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Influence of Demographic Profile On Acceptance of Internet Banking In A Non Metro City In Tamil Nadu, India An Empirical Study

JOHN JAYASEELAN RAMOLA PREMALATHA
  1. Research Scholar, VIT Business School, VIT University, Vellore, Tamil Nadu, India Postal Address: 124, Annai Theresa II St, Phase III, Sathuvachari, Vellore, 632 009, Tamil Nadu, South India. Author's Organizational Website: www.vit.ac.in Email: [email protected]
    Prof. J. Ramola Premalatha, Assistant Professor (Senior) of Commerce, VIT University is an internal part time research scholar at VIT Business School, VIT University, Vellore, Tamil Nadu, South India. Her research interest is marketing and has more than 15 years of teaching and specialized in accounting and finance. She has published 6 papers on internet banking.
Copyright: © John Jayaseelan Ramola Premalatha, 2014

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Abstract

The study examines the influence of demographic variables age, gender, marital status, educational level, occupation, monthly income and type of account held by the clients in non metro cities of Tamil Nadu, India. The paper builds on existing literature in demographic variables towards acceptance of internet banking. Quantitative analysis was carried out by collecting response through a well designed questionnaire to clients who have a bank account in the private sector banks and living in non metro cities of Tamil Nadu. A sample of 200 customers from private sector banks was surveyed from Vellore city which is a non metro city. To analyze the data, chi square tests were used in testing the association of the demographic variables with internet bank acceptance. Out of seven demographic variables associated with intention to use and the study revealed that five variables like age, gender, educational qualification, monthly income and type of account held illustrate significance towards acceptance of internet banking services in Vellore city. The study was done on private bank customers in Vellore city only hence the results cannot be generalized.

Keywords

Internet banking; customers; private sector banks; demographic variables; chi square test

INTRODUCTION

The banking industry is facing intense competition due to the entry of private sector and foreign banks in India. Private sector banks in India were the first to introduce internet for banking services in the country. Due to the late ingress into the field, private sector banks understood that reaching the customers in the inaccessible corners of the country is a very intricate task. The only way to endure in the industry is to get connected with customers from any place and any time through technology like the internet. Hence, they offered banking services to their customers through internet applications. India is among the Top 3 countries in the world with the highest number of internet users where11.4 percent of the Indian population use internet which is 5.4 percent of the world population (http://www.internetworldstats.com/top20.htm). In 2011, the statistics showed that less than 10 percent of bank customers use internet banking in India. Hence, to find out the reasons for non acceptance of internet banking, the study is done to analyze the influence of demographic variables on the acceptance of internet banking.
Studies have been made to find out the factors that influence the acceptance of internet banking, but extremely little studies are found in developing countries like India. No study has been done so far on the influence of demographic factors on the acceptance of internet banking by the private sector bank customers in non metro cities in Tamil Nadu, India. This led to the present study. There are seven new age private sector banks functioning in India which started functioning after 1996. They are ICICI, HDFC, AXIS, INDUSIND, Kotak Mahindra, Development Corporation bank and YES bank. Except Development Corporation bank and YES bank, other private sector banks are functioning in Vellore city. One of the metro cities in Tamil Nadu is Vellore which is surrounded by the industries. The urban population is about 43.13 percent (District census, 2011)

