Next-Generation Accounting Interviewing: A Comparison of Human and Embodied Conversational Agents (ECAs) as Interviewers

Abstract

Recent technological advances have made it possible to create automated, virtual interviewers, called embodied conversational agents (ECAs). We study how an ECA compares to a human interviewer in three experiments. In experiment 1, we show that two theoretically motivated factors—making the ECA facially and vocally similar to the interviewee—result in the ECA performing similarly to or better than human interviewers for six antecedents of disclosure quality. In two additional experiments, we show that employees are on average between 21 and 32 percent more likely to disclose violating internal controls to an ECA than to a human interviewer, even if the human interviewer has significant interviewing experience. These findings contribute to the ECA design literature by showing that similarity-enhancing features of ECAs increase the antecedents of disclosure. The findings also contribute to the accounting literature by demonstrating that ECA technology can increase the scope of interviewing in accounting without reducing interview quality.