Publication year
2020Number of pages
23 p.
Source
Journal of Service Management, 31, 2, (2020), pp. 203-225ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ BSI CW
Journal title
Journal of Service Management
Volume
vol. 31
Issue
iss. 2
Languages used
English (eng)
Page start
p. 203
Page end
p. 225
Subject
Communication and MediaAbstract
Purpose: Conversational agents (chatbots, avatars and robots) are increasingly substituting human employees in service encounters. Their presence offers many potential benefits, but customers are reluctant to engage with them. A possible explanation is that conversational agents do not make optimal use of communicative behaviors that enhance relational outcomes. The purpose of this paper is to identify which human-like communicative behaviors used by conversational agents have positive effects on relational outcomes and which additional behaviors could be investigated in future research. Design/methodology/approach: This paper presents a systematic review of 61 articles that investigated the effects of communicative behaviors used by conversational agents on relational outcomes. A taxonomy is created of all behaviors investigated in these studies, and a research agenda is constructed on the basis of an analysis of their effects and a comparison with the literature on human-to-human service encounters. Findings: The communicative behaviors can be classified along two dimensions: modality (verbal, nonverbal, appearance) and footing (similarity, responsiveness). Regarding the research agenda, it is noteworthy that some categories of behaviors show mixed results and some behaviors that are effective in human-to-human interactions have not yet been investigated in conversational agents. Practical implications By identifying potentially effective communicative behaviors in conversational agents, this study assists managers in optimizing encounters between conversational agents and customers. Originality/value: This is the first study that develops a taxonomy of communicative behaviors in conversational agents and uses it to identify avenues for future research.
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