Decision Making In Emergency Management

This assessment will provide a brief review of Hoekstra and Montz’s (2017) study ‘Decisions under duress: factors influencing emergency management decision making during Superstorm Sandy’. The purpose of this study was to investigate how emergency managers (EMs) make critical evacuation decisions in order to develop a decision-making model specifically for the complex role of EMs. Superstorm Sandy, the last tropical cyclone of the 2012 Northern Atlantic Hurricane season, was considered an extraordinary event due to its multi-hazard nature which made it difficult to characterize and subsequently respond to (Kunz, Mühr, Kunz-Plapp, Daniell, Khazai, Wenzel, Vannieuwenhuyse, Comes, Elmer, Schröter, Fohringer, Münzberg, Lucas, & Zschau, 2013, p. 2579). Utilising this complex weather event as a case study and Lindell & Perry’s (2012) Protective Action Decision model (PADM) as a foundation, Hoekstra and Montz have developed a decision-making model specifically for EMs that further distinguishes between “making a decision and actually taking action” (Hoekstra & Montz, 2013, p. 453). A critique of this study and its contribution to the field of decision making in EM will conclude this review.

Hoekstra and Montz utilised a qualitative method of data collection for this exploratory study. This involved conducting twenty-three in depth interviews with EMs in varying roles within Emergency Operations Centres (EOC) from across the affected areas in New York City and New Jersey (Hoekstra & Montz, 2017, p. 456). “The interviews elicited information regarding the process and complexity of decision making, gathering both explicit and tacit information that contributed to understanding what influences decision making” (Hoekstra & Montz, 2017, p. 456). Utilising the evidence obtained from participants, the study concluded that there are three components that influence the EM’s decision to evacuate: characteristics of the municipality; factors involving the individual EM; and weather information and storm characteristics (Hoekstra & Montz, 2017, p. 457). Hoekstra and Montz found that the model they were developing could distinguish between decision and action, as participating EMs were identified as either being influenced by: ‘accelerators’, such as a personal plea from the meteorologist in charge (MIC), the tone of forecasters, their own gut feelings, and the visualisation of the storm tracking left toward the coast; or, their own observations when deciding to take action, which included waiting for visual cues such as water reaching a certain level before ordering the evacuation (Hoekstra & Montz, 2017, p. 457).

Hoekstra and Montz concluded that these factors and various stages of decision making presented in their model built upon and complimented the works of others in the field. They had chosen to base their model on Lindell and Perry’s (2012) PADM instead of Tobin and Montz’s All hazards response model as it provides a more dynamic multistage model that more closely parallels the factors they hypothesised to influence EM decision making (Hoekstra & Montz, 2017, p. 455). The evidence they found above supported their hypothesis as the three sets of factors combined in various ways to impact decision making, and the ‘accelerating factors’ generated actions, which “parallels the findings of the PADM and hurricane response model by Lazo (2015)” (Hoekstra & Montz, 2017, p. 470). However, this study exceeds these works as it considers EMs as “individuals who often rely on a variety of non-weather-related sources; a recognition key to identifying opportunities for improved response in the future” (Hoekstra & Montz, 2013, p. 470).

The significance of this study’s contribution to the field of EM cannot be understated. There is a large body of research focused on public official and general public decision making during natural hazards and how weather information is utilised by decision makers (Baumgart et al, 2008; Erickson, 2019). However, the lack of research into decision making specific to the complex role of an EM is evident. Naturally occurring hazards account for a large portion of disasters worldwide, and due to the continued increase in global vulnerabilities due to climate change and an increasing population, understanding the EM decision-making process in relation to complex weather events will only become more critical. It is evident that through this study, Hoekstra and Montz have built a strong foundation for future research in this field. They have proposed a comprehensive model for decision making during extreme weather events specifically to the role of EMs, which has the potential to extend across all hazard types and enhance decision making capabilities.

