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  • × author_ss:"You, S."
  1. You, S.; Robert, L.P.: Subgroup formation in human-robot teams : a multi-study mixed-method approach with implications for theory and practice (2023) 0.00 
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    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.
    Type
    a
  2. Park, H.; You, S.; Wolfram, D.: Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields (2018) 0.00 
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    Abstract
    Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation-where data references are included alongside bibliographic references in the reference section of a publication-is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.
    Type
    a
  3. Robert Jr, L.P.; You, S.: Are you satisfied yet? : shared leadership, individual trust, autonomy, and satisfaction in virtual teams (2018) 0.00 
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    Abstract
    Despite the benefits associated with virtual teams, many people on these teams are unsatisfied with their experience. The goal of this study was to determine how to better facilitate satisfaction through shared leadership, individual trust, and autonomy. Specifically, in this study we sought a better understanding of the effects of shared leadership, team members' trust, and autonomy on satisfaction. We conducted a study with 163 individuals in 44 virtual teams. The results indicate that shared leadership facilitates satisfaction in virtual teams both directly and indirectly through the promotion of trust. Shared leadership moderated the relationships of individual trust and individual autonomy with satisfaction. Team-level satisfaction was a strong predictor of virtual team performance. We discuss these findings and the implications for theory and design.
    Type
    a

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