Date of Completion
5-3-2016
Embargo Period
5-1-2026
Keywords
groups, teams, emergency, decision-making, mining, mixed-methods, Naturalistic Decision Making
Major Advisor
Robert Henning
Associate Advisor
Dev Dalal
Associate Advisor
Launa Mallett
Field of Study
Psychology
Degree
Doctor of Philosophy
Open Access
Open Access
Abstract
Although the mining industry has made enormous strides in health and safety initiatives over the past few decades, the risk for life-threatening emergency situations is still present. In emergency situations, underground coal miners could be miles from the nearest exit and have to “self-escape” from a potentially hazardous environment. Although anecdotally self-escape from underground mine incidents most often occurs in a group context, the extent to which group behavior may affect the success of self-escape attempts has been vastly understudied. The present research effort attempts to address this gap by using an inductive, case study design to determine if any relationship exists between self-escape group behavior and emergency performance. Historical records of three actual, mine fire incidents and the seven groups of workers which had successfully escaped from them were the primary data source in this investigation. The aims of this study were three-fold: (1) operationalize and measure self-escape task performance using mine emergency Subject Matter Expert (SME) ratings on a Situational-Judgment Test; (2) assess variance in group processes within and between self-escape groups; and (3) determine the overlap between self-escape performance and group processes in order to develop a working model of self-escape survivability. Using this working model, findings were contextualized regarding applicability to the modern mining community as well as future naturalistic decision-making (NDM) related research.
Recommended Citation
Bauerle, Timothy J., "Ad Hoc Groups Engaged in Emergency Decision Making: A Mixed-Methods Study to Improve Successful Self-Escape from Underground Coal Fires" (2016). Doctoral Dissertations. 1130.
https://digitalcommons.lib.uconn.edu/dissertations/1130