A courier’s choice for delivery gigs in a real-world crowdshipping service with observed sender-courier preference discrepancy

Abstract

A courier’s choice for delivery gigs in a crowdshipping service is not well understood in the literature. Thus the objective of this study is to empirically investigate the crowdshipping (CS) couriers’ bidding preferences for delivery gigs, and how the gig features impact the gig delivery status of a real-world CS service in the United States. The delivery records were made available between 2015 and 2018. A descriptive analysis reveals that there exist significant preference discrepancies between the senders and the couriers in terms of package size, delivery time window, delivery distance, and delivery fee. Therefore, four features to capture the above discrepancy are specifically created from the data in predicting the bidding level and the delivery status. The bidding level which is measured by the number of bids received per gig is classified into low, medium, and high bidding levels to reflect the couriers’ preferences for the delivery gigs. The delivery status, labeled as delivered or undelivered, is affected by the couriers’ eventual choice of the delivery gigs. Five popular machine learning (ML) methods, namely Random Forest Decision Tree, Artificial Neural Network, eXtreme Gradient Boosting (XGBoost), Support Vector Machine, and Bayesian Network are applied to the predictions. Among them, the XGBoost is found to perform the best. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to explain and visualize how each feature influences the dependent variable (prediction target). The SHAP values provide an effective visualization and interpretability of the feature impact values and importance rankings, much like the coefficients of the traditional econometric based logit model. The paper further demonstrates that the ML models and the logit models produce consistent feature influences. Overall, the couriers are generally interested in the delivery gigs of extra-large and huge package sizes, medium to long delivery distance, insured packages, and flexible delivery time window. Discrepancy related features significantly influence couriers’ bidding behavior as expected. The study also reveals that gigs that receive a high number of bids do not translate into their eventual successful deliveries. Finally, policy and practical implications for improving the CS service particularly through pricing strategies are discussed.