BIROn - Birkbeck Institutional Research Online

    Enablers or inhibitors? Unpacking the emotional power behind in-vehicle AI anthropomorphic interaction: a dual‑factor approach by text mining

    Bai, S. and Yu, D. and Han, Chunjia and Yang, Mu and Islam, N. and Yang, Z. and Tang, R. and Zhao, J. (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 , ISSN 0018-9391.

    [img] Text
    TEM-23-0047.R2_Final version(23.10.23).pdf - Author's Accepted Manuscript
    Restricted to Repository staff only

    Download (2MB)
    [img]
    Preview
    Text
    52475a.pdf - Published Version of Record
    Available under License Creative Commons Attribution Non-commercial No Derivatives.

    Download (3MB) | Preview

    Abstract

    The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic AI assistants. There is still a lack of clarity regarding NEV users' attitudes towards this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, this study utilizes 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 multi-modality 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.

    Metadata

    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Innovation Management Research, Birkbeck Centre for
    Depositing User: Chunjia Han
    Date Deposited: 11 Dec 2023 13:56
    Last Modified: 11 Dec 2023 17:35
    URI: https://eprints.bbk.ac.uk/id/eprint/52475

    Statistics

    Activity Overview
    6 month trend
    9Downloads
    6 month trend
    114Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item