Jiang Pengtao, Niu Wanshu, Wang Qiaoli, Yuan Ruizhi, Chen Keyu
School of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China.
Behav Sci (Basel). 2024 Aug 2;14(8):671. doi: 10.3390/bs14080671.
In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.
近年来，随着人工智能（AI）应用形式和领域的不断拓展，用户对AI应用的接受度引起了学者和商业从业者越来越多的关注。尽管现有研究广泛探讨了用户对不同AI应用的接受情况，但对于不同AI应用在人机交互中所起的作用仍缺乏了解，这可能会限制对有关用户对AI接受情况的不一致研究结果的理解。本研究通过对2020年至2023年信息系统和营销学科顶级期刊上的AI接受度研究进行系统的文献综述来解决这一问题。基于对80篇论文的综述，本研究做出了以下贡献：（i）概述了AI接受度研究中使用的方法和理论框架；（ii）分别总结了用户对AI服务提供商和AI任务替代物接受反应的关键因素、潜在机制和理论化；（iii）对现有研究的局限性提出意见，并为未来研究提供指导。
