Toward theory-driven, quantitative performance measurement in ergonomics science: The abstraction hierarchy as a framework for data analysis

Measurement in ergonomics science has not kept pace with theorizing. As a result, it is rare to find measures of human performance that are simultaneously objective, quantitative, sensitive, and theoretically grounded. This article proposes a new set of measures, based on the abstraction hierarchy (AH) framework, that satisfies all of these criteria. Each level of the AH can be used to define a quantitative state space that can serve as a frame of reference for objective measurement. These state spaces are complementary because they provide different views of the same human-environment behaviour. Collectively, this set of measures can be used to determine if a participant is strongly or weakly coupled to functional or physical distal properties of the work domain. Data from a longitudinal study are used as a case study to test the value of these novel measures. The empirical results show that these AH-based measures provide unique insight into participants' behaviour that was not revealed by many, more traditional measures of performance. Because it is theoretically grounded, the set of measures proposed here has the potential to be generalized to diverse work domains for which it is possible to develop an AH representation.