Does Media Richness Influence the User Experience of Chatbots: A Pilot Study

Laurie Carmichael (), Sara-Maude Poirier (), Constantinos Coursaris (), Pierre-Majorique Léger () and Sylvain Sénécal ()
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Laurie Carmichael: HEC Montréal
Sara-Maude Poirier: HEC Montréal
Constantinos Coursaris: HEC Montréal
Pierre-Majorique Léger: HEC Montréal
Sylvain Sénécal: HEC Montréal

A chapter in Information Systems and Neuroscience, 2021, pp 204-213 from Springer

Abstract: Abstract From a user’s perspective, this pilot study investigates the contributors and irritants related to the media content format used by chatbots to assist users in an online setting. In this study, we use automated facial expression analysis (AFEA), which analyses users’ facial expressions and captures the valence of their lived experience. A questionnaire and a single-question interview were also used to measure the users’ perceived experience. All measures taken together allowed us to explore the effects of three media content formats (i.e., an interactive question and answer (Q&A), a video, and a link referring to a webpage) used in chatbots on both the lived and perceived experiences of users. In line with Media Richness Theory (MRT), our results show that an interactive Q&A might be an optimal chatbot design approach in providing users with sought-after information or assistance with transactions. Moreover, important avenues for future research emerge from this study and will be discussed.

Keywords: Chatbot; Media content format; Media richness theory; Task type; Automated facial expression analysis (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-88900-5_23

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DOI: 10.1007/978-3-030-88900-5_23

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