Dual humanness and trust in conversational AI: A person-centered approach
作者：
Highlights：
• Trust in conversational AI is the key to unleash the full business potential of the new technology.
• A dualistic humanness model for conversational AI based on its speaking and listening attributes is proposed.
• Person-centered analytics such as latent profile analysis and finite mixture modeling is used.
• Three distinctive humanness perception patterns are uncovered: para-human, para-machine, and asymmetric perception.
• The asymmetry between humanness perceptions regarding speaking and listening can impede morality-related trust.
摘要
•Trust in conversational AI is the key to unleash the full business potential of the new technology.•A dualistic humanness model for conversational AI based on its speaking and listening attributes is proposed.•Person-centered analytics such as latent profile analysis and finite mixture modeling is used.•Three distinctive humanness perception patterns are uncovered: para-human, para-machine, and asymmetric perception.•The asymmetry between humanness perceptions regarding speaking and listening can impede morality-related trust.
论文关键词：Artificial intelligence,Humanness perception,Trust,Person-centered approach,Finite mixture modeling
论文评审过程：Received 5 September 2020, Revised 15 December 2020, Accepted 29 January 2021, Available online 2 February 2021, Version of Record 19 February 2021.
论文官网地址：https://doi.org/10.1016/j.chb.2021.106727
