Journal
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
Volume 13, Issue 8, Pages 1979-2001Publisher
SPRINGER
DOI: 10.1007/s12369-020-00736-8
Keywords
Trust; Social robot; Child robot interaction; Human robot interaction; Meta-analysis; Review; Robot errors; Pro-social attitudes; Developmental psychology
Categories
Funding
- European Union's Horizon 2020 research and innovation programme [765955]
- Projekt DEAL
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The study aimed to identify factors influencing children's trust in robots. The findings suggest that more human-like attributes lead to decreased competency trust in robots. Additionally, the type of measure used can influence the direction of effects on social trust.
Although research on children's trust in social robots is increasingly growing in popularity, a systematic understanding of the factors which influence children's trust in robots is lacking. In addition, meta-analyses in child-robot-interaction (cHRI) have yet to be popularly adopted as a method for synthesising results. We therefore conducted a meta-analysis aimed at identifying factors influencing children's trust in robots. We constructed four meta-analytic models based on 20 identified studies, drawn from an initial pool of 414 papers, as a means of investigating the effect of robot embodiment and behaviour on both social and competency trust. Children's pro-social attitudes towards social robots were also explored. There was tentative evidence to suggest that more human-like attributes lead to less competency trust in robots. In addition, we found a trend towards the type of measure that was used (subjective or objective) influencing the direction of effects for social trust. The meta-analysis also revealed a tendency towards under-powered designs, as well as variation in the methods and measures used to define trust. Nonetheless, we demonstrate that it is still possible to perform rigorous analyses despite these challenges. We also provide concrete methodological recommendations for future research, such as simplifying experimental designs, conducting a priori power analyses and clearer statistical reporting.
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