Workload II: A future paradigm for analysis and measurement

Abstract

Well-established models of workload focus on the cognitive demands placed on an individual and the capacity of cognitive resources to respond to those demands. These models, and the range of measurement tools which have been used to capture workload in real world and laboratory settings have been vital in ensuring design of work to take into account workload over the past four decades. This paper will consider how we should now build on these well-established foundations to develop a new suite of tools and approaches suitable for 21st Century workplaces. In the past, it was impractical to apply detailed automatic capture of work (either through physiological measures of people, or through sensing of interaction) in situ in a workplace. Development of technologies, alongside changing attitudes towards sensing in workplace settings, mean that we now have new tools and large data sets potentially available to capture a wider range of elements of a workplace, potentially enriching our workload measurement data set, and making triangulation of methods routine and seamless. However, these enriched data sets will need to take into account the changed nature of work. Workplaces now see increasing collaboration between people and autonomous systems (Kaber 2017). Workplace tasks normally now involve multiple people, systems, technologies and artefacts. Typically, workload assessment tools have only considered single people and single roles (where multiple resource theory provides a framework to capture two or more elements of a single role). Similarly, unlike theories such as Situation Awareness (Endsley 1995) (Endsley 2015) workload theories do not tend to directly address the role of the social and organisational context on the effect of work, and it is proposed that through the adoption of established notions such as cognitive appraisal they can and should now do this. Finally, workload models tend to be based on the traditional human information processing model (Wickens et al. 2003), rather than directly acknowledging the joint cognitive systems nature (Hollnagel and Woods 2005) of the way that we work. This paper will address the above in the light of reviewing the strong foundations of workload theory, and building upon this to identify challenges and priorities for workload theories and tools for our future workplaces.