REVIEW OF LITERATURE

The study conducted by Mckinsey revealed that the customers those who use online services were young, learned and more affluent than average, but income and gender had less impact on consuming the services (The McKinsey Quarterly; 2001). This is apparent that the demographics had a role in influencing the utilization of online services.
In Murillo and Roisman’s (2004) report, the authors indicated that a bank’s decision to provide internet banking depends on the demographic profile of latent customers, as well as the location of the bank, which is located in a metropolitan area. The ultimate aim of Internet banking is to reach different demographic groups.
Jayawadhera and Foley, (2000) established that the internet banking user has been acknowledged as one who holds a high job position.
Block and Roering, (1976) and (Lewis, 1981) said demographic characteristics play a vital role in understanding the buying behavior of consumers in different segments. Sakkthivel, (2006) said when the demographic characteristics are recognized, that will help the companies to develop products and services according to their customers’ requirements, tastes and preferences. According to Rogers (2003), a new technology is adopted by a customer who is young, with good income and suitable level of education and is more reactive to new innovation than the non-adopter.
Literature shows that there is a strong association between age and the acceptance of technology. Young customers have a positive attitude and older customers have a negative attitude towards the acceptance of technology (Venkatesh and Morris (2000) and Guerrero et al, (2007). Flavian et al., (2006) said income, age and sex influence consumers’ decision to internet banking. Kim et al., (2005) found younger and welleducated consumers are more likely to adopt Internet banking. However, when individual’s age was associated with the level of education, the age effect varied across education groups. The findings showed that there are significant differences in terms of the demographics of these customers that use different payment methods. Solomon et al., (2005) illustrated that customers who are younger, with high salaries and holding higher positions where the adopters of internet banking in Malaysia. Gan et al., (2006) agreed that employment and education are the dominant variables that influence consumer’s choice of electronic banking and non-electronic banking channels in New Zealand.
Varsha Kuchar, (2012) found in India that 62 percent of the internet bank adopters are in the age group of 21-40years and there is no significant relationship between age and internet bank acceptance. Though gender has been suggested as a factor influencing internet banking acceptance, some studies argue that the internet is male dominated (Venkatesh and Morris, 2000).
Mirza et al. (2009) found that in most of the studies it is seen that more male use internet banking than females. Rogers (2003) found that income factor is expected to have less effect on technology acceptance. Karjaluoto et al., (2002); Mattila et al., (2003) and Sathye, (1999) showed that the customers who belong to upper middle class and have high-level occupations are more likely to use Internet banking. Kolodinsky et al. (2004) agreed educated individuals were more likely to adopt internet banking than those with less education. Datta (2010) examined the relationship between personal features and acceptance of the Internet banking. The results demonstrated that customer acceptance of Internet banking services is dependent on some individual characteristics followed by jobs related to the Internet, education level and their age and the older people with low levels of education were more resistant to these services than others.
Desai Chaitalivenkateshrao, (2012) revealed that education, gender, income plays an important role in the usage of internet banking. Inhibitory factors like trust, gender, education, culture, religion, security, and price have minimal effect on customer mindset towards internet banking.
Vinh Sum Chau, Liqing W.L.C. Ngai, (2010), studied the perceptions, attitudes and behaviour of the youth towards internet banking services in Brazil. The authors found that young people (age 16-29) have a more positive attitude towards accepting the Internet Banking Services than other user-groups. Though researchers, like Daniel (1999), Jayawardhena and Foley (2000), Karjaluoto et al. (2002), Mattila (2001) and Sathye (1999), indicated that demographic factors were significant in their acceptance model, they did not explain why the demographic factors had an impact on acceptance of Internet banking.

OBJECTIVE OF THE STUDY

The study aims at identifying the customer acceptance on internet banking in non metro cities in Tamil Nadu, India. Thus, this research sought to examine the influence of the key demographic characteristics which are age, gender, and marital status, educational level, occupation, monthly income and type of account held by the private sector bank customers in Vellore City, Tamil Nadu on the acceptance of internet banking.

HYPOTHESIS

The study is based on the following research hypothesis: Ho: There is no association between demographic variables and the acceptance of internet banking. The demographic variables of the respondents are age, gender, marital status, educational qualification, occupation, monthly income and type of account held by the customers

SAMPLE OF THE STUDY

The sample for the study is the private sector bank customers of Vellore district. With a view to proceed in a systematic way the following research methodology has been followed. To obtain detailed opinion of the customers, the research falls under the category of descriptive research. Both primary and secondary data were collected. To collect primary data questionnaire was prepared.
A pilot study was conducted to validate the reliability of the questionnaire. The study results revealed that the reliability value was above 0.8. Based on the pilot study changes were made to the questionnaire. The questionnaire included questions on demographic variables and the respondents were asked whether they use internet banking. The questionnaire was widely circulated to the customers of private sector banks in Tamil Nadu. The sample for the study was selected on the basis of stratified random sampling through direct method and also through email. About 250 questionnaires were being circulated and 235 responses were received, out of them 35 were incomplete. Using the remaining 200 completed questionnaire, the data are analyzed using SPSS 16 version using different tests like percentage analysis and chi square. The study was conducted in non metro cities of Tamil Nadu, hence the results cannot be generalized.
The period taken to collect the primary data was 6 months, i.e., June to November 2013. The secondary data is collected from magazines, journals, books and websites.

