To Be or Not to Be horizontal ellipsis Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents

• 成果类型: Article
• 署名作者: Chandra, Shalini; Shirish, Anuragini; Srivastava, Shirish C.
• 署名单位: Universite Paris Saclay; IMT - Institut Mines-Telecom; Institut Mines-Telecom Business School; Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2022.2127441
• 发表日期: 2022
• 页码: 969-1005
• 关键词: information-technology; cognitive absorption; media naturalness; brand engagement; virtual teamwork; decision-making; user acceptance; social presence; systems; trust

摘要

Driven by the need to provide continuous, timely, and efficient customer service, firms are constantly experimenting with emerging technological solutions. In recent times firms have shown an increased interest in designing and implementing artificial intelligence (AI)-based interactional technologies, such as conversational AI agents and chatbots, that obviate the need for having human service agents for the provision of customer service. However, the business impact of conversational AI is contingent on customers using and adequately engaging with these tools. This engagement depends, in turn, on conversational AI's similarity, or likeness to the human beings it is intended to replace. Businesses therefore need to understand what human-like characteristics and competencies should be embedded in customer-facing conversational AI agents to facilitate smooth user interaction. This focus on human-likeness for facilitating user engagement in the case of conversational AI agents is in sharp contrast to most prior information systems (IS) user engagement research, which is predicated on the instrumental value of information technology (IT). Grounding our work in the individual human competency and media naturalness literatures, we theorize the key role of human-like interactional competencies in conversational AI agents-specifically, cognitive, relational, and emotional competencies-in facilitating user engagement. We also hypothesize the mediating role of user trust in these relationships. Following a sequential mixed methods approach, we use a quantitative two-wave, survey-based study to test our model. We then examine the results in light of findings from qualitative follow-up interviews with a sampled set of conversational AI users. Together, the results offer a nuanced understanding of desirable human-like competencies in conversational AI agents and the salient role of user trust in fostering user engagement with them. We also discuss the implications of our study for research and practice.