知识管理
背景(考古学)
独创性
实施
影响力营销
营销
概念模型
业务
心理学
计算机科学
创造力
社会心理学
古生物学
数据库
关系营销
生物
程序设计语言
市场营销管理
作者
Ahmad A. Khanfar,Reza Kiani Mavi,Mohammad Iranmanesh,Denise Gengatharen
出处
期刊:Management Decision
[Emerald (MCB UP)]
日期:2025-01-08
标识
DOI:10.1108/md-05-2023-0838
摘要
Purpose Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area. Design/methodology/approach The authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption. Findings This study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels. Originality/value This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.
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