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The Binary Based Model (BBM) for Improved Human Factors method selection

The Binary Based Model (BBM) for Improved Human Factors method selection
The Binary Based Model (BBM) for Improved Human Factors method selection
Objective: this paper presents the BBM, a new approach to Human Factors (HF) method selection. The BBM helps practitioners select the most appropriate HF methodology in relation to the complexity within the target system.

Background: there are over 200 HF methods available to the practitioner, and little guidance to help choose between them.

Method: the BBM defines a HF ‘Problem Space’ comprising three complexity attributes. HF problems can be rated against these attributes and located in the ‘problem space’. In addition, a similar HF ‘approach space’ in which 66 predictive methods are rated according to their ability to confront those attributes, is defined. These spaces are combined into a ‘Utility Space’ in which problems and methods co-exist. In the Utility Space, the match between HF problems and methods can be formally assessed.

Results: the method space is split into octants to establish broad groupings of methods distributed throughout the space. 77% of the methods reside in octant 1 which corresponds to problems with low levels of complexity. This demonstrates that most HF methods are suited to problems in low complexity systems.

Conclusion: the location of 77% of the rated methods in octant 1 indicates that HF practitioners are underserved with methods for analysis of HF problems exhibiting high complexity.

Application: the BBM can be used by multidisciplinary teams to select the most appropriate HF methodology for the problem under analysis. All the materials and analysis are placed in the public domain for modification and consensus building by the wider HF community.
Fuzzy logic, HF methods, Method Selection, complexity
0018-7208
Holman, Matt
adbef54b-7510-45ab-b16a-a7af52a0042d
Walker, guy
be21812f-b1c8-45f1-9274-5a4498005205
Lansdown, Terry
458275cd-1e94-4b9b-9d1f-1edf12c05c4e
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Salmon, Paul
5398e747-09a5-47c2-9982-2906880c64c6
Read, Gemma J.M.
b581e346-d10e-43d6-bf04-a765780d4fdd
Holman, Matt
adbef54b-7510-45ab-b16a-a7af52a0042d
Walker, guy
be21812f-b1c8-45f1-9274-5a4498005205
Lansdown, Terry
458275cd-1e94-4b9b-9d1f-1edf12c05c4e
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Salmon, Paul
5398e747-09a5-47c2-9982-2906880c64c6
Read, Gemma J.M.
b581e346-d10e-43d6-bf04-a765780d4fdd

Holman, Matt, Walker, guy, Lansdown, Terry, Stanton, Neville, Salmon, Paul and Read, Gemma J.M. (2020) The Binary Based Model (BBM) for Improved Human Factors method selection. Human Factors. (doi:10.1177/0018720820926875).

Record type: Article

Abstract

Objective: this paper presents the BBM, a new approach to Human Factors (HF) method selection. The BBM helps practitioners select the most appropriate HF methodology in relation to the complexity within the target system.

Background: there are over 200 HF methods available to the practitioner, and little guidance to help choose between them.

Method: the BBM defines a HF ‘Problem Space’ comprising three complexity attributes. HF problems can be rated against these attributes and located in the ‘problem space’. In addition, a similar HF ‘approach space’ in which 66 predictive methods are rated according to their ability to confront those attributes, is defined. These spaces are combined into a ‘Utility Space’ in which problems and methods co-exist. In the Utility Space, the match between HF problems and methods can be formally assessed.

Results: the method space is split into octants to establish broad groupings of methods distributed throughout the space. 77% of the methods reside in octant 1 which corresponds to problems with low levels of complexity. This demonstrates that most HF methods are suited to problems in low complexity systems.

Conclusion: the location of 77% of the rated methods in octant 1 indicates that HF practitioners are underserved with methods for analysis of HF problems exhibiting high complexity.

Application: the BBM can be used by multidisciplinary teams to select the most appropriate HF methodology for the problem under analysis. All the materials and analysis are placed in the public domain for modification and consensus building by the wider HF community.

Text
Binary Methods Selection Paper REVISION 2 FINAL
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More information

e-pub ahead of print date: 18 June 2020
Keywords: Fuzzy logic, HF methods, Method Selection, complexity

Identifiers

Local EPrints ID: 442779
URI: http://eprints.soton.ac.uk/id/eprint/442779
ISSN: 0018-7208
PURE UUID: d65083dd-c89a-42c9-97d6-9c9df78a5fd0
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279

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Date deposited: 27 Jul 2020 16:30
Last modified: 26 Nov 2021 07:17

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Contributors

Author: Matt Holman
Author: guy Walker
Author: Terry Lansdown
Author: Neville Stanton ORCID iD
Author: Paul Salmon
Author: Gemma J.M. Read

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