scholarly journals A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Human-Robot Interaction

2014 ◽  
Author(s):  
Kristin E. Schaefer ◽  
Deborah R. Billings ◽  
James L. Szalma ◽  
Jeffrey K. Adams ◽  
Tracy L. Sanders ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Factors Influencing ◽  
Trust In Automation

A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction

2011 ◽  
Vol 53 (5) ◽  
pp. 517-527 ◽  
Author(s):  
Peter A. Hancock ◽  
Deborah R. Billings ◽  
Kristin E. Schaefer ◽  
Jessie Y. C. Chen ◽  
Ewart J. de Visser ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Factors Affecting

A Meta-Analysis of Factors Influencing the Development of Trust in Automation

2016 ◽  
Vol 58 (3) ◽  
pp. 377-400 ◽  
Author(s):  
Kristin E. Schaefer ◽  
Jessie Y. C. Chen ◽  
James L. Szalma ◽  
P. A. Hancock
Keyword(s):  
Meta Analysis ◽  
Factors Influencing ◽  
Trust In Automation

scholarly journals Analyze the factors influencing human-robot interaction using MCDM method

2020 ◽  
Author(s):  
R.K.A. Bhalaji ◽  
S. Bathrinath ◽  
S.G. Ponnambalam ◽  
S. Saravanasankar
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Factors Influencing ◽  
Mcdm Method

scholarly journals A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction

2021 ◽  
Author(s):  
Connor Esterwood ◽  
Kyle Essenmacher ◽  
Han Yang ◽  
Fanpan Zeng ◽  
Lionel Robert
Keyword(s):  
Meta Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Human Personality ◽  
Robot Acceptance

Let’s Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human–Robot Interaction at Work

2020 ◽  
pp. 001872082096643
Author(s):  
Sonja K. Ötting ◽  
Lisa Masjutin ◽  
Jochen J. Steil ◽  
Günter W. Maier
Keyword(s):  
Situation Awareness ◽  
Mental Workload ◽  
Meta Analysis ◽  
Human Robot Interaction ◽  
Effect Sizes ◽  
Robot Design ◽  
Specific Work ◽  
Design Features ◽  
Robot Interaction ◽  
Specific Effects

Objective This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.

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