Publication type: Conference paper
Type of review: Peer review (publication)
Title: Stereotype content model (SCM) and chatbots / conversational interfaces : an experiment comparing trust, competence and warmth dimensions
Authors: Seiler, Roger
Müller, Steffen
Beinert, Markus
et. al: No
Proceedings: Proceedings of the European Marketing Academy
Pages: 8468
Conference details: 48th Annual European Marketing Academy Conference (EMAC), Hamburg, Germany, 27-28 May 2019
Issue Date: 2019
Publisher / Ed. Institution: European Marketing Academy
ISBN: 978-3-9821146-0-6
Language: English
Subjects: Chatbot; Trust; Stereotype content model (SCM)
Subject (DDC): 006: Special computer methods
302: Social interaction
Abstract: With the rising popularity of chatbots this research paper seeks to answer the question if the stereotype content model (SCM) applies to the domain of chatbots or broader to conversational interfaces. This study answers this research question by conducting an experiment containing different stereotypes (lovable star and incompetent jerk). The SCM applies to the domain of chatbots. The lovable star stereotype chatbot is perceived as more trustworthy as well as more competent and warmer compared to the incompetent jerk. Participants pointed out that they want to know if they are talking to a chatbot and not to a real human. Nevertheless, further research is required regarding traditional text chatbots because the lovable star did not show higher trustworthiness than the text chatbot. Furthermore, these research results suggest, that data privacy is a further, important aspect as customers typically share information when engaging in a conversation with a chatbot.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Management and Law
Organisational Unit: International Management Institute (IMI)
Appears in collections:Publikationen School of Management and Law

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.