Improving retention: Detecting volunteer sabbaticals and reasons for taking a sabbatical

• In Event: Retaining Volunteers

Abstract

Volunteer-involving organizations are facing challenges associated with retaining volunteers against the background of rising turnover rates (Brudney & Meijs, 2009; Hustinx, 2010; Lockstone-Binney et al., 2022). Although most volunteer turnover can be explained by personal circumstances of the volunteer (e.g., Lancee & Radl, 2014), some can be explained by organizational factors leading to volunteer burnout or boreout (Hustinx, 2010). One strategy to cope with some personal circumstance or to prevent burnout or boreout among paid employees is a sabbatical (Carr & Tang, 2005). We define a volunteer sabbatical as an unusual longer pause between volunteer activities for the same organization. This research sets out to investigate whether volunteers do indeed take sabbaticals, what the reasons for these sabbaticals are, and whether volunteer-involving organizations should actively offer volunteer sabbaticals as an instrument to improve volunteer retention.

We use an unusually complete dataset and advanced methods to identify sabbaticals in volunteer-involving organizations. Our data are drawn from the Apache Software Foundation (ASF), which is an Open-Source Software (OSS) project-sponsoring nonprofit organization. ASF is an all-volunteer organization and relies on volunteers at all levels of the organization. Since its founding in 1999, thousands of volunteers have produced hundreds of OSS projects. We selected the ASF because it requires that nearly all communication take place in an online, transparent forum attributable to the individual. The communications are publicly available and offer an unparalleled opportunity to detect sabbaticals and examine reasons why they were taken by analyzing the digital traces when individuals contribute to a project.

We use Bayesian Mixture (Gelman, 2004) and Hidden Markov (Rabiner, 1989) models to determine deviations from typical patterns of contributions and use those to identify periods of time that reflect sabbaticals from a project. We then extract all emails the individual sent immediately prior to what we have labeled a volunteer sabbatical, sample from them, and hand code them for reasons given for changing their work patterns. These coded emails serve as a training set for an automatic text classifier that codes for those reasons as well as detecting new emergent patterns in the emails. This allows us to extract a complete set of reasons given for taking a sabbatical. Finally, by combining these methods, we are able to identify people who took sabbaticals and those who talked like they should take sabbaticals but did not and compare their long-term retention and activity in the project.

First of all, this research contributes to new research projects as it will establish the concept of volunteer sabbatical and present procedures to analyze these sabbaticals. This broadens our understanding of the volunteer journey of individuals, especially across the life span. This research contributes to our understanding of volunteer turnover and episodic volunteering as some sabbaticals might be misinterpreted as either stopping or retiring (Konieczny, 2018), or episodic behavior (Cnaan et al 2022). The research also offers possible practical advice to volunteer managers about offering sabbaticals to enable returning after ‘quitting’ for personal circumstances.

References

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Authors

• Curtis Atkisson, University of Massachusetts

• Brenda K. Bushouse, University of Massachusetts

• Lucas Meijs, Erasmus University Rotterdam

• Philine van Overbeeke, Rotterdam School of Management, Erasmus University Rotterdam