The mind in the machine:Estimating mind perception's effect on user satisfaction with voice-based conversational agents


Given the interest in imbuing voice-based conversational agents with humanlike features, understanding how this affects user satisfaction is ultimately important for business performance. Mind perception theory explains how ascribing the mental capacity for agency and experience to artificial intelligence shapes subsequent user attitudes. Hence, we estimate the effect of mind perception on satisfaction in users with high/low innovativeness using data from text-based online reviews, which better reflect actual usage than traditional surveys. Methodologically, where numerous controls affect the cause and outcome variables in a model, traditional machine learning methods produce biased estimates. We overcome this by deploying Double/Debiased Machine Learning (combined with text analytics). Results show that user satisfaction is increased by two forms of perceived experience: directed at moral agents, or moral patients. Perceived agency, however, has no significant influence. The increase in satisfaction from both types of perceived experience is stronger among users with high (vs. low) innovativeness.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Marketing & Strategy
College of Business and Social Sciences > Aston Business School
Funding Information: In Memoriam: Ramanathan Yoganathan (1952–2023).
Additional Information: Copyright © 2024, Elsevier. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Agency,Double Machine Learning,Experience,Latent Dirichlet Allocation,LIWC,Robots,Marketing
Publication ISSN: 1873-7978
Last Modified: 08 Jul 2024 08:28
Date Deposited: 08 May 2024 13:56
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Related URLs: https://www.sci ... 0778?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-03-01
Published Online Date: 2024-02-17
Accepted Date: 2024-02-10
Authors: Yoganathan, Vignesh (ORCID Profile 0000-0002-9285-4702)
Osburg, Victoria Sophie



Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 17 August 2025.

License: Creative Commons Attribution Non-commercial No Derivatives

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