ANALYSIS AND DISCUSSION

To analyze the hypothesis, based on the objective different demographic variables are considered. They are age, gender, and marital status, educational qualification, occupation, monthly income and type of account held by the customers.
The demographic profile of all respondents of the study is presented in the following Table 1 and analyzed using simple percentage analysis.
From Table 1, it is clear that the respondents of the study are from the age group of 18 years to 54 years. Most of them are in the age group of 25 years and 34 years which is 45.5 percent. Both male and female responded to the study, but the male is more which is 88.5 percent. The unmarried respondents are 67 percent; urban living respondents (Vellore City) are 72.5 percent. Among the respondents, graduates are 48.5 percent, engaged in business is 45.5 percent, earning between Rs. 20,000 and Rs. 30,000 p.m. are 36 percent. The respondents have savings account (45 percent) and current account (43 percent) with their banks.
To understand the acceptance of internet banking, the collected data are tabulated below and analyzed.
It is clear from the above Table that about 39.5 percent of the respondents use internet banking and remaining 60.5 percent do not use the services of internet banking.
Hence the study was done to find out whether the demographic profile influence for acceptance and non acceptance of internet banking in Vellore city, Tamil Nadu, India. Hence, chi square test was applied and the results are presented below:

CHI SQUARE TEST

To find out the association between age and acceptance of internet banking chi square has been applied The following Table 3 represents this data.
Table 3 shows the association between the age and acceptance of internet banking at 5% level of significance. Majority of the adopters and rejecters of internet banking belong to the age group of 25 years to 34 years which is 51.3 percent and 45.1 percent respectively. Respondents who are above 54 years do not use internet banking in Vellore city. We reject the null hypothesis since the p value is 0.027 which is less than 0.05. Hence there is a significant association between age of the respondents of private sector banks in Tamil Nadu and their acceptance of internet banking. These findings were not consistent with those of Gattiker (1992), Venkatesh and Morris (2000), that those who are less than 40 years old do not adopt technology. According to Trout (1999) mature customers are more conservative than young customers and resist new technologies. This study confirms the findings of Wang et al.(2003) and Yuan et al, (2010), that the most of internet banking subscribers belong to young generation and acceptance of internet banking among mature people is low.
To find out the association between gender and acceptance of internet banking chi square has been applied. The following Table 4 represents this data.
Table 4 depicts the association between gender and acceptance of internet banking. Majority of male respondents (96.2 percent) use internet banking whereas 16.4 percent of female respondents do not use internet banking. We reject the null hypothesis since the p value is 0.004 which is less than 0.05. Hence there is a significant association between gender of the respondents of private sector banks in Tamil Nadu on their acceptance of internet banking.
To find out the association between marital status and acceptance of internet banking chi square has been applied. The following Table 5 represents this data.
Table 5 depicts the association between marital status and acceptance of internet banking. Among the respondents unmarried respondents (69.2 percent) use internet banking whereas 65.6 percent of them do not use internet banking. We accept the null hypothesis since the p value is 0.352 which is more than 0.05. Hence there is no significant association between marital status of the respondents of private sector banks in Tamil Nadu on their acceptance of internet banking.
The findings are not consistent with the study by Mattila, et al (2003) and Gan et al. (2006) that married people use the internet banking more than the unmarried people.
To find out the association between educational qualification and acceptance of internet banking chi square has been applied. The following Table 6 represents this data.
Table 6 depicts the association between educational qualification and acceptance of internet banking. Majority of graduate respondents (37.2 percent) use internet banking whereas 59.8 percent of respondents with professional education do not use internet banking. The others category includes those who were with diploma degrees. All of them use internet banking in Tamil Nadu. We reject the null hypothesis since the p value is 0.000 which is less than 0.05. Hence there is a significant association between educational qualification of the respondents of private sector banks in Tamil Nadu and their acceptance of internet banking. This is similar to many studies that level of education has very significant impact on the acceptance of internet banking, but contrary to studies which say that as the education level increases the likelihood of adopting online services increase, (Mattila, et al , 2003;Laforet and Li, 2005; Yuan et al, 2010).
To find out the association between occupation and acceptance of internet banking chi square has been applied. The following Table 7 represents this data.
Table 7 depicts the association between occupation and acceptance of internet banking. Majority of professionals (35.2 percent) do not use internet banking whereas 56.4 percent of respondents doing various types of business use internet banking. The student category does not use internet banking in Tamil Nadu. The Table shows that the p value is 0.040 which is more than 0.05. So we accept the null hypothesis. Hence there is no significant association between occupations of the respondents of private sector banks in Tamil Nadu on their acceptance of internet banking. These results do not concur with those of Karjaluoto et al (2002) and Mattila et al (2003).
To find out the association between monthly income and acceptance of internet banking chi square has been applied. The following Table 8 represents this data.
Table 8 depicts the association between monthly income and acceptance of internet banking. Respondents belonging to earning Rs. 30,001 to Rs. 40,000 p.m (36.9 percent) do not use internet banking and Rs. 40,001 to Rs. 50,000 p.m use internet banking whereas 19.2 percent of respondents earning between Rs. 20,001 to Rs. 30,000 p.m do not use internet banking. The Table shows that the p value is 0.006 which is less than 0.05. So we reject the null hypothesis. Hence there is a significant association between monthly incomes of the respondents of private sector banks in Tamil Nadu on their acceptance of internet. It is clear that the highest income group have great use of information technology (Black et al. (2001), (Kolodinsky et al., 2000; Gartner, 2003b) Income level is found to be another significant demographic determinant for the acceptance of internet banking. It is same as the study made by Laforet and Li, 2005; Yuan et al, 2010, who found acceptance of internet banking is high among middle and upper income groups as compared to the low income groups.
To find out the association between type of account held and acceptance of internet banking chi square has been applied. The following Table 9 represents this data.
Table 9 depicts the association between type of account held and acceptance of internet banking. Majority of respondents who are having savings account with the banks adopt internet banking and majority of the customers who are having current account do not use internet banking. We accept the null hypothesis since the p value is 0.032 which is less than 0.05. Hence there is no significant association between types of account held with the private sector banks of Vellore city on their acceptance of internet banking.
Table 10 shows the descriptive statistics for all the observed demographic variables under study. The mean, standard deviation and number of respondents (N) who responded to the study are given in the table. It is concluded from the mean score that monthly income is the most important variable that influence customers to adopt the internet banking in India. It has the highest mean of 2.6500, followed by occupation and age with the mean of 2.4000. This led to the following analysis to build the relationship between them and the acceptance of internet banking.

