潜在Dirichlet分配
斯科普斯
背景(考古学)
领域(数学)
国家(计算机科学)
数据科学
商业智能
互联网
商业道德
政治学
人工智能
主题模型
知识管理
计算机科学
万维网
公共关系
法学
生物
古生物学
纯数学
数学
算法
梅德林
作者
Sandra María Correia Loureiro,João Guerreiro,Iis Tussyadiah
标识
DOI:10.1016/j.jbusres.2020.11.001
摘要
This study provides an overview of state-of-the-art research on Artificial Intelligence in the business context and proposes an agenda for future research. First, by analyzing 404 relevant articles collected through Web of Science and Scopus, this article presents the evolution of research on AI in business over time, highlighting seminal works in the field, and the leading publication venues. Next, using a text-mining approach based on Latent Dirichlet Allocation, latent topics were extracted from the literature and comprehensively analyzed. The findings reveal 18 topics classified into four main clusters: societal impact of AI, organizational impact of AI, AI systems, and AI methodologies. This study then presents several main developmental trends and the resulting challenges, including robots and automated systems, Internet-of-Things and AI integration, law, and ethics, among others. Finally, a research agenda is proposed to guide the directions of future AI research in business addressing the identified trends and challenges.
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