Journal
COMPUTERS IN HUMAN BEHAVIOR
Volume 138, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chb.2022.107415
Keywords
Anthropomorphic intelligent agents; Social role; Emotional attachment; Self-disclosure; Human-agent relationship
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This study explores the effects of anthropomorphism level and social role on users' emotional attachment, information disclosure tendency, and satisfaction in a smart home. The results suggest that high anthropomorphism and mentor role positively predict users' emotional attachment. Users tend to disclose more personal information to human-servant and robot-mentor IAs. Social presence is found to mediate the relationship between anthropomorphic design and emotional attachment. The findings highlight the importance of social role in anthropomorphic IA design and provide implications for theory and practice.
Owing to the development of anthropomorphic intelligent agent (IA) designs, users consider IAs as more than just inanimate tools. Previous studies have reported that anthropomorphic features can promote users' social feedback and aid in establishing intimate human-agent relationships. The present study examined the main and interaction effects of anthropomorphism level (a human-like IA vs. robot-like IA) and social role (servant vs. mentor) on emotional attachment, information disclosure tendency, and satisfaction in a smart home. The study participants were randomly assigned into four groups with balanced gender. The results indicate that high anthropomorphism and mentor role can positively predict users' emotional attachment. Additionally, users tend to disclose more personal information to the human-servant and robot-mentor IAs than the human-mentor and robot-servant IAs. Interestingly, social presence was determined to be a positive and significant mediator between anthropomorphic design and emotional attachment. The study findings highlight the importance of social role in anthropomorphic IA design and explain the mechanism of establishing effective human-agent relationships. Moreover, both theoretical and practical implications of these findings are analyzed.
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