Abstract
How to accurately predict customers’ adoption behavior is becoming more important and challenging to many credit card marketers as competition increases. This calls for more knowledge about the consumer utility function and the corresponding decision behavior. In this study, we challenge the commonly used logit model which implies linear utility function and constant marginal rate of substitution (MRS) with a neural network model that can accommodate nonlinear utility function and changing MRS between card attributes. Using the data from a national survey of credit card usage, we find that the neural network model significantly outperforms the logit in predicting consumer card adoption decisions. Our results indicate that consumers do not make linear tradeoffs between card attributes and the MRS between card features does not remain constant even within the same demographic group.
Published Version
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