Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg November 20, 2022

The Impact of Mobile Banking Adoption on Retail Banking in Germany

Die Auswirkungen der Einführung von Mobile Banking auf das Privatkundengeschäft in Deutschland
Michel Becker , Oscar Stolper and Andreas Walter

Zusammenfassung

Dieses Paper analysiert mögliche Änderungen im Privatkundengeschäft der Kunden nach der Einführung von Mobile Banking. Es wird auf einen qualitativ hochwertigen Datensatz auf Einzelkundenebene von einer deutschen Genossenschaftsbank zurückgegriffen und eine Differenz-von-Differenzen-Analyse für die Gruppe der Anwender und eine gematchte Stichprobe der Nicht-Anwender durchgeführt. Es kann ein starker Anstieg der Online-Banking-Transaktionen nach der Einführung des Mobile Banking beobachtet werden, während Transaktionen an Geldautomaten, Callcentern und Bankfilialen weniger häufig genutzt werden. Darüber hinaus überweisen Kunden häufiger Geld über digitale Kanäle und zahlen bargeldlos, nachdem sie Mobile Banking freigeschaltet haben, was zu Lasten von Offline-Geldüberweisungen und Bargeldabhebungen geht. Schließlich ist erkennbar, dass es wahrscheinlicher ist, dass Nutzer von Mobile Banking die von der Bank beworbenen Produkte kaufen und die aktive Nutzung von Mobile Banking die Loyalität der Kunden gegenüber der Bank erhöht.

This paper analyzes potential changes to clients’ retail banking behavior after they have adopted mobile banking. We draw on a high-quality data set at the level of the individual client, which we obtain from a German

Abstract

cooperative bank and perform difference-in-differences analyses for the group of adopting clients and a matched sample of non-adopters. We document a sharp increase in online banking transactions after mobile banking adoption, while ATM, call center and bank branch transactions are used less often. In addition, clients more often transfer money via digital channels and pay cashless after they have unlocked mobile banking, which comes at the expense of offline money transfers and cash withdrawals. Finally, mobile banking adopters are more likely to purchase the products promoted by the bank and active use of mobile banking increases clients’ loyalty with the bank.

Résumé

Le document analyse les changements potentiels du comportement des clients en matière de banque de détail après qu'ils aient adopté la banque mobile. Nous nous appuyons sur un ensemble de données de haute qualité au niveau du client individuel, que nous obtenons d'une banque coopérative allemande et effectuons des analyses de différence dans les différences pour le groupe de clients ayant adopté des services bancaires mobiles et un échantillon apparié de non-adoptants. Nous documentons une forte augmentation des transactions bancaires en ligne après l'adoption des services bancaires mobiles, tandis que les transactions aux guichets automatiques, aux centres d'appels et aux succursales bancaires sont moins souvent utilisées. En outre, les clients transfèrent plus souvent de l'argent via des canaux numériques et paient sans espèces après avoir déverrouillé les services bancaires mobiles, ce qui se fait au détriment des transferts d'argent hors ligne et des retraits d'espèces. Enfin, les adeptes de la banque mobile sont plus susceptibles d'acheter les produits promus par la banque et l'utilisation active de la banque mobile accroît la fidélité des clients envers la banque.

Bibliography

Abadie, A. & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267 https://doi.org/10.1111/j.l468-0262.2006.00655.xSearch in Google Scholar

Bartzsch, N., Schneider, F. & Uhl, M. (2019, 5. Juli). Cash use in Germany. Macroeconomic estimates of the extent of illicit cash use in Germany. Deutsche Bundesbank https://www.bundesbank.de/resource/blob/814448/76e7af84d33d83ba31f06894553556a6/mL/bargeldverwendung-in-deutschland-data.pdfSearch in Google Scholar

Becker, M., Stolper, O. & Walter, A. (2022). What Drives Mobile Banking Adoption? - An Empirical Investigation Using Transaction Data. Zeitschrift für Bankrecht und Bankwirtschaft, 34,1-11. https://doi.org/10.15375/zbb-2022-010310.15375/zbb-2022-0103Search in Google Scholar

