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

Abstract: Consumer technology adoption has long been a research topic in Marketing and Economics. One interesting stylized fact is that usage of new technologies by the elderly is consistently much lower than that by other age groups. Previous literature tries to rationalize this fact by arguing that the elderly have negative attitudes toward new technologies, or it is relatively more difficult for them to learn and use new technologies. If one estimates individuals’ adoption costs in a static choice model, these reasons would translate into higher adoption costs for the elderly. However, there is one potential explanation that has been neglected in the previous literature: the elderly have much shorter life horizons than the young, and consequently their total discounted benefits from adoption could also be much smaller. In order to capture this factor, we explicitly model consumers to be forward-looking and solve a finite horizon dynamic programming problem when deciding whether to adopt a new technology. We apply this framework to the case of ATM cards. To measure monetary benefits per period from ATM card adoption, we also explicitly model how consumers make cash withdrawal decisions. We estimate the structural parameters of our model by using a micro-level panel dataset, which consists of detailed demographic information, individuals’ adoption decisions of ATM cards and cash withdrawal patterns, and the number of ATM machines and interest rates over time, as provided by the Bank of Italy. The estimation results allow us to measure the relative importance of adoption costs and total discounted benefits in influencing consumers’ ATM card adoption decisions. We find evidence that the elderly do not have larger adoption costs for ATM cards in Italy -the lower ATM card adoption rate among the elderly can be explained in terms of differences in total discounted benefits of adoption across age groups. By conducting counterfactual experiments, we quantify how consumers’ ATM adoption decisions would be affected by changing (i) the amount of subsidies, (ii) interest rates, and (iii) number of ATMs.