Demographic Profile of Banking Self Service Technologies Users

Recently many industries have introduced technology-enabled services that do not need assistance from the service provider employee. These technologies are called ‘self-service technologies’ or ‘alternative delivery channels’ or ‘virtual channels’. These channels have been introduced along with traditional service (Fisher, 1998). ATMs, online banking are some of the SSTs, which have become very popular with the customers. However, encouraging customers to switch from traditional channel to virtual channel, is a big challenge. It will be useful for the company by giving it competitive advantage, only if customers adopt these new delivery channels making it cost effective for the banks. It will help reduce labor cost, capital expenditure of setting up new branches and employee training costs. Therefore, it is important to identify the factors leading to customer acceptance of these channels. The adoption of technology depends largely upon demographics of the target market. The impact of demographics on SSTs adoption and usage have been covered thoroughly in the recent past. Weijters et al. (2007) looked into the impact of factors like education, occupation, age and gender on customers’ intentions to use the channels. Venkatesh, Morris, Ackerman (2000) found that demographics like age of the customer and gender had an impact on the acceptance of technology. Similar results were also found by Morris and Venkatesh (2000). Therefore, it becomes imperative to study the demographic profile of the customer.

Rose and Ogunmokun (2013) suggested that demographic profile of customers had a huge influence on the adoption of self-service technologies. Younger customers compared to older customers were more likely to adopt Self-service technologies. Also, customers who were well-off and well read were more likely to adopt self-service technologies. The paper examines the profile of users of the technology-enabled channels. Marshall and Heslop (1988) suggested occupation and the education of customer played important role in the adoption of the ATM. Age acted as a hinderance in the adoption. The study also suggested that it was very important to know why customer was using self-service technology in order to understand if he would use the channel again or not. Leblanc (1990) suggested that the customers not using ATM are mostly those who preferred human interactions over technology interface. Security was also a huge concern for them. However, the factors affecting the adoption of ATM include easy availability of ATM kiosks and that they were easy to use. Customer who were high on education mostly adopted ATM and privacy was not a big issue for them. suggested that young customers easily adopted the channel while older customers were reluctant and preferred branch banking. Privacy was not a huge issue for the customers. Gender was also an important determinant as males preferred to use Internet Banking. Mattila et al. (2003) concluded that age was important deciding factor in the adoption of Internet Banking. The study based in Finland suggested that younger customers preferred the channel while the old found it difficult to use, complex and did not have the resouces to use it. There was also privacy issues because of which the channel was not very popular with the elderly. Mukherjee and Nath (2003) suggested that customers’ trust with online banking channel is largely dependent on the kind of interactions the customer has with the bank. Customers of different demographic profiles who were using online banking channel were taken for the study. Customers’ likelihood to use the channel and to stay committed to it depends to a large extent on the perceived trust. Both the service provider and the customer need to have same set of values for trust development. Akinci et al. (2004) concluded that males in the age group of 35 to 40, who preferred anytime-anywhere banking, were more inclined towards using technology. The study aimed to examine the behavior of customers who used Internet banking and those who did not. Customer demographics were considered for this. The study suggested that there was huge contrast in the characteristics of users and non-users of Internet banking. Customers’ preference for the channel helped in understanding his attitude towards Online banking in Turkey. Flavián, C., Guinaliu, M., & Torres, E. (2005) examined the factors responsible for customers likelihood of adopting of internet banking of the bank it is associated with. 633 customers formed a part of the sample. The effect of demographics on adoption was also examined. The study suggests that customers’ intentions to use online banking depend on customers trust with branch banking. Demographics like gender, age and income level affect adoption. Laforet S. and Li X. (2005) concluded that age and education level had no impact on adopton of internet and mobile banking. The study examined the online and mobile banking of customers. For this the profiles of customers were examined. The study examined demographics, attitude of customers and customer behavior. The study suggested that security and lack of resources, availability were the reasons for non-adoption of internet banking. Most male customer used online channels. and his age group decide whether he would use the channel or not. Demographics of customer played an important role in adoption behavior. Banks should have tried and targeted customers based on their demographic profiles.

To examine how performance of self-service technologies and the customers’ satisfaction from it and their intentions to use it, Public sector and private sector banks were taken. The objective of this study was to study the distribution of banking customers in public and private banks based on their demographic profile in Chandigarh region. 300 customers were taken from each bank using branch intercept technique. Only those respondents were taken in the study who are using internet and mobile banking services. In order to cover different segments’ of the society, five segments were selected. These included, Self-employed or businessman, Service class, Academicians and students, homemakers and others. An attempt was made to select at least 30 customers from each of these segments.

Here is an overview of respondents’ socio-economic characteristics as the socio-economic characteristics may have direct or indirect bearing on their perceptions shaping his intentions to use the technology.

The information contained in Table 1 showed that the highest proportion i.e. 46.00 percent of public sector respondents were from the age group of below 30 years followed by 34.00 percent from 30-40 years of age group.15.33 percent from 40-50 years of age and the lowest proportion i.e. 4.67 percent of them were from the age group of above 50 years.

