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

Area of Science:

• Human Factors and Ergonomics
• Systems Engineering
• Decision Support Systems

Background:

• Over 200 Human Factors (HF) methods exist, lacking clear guidance for selection.
• Practitioners face challenges in choosing appropriate HF methodologies for diverse system complexities.

Purpose of the Study:

• Introduce the Binary-Based Model (BBM) for systematic Human Factors (HF) method selection.
• Provide a framework to match HF problems with suitable methodologies based on system complexity.

Main Methods:

• Developed a "problem space" based on three complexity attributes to categorize HF problems.
• Created an "approach space" rating 66 predictive methods against complexity attributes.
• Integrated problem and approach spaces into a "utility space" for formal assessment of method-problem fit.

Main Results:

• The method space is divided into octants, with 77% of methods falling into Octant 1 (low complexity).
• This distribution indicates a concentration of HF methods for simple systems.
• A significant gap exists for methods addressing high-complexity HF problems.

Conclusions:

• The Binary-Based Model (BBM) facilitates informed HF methodology selection for multidisciplinary teams.
• The BBM provides a structured approach to address the challenge of selecting appropriate HF methods.
• All BBM components are publicly available for community adaptation and consensus.