About

Terry Taylor is the Milton W. Terrill Chaired Professor of Business Administration at the Haas School of Business, UC Berkeley. His research interests include artificial intelligence, labor, on-demand service platforms, social responsibility in operations management, and supply chain management. He serves as Associate Editor for the journals Management Science and Operations Research. Taylor has held positions at Columbia University’s Graduate School of Business and Dartmouth’s Tuck School of Business, and he was a business analyst at McKinsey & Company prior to his academic career. He received his PhD and BS from Stanford University. Taylor has been a faculty member at Haas since 2007, where he has also served as Associate Dean for Academic Affairs. His work has earned him several awards, including the Columbia Business School Dean’s Award for Teaching Excellence and the Berkeley Haas Earl F. Cheit Award for Excellence in Teaching.

Research topics

• Computer Science
• Business
• Market economy
• Economics
• Computer Security
• Finance
• Microeconomics
• Commerce
• Marketing
• Labour economics
• Law

Selected publications

• Designing Enterprise AI Systems: Hallucination, Creativity, and Moral Hazard

  SSRN Electronic Journal · 2026-01-01 · 2 citations

  preprintOpen accessSenior author
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• Co-Creating an Equity-Oriented, Discursive Space Within a Hybrid Rural and Remote Teacher Education Program

  2024-01-01

  article1st authorCorresponding
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• Shared-Ride Efficiency of Ride-Hailing Platforms

  Manufacturing & Service Operations Management · 2024 · 20 citations

  1st authorCorresponding
  • Computer Science
  • Business
  • Computer Science

  Problem definition: Ride-hailing platforms offering shared rides devote effort to reducing the trip-lengthening detours that accommodate fellow customers’ divergent transportation needs. By reducing shared-ride delay, improving shared-ride efficiency has the twin benefits of making shared rides more attractive to customers and increasing the number of customers a driver can serve per unit time. Methodology/results: We analytically model a ride-hailing platform that can offer individual rides and shared rides. We establish results that are counter to naive intuition: greater customer sensitivity to shared-ride delay and greater labor cost can reduce the value of improving shared-ride efficiency, and an increase in shared-ride efficiency can prompt a platform to add individual-ride service. We show that when network effects are small, increasing shared-ride efficiency pushes wages to extremes: if the current wage is high (low), increasing shared-ride efficiency pushes the wage higher (lower). We provide a sharp characterization of whether shared-ride efficiency and labor supply are complements or substitutes. We provide simple conditions under which increasing shared-ride efficiency reduces (alternatively, increases) labor welfare. We provide evidence that increasing shared-ride efficiency increases consumer surplus. Managerial implications: Our results inform a platform’s decision of whether to invest in improving shared-ride efficiency, as well as how to change its service offering and wage, as shared-ride efficiency improves. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0545 .

  PublisherDOI

• What is the Impact of Labor Productivity on the Optimal Staffing Level?

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Ride-Hailing Platforms: Competition and Autonomous Vehicles

  Manufacturing & Service Operations Management · 2022 · 101 citations

  Senior authorCorresponding
  • Computer Security
  • Computer Science
  • Business

  Problem definition: Ride-hailing platforms, which are currently struggling with profitability, view autonomous vehicles (AVs) as important to their long-term profitability and prospects. Are competing platforms helped or harmed by platforms’ obtaining access to AVs? Are the humans who participate on the platforms—driver-workers and rider-consumers (hereafter, agents)—collectively helped or harmed by the platforms’ access to AVs? How do the conditions under which access to AVs reduces platform profits, agent welfare, and social welfare depend on the AV ownership structure (i.e., whether platforms or individuals own AVs)? Academic/practical relevance: AVs have the potential to transform the economics of ride-hailing, with welfare consequences for platforms, agents, and society. Methodology: We employ a game-theoretic model that captures platforms’ price, wage, and AV fleet size decisions. Results: We characterize necessary and sufficient conditions under which platforms’ access to AVs reduces platform profit, agent welfare, and social welfare. The structural effect of access to AVs on agent welfare is robust regardless of AV ownership; agent welfare decreases if and only if the AV cost is high. In contrast, the structural effect of access to AVs on platform profit depends on who owns AVs. The necessary and sufficient condition under which access to AVs decreases platform profit is high AV cost under platform-owned AVs and low AV cost under individually owned AVs. Similarly, the structural effect of access to AVs on social welfare depends on who owns AVs. Access to individually owned AVs increases social welfare; in contrast, access to platform-owned AVs decreases social welfare—if and only if the AV cost is high. Managerial implications: Our results provide guidance to platforms, labor and consumer advocates, and governmental entities regarding regulatory and public policy decisions affecting the ease with which platforms obtain access to AVs.

  PublisherDOI

• Shared-Ride Efficiency of Ride-Hailing Platforms

  SSRN Electronic Journal · 2021-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Labor Welfare in On-Demand Service Platforms

  Manufacturing & Service Operations Management · 2021-04-06 · 172 citations

  articleSenior author

  Problem definition: An on-demand service platform relies on independent workers (agents) who decide how much time, if any, to devote to the platform. Some labor advocates have argued that an expansion of the labor pool hurts agents—by reducing the wage and agent utilization (i.e., the fraction of time an agent is busy serving customers). Motivated by concern for agent welfare, regulators are considering measures that reduce the labor pool size or that impose a floor on the nominal wage or effective wage (i.e., the product of the nominal wage and agent utilization). Are agents indeed hurt by an expansion in the labor pool size? Which type of wage-floor regulation is preferable? Are consumers hurt by the imposition of a wage floor? Academic/practical relevance: Because independent agents work without the traditional protections intended to ensure the welfare of employees, the welfare of those agents is an important concern. Methodology: We employ an equilibrium model that accounts for the interaction among price, wage, labor supply, customer delay, and demand. Results: Average labor welfare increases and then decreases in the labor pool size; that is, agents are harmed by an expansion in the labor pool size if and only if the labor pool size is sufficiently large. The effective wage floor is superior to the nominal wage floor in terms of labor welfare maximization. More generally, the two types of wage floors have structurally different effects on labor welfare, with a floor on the nominal wage only beneficial to agents if it is sufficiently small. Contrary to the conventional view that consumers are hurt by an effective wage floor (because they face a higher price, due to upward pressure on the wage, and longer delay, due to upward pressure on agent utilization), consumers actually benefit. Managerial implications: Regulators, labor advocates, platform managers, and agents benefit from understanding the forces that create and destroy labor welfare.

  PublisherDOI

• Labor Welfare in On-Demand Service Platforms

  SSRN Electronic Journal · 2020 · 21 citations

  Senior authorCorresponding
  • Labour economics
  • Business
  • Economics
  PublisherDOI

• Labor Welfare in On-Demand Service Platforms

  SSRN Electronic Journal · 2019-01-01 · 54 citations

  articleOpen accessSenior author
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• Ride-Hailing Platforms: Competition and Autonomous Vehicles

  SSRN Electronic Journal · 2019-01-01 · 32 citations

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Erica L. Plambeck

  27 shared

• J. M. Davidson

  University of Florida

  8 shared

• Patricia Gonce Morton

  University of Utah

  8 shared

• D.M. Sheppard

  Monash University

  8 shared

• J. A. Cameron

  McMaster University

  7 shared

• M. Neligan

  6 shared

• K. Salathia

  Ulster Hospital

  6 shared

• D. Boyle

  Ulster Hospital

  6 shared

Awards & honors

• Columbia Business School Dean’s Award for Teaching Excellenc…
• Earl F. Cheit Award for Excellence in Teaching
• Wickham Skinner Early-Career Research Accomplishments Award…