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Dynamics of Consumer Adoption of Financial Innovation: The Case of ATM Cards

Author

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  • Botao Yang

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Andrew T. Ching

    (Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada)

Abstract

We develop a structural consumer life-cycle model to investigate consumers' adoption and usage decisions of ATM cards. If consumers are forward-looking with a known discount factor, our framework can control for the heterogeneous life span faced by consumers of different ages, and hence measure adoption costs more accurately. Moreover, our framework can recover the monetary value of total adoption costs. To estimate our model, we use an Italian panel data set, which contains information on consumers' adoption decisions for ATM cards, and their cash withdrawal patterns before and after adoption. Our results suggest that one could significantly overestimate adoption costs for the elderly when ignoring their shorter life span. Our policy experiments show that a sign-up bonus targeted to the elderly could be much more effective if implemented as a limited-time offer rather than a permanent offer. Interestingly, if the sign-up bonus is permanent, younger consumers may strategically postpone adoption. This paper was accepted by Pradeep Chintagunta, marketing.

Suggested Citation

  • Botao Yang & Andrew T. Ching, 2014. "Dynamics of Consumer Adoption of Financial Innovation: The Case of ATM Cards," Management Science, INFORMS, vol. 60(4), pages 903-922, April.
  • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:4:p:903-922
    DOI: 10.1287/mnsc.2013.1792
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    References listed on IDEAS

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    5. Ulf Von Kalckreuth & Tobias Schmidt & Helmut Stix, 2014. "Using Cash to Monitor Liquidity: Implications for Payments, Currency Demand, and Withdrawal Behavior," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(8), pages 1753-1786, December.
    6. Haijing Hao & Rema Padman & Baohong Sun & Rahul Telang, 2018. "Quantifying the Impact of Social Influence on the Information Technology Implementation Process by Physicians: A Hierarchical Bayesian Learning Approach," Information Systems Research, INFORMS, vol. 29(1), pages 25-41, March.
    7. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    8. Francisco J. Callado-Muñoz & Jana Hromcová & Natalia Utrero-González, 2012. "Transformation of payment systems: the case of European Union enlargement," Applied Economics Letters, Taylor & Francis Journals, vol. 19(18), pages 1787-1791, December.
    9. Fernando E. Alvarez & David Argente & Diana Van Patten, 2022. "Are Cryptocurrencies Currencies? Bitcoin as Legal Tender in El Salvador," NBER Working Papers 29968, National Bureau of Economic Research, Inc.
    10. Marc Rysman & Scott Schuh, 2017. "New Innovations in Payments," Innovation Policy and the Economy, University of Chicago Press, vol. 17(1), pages 27-48.
    11. Kim Huynh & Philipp Schmidt-Dengler & Gregor W. Smith & Angelika Welte, 2017. "Adoption Costs of Financial Innovation: Evidence from Italian ATM Cards," Staff Working Papers 17-8, Bank of Canada.
    12. Francisco J. Callado-Muñoz & Jana Hromcová & Natalia Utrero-González, 2014. "Effects of Institutional Environment and Technology Development on Payment Choice," Working Papers wpdea1403, Department of Applied Economics at Universitat Autonoma of Barcelona.
    13. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
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