SUGGESTIONS

The study was primarily conducted to understand the impact of demographics in influencing the acceptance of internet banking in non metro city, Vellore city, in Tamil Nadu. An analysis on demographic variables of the internet banking users exposed that there is a significant association between the age, gender, educational qualification, occupation, monthly income and type of account held by the customers towards acceptance of internet banking in Vellore district, Tamil Nadu. It showed that 60.5 percent of the customers are reluctant to use internet banking services, so weight must be given to those customers who do not accept internet banking and proper training on the usage of internet banking should given to them. Also the demographic variables except gender and qualification have a strong relationship with the demographic variables. Hence bankers have to adopt the right strategies to attract customers living in non metro cities in Tamil Nadu. In light of the findings it is recommended that banks should understand the needs of customers and effective communication should be developed in order to realize latent benefits of internet banking and to remove the barriers influenced by the demographic profile of their customers in the budding Indian market particularly where a big fraction of customers living in urban areas. A point to be noted is that the private sector banks have to go a long way in attracting customers to accept internet banking. The insightful mapping of internet banking customers through demographics could enable their focus better.

CONCLUSION

The objective of the study is to find out the acceptance of internet banking offered by the private sector banks in a non metro city based on demographic variables. It is found that the majority of the customers are reluctant to use internet banking. The demographic variables except gender and qualification have a strong relationship with the acceptance of internet banking. Therefore, bank managers should have different strategies in targeting different groups of customers living in non metro cities in Tamil Nadu in terms of age, marital status, occupation, monthly income and type of account held in order to promote and encourage Internet banking adoption. Banks, who try to attract new customers will also benefit from an understanding of why customers do not adopt Internet banking. Bank managers can make use of such information to develop appropriate strategies to attract new customers to use Internet banking services. In general, if the bank managers have greater knowledge about the factors affecting their customers’ adoption of Internet banking, then they have greater ability to develop appropriate strategies and hence increase the internet banking adoption rate in the non metro cities.
 

Tables at a glance



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Table 1 Table 2 Table 3 Table 4 Table 5
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Table 6 Table 7 Table 8 Table 9 Table 10
 

References