Emotion vs. information : understanding the effect of AI-powered call systems on potential customer decision from a field experiment

Abstract

Emerging technologies such as neural networks, cloud computing, big data, and blockchain have paved the way for the development of artificial intelligence (AI), enabling AI to facilitate business operations. In particular, some organizations seek to leverage AI to replace human agents in positions involving sensitive customer information, with the aim of enhancing privacy protection. However, AI-human interaction tends to fall short of expectations in real-world settings due to the difference between humans and AI. To address this, a study will be conducted to explore the effect of implementing an AI-powered call system on potential customers compared to human agent calls. Leveraging a randomized field experiment conducted at a call center of a large securities company and a randomized online experiment, we investigated the mechanism resulting in the different impacts on customer behavior between humans and AI. The results show that voice-based AI calls trade off emotional and informational support: AI's informational advantages can raise intention, but empathy gaps can suppress it. These findings contribute to the literature on the application of technology in organizations and provide guidance to organizations on the effective implementation of AI systems, highlighting both the advantages and limitations of AI in customer-facing roles.