Affiliation or situation: what drives strategic decision-making in crisis response?

Journal of Managerial Issues, Summer, 2009 by Paul Drnevich, Rangaraj Ramanujam, Shailendra Mehta, Alok Chaturvedi

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METHODS

Sample Data

We test our hypotheses with combined data from an experiment (a U.S. Department of Homeland Security training exercise called Measured Response (MR)) and a simulation. We collected the data for our sample from both live agents (government officials participating in the exercise) and artificial agents from the simulation (a simulated smallpox outbreak in a major city). Given that such crisis events are extreme in magnitude and also rare, it is difficult to study JOC responses as a "real" event actually unfolds. Therefore, simulation methods are a highly valid, commonly-accepted, and frequently-used methodology to study crisis response (Chen et al., 2004; Eubank et al., 2004; Harrison et al., 2007).

The Measured Response Simulation. The exercise simulated a bio-terror "attack" on the population of a major city whose population was represented by tens of thousands of artificial agents. These "intelligent agents" contain four layers of "DNA" that represent the position, mobility, infectability, and well-being of an individual artificial agent. The simulation operates by modeling the rate of transmission as a function of population density, mobility, social structure, and lifestyle using an explicit spatial-temporal model. It uses the movement of individuals and the exposure of susceptible individuals to infection sources to model the spread of disease. In addition to standard epidemiological parameters such as reproductive rates of infection and disease propagation among individuals, the simulation platform also models the hosts and pathogens via several interrelated processes. These include age-specific susceptibility, infection propagation due to the exposure of wholly susceptible populations to the newly infectious population, and population immunity necessary to prevent the epidemic.

Simulation model verification and validation are critical for methodological credibility, however, the validation of models of terrorist attack events is extremely difficult and often impractical (Chen et al., 2004; Green and Kolesar, 2004; Harrison et al., 2007). To address this challenge and minimize validation issues, the simulation creators built and validated the model with data provided by government, scientific, and academic sources engaged in research and practice in this area (Chaturvedi et al., 2005). For example, they utilized U.S. Centers for Disease Control (CDC)-supplied pathogen-specific data to ensure that the outbreak and spread of the biological agent took place in a realistic manner. Finally, the simulated scenario itself depicts an attack in which uncertainty, response choice, speed, and resource intensity can significantly affect the outcomes in terms of infection rate, contagion spread, and public mood. (For more detail on the use and validity of simulation methodology, see Harrison et al. (2007) and Chaturvedi et al. (2005) for specific detail on the construction and validation of the simulation platform used in this study.)