Volunteer Management in Charity Storehouses: Experience, Congestion and Operational Performance
成果类型:
Article
署名作者:
Urrea, Gloria; Pedraza-Martinez, Alfonso J.; Besiou, Maria
署名单位:
University of Colorado System; University of Colorado Boulder; Indiana University System; Indiana University Bloomington; IU Kelley School of Business; Kuhne Logistics University
刊物名称:
PRODUCTION AND OPERATIONS MANAGEMENT
ISSN/ISSBN:
1059-1478
DOI:
10.1111/poms.13073
发表日期:
2019
页码:
2653-2671
关键词:
volunteer management
experience
learning
congestion
multi-method research
摘要:
We study volunteer management at a charity storehouse operated by a large faith-based organization. The storehouse runs entirely on volunteer efforts. We investigate the role of volunteer experience and storehouse congestion in the preparation of orders using a multi-method approach. First, we conduct a field study to explore these relationships and collect data at the level of volunteers' teams. These teams can pair volunteers with either different levels of experience (mixed pairing) or equal levels of experience (no-mixed pairing). Second, we estimate the effects of volunteer experience and storehouse congestion on the order processing times empirically. Third, we build a simulation model to study how operational decisions-volunteers' pairing in teams and whether to allow or impede storehouse congestion-affect two performance metrics: on-time order preparation rate and additional time to prepare the orders, in steady conditions. Then, we simulate disaster conditions at the storehouse, that is, simultaneous surges in supply of volunteers and demand of orders. Contrary to extant literature on team collaboration, we find that no-mixed pairing outperforms mixed pairing under disaster conditions with storehouse congestion. In fact, no-mixed pairing improves the on-time order preparation by 4.32% and the additional time to prepare the orders by 14.42% compared to mixed pairing. Moreover, under disaster conditions, a controlled congestion policy at the storehouse delivers the best performance metrics.
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