AI in business research

IF 2.8 4区 管理学 Q2 MANAGEMENT DECISION SCIENCES Pub Date : 2024-11-06 DOI:10.1111/deci.12655
Zhi Cao, Meng Li, Paul A Pavlou
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Abstract

Artificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI-related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ii) human perceptions of AI, and (iii) AI behavior. We also identify five principal research questions and offer suggestions for future research directions.

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商业研究中的人工智能
人工智能(AI)已经成为现代商业转型的关键力量,受到了从业者和学术界的广泛关注。随着人工智能相关研究呈指数级增长,我们提供了一个研究框架,旨在综合商业领域有关人工智能的现有文献。我们根据这一审查框架,对2010年至2023年期间在25家领先商业期刊上的人工智能研究进行了全面审查。具体来说,我们从三个研究角度回顾了文献:(i)人工智能应用,(ii)人类对人工智能的感知,以及(iii)人工智能行为。本文还提出了五个主要研究问题,并对未来的研究方向提出了建议。
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来源期刊
DECISION SCIENCES
DECISION SCIENCES MANAGEMENT-
CiteScore
12.40
自引率
1.80%
发文量
34
期刊介绍: Decision Sciences, a premier journal of the Decision Sciences Institute, publishes scholarly research about decision making within the boundaries of an organization, as well as decisions involving inter-firm coordination. The journal promotes research advancing decision making at the interfaces of business functions and organizational boundaries. The journal also seeks articles extending established lines of work assuming the results of the research have the potential to substantially impact either decision making theory or industry practice. Ground-breaking research articles that enhance managerial understanding of decision making processes and stimulate further research in multi-disciplinary domains are particularly encouraged.
期刊最新文献
Issue Information IN THIS ISSUE Issue Information In this issue Explanation seeking and anomalous recommendation adherence in human-to-human versus human-to-artificial intelligence interactions
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