Enablers or Inhibitors? Unpacking the Emotional Power Behind In-Vehicle AI Anthropomorphic Interaction: A Dual Factor Approach by Text Mining

Article

Bai, S., Yu, D., Han, C., Yang, M., Islam, N., Yang, Z., Tang, R. and Jiayuan, Z. 2023. Enablers or Inhibitors? Unpacking the Emotional Power Behind In-Vehicle AI Anthropomorphic Interaction: A Dual Factor Approach by Text Mining. IEEE Transactions on Engineering Management. 71, pp. 13149-13165. https://doi.org/10.1109/TEM.2023.3327500
AuthorsBai, S., Yu, D., Han, C., Yang, M., Islam, N., Yang, Z., Tang, R. and Jiayuan, Z.
Abstract

The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic artificial intelligence (AI) assistants. There is still a lack of clarity regarding NEV users' attitudes toward this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, in this article, we utilize text analysis techniques for the identification of AI interaction emotions, love and disgust (enablers and inhibitors) with significant influence on user satisfaction, and validates the improving role of multimodality on the effectiveness of anthropomorphic interaction. In addition, this study innovatively constructs a multidimensional corpus of modality × emotion, using structural topic modeling to uncover the constituent elements and real-time changes of love and disgust emotions in different modalities, from which development opportunities and improvement directions for AI anthropomorphic interaction technologies are identified. The findings provide new insights into the application of emotion analysis methods to improve users' intelligent service experience and provide a realistic reference for mitigating emerging technology resistance in the NEV industry.

JournalIEEE Transactions on Engineering Management
Journal citation71, pp. 13149-13165
ISSN0018-9391
Year2023
PublisherIEEE
Publisher's version
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/TEM.2023.3327500
Publication dates
Online29 Nov 2023
Publication process dates
Accepted11 Oct 2023
Deposited06 Dec 2023
FunderNational Social Science Fund of China
Copyright holder© 2023, The Authors
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