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Methods and lessons for business resilience and recovery surveys

Published

Author(s)

Maria Watson, Charlotte Brown, John Handmer, Cynthia Kroll, Anne Wein, Jennifer Helgeson, Adam Rose, Noah Dormady, Juri Kim

Abstract

Surveys are important tools in business resilience and recovery research because of their ability to capture disaggregated economic information; however, they can be difficult and costly due to business operational dynamics and the larger challenges of disaster research. The COVID-19 pandemic serves as a recent example where demand for business data was high across both research and practice. Yet, the methods and modes for collecting data were limited due to safety, health, and ethical concerns. This research seeks to address the lack of tailored guidance for conducting business resilience and recovery surveys by collecting and synthesizing instruments and best practices from previous survey efforts. These previous surveys were undertaken by a diverse group of organizations with varied research questions, objectives, and hazard events of interest. This paper discusses six broad lessons: clearly define purpose, objectives, and concepts; recognize that response rates will be low, consider disaster dynamics in the research design, address bias that can be exacerbated by disasters, take care to acknowledge the unique ethical considerations of disaster resilience surveys in the business and economic context, and verify and validate data at all stages of the survey process. These lessons, in addition to the published instruments themselves, support researchers or practitioners who wish to conduct their own business resilience and recovery surveys in the future.
Citation
International Journal of Disaster Risk Reduction

Citation

Watson, M. , Brown, C. , Handmer, J. , Kroll, C. , Wein, A. , Helgeson, J. , Rose, A. , Dormady, N. and Kim, J. (2023), Methods and lessons for business resilience and recovery surveys, International Journal of Disaster Risk Reduction, [online], https://doi.org/10.1016/j.ijdrr.2023.103743, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935211 (Accessed April 19, 2024)
Created May 25, 2023, Updated May 24, 2023