The Binary-Based Model (BBM) for Improved Human Factors Method Selection

Abstract

Objective This paper presents the Binary-Based Model (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 coexist. 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. About 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.