Abstract

This dissertation focuses on the intersection of operational decisions and their social implications, an area of growing importance as businesses increasingly optimize for efficiency while simultaneously facing pressures to consider environmental sustainability and technological adoption. In my research, I utilize field experiment and econometrics to study how innovations in delivery speed and algorithmic implementation influence human behavior and create broader impacts beyond immediate business outcomes. In Chapter 1, "Green E-commerce: Environmental Impact of Fast Delivery'', we study the impact of faster delivery on how consumers place orders (their order frequency and basket sizes) and the subsequent environmental implications. Specifically, we leverage a quasi-experiment involving the opening of a new local warehouse by Alibaba Group, which led to a half-day improvement in the delivery speed for local orders. Through a difference-in-differences analysis, we find that the delivery speed improvement not only increased consumers' monthly purchasing amount by 6.70%, but also increased monthly order frequency by a higher percentage (i.e., 7.74%) and reduced the average order basket size by 0.79%. These results collectively suggest that with faster delivery, consumers purchase more on the platform but do so in more frequent and smaller orders, which implies more packaging and transportation costs for each unit of product sold. Based on these results, we conduct a detailed calibration using both public and company-specific data to estimate the increase in the platform's carbon emissions due to faster delivery. We also explore and identify two mechanisms contributing to the phenomenon: order-splitting and category expansion. We combine these insights with heterogeneous treatment effect analysis to derive managerial implications for the e-commerce platform. In particular, we find that for a platform that implements a threshold shipping policy, raising the free shipping threshold may be more effective than raising the shipping fee to reduce the environmental and operational costs associated with faster delivery. In Chapter 2, "How Forced Intervention Facilitates Long-term Algorithm Adoption'', we investigate whether and why forced interventions can promote algorithm adoption and reduce algorithm aversion in the long term. Data from a leading online education company reveal that sales workers underutilize a new matching algorithm and often use it on low-quality leads. The company conducted a field experiment where sales workers were forced to use or not use the algorithm for three weeks. Experimental results show that forcing workers to use the algorithm during the experiment causally increases their algorithm usage one month after the experiment by 15.8 percentage points. We develop a theoretical model to derive empirical strategies for exploring the mechanisms behind this improvement. Contrary to the traditional literature focusing on habit formation, our findings suggest that learning is a key driver for long-term algorithm adoption among the workers. Specifically, forced algorithm usage allows workers to experience the algorithm's unbiased performance firsthand and positively adjust their beliefs about it. Consequently, after the experiment, the workers use the algorithm not only more frequently but also more on high-quality leads. The study provides empirical evidence that forced intervention can effectively improve long-term algorithm adoption among workers, which is crucial for continuous development of these technologies. More importantly, we demonstrate that forced intervention works by enabling workers to experience an algorithm's unbiased performance and adjust their prior misinformed assumptions about its effectiveness. This suggests that firms can implement extrinsic interventions or educational programs to help workers recognize the benefits of algorithms and develop unbiased beliefs about their capabilities, thus facilitating sustained algorithm usage.

Committee Chair

Dennis Zhang

Committee Members

Fuqiang Zhang; Jake Feldman; Jiankun Sun; Meng Liu; Xiaoyang Long

Degree

Doctor of Philosophy (PhD)

Author's Department

Supply Chain, Operations, and Technology

Author's School

Olin Business School

Document Type

Dissertation

Date of Award

5-22-2025

Language

English (en)

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