3120Heriot-Watt University, Edinburgh, UK.
5333University of the Sunshine Coast, Queensland, Australia.
Hum Factors. 2021 Dec;63(8):1408-1436. doi: 10.1177/0018720820926875. Epub 2020 Jun 18.
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.
There are over 200 HF methods available to the practitioner and little guidance to help choose between them.
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.
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.
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.
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.
本文提出了二进制模型（BBM），这是一种新的人因（HF）方法选择方法。BBM 有助于从业者根据目标系统中的复杂性选择最合适的 HF 方法。
有 200 多种 HF 方法可供从业者使用，但在选择方法时几乎没有指导。
BBM 定义了一个 HF“问题空间”，包括三个复杂性属性。HF 问题可以根据这些属性进行评分，并在“问题空间”中定位。此外，还定义了一个类似的 HF“方法空间”，其中 66 种预测方法根据其应对这些属性的能力进行评分。这些空间组合成一个“效用空间”，其中问题和方法共存。在效用空间中，可以正式评估 HF 问题和方法之间的匹配。
方法空间被分成八个象限，以建立方法的广泛分组，分布在整个空间中。大约 77%的方法位于对应于低复杂性问题的第一象限。这表明大多数 HF 方法适用于低复杂性系统中的问题。
77%的评分方法位于第一象限表明，HF 从业者在分析具有高复杂性的 HF 问题时，缺乏适当的方法。
BBM 可被多学科团队用于为正在分析的问题选择最合适的 HF 方法。所有的材料和分析都放置在公共领域，供更广泛的 HF 社区修改和达成共识。