Bruckmann, C., Eschelbach, M., Knümann, F., Korella, J., Novotny, J., Pietrowiak, A., Schwalm, C. & Wörlen, H. (2018, 9. Februar). Zahlungsverhalten in Deutschland 2017. Vierte Studie über die Verwendung von Bargeld und unbaren Zahlungsinstrumenten. Deutsche Bundesbank. Verfügbar unter: https://www.bundesbank.de/resource/blob/634056/ael0b24377fa62d6c5873886d8f48fld/mL/zahlungsverhalten-in-deutschland-2017-data.pdfSearch in Google Scholar

Campbell, D. & Frei, F. (2010). Cost Structure, Customer Profitability, and Retention Implications of Self-Service Distribution Channels: Evidence from Customer Behavior in an Online Banking Channel. Management Science, 56(1), 4-24. https://doi.org/10.1287/mnsc.1090.106610.1287/mnsc.1090.1066Search in Google Scholar

Carlin, B., Olafsson, A. & Pagel, M. (2019). FinTech and Consumer Financial Well-Being in the Information Age. Verfügbar unter: https://www.fdic.gov/bank/analytical/fintech/papers/carlin-paper.pdfSearch in Google Scholar

Chakravorti, B., Chaturvedi, R. S. & Mazzotta, B. (2016, 31. Mai). The Countries That Would Profit Most from a Cashless World. Verfügbar unter: https://hbr.org/2016/05/the-countries-that-would-profit-most-from-a-cashless-worldSearch in Google Scholar

Chen, P. Y. S. & Hitt, L. M. (2002). Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry. Information Systems Research, 13(3), 255-274 https://doi.Org/10.1287/isre.13.3.255.7810.1287/isre.13.3.255.78Search in Google Scholar

Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-202. https://doi.org/10.llll/j.2517-6161.1972.tb00899.x10.1007/978-1-4612-4380-9_37Search in Google Scholar

Deutsche Bundesbank (2019a, 17. Juni). The costs of payment methods in the retail sector. Deutsche Bundesbank. Verfügbar unter: https://www.bundesbank.de/resource/blob/800766/0462923c3587a2d98f2c2db5b71047ae/mL/2019-06-kosten-zahlungsmittel-data.pdfSearch in Google Scholar

Deutsche Bundesbank (2019b, 26. Juli). Statistics on Payments and Securities Trading, Clearing and Settlement in Germany 2014 to 2018. Deutsche Bundesbank. Verfügbar unter: https://www.bundesbank.de/resource/blob/621384/3c752951ebcl9c6adfc3c6cl0707a4e4/mL/zvs-daten-data.pdfSearch in Google Scholar

Deutsche Bundesbank (2020a). Number of OTC transactions. Verfügbar unter: https://www.bundesbank.de/dynamic/action/en/ statistics/time-series-databases/time-series-databases/759784/759784?statisticType=BBK_ITS&listld=www_sl3b_zvs08a&treeAnchor=BANKENSearch in Google Scholar

Deutsche Bundesbank (2020b). Number of payment transactions. Verfügbar unter: https://www.bundesbank.de/dynamic/action/en/statistics/time-series-databases/time-series-databases/759784/759784?statisticType=BBK_ITS8&listld=www_sl3b_zvs04a&treeAnchor=BANKENSearch in Google Scholar

Deutsche Bundesbank (2020c). Number of transactions per type of terminal. Verfügbar unter: https://www.bundesbank.de/dynamic/action/en/statistics/time-series-databases/time-series-databases/759784/759784?statisticType=BBK_ITS8&listld=www_sl3b_zvs04a&treeAnchor=BANKENSearch in Google Scholar

