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

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 cuttingedge 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.

Impact of Anthropomorphic Design on User Sentiment and Sustained Use Intention towards Household Healthcare

“…Sentiment towards the anthropomorphic design of the system could be divided into two dimensions: cognitive and emotional [10]. In terms of the cognitive dimension, users are more concerned about system transparency [11].…”

“…In empirical research, the sentiment feedback mechanism in an AI context is divided into positive and negative dimensions [14]. However, due to the diverse application areas of AI, such as autonomous driving, and robotics, there is a strong heterogeneity in the equipment used, application scenarios, and user identity characteristics of AI technology [3,9,10]. This leads to potentially divergent results in empirical research and prevents general conclusions about user sentiment in intelligent systems from being drawn.…”