机器/人类对学习和发展实践的影响:一项跨中小微企业、非政府组织和跨国公司的研究

IF 3.3 3区 管理学 Q1 INDUSTRIAL RELATIONS & LABOR
D. Dutta, Anasha Kannan Poyil
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引用次数: 0

摘要

在日益动态的环境中,学习在发展中的重要性可以帮助个人和组织适应混乱。人工智能(AI)正在成为一种颠覆性技术,越来越多地被各种人力资源管理(HRM)功能所采用。然而,人工智能的学习和发展(L&D)采用是滞后的,并且需要基于内部/外部环境和组织类型来理解这种低采用率。在开放系统理论的基础上,采用技术在实践的视角,作者研究了各种学习与开发方法以及人类和技术机构的角色,这些方法是由不同的结构、不同类型的组织和人工智能在学习与开发中的使用实现的。设计/方法/方法通过定性访谈设计,收集了来自中小微企业、非政府组织和跨国公司组织的27名主要利益相关者和L&D专业人士的数据。作者采用Gioia的定性研究方法对收集到的数据进行专题分析。作者认为,人力和技术机构制定了与其内部/外部环境、资源可用性和技术采用相一致的组织协议和结构。虽然已经确定了人工智能在L&D领域应用滞后的原因,但人工智能支持L&D的未来潜力也在显现。作者对人类和技术中介互动的社会化进行了理论化,并在不同规模、行业、部门和内部/外部背景的组织中发展了三种新兴的学习与发展结构。研究局限/启示本研究以开放系统理论(OST)和实践中的技术为基础,论证了人类活动、技术进步和能力以及结构化环境之间的相互依存和不可分割性。作者研究了学习与发展中人工智能采用滞后的原因,以及代理焦点如何根据组织的内部/外部环境而变化。原创性/价值虽然人工智能人力资源管理奖学金主要依靠心理学理论来检验影响和结果,但作者采用了OST和技术在实践中的视角来解释组织背景、资源和技术采用如何影响L&D。本研究调查了人工智能技术在L&D中的应用及其促成因素,这方面的研究尚未得到充分的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The machine/human agentic impact on practices in learning and development: a study across MSME, NGO and MNC organizations
PurposeThe importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.Design/methodology/approachThrough a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.FindingsThe authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.Research limitations/implicationsThe study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.Originality/valueWhile AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.
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来源期刊
Personnel Review
Personnel Review Multiple-
CiteScore
7.10
自引率
7.70%
发文量
133
期刊介绍: Personnel Review (PR) publishes rigorous, well written articles from a range of theoretical and methodological traditions. We value articles that have high originality and that engage with contemporary challenges to human resource management theory, policy and practice development. Research that highlights innovation and emerging issues in the field, and the medium- to long-term impact of HRM policy and practice, is especially welcome.
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