Fisher, L. D. & Lin, D. Y. (1999). Time-dependent covariates in the Cox proportional-hazards regression model. Annual Review of Public Health, 20(1), 145-157. https://doi.org/10.1146/annurev.publhealth.20.l.14510.1146/annurev.publhealth.20.1.145Search in Google Scholar

Harder, V. S., Stuart, E. A. & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15(3), 234-249. https://doi.org/10.1037/a001962310.1037/a0019623Search in Google Scholar

Judson, R. (2017). The Death of Cash? Not So Fast: Demand for U.S. Currency at Home and Abroad, 1990-2016. International Cash Conference 2017 - War on Cash: Is there a Future for Cash?. Verfügbar unter: https://www.econstor.eu/bitstream/10419/162910/l/Judson.pdfSearch in Google Scholar

Jünger, M. & Mietzner, M. (2020). Banking goes digital: The adoption of FinTech services by German households. Finance Research Letters, 34,101260. https://doi.org/10.1016/j.frl.2019.08.00810.1016/j.frl.2019.08.008Search in Google Scholar

Kalckreuth, U. V., Schmidt, T. & Stix, H. (2014). Using Cash to Monitor Liquidity: Implications for Payments, Currency Demand, and Withdrawal Behavior. Journal of Money, Credit and Banking, 46(8), 1753-1786. https://doi.org/10.llll/jmcb.1216510.1111/jmcb.12165Search in Google Scholar

Lee, I. & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35-46. https://doi.org/10.1016/j.bushor.2017.09.00310.1016/j.bushor.2017.09.003Search in Google Scholar

Levi, Y. & Benartzi, S. (2020). Mind the App: Mobile Access to Financial Information and Consumer Behavior. SSRN Electronic Journal https://doi.org/10.2139/ssrn.355768910.2139/ssrn.3557689Search in Google Scholar

Lu, B. (2005). Propensity Score Matching with Time-Dependent Covariates. Biometrics, 61(3), 721-728. https://doi.org/10.llll/j.1541-0420.2005.00356.x10.1111/j.1541-0420.2005.00356.xSearch in Google Scholar

Massi, M., Sullivan, G., Strauß, M. & Khan, M. (2019, 28. Mai). How cashless payments help economies grow. Verfügbar unter: https://www.bcg.com/dede/publications/2019/cashless-payments-help-economies-growSearch in Google Scholar

Meta. (2019, 22. April). Understanding how mobile-first millennials are reshaping expectations in the retail banking industry. Facebook IQ. Verfügbar unter: https://www.facebook.com/business/news/insights/understanding-how-mobile-first-millennials-are-reshaping-expectations-in-the-retail-banking-industry-auSearch in Google Scholar

Rangaswamy, A. & van Bruggen, G. H. (2005). Opportunities and challenges in multichannel marketing: An introduction to the special issue. Journal of Interactive Marketing, 19(2), 511. https://doi.org/10.1002/dir.2003710.1002/dir.20037Search in Google Scholar

Rosenbaum, P. R. (2009). Design of Observational Studies (Springer Series in Statistics) (2010.Aufl.). Springer.Search in Google Scholar

Soysal, G. & Krishnamurthi, L. (2016). How Does Adoption of the Outlet Channel Impact Customers’ Spending in the Retail Stores: Conflict or Synergy? Management Science, 62(9), 2692-2704 https://doi.org/10.1287/mnsc.2015.226210.1287/mnsc.2015.2262Search in Google Scholar

Tee, H. H. &Ong, H. B. (2016). Cashless payment and economic growth. Financial Innovation, 2(1) https://doi.org/10.1186/s40854-016-0023-z10.1186/s40854-016-0023-zSearch in Google Scholar

Xu, K., Chan, J., Ghose, A. & Han, S. P. (2017). Battle of the Channels: The Impact of Tablets on Digital Commerce. Management Science, 63(5), 1469-1492. https://doi.org/10.1287/mnsc.2015.240610.1287/mnsc.2015.2406Search in Google Scholar

