A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction

2021 ◽  
Vol 6 (58) ◽  
Author(s):  
E. Roesler ◽  
D. Manzey ◽  
L. Onnasch
Keyword(s):  
Meta Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction

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

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

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

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.

Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses

2020 ◽  
pp. 001872082092208 ◽  
Author(s):  
P. A. Hancock ◽  
Theresa T. Kessler ◽  
Alexandra D. Kaplan ◽  
John C. Brill ◽  
James L. Szalma
Keyword(s):  
Meta Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Factors Associated ◽  
Complex Interactions ◽  
Analytic Review ◽  
Meta Analyses ◽  
Trust Factors

Objective The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. Background Human–robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs. Method The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis. Results Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI. Conclusion The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered. Application The identified trust factors can be used in order to promote appropriate levels of trust in robots.

scholarly journals Human-like Robots and the Uncanny Valley: A Meta-Analysis of User Responses Based on the Godspeed Scales

2021 ◽  
Author(s):  
Martina Mara ◽  
Markus Appel ◽  
Timo Gnambs
Keyword(s):  
Adverse Effects ◽  
Random Effects ◽  
Meta Analysis ◽  
Human Robot Interaction ◽  
Curvilinear Relationship ◽  
Uncanny Valley ◽  
Robot Interaction ◽  
Total N ◽  
The Uncanny ◽  
Human Likeness

In the field of human-robot interaction, the well-known uncanny valley hypothesis proposes a curvilinear relationship between a robot’s degree of human likeness and the observers’ responses to the robot. While low to medium human likeness should be associated with increasingly positive responses, a shift to negative responses is expected for highly anthropomorphic robots. As empirical findings on the uncanny valley hypothesis are inconclusive, we conducted a random-effects meta-analysis of 49 studies (total N = 3,556) that reported 131 evaluations of robots based on the Godspeed scales for anthropomorphism (i.e., human likeness) and likability. Our results confirm more positive responses for more human-like robots at low to medium anthropomorphism, with moving robots rated as more human-like but not necessarily more likable than static ones. However, because highly anthropomorphic robots were sparsely utilized in previous studies, no conclusions regarding proposed adverse effects at higher levels of human likeness can be made at this stage.

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