Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-25504-5_8
Title: Making robots persuasive: The influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power
Authors: Ham, J.
Bokhorst, R.
Cuijpers, R.
Van Der Pol, D.
Cabibihan, J.-J. 
Keywords: Gazing
Gestures
Head Movement
Nao
Persuasion
Persuasive Robotics
Persuasive Technology
Social Robotics
Storytelling Robot
Issue Date: 2011
Source: Ham, J.,Bokhorst, R.,Cuijpers, R.,Van Der Pol, D.,Cabibihan, J.-J. (2011). Making robots persuasive: The influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7072 LNAI : 71-83. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-25504-5_8
Abstract: Social agency theory suggests that when an (artificial) agent combines persuasive strategies, its persuasive power increases. Therefore, we investigated whether a robot that uses two persuasive strategies is more persuasive than a robot that uses only one. Because in human face-to-face persuasion two crucial persuasive strategies are gazing and gestures, the current research investigated the combined and individual contribution of gestures and gazing on the persuasiveness of a storytelling robot. A robot told a persuasive story about the aversive consequences of lying to 48 participants. The robot used persuasive gestures (or not) and gazing (or not) to accompany this persuasive story. We assessed persuasiveness by asking participants to evaluate the lying individual in the story told by the robot. Results indicated that only gazing independently led to increased persuasiveness. Using persuasive gestures only led to increased persuasiveness when the robot combined it with (the persuasive strategy of) gazing. Without gazing, using persuasive gestures diminished robot persuasiveness. The implications of the current findings for theory and design of persuasive robots are discussed. © 2011 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/70879
ISBN: 9783642255038
ISSN: 03029743
DOI: 10.1007/978-3-642-25504-5_8
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