Xue, M., Hitt, L. M. & Harker, P. T. (2007). Customer Efficiency, Channel Usage, and Firm Performance in Retail Banking. Manufacturing & Service Operations Management, 9(4), 535- 558. https://doi.org/10.1287/msom.1060.013510.1287/msom.1060.0135Search in Google Scholar

Xue, M., Hitt, L. M. &Chen, P. Y. (2011). Determinants and Outcomes of Internet Banking Adoption. Management Science, 57(2), 291-307. https://doi.org/10.1287/mnsc.1100.118710.1287/mnsc.1100.1187Search in Google Scholar

Appendix

A.1 Variable Definitions

Tab. 6

Definition of variables.

Variable Definition
Demographics
Age Age of the customer (in years)
Male Indicator variable, which equals one, if the customer is male, and zero otherwise
Channel usage
Mobile banking Count of mobile banking transactions (per month)
Online banking Count of online banking transactions (per month)
ATM Count of ATM transactions (per month)
Call center Count of call center transactions (per month)
Branch Count of branch transactions (per month)
Payment behavior
Offline transfers Count of money transfers, which are performed through nondigital channels (ATM, call center, branch) (per month)
Online transfers Count of money transfers, which are performed through digital channels (online banking, mobile banking) (per month)
Cash withdrawals Count of cash withdrawals (ATM, branch) (per month)
Cash deposits Count of cash deposits (ATM, branch) (per month)
Debit card payments Count of debit card payments (per month)
Credit card payments Count of credit card payments (per month)
Business intensity
Product usage Count of distinct product categories (current, loan, deposit or custody account, credit card, share in the company/bank, building society savings, insurance), which are used by the customer (end of month)
Customer profitability Monthly gross margin, measured by the banks internal control system (in euro)
Wealth status
Salary Sum of all salary or pension payments (per month in euro)
Balance of currents Sum of balances of current accounts (end of month in euro)
Balance of deposits Sum of balances of deposit accounts (end of month in euro)
Balance of investments Sum of balances of investment accounts (end of month in euro)
Balance of loans Sum of balances of loan accounts (end of month in euro)

A.2 Types of Transactions per Channel

Tab. 7

Distribution of transaction types across banking channels. The table shows the mean of the number of clients’ monthly transactions per channel. The table provides descriptive statistics after customers’ first usage of the respective channel. Thus, we remove observations of those customers, who do not use the respective channel in order to reduce skewness of our descriptive results. As customers are able to inquire multiple accounts during one login-session, the online and mobile banking transaction counts for account inquiry are higher than those for Login.

Transaction Mean
Branch
Sum of all transactions 1.27
thereof money transfer 1.00
thereof money withdrawal 0.19
thereof money deposit 0.06
thereof print of account statement 0.02
Call center
Sum of all transactions 1.97
thereof account inquiry 0.71
thereof general call 0.62
thereof money transfer 0.64
ATM
Sum of all transactions 6.43
thereof money withdrawal 2.71
thereof print of account statement 2.20
thereof account inquiry 1.29
thereof money transfer 0.12
thereof money deposit 0.11
Online banking
Sum of all transactions 31.07
thereof account inquiry 24.25
thereof Login 4.35
thereof money transfer 1.87
thereof view account statement 0.57
thereof other service 0.03
Mobile banking
Sum of all transactions 5.53
thereof account inquiry 4.25
thereof Login 1.18
thereof money transfer 0.06
thereof view account statement 0.04
thereof other service 0.00

A.3 Matching Results for the DiD Analysis

Fig. 4 This figure displays the standardized bias across covariates before and after matching. We perform risk-set matching, in which a treated client is matched to a not-yet-treated client, who exhibit similar time-dependent covariates up to the moment when the treatment occurs. We perform matching with replacement and match each treated unit with the three closest untreated neighbors. We enforce exact matching on month of adoption and an indicator variable, whether or not the customer already uses online banking. The figure’s x-axis shows the standardized bias (SB), y-axis lists the covariates of interest. The SB before matching (Unmatched) is visualized by squares; the SB after matching (3NN with replacement) is visualized by triangles. Vertical dotted lines visualize the performance threshold of Harder et al. (2010), who recommend a SB of less than 0.1.
Fig. 4

