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Motivation to interaction media: The impact of automation trust and self-determination theory on intention to use the new interaction technology in autonomous vehicles

INTRODUCTION: This research investigated the effects of three psychological needs (competence, autonomy, and relatedness) of self-determination theory (SDT) and automation trust on the intention of users to employ new interaction technology brought by autonomous vehicles (AVs), especially interactio...

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Detalles Bibliográficos
Autores principales: Xie, Yubin, Zhou, Ronggang, Chan, Alan Hoi Shou, Jin, Mingyu, Qu, Miao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945264/
https://www.ncbi.nlm.nih.gov/pubmed/36844336
http://dx.doi.org/10.3389/fpsyg.2023.1078438
Descripción
Sumario:INTRODUCTION: This research investigated the effects of three psychological needs (competence, autonomy, and relatedness) of self-determination theory (SDT) and automation trust on the intention of users to employ new interaction technology brought by autonomous vehicles (AVs), especially interaction mode and virtual image. METHOD: This study focuses on the discussion from the perspective of psychological motivation theory applied to AV interaction technology. With the use of a structured questionnaire, participants completed self-report measures related to these two interaction technologies; a total of 155 drivers’ responses were analyzed. RESULT: The results indicated that users’ intentions were directly predicted by their perceived competence, autonomy, and relatedness of SDT and automation trust, which jointly explained at least 66% of the variance in behavioral intention. In addition to these results, the contribution of predictive components to behavioral intention is influenced by the type of interaction technology. Relatedness and competence significantly impacted the behavioral intention to use the interaction mode but not the virtual image. DISCUSSION: These findings are essential in that they support the necessity of distinguishing between types of AV interaction technology when predicting users’ intentions to use.