Workload II: A Future Paradigm for Analysis and Measurement

  • Sarah SharplesEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 824)


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.


Workload Measurement Systems ergonomics 



Thanks to my fellow panelists Peter Hancock, Chris Wickens, Karel Brookhuis and Luca Longo and the audience at the Workload conference, 2017 in Dublin, which provided the inspiration for this paper.


  1. Bainbridge L (1983) Ironies of automation. In: Rasmussen J, Duncan K, Leplat J (eds) New technology and human error. Wiley, Chichester, pp 271–283Google Scholar
  2. Blandford A, Furniss D (2006) DiCoT: A methodology for applying distributed cognition to the design of teamworking systems. Springer, HeidelbergGoogle Scholar
  3. Cox T, Mackay CJ (1985) The measurement of self-reported stress and arousal. Br J Psychol 76:183–186CrossRefGoogle Scholar
  4. Edwards T, Sharples S, Kirwan B, Wilson JR, Balfe N (2014) Identifying Markers of Performance Decline in Air Traffic Controllers. Paper presented at the proceedings of the 5th international conference on applied human factors and ergonomics AHFE 2014, Kraków, PolandGoogle Scholar
  5. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64CrossRefGoogle Scholar
  6. Endsley MR (2015) Situation awareness misconceptions and misunderstandings. J Cognit Eng Decis Making 9(1):4–32. Scholar
  7. Hancock P, Meshkati N (eds) (1988) Human mental workload. North-Holland, AmsterdamGoogle Scholar
  8. Hart SG, Staveland LE (1988) Development of the NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock PA, Meshkati N (eds) Human mental workload. North-Holland, Amsterdam, pp 139–183CrossRefGoogle Scholar
  9. Hollnagel E (2012) FRAM: The functional resonance analysis method. CRC Press, LondonGoogle Scholar
  10. Hollnagel E, Wears RL, Braithwaite J (2015) From Safety-I to Safety-II: A white paper. The resilient health care net: published simultaneously by the University of Southern Denmark, University of Florida, USA, and Macquarie University, AustraliaGoogle Scholar
  11. Hollnagel E, Woods DD (2005) Joint cognitive systems: foundations of cognitive systems engineering. Taylor & Francis, LondonCrossRefGoogle Scholar
  12. Hutchins E (1995) Cognition in the wild. MIT Press, CambridgeGoogle Scholar
  13. Kaber DB (2017) A conceptual framework of autonomous and automated agents. Theoretical Issues in Ergonomics ScienceGoogle Scholar
  14. Kahneman D (1973) Attention and effort. Prentice Hall, Englewood CliffsGoogle Scholar
  15. Karasek RA (1979) Job demands, job decision latitude, and mental strain: implications for job redesign. Adm Sci Q 24(2):285–308. Scholar
  16. Maior HA, Pike M, Sharples S, Wilson ML (2015) Examining the reliability of using fNIRS in realistic HCI settings for spatial and verbal tasks. Paper presented at the proceedings of the 33rd annual ACM conference on human factors in computing systemsGoogle Scholar
  17. Marinescu AC, Sharples S, Ritchie AC, Sánchez López T, McDowell M, Morvan HP (2018) Physiological parameter response to variation of mental workload. Hum Factors 60(1):31–56CrossRefGoogle Scholar
  18. Moray N (ed) (1979) Mental workload: Its theory and measurement. Plenum Press, New YorkGoogle Scholar
  19. Neisser U (1976) Cognition and reality. W.H. Freeman, San FranciscoGoogle Scholar
  20. Pickup L, Wilson JR, Norris BJ, Mitchell L, Morrisroe G (2005a) The Integrated Workload Scale (IWS): a new self-report tool to assess railway signaller workload. Appl Ergonom 36(6):681–693CrossRefGoogle Scholar
  21. Pickup L, Wilson JR, Sharples S, Norris B, Clarke T, Young MS (2005b) Fundamental examination of mental workload in the rail industry. Theoret Issues Ergonom Sci 6(6):463–482CrossRefGoogle Scholar
  22. Porcheron M, Fischer JE, Reeves S, Sharples S (2018) Voice interfaces in everyday life. Paper presented at the proceedings of the 2018 CHI conference on human factors in computing systemsGoogle Scholar
  23. Salas E, Sims DE, Burke CS (2005) Is there a “big five” in teamwork? Small Group Res 36(5):555–599CrossRefGoogle Scholar
  24. Sharples S, Edwards T, Balfe N, Wilson JR (2012) Inferring cognitive state from observed behaviour. Paper presented at the AHFE 2012, San FranciscoGoogle Scholar
  25. Sharples S, Megaw ED (2015) The definition and measurement of human workload. In: Wilson JR, Sharples S (eds) Evaluation of human work, 4th edn. CRC Press, Boca Raton, pp 515–548Google Scholar
  26. Sharples S, Millen L, Golightly D, Balfe N (2011) The impact of automation on rail signalling operations. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 225(3):179–191CrossRefGoogle Scholar
  27. Tattersall AJ, Foord PS (1996) An experimental evaluation of instantaneous self-assessment as a measure of workload. Ergonomics 39:740–748CrossRefGoogle Scholar
  28. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage Sci 46(2):186–204CrossRefGoogle Scholar
  29. Vicente KJ (1999) Cognitive work analysis: Toward safe, productive, and healthy computer-based work. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  30. Wickens CD (2008) Multiple resources and mental workload. Hum Factors 50:449–455CrossRefGoogle Scholar
  31. Wickens CD, Gordon-Becker SE, Liu YS, Lee J (2003) An introduction to human factors engineering. Prentice Hall, Upper Saddle RiverGoogle Scholar
  32. Wilson JR (2014) Fundamentals of systems ergonomics/human factors. Appl Ergonom 45:5–13CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Human Factors Research Group, Faculty of EngineeringUniversity of Nottingham, University ParkNottinghamUK

Personalised recommendations