To better understand the external factors that influence the performance and decisions of aviators involved in Naval aviation mishaps.
Background:
Mishaps in complex activities, ranging from aviation to nuclear power operations, are often the result of interactions between multiple components within an organization. The Naval aviation mishap database contains relevant information, both in quantitative statistics and qualitative reports, that permits analysis of such interactions to identify how the working atmosphere influences aviator performance and judgment.
Method:
Results from 95 severe Naval aviation mishaps that occurred from 2011 through 2016 were analyzed using Bayes’ theorem probability formula. Then a content analysis was performed on a subset of relevant mishap reports.
Results:
Out of the 14 latent factors analyzed, the Bayes’ application identified 6 that impacted specific aspects of aviator behavior during mishaps. Technological environment, misperceptions, and mental awareness impacted basic aviation skills. The remaining 3 factors were used to inform a content analysis of the contextual information within mishap reports. Teamwork failures were the result of plan continuation aggravated by diffused responsibility. Resource limitations and risk management deficiencies impacted judgments made by squadron commanders.
Conclusion:
The application of Bayes’ theorem to historical mishap data revealed the role of latent factors within Naval aviation mishaps. Teamwork failures were seen to be considerably damaging to both aviator skill and judgment.
Application:
Both the methods and findings have direct application for organizations interested in understanding the relationships between external factors and human error. It presents real-world evidence to promote effective safety decisions.
AndreA.WickensC.MoormanL. (1991). Display formatting techniques for improving situation awareness in the aircraft cockpit. The International Journal of Aviation Psychology, 1(3), 205–218.
2.
AvenT. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13.
3.
BakxG. C. H.NyceJ. N. (2012). Is redundancy enough?: A preliminary study of Apache crew behaviour. Theoretical Issues in Ergonomics Science, 14(6), 531–545.
4.
BellandK.OlsenC.LawryR. (2010). Carrier air wing mishap reduction using a human factors classification system and risk management. Aviation, Space, and Environmental Medicine, 81(11), 1028–1032.
5.
BourgeonL.ValotC.VacherA.NavarroC. (2011). Study of perseveration behaviors in military aeronautical accidents and incidents: Analysis of plan continuation errors. Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting (pp. 1730–1734). Santa Monica, CA: Human Factors and Ergonomics Society.
6.
ChenW.HuangS. P. (2014). Human reliability analysis in aviation maintenance by a Bayesian network approach. In FrangopolD.DeodatisG. (Eds.), Safety, reliability, risk and life-cycle performance of structures and infrastructures (pp. 2091–2096). London: CRC Press.
7.
ChildsJ. M.SpearsW. D.ProphetW. W. (1983). Private pilot flight skill retention 8, 16, and 24 months following certification (Accession Number ADA133400). Daytona Beach, FL: Embry-Riddle Aeronautical University.
8.
CohenT. N.WiegmannD. A.ShappellS. A. (2015). Evaluating the reliability of the Human Factors Analysis and Classification System. Aerospace Medicine and Human Performance, 86(8),728–735.
9.
DarleyJ. M.LataneB. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8(4), 377–383.
10.
DekkerS. (2003). Illusions of explanation: A critical essay on error classification. The International Journal of Aviation Psychology, 13(2), 95–106.
11.
DekkerS. (2014). The field guide to understanding “human error” (3rd ed.). Burlington, VT: Ashgate Publishing Company.
12.
Department of Defense. (2011). Mishap notification, investigation, reporting, and record keeping (DOD Instruction 6055.07). Washington, DC: Author.
13.
Department of the Navy. (2014). Naval aviation safety management system (OPNAV Instruction 3750.6S). Washington, DC: Author.
14.
Department of the Navy. (2018). Operational risk management (OPNAV Instruction 3500.39D). Washington, DC: Author.
EndsleyM. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64.
17.
EndsleyM. (2015). Situation awareness misconceptions and misunderstandings. Journal of Cognitive Engineering and Decision Making, 9(1), 4–32.
18.
EricssonK. A.KrampeR. T.Tesch-RömerC. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
19.
FittsP.JonesR. (1947). Analysis of factors contributing to 460 “pilot error” experiences in operating aircraft controls (AMC Memorandum Report TSEAA−694−12). Dayton, OH: Wright-Patterson AFB, Air Material Command.
HsiaoY. L.DruryC.WuC.PaquetV. (2013a). Predictive models of safety based on audit findings: Part 1: Model development and reliability. Applied Ergonomics, 44(2), 261–273.
22.
HsiaoY. L.DruryC.WuC.PaquetV. (2013b). Predictive models of safety based on audit findings: Part 2: Measurement of model validity. Applied Ergonomics, 44(4), 659–666.
23.
KrulakD. C. (2004). Human factors in maintenance: Impact on aircraft mishap frequency and severity. Aviation, Space, and Environmental Medicine, 75(5), 429–432.
24.
LiW. C.HarrisD. (2006). Pilot error and its relationship with higher organizational levels: HFACS analysis of 523 accidents. Aviation, Space, and Environmental Medicine, 77(10), 1056–1061.
