Title: Transport Priority and Composition Effects in Liver Transplantation

Speaker: Baojun Jiang

Time: November 27^th, 2025 (Thursday) 15:00

Venue: 405, School of Business, East China University of Science and Technology

Organizer: School of Business, East China University of Science and Technology

Guest Bio:
  Professor Baojun Jiang currently serves as a tenured full professor of marketing at Washington University in St. Louis' Olin Business School. He is an internationally renowned scholar in the interdisciplinary field of shared economy, platform strategy, and operational marketing. His research deeply integrates behavioral economics and game theory, focusing on cutting-edge topics such as IoT supply chains, peer-to-peer sharing, and consumer fairness concerns. He systematically analyzes platform business models, channel coordination, and product innovation strategies. His work has been published in top-tier journals including Marketing Science, Management Science, Journal of Marketing Research, and Manufacturing & Service Operations Management (UTD24/FT50). He has received the Don Lehmann Best Paper Award from the American Marketing Association, been selected for the MSI Scholar Program, and serves as a Senior Editor for Production and Operations Management. Professor Jiang holds a Ph.D. from Carnegie Mellon University and multiple degrees in science and engineering. He specializes in applying interdisciplinary methodologies to solve complex business challenges, with his research providing critical theoretical foundations for corporate competition and policy design in digital environments.

Synopsis:

  China’s Green Path established nationwide transport priority for donor organs to reduce delays and improve liver transplantation outcomes. We document that, following the policy, system-wide average cold ischemia time (CIT) increased and post-transplant survival declined, even though—by design—transport performance for nonlocal organs should have improved. We explain this pattern through a composition mechanism: by shortening travel times, the policy expanded the feasible set of nonlocal livers that previously arrived too late to be used. Hospitals subsequently accepted and transplanted more nonlocal grafts—organs that, even with faster routes, entail inherently longer CIT than local grafts—thereby raising the average CIT across all transplants and offsetting the direct logistics gain. We formalize this mechanism in a model of hospital acceptance and patient matching. The model yields a transparent condition under which average CIT rises after transport priority: when the increase in nonlocal organ availability and acceptance dominates the per-organ CIT savings from faster transport. The model also clarifies how hospital decision rules and patient severity interact with policy to shape outcomes. Empirically, we combine a time-based policy design with a provenance-free method to separate nonlocal from local cases using the distribution of CIT, allowing us to isolate the policy’s direct effect on nonlocal CIT while absorbing contemporaneous changes in local logistics and learning-by-doing in hospital operations. We then test the model’s predictions on case mix (nonlocal share, acceptance of marginal grafts, recipient severity) and outcomes (survival). Our findings highlight a system-level trade-off: logistics reforms that accelerate individual deliveries can worsen aggregate outcomes via equilibrium re-sorting of cases. For policy and management, we caution against KPI regimes that evaluate only transplanted patients and point to guardrails (e.g., eligibility/threshold rules) that preserve urgency and curb price-driven erosion of priority.