The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
To Be or Not to Be …Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents
Abstract
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…
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“These results extend prior IS research that has examined conversational agents primarily through service enhancement, trust formation, and operational efficiency (Han et al. 2023;Saffarizadeh et al. 2024;Chandra et al. 2022).”
From Not a Silver Bullet for Loneliness: How Attachment and Age Shape Intimacy with AI Companions (2026) · cites this paper as Chandra et al. 2022
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Metadata
- Authors
- Chandra, S., Shirish, A., & Srivastava, S. C.
- Affiliations
- S P Jain School of Global Management, Singapore
- Université Paris-Saclay, Univ Evry, IMT-BS, LITEM, Evry-Courcouronnes, France
- HEC Paris, Jouy en Josas Cedex, France
- Venue
- Journal of Management Information Systems
- Publisher
- Informa UK Limited
- Published
- 2022-10-02
- Volume
- 39
- Issue
- 4
- Pages
- 969-1005
- ISSN
- 0742-1222
- 1557-928X
- Cited by
- 315
- References
- 30
- Language
- en
- Funders
- HEC Paris Foundation
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