Language
English (en)
Date of Award
Spring 5-15-2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Chair and Committee
Dennis J. Zhang Fuqiang Zhang
Committee Members
Tat Y. Chan, Hengchen Dai, Jacob Feldman,
Abstract
The objective of this dissertation is to study the emerging operations issues on data-driven platforms and digital operations. With the increasing availability of data and the development of information technologies, platforms process a large amount of data in order to efficiently make daily operational decisions. Understanding human behaviors and the human-algorithm connection is instrumental to the success of this process. In my research, I implement field experiments and use structural models to study in-warehouse worker behavior and out-of-warehouse customer behavior in the last mile of logistics.
In Chapter 1, “The Impacts of Algorithmic Work Assignment on Fairness Perceptions and Productivity: Evidence from Field Experiments”, we study in-warehouse worker behavior. We study how algorithmic (vs. human-based) task assignment processes change task recipients' fairness perceptions and productivity. In a 15-day-long field experiment with Alibaba Group in a warehouse where workers pick products following orders (or “pick lists”), we randomly assigned half of the workers to receive pick lists from a machine that ostensibly relied on an algorithm to distribute pick lists, and the other half to receive pick lists from a human distributor. Despite that we used the same underlying rule to assign pick lists in both groups, workers perceive the algorithmic (vs. human-based) assignment process as fairer by 0.94-1.02 standard deviations. This yields productivity benefits: receiving tasks from an algorithm (vs. a human) increases workers' picking efficiency by 15.56%-17.86%. These findings persist beyond the first day when workers were involved in the experiment, suggesting that our results are not limited to the initial phrase when workers might find algorithmic assignment novel. We replicate the main results in another field experiment involving a nonoverlapping sample of warehouse workers. We also show via online experiments that people in the U.S. also view algorithmic task assignment as fairer than human-based task assignment. We demonstrate that algorithms can have broader impacts beyond offering greater efficiency and accuracy than humans: introducing algorithmic assignment processes may enhance fairness perceptions and productivity. This insight can be utilized by managers and algorithm designers to better design and implement algorithm-based decision making in operations.
In Chapter 2, “The Value of Logistic Flexibility in E-commerce”, we study out-of-warehouse customer behavior in the last mile of logistics. We use the opening of hundreds of pick-up stations as a natural experiment to study the impact of these stations on consumers. We find that the introduction of pick-up stations has increased total sales by 3.9%. In contrast with past literature, we show that shipping time reduction is not the driving factor on the impact of pick-up stations. Yet, the logistic flexibility introduced by pick-up stations explains the sales impact. To explicitly examine how logistic flexibility affects consumers' decisions on purchases, we develop and estimate a structural model of consumer choice. In our model, consumers value two types of logistics flexibility---the flexibility to pick up their items at their preferred time, denoted as the value of time flexibility, and the flexibility to delay pickup decisions until after packages arrive at a local station, denoted as the value of choice flexibility. We show that the value of time flexibility accounts for 76.2% of the impact on sales, while the value of choice flexibility accounts for the remaining 23.8%. Using our estimated model, we develop a counterfactual strategy in building pick-up stations that could achieve the sales lift with 56.4%-63.6% fewer stations. Last but not least, using our estimated time flexibility, we also develop a novel shipping strategy without pick-up stations that could improve sales by 8.4%. Our estimates suggest that our counterfactual logistic strategies could increase consumer welfare by 2.0%-10.0%.
Recommended Citation
Bai, Bing, "Data-driven Platform and Digital Operations" (2023). Olin Business School Electronic Theses and Dissertations. 20.
https://openscholarship.wustl.edu/olin_etds/20