Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

Abstract

Fueled by the widespread adoption of algorithms and artificial intelligence (AI), the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging two design features: identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response rate. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response rate, conversation length, and order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot's identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure.
Finally, we propose one plausible explanation for the performance improvement---the enhanced trust between humans and algorithms and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform.

Keywords: Chatbot, Field Experiment, Artificial Intelligence, Operational Transparency, Anthropomorphism