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Research on Users’ Trust in Customer Service Chatbots Based on Human-Computer Interaction

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Big Data and Social Computing (BDSC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1640))

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As Human-Computer Interaction (HCI) moves towards deep collaboration, it is urgent to study users’ trust in chatbots. This study takes customer service chatbots as an example. Firstly, literature review is conducted on the relevant research on users’ trust in chatbots, and the value chain model of customer service chatbots is analyzed. Taking Taobaoxiaomi as the specific research object, we conducted in-depth interviews with 18 users, organized the interview data with value focused thinking method (VFT), constructed the users’ trust model of customer service chatbots, and carried out an empirical test by questionnaire survey. The results show that professionalism, response speed and predictability have positive effects on users’ trust in chatbots, while ease of use and human-likeness have no significant positive effects on users’ trust. Besides, brand trust has a positive impact on users’ trust in chatbots, risk perception negatively affects users’ trust in chatbots, and human support has no significant negative effect on users’ trust. Finally, privacy concerns have a moderating effect on environmental factors (brand trust, risk et al.). This study will deepen the understanding of human-computer trust and provide reference for the industry to improve chatbots and enhance users’ trust.

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This work is supported by 2019, Digital Transformation in China and Germany: Strategies, Structures and Solutions for Ageing Societies, GZ 1570. Also supported by the Research Project of Shanghai Science and Technology Commission (No.20dz2260300) and The Fundamental Research Funds for the Central Universities.

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Correspondence to Feng Liu or Jiayin Qi .

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Lv, Y., Hu, S., Liu, F., Qi, J. (2022). Research on Users’ Trust in Customer Service Chatbots Based on Human-Computer Interaction. In: Meng, X., Xuan, Q., Yang, Y., Yue, Y., Zhang, ZK. (eds) Big Data and Social Computing. BDSC 2022. Communications in Computer and Information Science, vol 1640. Springer, Singapore.

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  • Print ISBN: 978-981-19-7531-8

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