Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice
