Tipping phenomenon has been widely observed with broad social-economic impacts. Digital nudging using ‘default tip options’ on iPad-like payment devices is increasingly adopted by the service providers, in order to increase the size of the tip that can greatly impact service industry worker’s income. The current practice is to use one set of standard ‘default tip options’ regardless of whatever kind of service to be delivered. This paper proposes a data-driven approach to designing smart tipping nudge that enables customized ‘default tip options’ tailored to varying tendency of different services for potentially high or low tip amounts. For a behavioral economic analysis of the tipping behavior, we apply prospect-theoretic value functions to model the tip amount as the consumer perceived value of service quality. A field experimental study in a fast-causal business is reported to demonstrate the potential of the proposed data-driven approach to smart tip nudging.

Keywords
Behavioral economics, Prospect theory, Tipping behavior, Incentive design, Service delivery system.