Toggle Main Menu Toggle Search

Open Access padlockePrints

More than just a chat: a taxonomy of consumers’ relationships with conversational AI agents and their well-being implications

Lookup NU author(s): Dr Amani Alabed, Professor Diana Gregory-SmithORCiD, Dr Rebecca CaseyORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Purpose:This paper aims to study the role of self-concept in consumer relationships with anthropomorphised conversational artificially intelligent (AI) agents. First, the authors investigate how the self-congruence between consumer self-concept and AI and the integration of the conversational AI agent into consumer self-concept might influence such relationships. Second, the authors examine whether these links with self-concept have implications for mental well-being. Design/methodology/approach: This study conducted in-depth interviews with 20 consumers who regularly use popular conversational AI agents for functional or emotional tasks. Based on a thematic analysis and an ideal-type analysis, this study derived a taxonomy of consumer–AI relationships, with self-congruence and self–AI integration as the two axes. Findings: The findings unveil four different relationships that consumers forge with their conversational AI agents, which differ in self-congruence and self–AI integration. Both dimensions are prominent in replacement and committed relationships, where consumers rely on conversational AI agents for companionship and emotional tasks such as personal growth or as a means for overcoming past traumas. These two relationships carry well-being risks in terms of changing expectations that consumers seek to fulfil in human-to-human relationships. Conversely, in the functional relationship, the conversational AI agents are viewed as an important part of one’s professional performance; however, consumers maintain a low sense of self-congruence and distinguish themselves from the agent, also because of the fear of losing their sense of uniqueness and autonomy. Consumers in aspiring relationships rely on their agents for companionship to remedy social exclusion and loneliness, but feel this is prevented because of the agents’ technical limitations. Research limitations/implications: Although this study provides insights into the dynamics of consumer relationships with conversational AI agents, it comes with limitations. The sample of this study included users of conversational AI agents such as Siri, Google Assistant and Replika. However, future studies should also investigate other agents, such as ChatGPT. Moreover, the self-related processes studied here could be compared across public and private contexts. There is also a need to examine such complex relationships with longitudinal studies. Moreover, future research should explore how consumers’ self-concept could be negatively affected if the support provided by AI is withdrawn. Finally, this study reveals that in some cases, consumers are changing their expectations related to human-to-human relationships based on their interactions with conversational AI agents. Practical implications: This study enables practitioners to identify specific anthropomorphic cues that can support the development of different types of consumer–AI relationships and to consider their consequences across a range of well-being aspects. Originality/value: This research equips marketing scholars with a novel understanding of the role of self-concept in the relationships that consumers forge with popular conversational AI agents and the associated well-being implications.


Publication metadata

Author(s): Alabed A, Javornik A, Gregory-Smith D, Casey R

Publication type: Article

Publication status: Published

Journal: European Journal of Marketing

Year: 2023

Pages: Epub ahead of print

Online publication date: 04/09/2023

Acceptance date: 17/08/2023

Date deposited: 07/09/2023

ISSN (print): 0309-0566

ISSN (electronic): 1758-7123

Publisher: Emerald Publishing Limited

URL: https://doi.org/10.1108/EJM-01-2023-0037

DOI: 10.1108/EJM-01-2023-0037

ePrints DOI: 10.57711/6bza-1v07


Altmetrics

Altmetrics provided by Altmetric


Share