The analysis further indicated that the highest proportion i.e. 42.67 percent of private sector

percent of them were from the age group of 40-50 years followed by 6.00 percent from above 50 years of age. The pattern of age distribution of respondents differed significantly between public and private sector banks as indicated by the calculated chi-square value of 33.71. Younger customers dominated the private sector banks as compared to the public sector banks.

A perusal of Table 2 showed that majority i.e. 63.33 percent and 75.33 percent of the respondents in public sector and private sector banks were males while the remaining respective proportion of 36.67 and 24.67 percent were females.

Though the pattern of gender distribution was similar in both the sectors, but the proportion of male respondents was significantly higher in private sector banks as compared to that in public sector banks. This is also confirmed by the significant chi-square value of 10.16.

Table 3 clearly shows that the highest proportion i.e. 59.33 percent of the public sector respondents was married followed by 37.33 percent unmarried persons. There were only 3.33 percent who were divorcee. Private sector also depicted a similar pattern of distribution according to marital status.

The highest proportion i.e. 62.00 percent of them were married followed by 36.00 unmarried, while there were only 2.00 percent who were divorcee. The chi-square value of 1.25 indicated that there was no significant difference in marital status pattern in both the sectors. i.e. 56.67 percent of public sector respondents was graduates, followed by 26.00 percent undergraduates. While 10.00 percent were postgraduates, the lowest proportion i.e. 7.33 percent of them was having some professional diploma or degree after graduation or post-graduation. In private sector, the highest proportion i.e. 56.00 percent was graduates followed by 17.33 percent postgraduates.

Respondents having a some professional diploma or degree after graduation or post-graduation were 14.67 followed by 11.33 percent was undergraduates. The analysis showed that in public sector, 17.33 percent of respondents were above graduation while the same was 32.67 percent in private sector. The proportion of undergraduates was 26.00 percent in public sector and only 11.33 percent in private sector. The education level was significantly higher in private sector banks as compared to that in public sector banks. This finding is also confirmed by the chi-square value of 31.49.

Table 5 revealed that the highest proportion i.e. 36.67 percent of public sector respondents was doing some service, followed by 24.67 percent self-employed or business persons and 19.33 percent academicians or students.

As much as 14.67 percent were homemakers and 4.67 percent were doing some other occupation. In private sector banks, the highest proportion i.e. 46.00 percent was service persons, followed by 32.67 percent self-employed or business persons and 11.33 proportion of respondents doing some sort of self-employed/business activity and servicemen was significantly higher in private sector banks as compared to that in public sector banks. On the other hand, the proportion of homemakers was significantly higher in public sector banks as compared to that in private sector banks. This finding is also supported by the chi-square value of 22.44.

As per the information contained in Table 6, the highest proportion i.e. 54.67 percent of public sector respondents was having a monthly income of Rs. 25000 to Rs. 50000, followed by 26.00 with an income of less than Rs. 25000. The lowest proportion i.e. 4.00 percent of them was enjoying an income of more than Rs. 75000 while 15.33 percent of customers had a monthly income of Rs. 50000 to Rs. 75000.

In private sector banks, the highest proportion i.e. 38.67 percent of respondents was having a monthly income of Rs. 25000 to Rs. 50000, followed by 26.00 percent with an income of less than Rs.25000. As much as 17.33 percent of respondents were enjoying an income of above Rs. 75000 and 18.00 percent having an income of Rs.50000 to Rs. 75000. The proportion of respondents having income of above Rs. 50000 was 19.33 percent in public sector and 35.33 percent in private sector banks. The income of private sector banks respondents was significantly higher than that of public sector banks respondents as indicated by the chi-square value of 33.87.

Majority i.e. 63.33 percent and 75.33 percent of the respondents in public sector and private sector banks were males. However, the proportion of male respondents was significantly higher in private sector banks as compared to that in public sector banks. The highest proportion i.e. 59.33 percent of the public sector respondents was married. Private sector also depicted a similar pattern of distribution according to marital status. The highest proportion i.e. 56.67 percent of public sector respondents was graduates. Similarly, in private sector, the highest proportion i.e. 56 percent was graduates. The education level was respondents was doing some service, while in private sector banks, the highest proportion i.e. 46 percent were service persons. The proportion of respondents doing some sort of self-employed/business activity and servicemen was significantly higher in private sector banks as compared to that in public sector banks. On the other hand, the proportion of homemakers was significantly higher in public sector banks as compared to that in private sector banks. The highest proportion i.e. 54.67 percent of public sector respondents were having a monthly income of Rs. 25000 to Rs. 50000 and this figure came to be 38.67 percent in private sector banks. Overall, the analysis showed that the younger people with higher level of education and income were associated with the private sector banks as compared to the public sector banks. The clientele of young customers is more in private sector banks. These banks need to devise special packages to attract more customers from this category.There are more male customers compared to females. Banks, both, public sector and private sector, need to have specific plans to attract more female customers. Study reveals that young, highly educated and high-income customers are associated with private sector banks. It reflects that SSTs have not picked up with older age groups in public sector banks. The convenience of using these channels should be promoted in the banks.

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Assistant Professor, Punjabi University Regional Centre for IT and Management, Mohali tarannummohan@gmail.com