third position is ICICI bank with SBI in fourth position. At the bottom we have Punjab National Bank. The index obtained is very helpful in identifying the position or overall status of E-banking service of a particular bank under study. From the mean scores obtained the banking organizations can identify and measure the difference in scores from the top position and differences of scores with its competitors. Since the index is developed based on sample from each bank under proportional allocation of number of customers, hence few banks have very low respondents as population is very low. As such the index cannot be generalized and the results may be different with larger respondents, location and many other factors.

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