Mobile futures knowledge: From research policy to research and public policy?

Heino, Hanna; Hautala, Johanna

doi:10.1016/j.geoforum.2020.12.007

Cited by 4 publications

(1 citation statement)

References 66 publications

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“…This means drawing out implications at each stage and presenting them for consideration beyond the FFL group. However, this represents a key role for HRD practitioners as facilitators because FFL can be understood as a knowledge-creation process (Heino and Hautala, 2021) that feed strategic decision-making. Further, findings from FFL can be blocked or stymied through the lack of support from others, particularly leaders who have not been part of the process.…”

Section: Towards Futures and Foresight Learning In Human Resource Dev...mentioning

confidence: 99%

Futures and foresight learning in HRD

Gold

Jolliffe²,

Stewart³

et al. 2022

EJTD

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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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Section: Towards Futures and Foresight Learning In Human Resource Dev...mentioning

confidence: 99%

Futures and foresight learning in HRD

Gold

Jolliffe²,

Stewart³

et al. 2022

EJTD

View full text Add to dashboard Cite

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Transition co-design dynamics in high level policy processes

et al. 2023

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Can robots possess knowledge? Rethinking the DIK(W) pyramid through the lens of employees of an automotive factory

Hautala

2021

Humanit Soc Sci Commun

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Knowledge, information, and data are increasingly processed in human–robot collaboration. This study tackles two requirements for revising the concepts of knowledge, information, and data. First is developing robots’ knowledge capabilities and transparency and ensuring effective division of tasks between humans and robots to increase the productivity of robotised factories. Employees’ interpretations of robots’ abilities to possess knowledge reveal their assumptions of robots’ possibilities and limitations to create knowledge-based products with humans. Second, the classic DIK(W) pyramid of data, information, knowledge, and wisdom is a theoretical construct requiring additional empirical research. This empirical exploratory study develops the DIK(W) further and applies it as a tool to understand employees’ perspectives of robots and knowledge. Do people believe robots possess knowledge? What kind of knowledge can (or cannot) robots possess? A survey (n = 269) was collected from the most robotised factory in Finland, Valmet Automotive. Half of the respondents think robots can possess knowledge, but only with humans. These respondents were more likely to trust robots compared to those who think robots cannot possess knowledge. As the key contribution, the DIK(W) pyramid is reconceived by (i) acknowledging robots and humans, (ii) turning the pyramid upside down, and (iii) recognising knowledge as a dividing concept.

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Mobile futures knowledge: From research policy to research and public policy?

Cited by 4 publications

References 66 publications

Futures and foresight learning in HRD

Futures and foresight learning in HRD

Transition co-design dynamics in high level policy processes

Can robots possess knowledge? Rethinking the DIK(W) pyramid through the lens of employees of an automotive factory

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