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Effect of Payment Mechanisms on the Replacement Time of Durable Products Purchase
Aruna Divya
Kanchan Mukherjee
出版
SSRN
, 2015
URL
http://books.google.com.hk/books?id=25DhzwEACAAJ&hl=&source=gbs_api
註釋
Replacement timing decisions of durable goods have been studied by scholars from both normative and descriptive perspectives. Normative models are based on the optimization of some utility based objective function which then suggest an optimal time for replacing a durable good. On the other hand, studies by behavioral researchers using simulated lab-experiments show that individuals do not always make optimal replacement decisions but instead exhibit deviations in systematic ways. For example, individuals were observed to replace old durables at a slower rate than that predicted by normative models and seemed to anchor on some mental threshold for making replacement decisions. The present study adds to the literature on replacement decisions by uncovering systematic linkages between different payment methods at the time of purchase of durable goods and the replacement timing of the same. We also explore the psychological processes underlying these systematic effects. We start by proposing a behavioral model that draws on findings from the mental accounting, coupling effects and cognitive dissonance literatures to predict that people who make cash purchases are more likely to replace the durable earlier compared to people who make the purchase through loans and make EMI payments. Our arguments are based on how payment methods (cash or EMI) influence the consumption experiences of durable goods, which ultimately impacts the replacement time. We propose that the strength of coupling, which is the degree of association between payment and consumption instances, depends on different payment methods and differentially influences the motivation of individuals to close their mental accounts on one hand and the consumption experiences due to cognitive dissonance effects on the other. We conduct two lab and one field experiment to test our model predictions and find support for the same.