Exploring factors that influence human peer reviewers' acceptance of AI peer review as a teammate

Purpose Academic publication is one of the significant parts of higher education (HE). Given the pressures on the traditional peer-review system with the increasing number of submitted manuscripts, the rapid advancement of AI attracts interest in AI adoption in the peer review process. There is growing concern that AI has transformed the peer review process, which requires efforts to explore and establish human–AI hybrid collaboration for the peer review ecosystem. To better understand the fundamental questions about AI peer review regarding the quality and ethics of reviewers' reports for manuscript evaluation, and to further understand the human reviewers' acceptance of AI as a teammate, this paper develops a conceptual framework from the perspectives of technology acceptance and team trust for a comprehensive analysis. Design/methodology/approach The study adopts a theory-building approach that integrates literature on technology acceptance and team trust. A conceptual framework is proposed, which extends physical, psychological, technological and ethical factors that reflect the characteristics of human reviewers' acceptance of AI in the peer review process. The framework also integrates the exploration of team trust in human–AI collaboration, which sheds light on how human reviewers recognize AI peer review. Findings Drawing from the unified theory of acceptance and use of technology (UTAUT), in addition to the existing components, the modified element “satisfaction with AI-generated report” specifically reflects the peer review process. The proposed conceptual framework integrates “ethical dilemmas” as an influential factor when considering human reviewers' acceptance of AI. To provide a comprehensive approach, the framework takes “perceived trustworthiness” and “perceived similarity” into account from the perspective of team trust, which explores the formation of an optimal human–AI team. Originality/value By enriching UTAUT with the insightful factors from AI peer review, this paper contributes a novel lens to the research fields of ethical AI application and human–AI teams. The proposed conceptual framework offers implications for the formulation of AI peer review policies, the ethical practices of AI peer review and the optimal formation of human–AI teams, particularly by understanding the psychological, physical and ethical factors that challenge AI peer review. The suggested research directions give fresh impetus to future research with potential research opportunities.