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Improving Worker Engagement in the Ride-Sharing Economy with Human-Centered Data Science

Office of Alumni and External Relations    2021-01-18

Improving Worker Engagement in the Ride-Sharing Economy with Human-Centered Data Science

Guest: Teng Ye, Ph.D. candidate, University of Michigan

Host: Wei Xuan, Assistant Professor, Antai

Time: Wednesday, Jan 20th, 2021, 10:00-11:30

Venue: Zoom (for conference number and password, pleas send email to xuziqing@sjtu.edu.cn by 10:00, Jan 20th)

Abstract:

While the sharing economy provides flexible and low-barrier jobs for millions of workers globally, a lack of both organization identity and social bonds contributes to the high attrition rate experienced by the sharing-economy platforms. Understanding how to incentivize and engage workers is thus critical. To improve worker engagement, we propose a human-centered data science framework that synergizes strengths across machine learning, causal inference, field experiment, and social science theories. In this talk, I will present our empirical work in collaboration with a leading ride-sharing platform. Inspired by social identity theory and contest theory, we first use a large-scale field experiment to examine the impact of organizing drivers into teams and engaging teams into team contests. We then combine machine learning and causal inference to unpack the heterogeneous treatment effect of team contests at the individual level. Our work identifies directly actionable insights for contest design and team recommender systems. More future directions will be discussed to showcase the effectiveness and flexibility of applying the human-centered data science framework to improve worker performance at large.

Bio

Teng Ye is a Ph.D. candidate at the University of Michigan, Ann Arbor. Her research interests are in human-centered data science, which is at the intersection of data science and social sciences via information systems. Her work is characterized by deploying interdisciplinary solutions to address real-world problems by understanding, predicting, and intervening in human behavior. She has been passionate about a variety of application domains, such as the sharing economy, crowdsourcing, crowdfunding, healthcare, and data science for social good. She has been spotlighted by the Rising Stars in Data Science (CDAC, UChicago).

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