Literature DB >> 33501226

In the Wild HRI Scenario: Influence of Regulatory Focus Theory.

Roxana Agrigoroaie1, Stefan-Dan Ciocirlan1, Adriana Tapus1.   

Abstract

Research related to regulatory focus theory has shown that the way in which a message is conveyed can increase the effectiveness of the message. While different research fields have used this theory, in human-robot interaction (HRI), no real attention has been given to this theory. In this paper, we investigate it in an in the wild scenario. More specifically, we are interested in how individuals react when a robot suddenly appears at their office doors. Will they interact with it or will they ignore it? We report the results from our experimental study in which the robot approaches 42 individuals. Twenty-nine of them interacted with the robot, while the others either ignored it or avoided any interaction with it. The robot displayed two types of behavior (i.e., promotion or prevention). Our results show that individuals that interacted with a robot that matched their regulatory focus type interacted with it significantly longer than individuals that did not experience regulatory fit. Other qualitative results are also reported, together with some reactions from the participants.
Copyright © 2020 Agrigoroaie, Ciocirlan and Tapus.

Entities:  

Keywords:  HRI; acceptance; in the wild; regulatory focus; social robotics

Year:  2020        PMID: 33501226      PMCID: PMC7805998          DOI: 10.3389/frobt.2020.00058

Source DB:  PubMed          Journal:  Front Robot AI        ISSN: 2296-9144


  6 in total

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6.  Making message recipients "feel right": how nonverbal cues can increase persuasion.

Authors:  Joseph Cesario; E Tory Higgins
Journal:  Psychol Sci       Date:  2008-05
  6 in total
  2 in total

Review 1.  Trust in Robots: Challenges and Opportunities.

Authors:  Bing Cai Kok; Harold Soh
Journal:  Curr Robot Rep       Date:  2020-09-03

2.  Hurry Up, We Need to Find the Key! How Regulatory Focus Design Affects Children's Trust in a Social Robot.

Authors:  Natalia Calvo-Barajas; Maha Elgarf; Giulia Perugia; Ana Paiva; Christopher Peters; Ginevra Castellano
Journal:  Front Robot AI       Date:  2021-07-08
  2 in total

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