Abstract: Although conversational agents (CAs) are increasingly used for providing purchase recommendations, important design questions remain. Our research project seeks to examine whether processing fluency is a novel mechanism to explain how recommendation modality (speech vs. text) shapes recommendation evaluations, the intention to follow the recommendation, and how modality interacts with the style of recommendation presentation. Our main goal is to identify under which conditions a specific modality (speech-based or text-based CAs) is superior in terms of processing fluency and consumer responses. Our first research findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. Moreover, the findings underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal recommendation presentation. For example, numerical explanations increase processing fluency and purchase intention of both recommendation modalities. For practice, we offer actionable implications on how to make effective sales agents out of CAs.
Melanie Schwede () is a Ph.D. student and research associate at the chair of Marketing and Innovation Management at the University of Goettingen, Germany. Her research focuses on artificial intelligence-based assistants (e.g., voice assistants, chatbots and service robots) in the service frontline.