Using policy capturing to examine tipping decisions

Participants (n = 15) made tipping decisions for 80 restaurant situations. A policy-capturing analysis was then conducted for each participant to quan titatively describe relations between his or her judgments and the informat ion used to make those judgments. Participants possessed reliable, simple, and nonconfigural models. The majority of these individual models heavily w eighted bill-size information. In addition, service-quality, server-friendl iness, or food-quality information affected tipping decisions, to a lesser extent; for a number of individuals. Atmosphere, server gender, and restaur ant cleanliness information were not considered in any tipping model. Unlik e affect, social desirability, and gender, participants' dining-out frequen cy was related to the types of information used when tipping. Finally, clus ter analysis of the models revealed 11 general approaches to tipping.