Motivation and Participation of Digital Volunteer Communities in Humanitarian Assistance: Models and Incentives for Closing the Gap to Decision Makers

Professor Dr.-Ing. Frank Fiedrich
Bergische Universität Wuppertal, Fachbereich D, Abteilung Sicherheitstechnik, Lehrstuhl für Bevölkerungsschutz, Katastrophenhilfe und Objektsicherheit, Lise-Meitner-Straße 11-13, 42119 Wuppertal

Since a number of years, Volunteered Geographic Information (VGI) plays an important role in the management of large scale disasters and in Humanitarian Assistance. Digital volunteer communities such as the CrisisMapper community, the Standby Task Force, the UAViators or Virtual Operations Support Teams (VOST) have a high potential to improve the decision making processes by providing adequate data which otherwise would not be available. While a lot of research focuses on the extraction, validation and visualization of VGI data the motivational aspects of these communities are hardly understood. A better understanding of motivational and impeding factors would help to identify critical success factors and incentives in order to increase not only the number of volunteers but also the quality of the provided data. The proposed research project will provide a categorisation scheme for different volunteer groups based on their degree of organizational structure and the degree of the individual linkage of the volunteers to the respective community. We will analyse motivational success factors for selected volunteer groups with a mixed method approach including surveys, interviews and focus groups. On the other side, the information needs of the humanitarian response groups will be analysed and compared to the potential tasks of the volunteer communities. We assume that a closer link between volunteers and responders will not only increase the quality of the provided data but also the motivation of the volunteers and therefore help to close the current gap and increase the collaboration between volunteers and responders. The proposed research will also analyse passive digital volunteers like Twitter users. This group is often not aware that they provide valuable VGI data. Based on the content analysis of Twitter data from past events different categories of this specific volunteer group will be identified. Comparisons to the results from the motivational study and semistructured interviews will be used to develop a model for the activation of passive VGI contributors. The results of the proposed research will also provide valuable links to other research aspects of the SPP 1894 priority programme like big data analysis, visualization and quality assurance.