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Crowd-shipping for urban food rescue logistics

Crowd-shipping for urban food rescue logistics The purpose of this research is to gain a better understanding of how a crowd-shipping platform can achieve a critical mass of senders and carrier crowd members to yield network effects that are necessary for the platform to grow and thrive. Specifically, this research studies the participation decisions of both senders and carriers over time and the impacts of the resulting feedback loop on platform growth and performance.Design/methodology/approachAn agent-based model is developed and used to study dynamic behavior and network effects within a simulated crowd-shipping platform. The model allows both carriers and senders to be represented as autonomous, heterogeneous and adaptive agents, whose decisions to participate in the platform impact the participation of other agents over time. Survey data inform the logic governing agent decisions and behaviors.FindingsThe feedback loop created by individual sender and carrier agents' participation decisions generates complex and dynamic network effects that are observable at the platform level. Experimental results demonstrate the importance of having sufficient crowd carriers available when the platform is initially launched, as well as ensuring that sender and carrier participation remains balanced as the platform grows over time.Research limitations/implicationsThe model successfully demonstrates the power of agent-based modeling (ABM) in analyzing network effects in crowd-shipping systems. However, the model has not yet been fully validated with data from a real-world crowd-shipping platform. Furthermore, the model's geographic scope is limited to a single census tract. Platform behavior will likely differ across geographic regions, with varying demographics and sender/carrier density.Practical implicationsThe modeling approach can be used to provide the manager of a volunteer-based crowd-shipping program for food rescue with insights on how to achieve a critical mass of participants, with an appropriate balance between the number of restaurant food donation delivery requests and the number of crowd-shippers available and willing to make those deliveries.Social implicationsThis research can help a crowd-shipping platform for urban food rescue to grow and become self-sustainable, thereby serving more food-insecure people.Originality/valueThe model represents both senders and the carrier crowd as autonomous, heterogeneous and adaptive agents, such that network effects resulting from their interactions can emerge and be observed over time. The model was designed to study a volunteer crowd-shipping platform for food rescue, with participant motivations driven by personal values and social factors, rather than monetary incentives. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Physical Distribution & Logistics Management Emerald Publishing

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0960-0035
DOI
10.1108/ijpdlm-01-2020-0001
Publisher site
See Article on Publisher Site

Abstract

The purpose of this research is to gain a better understanding of how a crowd-shipping platform can achieve a critical mass of senders and carrier crowd members to yield network effects that are necessary for the platform to grow and thrive. Specifically, this research studies the participation decisions of both senders and carriers over time and the impacts of the resulting feedback loop on platform growth and performance.Design/methodology/approachAn agent-based model is developed and used to study dynamic behavior and network effects within a simulated crowd-shipping platform. The model allows both carriers and senders to be represented as autonomous, heterogeneous and adaptive agents, whose decisions to participate in the platform impact the participation of other agents over time. Survey data inform the logic governing agent decisions and behaviors.FindingsThe feedback loop created by individual sender and carrier agents' participation decisions generates complex and dynamic network effects that are observable at the platform level. Experimental results demonstrate the importance of having sufficient crowd carriers available when the platform is initially launched, as well as ensuring that sender and carrier participation remains balanced as the platform grows over time.Research limitations/implicationsThe model successfully demonstrates the power of agent-based modeling (ABM) in analyzing network effects in crowd-shipping systems. However, the model has not yet been fully validated with data from a real-world crowd-shipping platform. Furthermore, the model's geographic scope is limited to a single census tract. Platform behavior will likely differ across geographic regions, with varying demographics and sender/carrier density.Practical implicationsThe modeling approach can be used to provide the manager of a volunteer-based crowd-shipping program for food rescue with insights on how to achieve a critical mass of participants, with an appropriate balance between the number of restaurant food donation delivery requests and the number of crowd-shippers available and willing to make those deliveries.Social implicationsThis research can help a crowd-shipping platform for urban food rescue to grow and become self-sustainable, thereby serving more food-insecure people.Originality/valueThe model represents both senders and the carrier crowd as autonomous, heterogeneous and adaptive agents, such that network effects resulting from their interactions can emerge and be observed over time. The model was designed to study a volunteer crowd-shipping platform for food rescue, with participant motivations driven by personal values and social factors, rather than monetary incentives.

Journal

International Journal of Physical Distribution & Logistics ManagementEmerald Publishing

Published: May 27, 2021

Keywords: Food rescue; Agent-based modeling; Crowd-shipping

References