Understanding Users’ Acceptance of Artificial Intelligence Applications: A Literature Review

Pengtao Jiang, Wanshu Niu, Qiaoli Wang, Ruizhi Yuan, Keyu Chen

Research output: Journal PublicationReview articlepeer-review

Abstract

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.

Original languageEnglish
Article number671
JournalBehavioral Sciences
Volume14
Issue number8
DOIs
Publication statusPublished - Aug 2024

Keywords

  • AI
  • AI service provider
  • AI task substitute
  • systematic literature review
  • user acceptance

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Development
  • Genetics
  • General Psychology
  • Behavioral Neuroscience

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