Subgroup formation in human–robot teams: A multi‐study mixed‐method approach with implications for theory and practice. (10th February 2022)

• Record Type: Journal Article
• Authors: You, Sangseok; Robert, Lionel P.
• Other Names: Jarrahi Mohammad Hossein guestEditor.; Lutz Christoph guestEditor.; Boyd Karen guestEditor.; Oesterlund Carsten guestEditor.; Willis Matthew guestEditor.
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 74:Number 3(2023)
• Page Start: 323
• Page End: 338
• Publication Date: 2022-02-10
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.24626
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract: Human–robot teams represent a challenging work application of artificial intelligence (AI). Building strong emotional bonds with robots is one solution to promoting teamwork in such teams, but does this come at a cost in the form of subgroups? Subgroups—smaller divisions within teams—in all human teams can undermine teamwork. Despite the importance of this question, it has received little attention. We employed a mixed‐methods approach by conducting a lab experiment and a qualitative online survey. We (a) examined the formation and impact of subgroups in human–robot teams and (b) obtained insights from workers currently adapting to robots in the workplace on mitigating impacts of subgroups. The experimental study (Study 1) with 44 human–robot teams found that robot identification (RID) and team identification (TID) are associated with increases and decreases in the likelihood of a subgroup formation, respectively. RID and TID moderated the impacts of subgroups on teamwork quality and subsequent performance in human–robot teams. Study 2 was a qualitative study with 112 managers and employees who worked collaboratively with robots. We derived practical insights from this study that help situate and translate what was learned in Study 1 into actual work practices.