Volunteers’ Capabilities and Their Perceived Satisfaction and Performance in Volunteering Tasks During Socio-Natural Disasters

Volunteers can be a valuable human resource for disaster risk management. In the aftermath of natural hazard events affecting vulnerable communities, there are many situations where there is an urgent need to properly match volunteers to volunteering tasks in order to fully harness their capabilities. Our study explores the relationships between volunteers capabilities and their perceived satisfaction and performance in conducting volunteering tasks. We carried out a cross-sectional study recruiting a convenience sample of 170 volunteers (spontaneous and affiliated volunteers) who previously participated performing volunteering tasks during socio-natural disasters in Chile. We collected information on sociodemographics, experience in volunteering activities, capabilities (i.e. competencies and relatively stable psychological attributes), and perceived satisfaction and performance in volunteering tasks. Predictive models based on stepwise logistic regressions were fitted, with areas under the receiver operating characteristics curve (AUC) being estimated to assess the discrimination capacity of the predictive models. A heterogeneous set of sociodemographic characteristics, experiences in volunteering activities, and capabilities were statistically significant predictors for each of the assessed volunteering tasks. Most of the predictive models had an adequate discriminatory capacity (AUC >0.70), with models predicting performance (AUC >0.80) exhibiting the best discriminatory capacity. Knowledge of volunteering tasks – a competency – was the most consistent predictor of perceived satisfaction and performance in volunteering tasks. Our findings are a step forward in matching volunteers to volunteering tasks during emergencies from socio-natural disasters. In addition, some of the observed characteristics (e.g. knowledge) could be trained to improve volunteers’ satisfaction and performance. © 2022 Elsevier Ltd