2023
By Carolin Ischen, Theo Araujo, Jochen Peter, and Alain Starke
Conversational agents (CAs) can make personalized product- or service-related recommendations based on user input, and allow for repeated interactions with their users over time. This study distinguishes between within-session effects which refer to the (longitudinal) effects of one-shot personalized recommendations, and between-session effects which refer to the effects of a CA remembering user input from previous interactions (conversational memory). We test the persuasive effects of these two types of personalization. This project makes a methodological contribution: It extends our conversational agent research toolkit by (1) integrating recommender systems and (2) working with conversational memory over time.