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Determinants of Transactional Internet Banking

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Abstract

The decision of credit unions in the United States to adopt transactional web-based services is consistent with profit-maximization behavior. Credit unions adopt transactional internet banking services when they provide a higher proportion of consumer loans and when there is increased competition from other financial institutions. They adopt transactional internet banking to attract new customers. The larger the credit union the higher the probability of adoption of transactional internet banking. The probability of adoption of transactional banking is directly related to credit unions’ efficiency and indirectly related to loan delinquencies. We also find that the probability of credit unions offering transactional internet banking is positively related to the percentage of the young population in the counties where credit unions are located.

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Notes

  1. Hernando and Nieto (2007) show that the adoption of internet banking as a delivery channel leads to a reduction in a bank’s overhead expenses.

  2. Our definition is similar to DeYoung (2005), who defines a transactional website as a website offering services such as money transfers, online payments, and reviewing account balances.

  3. DeYoung et al. (2007) investigate the effect of internet adoption for U.S. community banks and report an increase in profitability through deposit-related charges. Hernando and Nieto (2007) use a sample of Spanish banks to find that, over time, online banking was associated with lower costs and higher profitability. Onay and Ozsoz (2013) investigate as to how internet banking affects the deposit collection and loan generation per branch in Turkish banks. They find that internet banking results in higher profits, deposits and lending per branch. They also find evidence that suggests that the adoption of internet banking depresses banks’ overall profitability and their interest income, while positively influencing non-interest income.

  4. Though Dow (2007) and Pana et al. (2015) are the only papers that are related to the determinants of transactional banking, Goddard et al.’s (2009) work on credit unions asserts the importance of internet technology. Goddard et al. (2009) identify the determinants of acquisition for credit unions and provide empirical links between the technological capabilities of credit unions and its hazard of acquisition. They find that during 2001–2006 there was sustained growth in the use of internet technology and that credit unions without any website were at highest risk of acquisition.

  5. We acknowledge that there are differences in credit unions and banks because of the difference in tax structure and the common bond requirements associated with credit unions. Hence, more tests need to be done using bank level data to ascertain these findings. We thank an anonymous referee for pointing out this limitation of our paper.

  6. Credit unions could differentiate themselves by providing more online services (currency exchange, wire transfers, etc.), stronger security (more data encryption), and a more functional user interface.

  7. We thank an anonymous referee for suggesting the use of Herfindahl index as a measure of the level of competition.

  8. Due to unavailability of data we don’t include all banking institutions in a county to measure the HHI score. We acknowledge that our measure of competition may be biased as we don’t include all the banking institutions in county. This bias works in our favor though. If we witness competition to be significant determinant of transactional internet banking with a weaker measure, then it is expected to be even more significant for a stronger measure of competition i.e. if we include all banking institutions in county.

  9. We acknowledge that increasing the number of customers is not the only way to increase growth for a credit union. Growth can be achieved through improved profit margins and increased revenues. We use the number of members because of the availability of the data on penetration ratio provided by the credit unions. We thank an anonymous referee for pointing out this limitation of our test.

  10. We thank an anonymous referee for suggesting the use of delinquency ratio as one of the measures of financial condition of a credit union.

  11. Burden is defined as non-interest expense minus non-interest income. A lower burden improves profitability in the subsequent banking model. NI = [NII − Burden − PLL][1 − T].

  12. https://www.bbva.com/en/data/8663082015/141216_US_BW_BankMillennials.pdf

  13. http://www.federalreserve.gov/econresdata/consumers-and-mobile-financial-services-report-201403.pdf

  14. http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-2014-NA-Consumer-Digital-Banking-Survey.pdf

  15. The logistic regressions are run both as cross-sectional for each year in the dataset and as panel for all years in dataset.

  16. We thank an anonymous referee for suggesting the inclusion of a discussion on common bond and its effect on the size of the credit union and thereby its influence on the adoption of transactional websites.

  17. Corrocher (2006) proxies for the number of retail bank account holders by computing customers’ deposits as a fraction of total deposits.

  18. Bowerman and O’Connell (1990), Myers (1990), Studenmund (2001), Gujarati and Porter (2003) and Balatbat et al. (2004) suggest the use of variance inflation factor (VIF) as a measure of multicollinearity. Bowerman and O’Connell (1990), Myers (1990) Gujarati and Porter (2003) and Balatbat et al. (2004) suggest that if the largest VIF value is greater than 10, then there may be issues of multicollinearity whereas Studenmund (2001) suggests that a VIF value greater than 5 may indicate a multicollinearity problem.

  19. There is no endogeneity involved in competition and age of members. Increased competition and age of members may motivate a credit union to adopt internet banking, but adoption of internet banking by a credit union does not increase the competition it faces from other banks, and adoption of internet banking surely does not change the demographics of the population where the credit union is located.

  20. The aim of this paper is to find the determinants of transactional internet banking, and the difference-in-difference test that we use is for alleviating endogeneity concerns only. We acknowledge that for investigating the effect of adoption of transactional banking the second difference approach of Butler and Cornaggia (2011) is more appropriate. We thank an anonymous referee for making this suggestion.

  21. There are 513 credit unions that adopt basic or informational and interactive websites. As the goal of the paper is to find determinants of transactional banking, we ignore these 513 credit unions from our DID analysis.

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Authors and Affiliations

  1. Department of Finance, College of Business Administration, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, USA

    Krishnan Dandapani

  2. RB 239A, Department of Finance, College of Business Administration, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, USA

    Edward R. Lawrence

  3. Department of Accounting and Finance, 506 Owen College of Business, Eastern Michigan University, Ypsilanti, MI, 48197, USA

    Jodonnis Rodriguez

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  1. Krishnan Dandapani
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  2. Edward R. Lawrence
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  3. Jodonnis Rodriguez
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Correspondence to Edward R. Lawrence.

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Dandapani, K., Lawrence, E.R. & Rodriguez, J. Determinants of Transactional Internet Banking. J Financ Serv Res 54, 243–267 (2018). https://doi.org/10.1007/s10693-016-0268-8

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  • DOI: https://doi.org/10.1007/s10693-016-0268-8

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