The road crash environment is a complex, dynamic, and dangerous workplace for responders at the scene. Decisions made by senior officers from responder agencies can alter the survival outcomes of road crash victims and the safety of responder crews. Although no previous studies have utilized naturalistic decision making principles to research traffic incident management, the characteristics of the workplace seem ideally suited to it. This study used critical decision method (CDM) interviews and the recognition-primed decision model to analyze decisions made by experienced senior responders at traffic incidents. CDM interviews were conducted with officers from three agencies: police, emergency services, and road authority traffic officers. The results revealed useful and novel information about the traffic incident management system, for example, interagency coordination issues. The process identified key decision points at incidents as well as countermeasures to the identified issues using decision-centered design. These countermeasures were subsequently validated by subject matter experts.
AshooriM.BurnsC. (2013). Team cognitive work analysis structure and control tasks. Journal of Cognitive Engineering and Decision Making, 7, 123–140.
2.
AshooriM.BurnsC. M.d’EntremontB.MomtahanK. (2014). Using team cognitive work analysis to reveal healthcare team interactions in a birthing unit. Ergonomics, 57, 973–986.
3.
CattermoleV.HorberryT.WallisG.CloeteS. (2014, November). An operator-centred investigation of safety issues for emergency responders at traffic incidents. Paper presented at the Australasian Road Safety Research, Policing, and Education Conference, Melbourne, Australia.
4.
ChamblissB. (2015). Macrocognition: A theory of distributed minds and collective intentionality. Philosophical Psychology. Advance online publication.
5.
CharlesP. (2007). Traffic incident management guide to best practices (Austroads Research Report AP-R298/07). Sydney, Australia: Austroads Incorporated.
6.
CrandallB.Getchell-ReiterK. (1993). Critical decision method: A technique for eliciting concrete assessment indicators from the intuition of NICU nurses. Advances in Nursing Science, 16, 42–51.
7.
Department of Transport and Main Roads. (2016). Queensland road crash database. Brisbane, Australia: Author.
8.
EricssonK. A.CharnessN.FeltovichP. J.HoffmanR. R. (Eds.). (2006). The Cambridge handbook of expertise and expert performance. Cambridge, UK: Cambridge University Press.
9.
Federal Signal Corporation. (2012). Risk reduction for emergency response. Retrieved from https://www.federalsignal.com/
10.
FioreS. M.RosenM. A.Smith-JentschK.SalasE.LetskyM.WarnerN. (2010). Toward an understanding of macrocognition in teams: Predicting processes in complex collaborative contexts. Human Factors, 52, 203–224.
11.
FischerJ.KrzmarzickA.MenonA.ShankwitzC. (2012). Performance analysis of squad car lighting, retro-reflective markings, and paint treatments to improve safety at roadside traffic stops (Report CTS 12-13). Minneapolis: University of Minnesota.
12.
HoffmanR. R.CrandallB.ShadboltN. (1998). Use of the critical decision method to elicit expert knowledge: A case study in the methodology of cognitive task analysis. Human Factors, 40, 254–276.
13.
HoffmanR. R.McNeeseM. D. (2009). A history for macrocognition. Journal of Cognitive Engineering and Decision Making, 3, 97–110.
14.
HorberryT.CookeT. (2014). Operator acceptance of new technology for industrial mobile equipment. In HorberryT.StevensA.ReganM. (Eds.), Driver acceptance of new technology: Theory, measurement and optimisation (pp. 227–239). Farnham, UK: Ashgate.
15.
KleinG. (1999). Sources of power: How people make decisions. Cambridge, MA: MIT Press.
16.
KleinG. (2008). Naturalistic decision making. Human Factors, 50, 456–460.
17.
KleinG.CalderwoodR.Clinton-CiroccoA. (2010). Rapid decision making on the fire ground: The original study plus a postscript. Journal of Cognitive Engineering and Decision Making, 4, 186–209.
18.
KleinG.CalderwoodR.MacgregorD. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man and Cybernetics, 19, 462–472.
19.
KleinG.WrightC. (2016). Macrocognition: From theory to toolbox. Frontiers in Psychology, 7, 54.
20.
LipshitzR.KleinG.OrasanuJ.SalasE. (2001). Taking stock of naturalistic decision making. Journal of Behavioral Decision Making, 14, 331–352.
21.
LumH. C.FioreS. M.RosenM. A.SalasE. (2008). Complexity in collaboration: Developing an understanding of macrocognition in teams through examination of task complexity. In Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting (pp. 1425–1429). Santa Monica, CA: Human Factors and Ergonomics Society.
22.
MilitelloL.CrandallB. (1999). Critical incident/critical decision method. In JonassenD.JonassenH.TessmerM.HannumW. H. (Eds.), Task analysis methods for instructional design (pp. 181–192). Mahwah, NJ: Lawrence Erlbaum.
23.
MilitelloL.LimL. (1995). Patient assessment skills: assessing early cues of necrotizing enterocolitis. The Journal of Perinatal & Neonatal Nursing, 9(2), 42-52.
24.
NaikarN. (2010). A comparison of the decision ladder template and the recognition-primed decision model (No. DSTO-TR-2397). Victoria, Australia: Defence Science and Technology Organisation, Air Operations Division.
25.
OrasanuJ.ConnollyT. (1993). The reinvention of decision making. Westport, CT: Ablex.
26.
PoratT.KostopoulouO.WoolleyA.DelaneyB. C. (2015). Eliciting user decision requirements for designing computerized diagnostic support for family physicians. Journal of Cognitive Engineering and Decision Making, 10, 57–73.
27.
RasmussenJ. (1997). Risk management in a dynamic society: A modelling problem. Safety Science, 27, 183–213.