This study was carried out by two people who have been heavily involved in research in this area of EM, providing a strong sense of validity and reliability to its contents. Hoekstra, who holds a bachelor’s degree in environmental, atmospheric, and oceanic sciences, has written several peer-reviewed articles integrating meteorology and social science over the past decade (Linked in, 2020). Montz alone has contributed to over 93 publications on natural hazards and environmental impact analysis particularly as it relates to the vulnerability of communities to the impacts climate change as projected in future flood scenarios (Researchgate, 2020). They should also be commended for their use of a case study instead of a simulation which is more commonly seen in studies of decision making.

A critique of the study includes concern that Superstorm Sandy occurred in October 2012; however, the interviews were not conducted until June 2014. It is unlikely that the participants were able to accurately recall all details of their decision making after such a long period of time, which brings the reliability of the data into question. Although the study asserts that the findings can be generalised across other weather events, twenty-three interviews in relation to one weather event is insufficient to make such a bold claim. Further studies would need to be utilised to understand the decisions made during other extreme weather events, in other locations and with varying EMs possessing different levels of knowledge and experience to confirm its validity and reliability.

Hoekstra and Montz (2017) set out to develop a decision-making model specific to the complex role of EMs by interviewing EMs involved in the evacuation decision making process during Superstorm Sandy. They have identified the factors influencing not only decisions, but also those that determine how and when those decisions are acted upon. The model they have developed is unique in hazards literature as it recognises that EMs are individuals who follow a separate model to that of the general public and officials; this recognition is key to identifying opportunities for improved response and is a significant contribution to future research in this field. However, although the authors have provided a strong foundation for future study in this field, the limited scope of the study makes it hard to definitively state that the findings can be generalised across all hazard types.

References

1. Baumgart, L. A., Bass, E. J., Kloesel, K., & Philips, B. (2008). Emergency Management Decision Making during Severe Weather. Weather and Forecasting. American Meteorological Study. Vol. 23. No. 6. Retrieved from: https://www.alnap.org/help-library/emergency-management-decision-making-during-severe-weather
2. Erickson, S. E. (2019). The Federal Emergency Management Agency: A New Era of Weather Disaster Management. University of Oklahoma Graduate College. Retrieved from: https://shareok.org/bitstream/handle/11244/320367/2019_Erickson_Somer_Dissertation.pdf?sequence=5&isAllowed=y
3. Hoekstra, S. & Montz, B. (2017). Decisions under duress: factors influencing emergency management decision making during Superstorm Sandy. Natural Hazards. Vol. 88. Retrieved from: file:///C:/Users/Beth/AppData/Local/Packages/Microsoft.MicrosoftEdge_8wekyb3d8bbwe/TempState/Downloads/Decisions_under_duress_factor.pdf
4. Kunz, M., Mühr, B., Kunz-Plapp, T., Daniell, J. E., Khazai, B., Wenzel, F., Vannieuwenhuyse, M., Comes, T., Elmer, F., Schröter, K., Fohringer, J., Münzberg, T., Lucas, C., & Zschau, J. (2013). Investigation of superstorm Sandy 2012 in a multi-disciplinary approach. Natural Hazards and Earth System Sciences. Vol. 13. Retrieved from: https://www.nat-hazards-earth-syst-sci.net/13/2579/2013/nhess-13-2579-2013.pdf
5. Lindell, M. K., & Perry, R. W. (2012). The protective action decision model: theoretical modifications and additional evidence. Risk Analysis: An International Journal. Vol. 32. No. 4. Retrieved from: https://www.researchgate.net/publication/51234490_The_Protective_Action_Decision_Model_Theoretical_Modifications_and_Additional_Evidence
6. Linked in. (2020). Stephanie Hoekstra: Program Director of Applied Sciences at UCLA Extension. Retrieved from: https://www.linkedin.com/in/stephanie-hoekstra-b075672a
7. Researchgate. (2020). Burrell E. Montz: East Carolina University. Retrieved from https://www.researchgate.net/profile/Burrell_Montz