The purpose of this paper is to examine factors that influence early adopters’ intention to use AI chatbots, emphasizing diverse factors such as personalization, conversational ability, social influence and trust.
Data collected from 323 US adults were analyzed using structural equation modeling (SEM) to examine the relationships between interaction factors and early adopters’ attitudes and intentions toward AI chatbot adoption.
Users’ attitude toward adopting AI chatbots is influenced by system affordances such as conversational ability, personalization, as well as users’ trust in AI chatbots. Social influence does not significantly impact usage intentions, but it positively affects users' belief in the perceived usefulness of AI chatbots.
System designers and developers must enhance features related to conversational ability while simultaneously ensuring that AI chatbots have user-friendly interfaces that afford users low barriers of entry. Additionally, efforts should be made to enhance training data quality and optimize interaction models to support more effective and adaptive communication between users and AI-powered systems.
The rapid adoption of large language models will undoubtedly have a transformative effect on modern life and society. Therefore, a comprehensive understanding of the phenomenon is needed. The current study proposes a novel information model situated on the determinants that influence user acceptance of AI-powered chatbots. We extend the technology acceptance model by assessing interaction factors offered by this new technology, as well as users' beliefs about intended use and resulting outcomes in the context of seeking information.