This figure displays the standardized bias across covariates before and after matching. We perform risk-set matching, in which a treated client is matched to a not-yet-treated client, who exhibit similar time-dependent covariates up to the moment when the treatment occurs. We perform matching with replacement and match each treated unit with the three closest untreated neighbors. We enforce exact matching on month of adoption and an indicator variable, whether or not the customer already uses online banking. The figure’s x-axis shows the standardized bias (SB), y-axis lists the covariates of interest. The SB before matching (Unmatched) is visualized by squares; the SB after matching (3NN with replacement) is visualized by triangles. Vertical dotted lines visualize the performance threshold of Harder et al. (2010), who recommend a SB of less than 0.1.

A.4 Payments Statistics of Deutsche Bundesbank

Tab. 8

Key figures explaining the nationwide payment behavior in Germany based on Deutsche Bundesbank (2020a,b,c). Column 1 shows the dimension of interest. Columns 2 to 10 document the number of transactions in millions in that particular year. Percentage changes relative to the preceding year are displayed in parentheses.

2010 2011 2012 2013 2014 2015 2016 2017 2018
Online Money Transfers 940.2 896.5 847.2 816.1 623.5 612.2 570.5 521.3 482.9
[/] [4.6%] [5.5%] [3.7%] [23.6%] [1.8%] [6.8%] [8.6%] [7.4%]
Online Money Transfers 4931.6 5176.0 5303.8 5401.3 5009.6 5407;5 5615.7 5777.3 5985.2
[/] [5.0%] [2.5%] [1.8%] [7.3%] [7.9%] [3.9%] [2.9%] [3.6%]
Cash Withdrawals 2326.4 2377.4 2385.0 2352.8 2256.8 2359.6 2345.6 2271.7 2223.5
[/] [2.2%] [0.3%] [1.4%] [4.1%] [4.6%] [0.6%] [3.2%] [2.1%]
DeCash Deposits 275.0 279.6 273.8 271.9 262.7 265.5 265.2 257.1 257.2
[/] [1.7%] [2.1%] [0.7%] [3.4%] [1.1%] [0.1%] [0.6%] [0.6%]
Debit Card Payments 2196.3 2399.7 2579.1 2885.3 2595.1 2722.6 2963.4 3275.4 3913.8
[/] [9.3%] [7.5%] [11.9%] [10.1%] [4.9%] [8.8%] [10.5%] [19.5%]
Credit Card Payments 447.9 501.2 559.7 681.5 762.5 879.0 984.0 1100.8 1260.3
[/] [11.9%] [11.7%] [21.8%] [11.9%] [15.3%] [11.9%] [11.9%] 1260.3

A.5 Detailed Results on Customer Churn

Tab. 9

Detailed results of difference-in-differences regression for active mobile banking users, having 12-month customer churn on the left hand side (LHS). The LHS equals 1, whether the client leaves the bank within the next 12 months, and zero otherwise. The treatment group of the DiD regression comprises 3,907 clients, who adopt mobile banking before January 2018, are observable at least one month before adoption and perform above median monthly mobile banking transactions after their adoption. The control group comprises matched controls of not-yet-treated clients.

12-month customer churn rate
Treatment -0.0000
(0.0000)
Post 0.0220***
(0.0016)
Treatment x Post -0.0076**
(0.0028)
Number of Observations 32,813
R2 (full model) 0.0120
Adj. R2 (full model) 0.0108
Published Online: 2022-11-20
Published in Print: 2022-11-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 2.12.2022 from frontend.live.degruyter.dgbricks.com/document/doi/10.1515/zfgg-2022-0019/html
Scroll Up Arrow