人力资源开发中人工智能的系统文献计量学审查:对人力资源开发研究人员、从业人员和决策者的启示

Salima Hamouche, Norffadhillah Rofa, Annick Parent-Lamarche
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引用次数: 0

摘要

目的人工智能(AI)将极大地改变人力资源开发(HRD)领域的游戏规则。聊天 GPT 的推出加速了其发展进程,并扩大了其对组织和员工的影响。本研究旨在采用文献计量学方法回顾和研究人力资源开发中的人工智能文献。使用 Scopus 查找该领域的研究。结果研究结果表明,大多数被引用的文献和作者主要来自计算机科学领域,强调机器学习而非人类学习。本研究为研究人员、管理人员、人力资源开发从业人员和政策制定者提供了见解和建议。优先发展人类和机器变得至关重要,因为只关注机器可能会对员工技能的可持续性和长期职业前景构成风险。因此,本研究提出了一种相对尚未探索的方法来研究这一主题。它对这一主题进行了直观而有条理的概述。此外,它还强调了研究集中的领域和被忽视的领域。本研究的一个重要贡献是揭示了更多源自计算机科学的研究,以及对机器学习而非人类学习的关注,这可能会促进与不同领域专家的跨学科合作,拓宽工作场所技术与学习的研究范围。
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Systematic bibliometric review of artificial intelligence in human resource development: insights for HRD researchers, practitioners and policymakers

Purpose

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

Design/methodology/approach

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

Findings

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

Practical implications

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

Originality/value

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

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来源期刊
CiteScore
5.10
自引率
13.60%
发文量
53
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