Aiming at a data driven definition of volunteer types: The key to improved volunteer management practices

Abstract

Due to the huge heterogeneity of volunteering, generalizability of context specific findings from the literature regarding volunteer management practices is often limited. Furthermore, it seems that practitioner recommendations are consequently often too narrow or at times contrasting. To deal with this gap, we aim at a data driven approach to cluster volunteers into more homogeneous types, in order to enable (a) comparability of various volunteer contexts, and (b) differentiation of volunteer management strategies. Therefore, we apply an exploratory factor analysis, a cluster analysis and a canonical correlation analysis on a representative nationwide survey in Germany regarding volunteering behavior. Findings are however not robust and not suitable for further substantial interpretation, as the multivariate characteristics of the constructs probed for in the German Survey on Volunteering (GSV) are of limited quality (at least for our statistical analysis). Hence, we clarify the value of more elaborate questions in future large-scale data collection, and we discuss the remaining trade-off in the literature regarding generalizable but limited findings, versus more robust but context specific findings.