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Developing a Predictive Model Using Logistic Regression to Portend the Intention to Go Cashless in Mumbai Using Constructs of Technology Readiness Index

The paper is an attempt to understand barriers and drivers of cashless transaction and using this understanding to finally develop a model to predict the propensity of an individual residing in Mumbai, an Indian metropolis, to adopt cashless transaction. The understanding and the model is based on the variables espoused by Theory of Technology Readiness Index. The data on the variables were collected through a self administered survey of 815 residents of Mumbai. The model is built using binary logistic regression. The model increases the classification accuracy by 22 points (44 percent). All the variables of the theory of technology readiness are significant except the variable which measures the innovation index of an individual. The variables of insecurity and discomfort are more useful in classification as compared to innovation and optimism. The developed model can be used by marketers to target customers who are pro cashless and vice versa. The finding also gives the policy makers a clue of the barriers to adopt cashless transaction in Mumbai.