Purpose – The purpose of this paper is to introduce applied improvisation (AI) as a tool for training humanitarian aid workers. AI incorporates principles and practices from improvisational theatre into facilitation and training. It is an excellent modality for training aid workers to deal with crisis and disaster scenarios where decision-making and collaboration under pressure are critical. Design/methodology/approach – This paper provides a theoretical base for understanding skills needed in disaster response and provides a case for innovative training that goes beyond the current standard. AI principles, activities and case examples are provided. Interviews with development experts who have participated in AI training are excerpted to reveal the impact and promise of this methodology. Findings – Different from typical training and games, which simulate potential crisis scenarios, AI works with participants in developing the skills necessary for success in disaster situations. The benefit is that workers are better prepared for the unexpected and unknown when they encounter it. Research limitations/implications – The current paper is based on author observation, experience and participant interviews. While AI is consistently transformative and successful, it would benefit from more rigorous and structured research to ground the findings more deeply in larger evidence based processes. Practical implications – The authors offer specific activities, resources for many others and practical application of this modality for training purposes. Social implications – Its application has tremendous benefits in training for specific skills, in creating greater cohesion and satisfaction in work units and breaking down culture and language barriers. Originality/value – This work is original in introducing these training methods to humanitarian aid contexts in general, and disaster preparedness and response in particular.
Journal of Humanitarian Logistics and Supply Chain Management – Emerald Publishing
Published: Apr 7, 2015
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