| Abstract: |
The decentralized nature of gig platforms gives rise to a new employment model with freelancers. However, recent industry debates have questioned whether the freelancer model should be the preferred model for the gig economy, and there have been regulatory attempts to restrict its use. This research aims to uncover new insights by examining the behavioral differences between freelancers and employees and their impact on gig platforms. Partnering with a food delivery platform, we conducted a field experiment by randomly assigning customer orders to freelancers or employees. Our findings reveal an efficiency-quality trade-off between the two employment models. On the one hand, freelancers exhibit longer order completion times, indicating lower system efficiency. This inefficiency stems from the decentralized nature of freelancer operations, which can lead to suboptimal order selection and insufficient order batching. These issues can be mitigated with employees, as platforms can centrally coordinate order assignments. On the other hand, freelancers outperform employees in on-time performance. Our empirical evidence further reveals that this difference stems from freelancers' tendency to prioritize urgent orders that have a higher risk of missing the promised delivery times, even at the cost of traveling longer distances overall. In contrast, employees optimize routes to minimize travel distances, sometimes compromising service quality by neglecting customer commitments. These findings provide implications for developing tailored operational strategies to improve worker performance based on their employment type. Platforms can enhance freelancer efficiency by providing order-picking and routing recommendations, while motivating employees to prioritize customer satisfaction through additional incentives for on-time performance.
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Biography:
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Yao Cui is an associate professor of operations, technology, and information management at the Samuel Curtis Johnson Graduate School of Management, part of the Cornell SC Johnson College of Business at Cornell University. His research interests center around operational innovation, specifically examining how new technologies can be leveraged to develop novel operations strategies in supply chains, the gig economy, and the hospitality industry. Using both analytical and empirical methodologies, his research aims to uncover innovative solutions to practical problems facing these industries.
Yao’s research articles have been published in leading journals such as Management Science, Manufacturing & Service Operations Management, and Production and Operations Management. His research has been recognized with several awards such as the INFORMS Service Science Section Best Student Paper Award, the INFORMS TIMES Best Working Paper Award, the Digital Supply Chain and Supplier Diversity Conference Best Paper Award, and the INFORMS Public Sector OR Best Paper Award. At Johnson, he was the recipient of the 2020 Half Century Faculty Research Fellowship and the 2017 Clifford H. Whitcomb Faculty Fellowship. He holds editorial positions in leading journals such as associate editor for Manufacturing & Service Operations Management, senior editor for Production and Operations Management, and associate editor for Naval Research Logistics.
Yao teaches the operations management core course in the EMBA and MBA programs, and revenue management analytics for doctoral students. In 2023, he was named one of the 40-Under-40 Best MBA Professors by Poets&Quants. Prior to joining Johnson, he received his doctoral degree from the Stephen M. Ross School of Business at the University of Michigan and his bachelor’s degree from the Department of Industrial Engineering at Tsinghua University.
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