Banking sector has witnessed a rapid shift in terms of distribution of services by employing automated, self-service modes built on ATM, card, Internet and mobile phone based platforms. The goal of banks in India is to promote and build up these alternate modes of delivery through new off-site ATM installations, sharing of networks, free access, offers and discounts and bundling of services. While a time-conscious, convenienceoriented city customer looks forward to these services, the urban and semi-urban customer is exposed to only a few of the above marketed benefits. This study was conducted to develop an understanding of adoption of e-banking services among the consumers in urban and semi-urban regions. The analyses revealed the influence of demographic variables, experience with computer technologies and medium of information as predictors of channel adoption. Depending upon banking behavior, four segments of consumers were identified with the help of factor analysis. Logistic regression was used to analyze the significance of different levels of socio-techno-demographic variables and behavioral factors on the propensity to adopt ATM and Internet banking at an individual level.

Banking sector has witnessed major technological shifts in the areas of distribution of services, real time internal management information, transfer of funds and financial engineering of new products and services (Grosse, 1997). Electronic-banking (e-banking) facilitates the provision of information or services to customers using several different methods of delivery (Daniel, 1999), such as ATMs, online banking, debit cards, credit cards, smart cards and mobile banking. It is primarily concerned with accessing information, transferring funds and purchasing financial products and services (Sathye, 1999). Any acceptance of technology-enabled service depends mainly on its perceived ease of use, security, awareness of benefits and above all reliability. Accessibility of the source, availability of uninterrupted power supply, Internet connection, technical staff and bundling of services can speed up the adoption and hence usage rates. The question to ponder is whether the marketing efforts of the banks have motivated at least those customer segments which are capable of utilizing technology-enabled services. The benefits of technology can be realized only if the service providers can understand the factors that influence the acceptance of new products and create a climate in which technological advantages can be embraced by a majority of consumers instead of just a few techno-savvy consumers (Kolodinsky et al., 2004).

The primary objective of this study is to gain an understanding of the adoption behaviors of e-banking services by customers in urban and semi-urban offices of commercial banks in India. Given the demographic shifts resulting from changes in age profile and household, customer preferences towards e-banking is also evident in these regions; banks are committed to build a value-creating customer franchise to woo the ‘customer wallet’ in these groups too. Although banks may invest heavily in new delivery channels, the success and sustainability of these channels critically lie in the ability to convert that investment into lower distribution costs (Vepa Kamesam, 2003). As a group, urban and semi-urban offices constitute 62.5% of total number of offices and share 23% of deposits and 17% of credit at all India level. Table 1 provides the statistics of growth rates of deposits and credit at all India level. Given the fact that customers in India, as in most other countries, feel uncomfortable without a bank-branch close to them (Sengupta and Thomas, 2005), the per capita level per branch is 15,000 (RBI, 2009). Banks are making all efforts to move bulk but small-volume cash transactions to other delivery channels by motivating customers.

Bank Management Journal, Indian Banks, Asset Liability Management, Data Filtering, Least Absolute Deviation, Decision-Making Group, Commercial Banks, Ordinary Least Square, Banking Industry, Kenyan Banks, Least Squares Regression, Mutual Fund Industry, Linear Programming, Financial Markets, Capital Required Adequacy Ratio, Public Sector Banks.