25.
LipshitzR.KleinG.OrasanuJ.SalasE. (2001). Taking stock of naturalistic decision making. Journal of Behavioral Decision Making, 14(5), 331–352.
26.
LundbergJ.RollenhagenC.HollnagelE. (2009). What-You-Look-For-Is-What-You-Find: The consequences of underlying accident models in eight accident investigation manuals. Safety Science, 47(10), 1297–1311.
27.
MeshkatiN. (1991). Human factors in large-scale technological systems’ accidents: Three Mile Island, Bhopal, Chernobyl. Industrial Crisis Quarterly, 5(2), 133–154.
28.
NormanD. A. (1988). The psychology of everyday things. New York, NY: Harper & Row.
29.
O’ConnorP.O’DeaA. (2007). The U.S. Navy’s aviation safety program: A critical review. International Journal of Applied Aviation Studies, 7(2), 312–328.
30.
O’ConnorP.WalkerP. (2011). Evaluation of a human factors analysis and classification system as used by simulated aviation mishap boards. Aviation, Space, and Environmental Medicine, 82(1), 44–48.
31.
OrasanuJ. (2010). Flight crew decision-making. In KankiB.HelmreichR.AncaJ. (Eds.), Crew resource management (2nd ed., pp. 147–179). San Diego, CA: Elsevier.
32.
OrasanuJ.FischerU.DavisonJ. (2002). Risk perception: A critical element of aviation safety. IFAC Proceedings Volumes, 35(1), 49–58.
33.
OrasanuJ.MartinL.DavisonJ. (2002). Cognitive and contextual factors in aviation accidents. In SalasE.KleinG. (Eds.), Naturalistic decision making (pp. 343–358). Mahwah, NJ: Lawrence Erlbaum.
34.
PattersonJ. M.ShappellS. A. (2010). Operator error and system deficiencies: Analysis of 508 mining incidents and accidents from Queensland, Australia using HFACS. Accident Analysis & Prevention, 42(4), 1379–1385.
35.
RamoniM.SebastianiP. (2007). Bayesian methods. In BertholdM.HandD. (Eds.), Intelligent data analysis: An introduction (2nd ed., pp. 131–168). New York, NY: Springer.
36.
RasmussenJ. (1983). Skills, rules, and knowledge: Signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, 257–266.
37.
ReasonJ. (1990). Human error. New York, NY: Cambridge University Press.
38.
SnookS. A. (2000). Friendly fire: The accidental shootdown of U.S. Black Hawks over northern Iraq. Princeton, NJ: Princeton University Press.
39.
TaibI. A.McIntoshA. S.CaponecchiaC.BaysariM. T. (2011). A review of medical error taxonomies: A human factors perspective. Safety Science, 49(5), 607–615.
40.
TamborelloF. P.TraftonJ. G. (2017). Human error as an emergent property of action selection and task place-holding. Human Factors, 59(3), 377–392.
41.
TheophilusS. C.EsenowoV. N.ArewaA. O.IfelebueguA. O.NnadiE. O.MbanasoF. U. (2017). Human factors analysis and classification system for the oil and gas industry (HFACS-OGI). Reliability Engineering & System Safety, 167, 168–176.
42.
TvaryanasA. P.ThompsonW. T. (2008). Recurrent error pathways in HFACS data: Analysis of 95 mishaps with remotely piloted aircraft. Aviation, Space, and Environmental Medicine, 79(5), 525–532.
43.
WalkerP.O’ConnorP.PhillipsH.HahnR.DalitschW. (2011). Evaluating the utility of DOD HFACS using lifted probabilities. Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting (pp. 1793–1797). Los Angeles, CA: Human Factors and Ergonomics Society.
44.
WeberD. E.DekkerS. W. (2017). Assessing the sharp end: Reflections on pilot performance assessment in the light of Safety Differently. Theoretical Issues in Ergonomics Science, 18(1), 59–78.
45.
WiegmannD.ShappellS. (1997). Human factors analysis of postaccident data: Applying theoretical taxonomies of human error. The International Journal of Aviation Psychology, 7(1), 67–81.
46.
WiegmannD.ShappellS. (2001). Human error analysis of commercial aviation accidents: Application of the Human Factors Analysis and Classification System (HFACS). Aviation, Space, and Environmental Medicine, 72(11), 1006–1016.
47.
WiegmannD.ShappellS. (2003). A human error approach to aviation accident analysis: The Human Factors Analysis and Classification System. Burlington, VT: Ashgate Publishing Company.
48.
WinklerR. (2003). An introduction to Bayesian inference and decision (2nd ed.). Gainesville, FL: Probabilistic Publishing.
49.
WoodsD. D.CookR. I. (1999). Perspectives on human error: Hindsight biases and local rationality. In DursoF.NickersonR. (Eds.), Handbook of applied cognition (pp. 141–171). New York, NY: John Wiley & Sons.
50.
YinR. K. (2016). Qualitative research from start to finish (2nd ed.). New York, NY: Guilford Press.
