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A Methodological approach to supporting organisational learning

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Author(s)
Mulholland, Paul
Zdrahal, Zdenek
Domingue, John
Hatala, Marek
Bernardi, Ansgar

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URI
http://hdl.handle.net/20.500.12424/2454816
Online Access
http://oro.open.ac.uk/2980/1/Organisational_Learning.pdf
Abstract
Many organizations need to respond quickly to change and their workers need to regularly develop new knowledge and skills. The prevailing approach to meeting these demands is on-the-job training, but this is known to be highly ineffective, cause stress and devalue workplace autonomy. Conversely, organizational learning is a process through which workers learn gradually in the work context through experience, reflection on work practice and collaboration with colleagues. Our approach aims to support and enhance organizational learning around enriched work representations. Work representations are tools and documents used to support collaborative working and learning. These are enriched through associations with formal knowledge models and informal discourse. The work representations, informal discourse and associated knowledge models together form on organizational memory from which knowledge can be retrieved later. Our methodological approach to supporting organizational learning is drawn from three industrial case studies concerned with machine maintenance, team planning and hotline support. The methodology encompasses development and design activities, a description of the roles and duties required to sustain the long-term use of the tools, and applicability criteria outlining the kind of organizations that can benefit from this approach.
Date
2001-09
Type
Journal Item
Identifier
oai:oro.open.ac.uk:2980
http://oro.open.ac.uk/2980/1/Organisational_Learning.pdf
Mulholland, Paul <http://oro.open.ac.uk/view/person/pm223.html>; Zdrahal, Zdenek <http://oro.open.ac.uk/view/person/zz3.html>; Domingue, John <http://oro.open.ac.uk/view/person/jbd2.html>; Hatala, Marek and Bernardi, Ansgar (2001). A Methodological approach to supporting organisational learning. International Journal of Human-Computer Studies, 55(3) pp. 337–367.
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