Futures and foresight learning in HRD

J. Gold, Patricia Jolliffe, J. Stewart, C. Glaister, S. Halliday
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引用次数: 2

Abstract

Purpose The purpose of this paper is to argue that human resource development (HRD) needs to embrace and include futures and foresight learning (FFL) as a new addition to its field of theorising and practice. The question to consider is: How can FFL become a new feature of HRD? A key part of the authors’ argument is that the inclusion of FFL will enable HRD to add to the success of any organisation and make a vital contribution to the management of people at work. Design/methodology/approach This paper firstly considers some of the debates surrounding the meaning of HRD. The authors suggest that instability of the time serves to disturb any comforts that have been created in HRD and that there is a need to consider how there might be different futures for what we still call HRD in research, practice and praxis. This paper then considers how FFL might become one possibility for expanding the existing boundaries of HRD. The authors characterise futures and foresight as a learning process, which provides new but complementary features to what is already considered as HRD. This paper will show how FFL can lead to organisation's success and the way this can be achieved. Findings There is a wide variety of meanings of the term HRD; however, HRD is still cast as a “weakened profession” which has to play a subservient role to others in the workplace. Over the last 15 years, the expansion of the meaning of HRD has been seen as evidence of its evolving and emerging nature and development based on a co-creation with other disciplines. This creates a space for FFL, defined as an ongoing learning process to find predictable, probable, possible and/or a variety of long-term futures. FFL embraces three key processes of scanning, futuring and reconfiguring, all of which contain a high potential for participants and others to learn as they proceed, providing outcomes at each stage. FFL has been shown to enhance organisation performance and success and HRD interventions can play a key part in implementation. This represents a significant opportunity for the HRD profession to move from weakness towards strength. Research limitations/implications For HRD researchers, while FFL is not yet on its radar, the authors would argue that the uncertainties of the future require that more attention be given to what might lie ahead. Indeed, HRD researchers need to ask the question: What is the future of HRD research? In addition, if the authors’ call for FFL to be included in the practice of HRD, such practice will itself provide new pathways for HRD research. Further research questions might include: To what extent is FFL practiced in organisations and what role do HRD practitioners play in delivery? How does FFL impact on organisation behaviour and outcomes? What new products and services emerge from FFL? What new skills are required to deliver FFL? Can FFL enhance the status of HRD practitioners in the work place and its role in decision-making? and How can the HRD profession develop as a hybrid profession with respect to machine learning (ML)/artificial intelligence (AI)? Practical implications FFL produces outcomes that have importance for strategy, HRD practitioner can learn to facilitate FFL by action learning and in leadership development programmes. FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. FFL offers those involved in HRD a significant opportunity to transfer ideas into practice that have an impact on organisation sustainability. HRD can play a significant role in the design and delivery of ML and AI projects. Originality/value This paper concludes with a call for embracing FFL as a challenging but important addition to how we talk about learning at work. The authors argue that FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. At its centre, FFL involves learning by people, groups, organisations and machines and this has to be of concern to HRD.
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人力资源开发中的未来与前瞻学习
本文的目的是论证人力资源开发(HRD)需要拥抱和包括未来和前瞻学习(FFL)作为其理论和实践领域的一个新的补充。要考虑的问题是:FFL如何成为HRD的新功能?作者的论点的一个关键部分是,FFL的纳入将使人力资源开发能够增加任何组织的成功,并对工作人员的管理做出重要贡献。设计/方法论/方法本文首先考虑了围绕人力资源开发意义的一些争论。作者认为,时代的不稳定性扰乱了在人力资源开发中创造的任何舒适,有必要考虑我们在研究、实践和实践中仍然称之为人力资源开发的未来可能会有什么不同。然后,本文考虑FFL如何成为扩展现有HRD边界的一种可能性。作者将未来和远见描述为一种学习过程,它为已经被认为是人力资源开发的东西提供了新的但互补的特征。本文将展示FFL如何导致组织的成功以及实现这一目标的方式。研究结果人力资源开发一词有多种含义;然而,人力资源开发仍然被认为是一个“被削弱的职业”,必须在工作场所扮演屈从于他人的角色。在过去的15年中,人力资源开发含义的扩展被视为其在与其他学科共同创造的基础上不断演变和新兴的性质和发展的证据。这为FFL创造了一个空间,FFL被定义为一个持续的学习过程,以发现可预测的、可能的、可能的和/或各种长期的未来。FFL包含扫描、未来和重新配置三个关键过程,所有这些过程都包含了参与者和其他人在进行过程中学习的高潜力,并在每个阶段提供结果。FFL已被证明可以提高组织绩效和成功,人力资源开发干预可以在实施中发挥关键作用。这是人力资源开发行业从弱势走向强势的重要机会。研究限制/影响:对于人力资源研究人员来说,虽然FFL还没有进入他们的视野,但作者认为,未来的不确定性需要更多地关注未来可能发生的事情。事实上,人力资源开发研究人员需要问这样一个问题:人力资源开发研究的未来是什么?此外,如果作者呼吁将FFL纳入人力资源开发实践,这种实践本身将为人力资源开发研究提供新的途径。进一步的研究问题可能包括:FFL在组织中的实践程度如何?人力资源开发从业者在交付中扮演什么角色?FFL如何影响组织行为和结果?FFL会推出哪些新产品和服务?提供FFL需要哪些新技能?FFL能否提高人力资源开发从业者在工作场所的地位及其在决策中的作用?人力资源开发职业如何发展成为机器学习(ML)/人工智能(AI)的混合职业?FFL产生的结果对战略具有重要意义,人力资源开发从业者可以通过行动学习和领导力发展计划来学习促进FFL。FFL提供了一个重要的机会来提高人力资源开发在组织内外的重要性。FFL为参与人力资源开发的人员提供了将对组织可持续性有影响的想法转化为实践的重要机会。人力资源开发可以在机器学习和人工智能项目的设计和交付中发挥重要作用。原创性/价值本文最后呼吁将FFL作为我们谈论工作学习的一个具有挑战性但重要的补充。作者认为,FFL提供了一个重要的机会来提高人力资源开发在组织内外的重要性。FFL的核心是人、团体、组织和机器的学习,这必须引起人力资源开发人员的关注。
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来源期刊
CiteScore
5.10
自引率
13.60%
发文量
53
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