ExtraBot vs IntroBot: The Influence of Linguistic Cues on Communication Satisfaction

Conversational agents (CA) have emerged as a new type of dialogue systems, able to simulate human conversation. However, research suggests that current CAs fail to provide convincing interactions due to a lack of satisficing communication with users. To address this problem, we propose the idea of a personality adaptive CA that could enhance communication satisfaction during a user\u27s interaction experience. As personality differences manifest themselves in language cues, we investigate in an experiment, whether linguistic styles have an influence regarding a user\u27s communication satisfaction, when interacting with a CA. The results show that users perceive greater satisfaction when communicating with an extraverted CA (ExtraBot) than with an introverted CA (IntroBot). The outcomes of our study highlight that different linguistic styles can influence the course of the conversation and determine whether the user is satisfied with the communication and sees any value in the interaction with the CA