Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation
Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation
This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.
risk intelligence, competitive intelligence, military intelligence, risk radar
644-658
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616
Ojiako, Udechukwu
b4eaf8f0-7f7a-49aa-903c-d208227eb5c6
Wang, Victoria
fe831e70-28a9-4b12-9942-aa5c2e260f12
Lin, Fenfang
d2d0fe76-3e6f-488b-94c2-6b0f4c9f08eb
Chipulu, Maxwell
12545803-0d1f-4a37-b2d2-f0d21165205e
April 2019
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616
Ojiako, Udechukwu
b4eaf8f0-7f7a-49aa-903c-d208227eb5c6
Wang, Victoria
fe831e70-28a9-4b12-9942-aa5c2e260f12
Lin, Fenfang
d2d0fe76-3e6f-488b-94c2-6b0f4c9f08eb
Chipulu, Maxwell
12545803-0d1f-4a37-b2d2-f0d21165205e
Marshall, Alasdair, Ojiako, Udechukwu, Wang, Victoria, Lin, Fenfang and Chipulu, Maxwell
(2019)
Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation.
International Journal of Forecasting, 35 (2), .
(doi:10.1016/j.ijforecast.2018.07.015).
Abstract
This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.
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Accepted/In Press date: 2 July 2018
e-pub ahead of print date: 23 October 2018
Published date: April 2019
Keywords:
risk intelligence, competitive intelligence, military intelligence, risk radar
Identifiers
Local EPrints ID: 424533
URI: http://eprints.soton.ac.uk/id/eprint/424533
ISSN: 0169-2070
PURE UUID: d7d69930-5fd0-47e1-a4b2-fecb962f4228
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Date deposited: 05 Oct 2018 11:38
Last modified: 16 Mar 2024 07:02
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Contributors
Author:
Udechukwu Ojiako
Author:
Victoria Wang
Author:
Maxwell Chipulu
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