A Scoping Review of Understandings, Antecedents, and Outcomes of Human-AI Teaming Based on Bibliometric Network Analysis

Human-AI teaming aspires to create a symbiotic, interdependent collaboration between humans and AI at work. The concept is being investigated from various disciplinary perspectives, e.g., engineering, data sciences or psychology. The goal of this scoping review is to examine the extent, range, and nature of current research activities. Specifically, the aim is to give an overview of the definitory understandings of human-AI teaming and of the current, interdisciplinary state of empirically investigated and theoretically discussed antecedents and outcomes of human-AI teaming. Results will be based on a systematic literature search in the databases “Scopus” and “EBSCOhost”. English and German peer-reviewed journal article, journal proceedings, or book chapter (not limited to empirical articles) that were published since 2021 and that are concerned with human-AI teaming in a work context are included. The articles will first be screened by two researchers regarding their eligibility (carried out using the research review software “Rayyan” (https://www.rayyan.ai/)). To identify research streams concerning the understanding of human-AI teaming, a bibliometric network analysis will be conducted with the aim of grouping publications into clusters based on their relationships in the citation network (bibliometric coupling: “who cites who”). The emerging clusters will be analysed with regards to their definitory understanding of human-AI-teaming. This will be done based on qualitative content analysis with an inductive coding process executed by three researchers and documented in Excel. Additionally, investigated/ proposed antecedents and outcomes of human-AI-teaming are subsumed. The scoping review will be conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).