Qualitative Interview Techniques for Human-Robot Interactions

Bethel, Cindy L.; Cossitt, Jessie E.; Henkel, Zachary; Baugus, Kenna

doi:10.1007/978-3-030-42307-0_6

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The data collection consisted of audio-recorded semi-structured interviews ( Adams, 2015 ; Bethel et al, 2020 ) conducted in German in June and July 2022 with approval of Bielefeld University’s ethics review committee (reference no. 2022-084).…”

Section: Methodsmentioning

confidence: 99%

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Should robots be polite? Expectations about politeness in human–robot interaction

Lumer,

Buschmeier

2023

Front. Robot. AI

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Interaction with artificial social agents is often designed based on models of human interaction and dialogue. While this is certainly useful for basic interaction mechanisms, it has been argued that social communication strategies and social language use, a “particularly human” ability, may not be appropriate and transferable to interaction with artificial conversational agents. In this paper, we present qualitative research exploring whether users expect artificial agents to use politeness—a fundamental mechanism of social communication—in language-based human-robot interaction. Based on semi-structured interviews, we found that humans mostly ascribe a functional, rule-based use of polite language to humanoid robots and do not expect them to apply socially motivated politeness strategies that they expect in human interaction. This study 1) provides insights for interaction design for social robots’ politeness use from a user perspective, and 2) contributes to politeness research based on the analysis of our participants’ perspectives on politeness.

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Section: Methodsmentioning

confidence: 99%

“…In a later step, codes are grouped and iteratively analyzed to find themes. This is a tried and tested evaluation method for qualitative research in human–robot and more generally human–computer interaction research ( Bethel et al, 2020 ). A similar approach was also used by Clark et al (2019) .…”

Section: Methodsmentioning

confidence: 99%