T RUSTING AND W ORKING WITH R OBOTS : A R ELATIONAL D EMOGRAPHY T HEORY OF P REFERENCE FOR R OBOTIC OVER H UMAN C O-W ORKERS

• 成果类型: Article
• 署名作者: You, Sangseok; Robert Jr, Lionel P.
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2023/17403
• 发表日期: 2024
• 页码: 1297-1330
• 关键词: surface-level diversity; factors affecting trust; Global virtual teams; RELATIONAL DEMOGRAPHY; TASK INTERDEPENDENCE; decision-making; social identity; next-generation; mind perception; swift trust

摘要

Organizations are facing the new challenge of integrating humans and robots into one cohesive workforce. Relational demography theory (RDT) explains the impact of dissimilarities on when and why humans trust and prefer to work with others. This paper proposes RDT as a useful lens to help organizations understand how to integrate humans and robots into a cohesive workforce. We offer a research model based on RDT and examine dissimilarities in gender and co-worker type (human vs. robot) along with dissimilarities in work style and personality. To empirically examine the research model, we conducted two experiments with 347 and 422 warehouse workers, respectively. The results suggest that the negative impacts of gender, work style, and personality dissimilarities on swift trust depend on the co-worker type. In our experiments, gender dissimilarity had a stronger negative impact on swift trust in a robot co-worker, while work style and personality had a weaker negative impact on swift trust in a robot co-worker. Also, swift trust in a robot co-worker increased the preference for a robot co-worker over a human co-worker, while swift trust in a human co-worker decreased such preferences. Overall, this research contributes to our current understanding of human-robot collaboration by identifying the importance of dissimilarity from the perspective of RDT.